From 02f54d4fc265fad629a475e5f9f4e44fb91a1842 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Fri, 17 Apr 2026 13:11:03 -0500 Subject: [PATCH 01/30] docs: design for loading real Snowplow source via Athena CTAS into S3 Tables Keeps target schema identical to the synthetic loader so the existing dbt-snowplow-web wiring works unchanged; only sources.yml gets an identifier override. Co-Authored-By: Claude Opus 4.7 (1M context) --- ...-04-17-athena-to-s3tables-source-design.md | 148 ++++++++++++++++++ 1 file changed, 148 insertions(+) create mode 100644 specs/2026-04-17-athena-to-s3tables-source-design.md diff --git a/specs/2026-04-17-athena-to-s3tables-source-design.md b/specs/2026-04-17-athena-to-s3tables-source-design.md new file mode 100644 index 0000000..7c248b5 --- /dev/null +++ b/specs/2026-04-17-athena-to-s3tables-source-design.md @@ -0,0 +1,148 @@ +# Load real Snowplow source into S3 Tables via Athena CTAS + +## Goal + +Run the existing dbt-snowplow-web pipeline on real Snowplow atomic events (from +the Glue-managed Iceberg table `analytics_glue.hooli_events_0417_v2`) and +verify all derived tables are non-empty. + +## Constraints + +- `dbt-embucket` only reads tables that live in the S3 Tables bucket Embucket is + volumed to (`arn:aws:s3tables:us-east-2:767397688925:bucket/snowplow`, + mapped as database `demo`). +- Embucket's `COPY INTO` is path-based and cannot traverse Iceberg metadata, so + it cannot be pointed at the source warehouse prefix directly. +- The source table is Iceberg v2 in Glue, partitioned by + `day(load_tstamp), event_name`, ~2.12M rows / ~2.44 GB across 634 files. + +## Approach + +Have Athena do both sides: read the Glue Iceberg source, write a new Iceberg +table directly into the Embucket S3 Tables bucket via the existing Lake +Formation federated catalog. No staging parquet, no Embucket `COPY INTO`. dbt +then reads the new table through Embucket. + +### Environment prerequisites (already in place) + +- Glue federated catalog `767397688925:s3tablescatalog` registered against + `arn:aws:s3tables:us-east-2:767397688925:bucket/*`. Addresses the `snowplow` + bucket's namespaces from Athena as `"s3tablescatalog/snowplow"."atomic"`. +- Lake Formation resource `arn:aws:s3tables:us-east-2:767397688925:bucket/*` + registered with `WithFederation=true`. +- Source table visible to Athena as + `awsdatacatalog.analytics_glue.hooli_events_0417_v2`. + +### Target schema policy + +Keep the target identical to today's `demo.atomic.events`: +- **117 atomic columns** plus **7 snowplow-context/unstruct columns** used by + dbt-snowplow-web (`web_page_1`, `ua_parser_context_1`, `yauaa_context_1`, + `consent_preferences_1`, `cmp_visible_1`, `iab_spiders_and_robots_1`, + `web_vitals_1`). +- 3 of those 7 context columns **exist** in the source and are serialised to + JSON strings; 4 **are absent** in the source and are written as `NULL` to + preserve the package's schema contract. +- All 100+ other typed context/unstruct columns in the source are dropped. + +### Type reconciliation (done in the CTAS SELECT) + +| Source (Iceberg) | Target (Embucket `create_table.sql`) | Cast | +|---|---|---| +| `timestamptz` (all tstamps) | `TIMESTAMP_NTZ` | `CAST(... AS TIMESTAMP)` — strips tz | +| `decimal(18,2)` (tr_*, ti_*) | `DOUBLE` | `CAST(... AS DOUBLE)` | +| `double` `se_value` | `STRING` | `CAST(... AS VARCHAR)` | +| `string` `br_colordepth` | `INTEGER` | `TRY_CAST(... AS INTEGER)` | +| struct/list contexts | `VARIANT` | `CAST(... AS JSON)` (serialises to JSON string) | + +`CAST(struct AS JSON)` in Athena produces a VARCHAR JSON payload that Embucket +stores as VARIANT, matching the existing synthetic fixture's shape. + +### Target table name + +`demo.atomic.events_0416` (new name, does not touch the existing +`demo.atomic.events` produced by the synthetic loader). Source configuration +in dbt is redirected to it. + +### Partitioning + +Preserve the source's partition spec on the target: +`WITH (partitioning = ARRAY['day(load_tstamp)', 'event_name'])`. This matches +the Snowplow incremental tuning already in `dbt_project.yml` +(`snowplow__session_timestamp: load_tstamp`) and the source's own layout. + +### dbt wiring + +Update `models/sources.yml` with an `identifier: events_0416` override on the +existing `atomic.events` source. This leaves `snowplow__events: +"{{ source('atomic','events') }}"` in `dbt_project.yml` untouched — all +references resolve to the new physical table with no model edits. + +### Verification steps + +1. Count rows visible to Embucket: run `SELECT COUNT(*) FROM demo.atomic.events_0416` + via `scripts/embucket_client.py run_sql` and expect ~2.12M. +2. Sanity-read one row: confirm `load_tstamp`, `event_id`, and a VARIANT column + deserialise. +3. `uv run dbt seed --profiles-dir .` +4. `uv run dbt run --profiles-dir .` — expect 18 models to build. +5. `dbt show` the three headline derived tables; each should be non-empty: + - `demo.atomic_derived.snowplow_web_page_views` + - `demo.atomic_derived.snowplow_web_sessions` + - `demo.atomic_derived.snowplow_web_users` + +## Components + +### `scripts/ctas_from_glue.sql` + +New file. The Athena CTAS statement with the full column projection and casts +described above. Kept in-repo so the load is reproducible. + +### `scripts/load_from_glue.py` + +New file. Orchestrates: +1. Drop `s3tablescatalog/snowplow.atomic.events_0416` if it exists (idempotent + reruns). +2. Start Athena query from `ctas_from_glue.sql` using `boto3` Athena client. +3. Poll until completion; print row count on success, throw on failure. + +Takes `--athena-workgroup`, `--query-output-location` +(`s3://.../athena-results/`) as args, with sensible defaults resolved from +environment. Uses the current AWS credentials (no Embucket Lambda involved). + +### `models/sources.yml` edit + +Add `identifier: events_0416` under the `events` table entry so dbt's +`source('atomic','events')` resolves to the Athena-written table. + +### `README.md` addendum + +New "Loading real Snowplow data" section pointing at `load_from_glue.py` as the +alternative to step 6. Keeps the synthetic quickstart path as the default. + +## Risks / open issues + +1. **Embucket reading Athena-written Iceberg**: unverified end-to-end today. If + read fails on a type (most likely candidate: nested JSON VARIANT round-trip), + mitigation is to adjust the cast in `ctas_from_glue.sql` — the SQL is the + single point of control. +2. **Partition transform support on read**: Embucket should ignore partition + spec on reads (it's metadata), but if pruning misbehaves we can re-run CTAS + unpartitioned as a fallback. +3. **Glue federated catalog write permissions**: CTAS from Athena requires the + query's Lake Formation principal to hold `CREATE_TABLE` on + `s3tablescatalog/snowplow.atomic`. First run may surface a perms gap; noted + so we don't treat it as a code bug. +4. **Absent context columns**: 4 of the 7 snowplow contexts referenced by the + current dbt_project.yml are not present in the source. Models gated on + those flags (iab, cwv, consent) will produce empty/null-only derived rows. + That's acceptable for a "does the pipeline run" smoke test; flag values in + `dbt_project.yml` can be disabled later if the test is re-scoped to + "populates every derived table". + +## Out of scope + +- Changing `create_table.sql` / the synthetic load path. +- Adding `dbt-snowplow-web` config flags for context coverage. +- Benchmarking runtime; this spec only targets coverage verification. +- Cross-account plumbing (source and target live in the same account). From 58c57fd3a6f22baa945f1eae487cd399b468d20e Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Fri, 17 Apr 2026 15:35:16 -0500 Subject: [PATCH 02/30] feat: load real Snowplow source via Athena CTAS, wire dbt to events_0416 Adds scripts/ctas_from_glue.sql + scripts/load_from_glue.py to materialise the Hooli Iceberg-in-Glue source into the Embucket S3 Tables bucket as atomic.events_0416 (filtered to the post-realism-fix cutover at 2026-04-17 19:47 UTC). Points dbt-snowplow-web at the new table via snowplow__events_table, removes synthetic-specific var overrides, and makes the package's five context flags explicit-off. Co-Authored-By: Claude Opus 4.7 (1M context) --- dbt_project.yml | 20 ++--- scripts/ctas_from_glue.sql | 176 +++++++++++++++++++++++++++++++++++++ scripts/load_from_glue.py | 149 +++++++++++++++++++++++++++++++ 3 files changed, 333 insertions(+), 12 deletions(-) create mode 100644 scripts/ctas_from_glue.sql create mode 100644 scripts/load_from_glue.py diff --git a/dbt_project.yml b/dbt_project.yml index 272d8dc..c6cf48a 100644 --- a/dbt_project.yml +++ b/dbt_project.yml @@ -11,17 +11,13 @@ dispatch: vars: snowplow__start_date: '2026-04-13' - snowplow__events: "{{ source('atomic', 'events') }}" snowplow__atomic_schema: 'atomic' - # The fake event generator pins collector_tstamp to a fixed 2h52m window - # (generator-time never advances), so snowplow_web's default - # snowplow__session_timestamp=collector_tstamp cannot make incremental - # progress — every run re-scans the entire events table. load_tstamp *does* - # advance (it's the loader commit time), so override to it. atomic.events - # must also be partitioned by day(load_tstamp) for pruning to match. - snowplow__session_timestamp: load_tstamp - # Narrow the incremental lookback from 6h → 1h. With hour() partitioning - # on load_tstamp this should reduce the number of partitions Snowplow - # reads at the base layer. - snowplow__lookback_window_hours: 1 + snowplow__events_table: 'events_0416' + # Context flags — set to true once the corresponding context column is + # populated (see scripts/ctas_from_glue.sql). web_page_1 is always on. + snowplow__enable_iab: false + snowplow__enable_ua: false + snowplow__enable_yauaa: false + snowplow__enable_consent: false + snowplow__enable_cwv: false diff --git a/scripts/ctas_from_glue.sql b/scripts/ctas_from_glue.sql new file mode 100644 index 0000000..f56cf16 --- /dev/null +++ b/scripts/ctas_from_glue.sql @@ -0,0 +1,176 @@ +-- Athena CTAS: copy Glue Iceberg source into the Embucket S3 Tables bucket. +-- +-- Source: awsdatacatalog.analytics_glue.hooli_events_0417_v2 (Iceberg v2 in Glue). +-- Target: "s3tablescatalog/snowplow"."atomic"."events_0416" (new Iceberg table +-- in the bucket Embucket Lambda has volumed as database `demo`). +-- +-- Column list and ordering mirror scripts/create_table.sql so the existing +-- dbt-snowplow-web wiring works unchanged. Type casts reconcile differences +-- between the source's Iceberg types and the target schema: +-- timestamptz -> timestamp (strip tz) +-- decimal(18,2) -> double (tr_*, ti_* monetary cols) +-- int txn_id -> varchar +-- double se_value -> varchar +-- string br_colordepth -> integer (via TRY_CAST) +-- list/struct context cols -> varchar (JSON) (via JSON_FORMAT) +-- +-- Only the 3 context columns that exist in the source are populated; the 4 +-- absent ones (consent_preferences, cmp_visible, iab_spiders_and_robots, +-- web_vitals) are emitted as NULL so the column set matches the synthetic +-- schema. + +CREATE TABLE events_0416 +WITH ( + table_type = 'ICEBERG', + is_external = false, + format = 'PARQUET', + write_compression = 'ZSTD', + partitioning = ARRAY['day(load_tstamp)', 'event_name'] +) AS +SELECT + app_id, + platform, + CAST(etl_tstamp AS TIMESTAMP) AS etl_tstamp, + CAST(collector_tstamp AS TIMESTAMP) AS collector_tstamp, + CAST(dvce_created_tstamp AS TIMESTAMP) AS dvce_created_tstamp, + event, + event_id, + CAST(txn_id AS VARCHAR) AS txn_id, + name_tracker, + v_tracker, + v_collector, + v_etl, + user_id, + user_ipaddress, + user_fingerprint, + domain_userid, + domain_sessionidx, + network_userid, + geo_country, + geo_region, + geo_city, + geo_zipcode, + geo_latitude, + geo_longitude, + geo_region_name, + ip_isp, + ip_organization, + ip_domain, + ip_netspeed, + page_url, + page_title, + page_referrer, + page_urlscheme, + page_urlhost, + page_urlport, + page_urlpath, + page_urlquery, + page_urlfragment, + refr_urlscheme, + refr_urlhost, + refr_urlport, + refr_urlpath, + refr_urlquery, + refr_urlfragment, + refr_medium, + refr_source, + refr_term, + mkt_medium, + mkt_source, + mkt_term, + mkt_content, + mkt_campaign, + se_category, + se_action, + se_label, + se_property, + CAST(se_value AS VARCHAR) AS se_value, + tr_orderid, + tr_affiliation, + CAST(tr_total AS DOUBLE) AS tr_total, + CAST(tr_tax AS DOUBLE) AS tr_tax, + CAST(tr_shipping AS DOUBLE) AS tr_shipping, + tr_city, + tr_state, + tr_country, + ti_orderid, + ti_sku, + ti_name, + ti_category, + CAST(ti_price AS DOUBLE) AS ti_price, + ti_quantity, + pp_xoffset_min, + pp_xoffset_max, + pp_yoffset_min, + pp_yoffset_max, + useragent, + br_name, + br_family, + br_version, + br_type, + br_renderengine, + br_lang, + br_features_pdf, + br_features_flash, + br_features_java, + br_features_director, + br_features_quicktime, + br_features_realplayer, + br_features_windowsmedia, + br_features_gears, + br_features_silverlight, + br_cookies, + TRY_CAST(br_colordepth AS INTEGER) AS br_colordepth, + br_viewwidth, + br_viewheight, + os_name, + os_family, + os_manufacturer, + os_timezone, + dvce_type, + dvce_ismobile, + dvce_screenwidth, + dvce_screenheight, + doc_charset, + doc_width, + doc_height, + tr_currency, + CAST(tr_total_base AS DOUBLE) AS tr_total_base, + CAST(tr_tax_base AS DOUBLE) AS tr_tax_base, + CAST(tr_shipping_base AS DOUBLE) AS tr_shipping_base, + ti_currency, + CAST(ti_price_base AS DOUBLE) AS ti_price_base, + base_currency, + geo_timezone, + mkt_clickid, + mkt_network, + etl_tags, + CAST(dvce_sent_tstamp AS TIMESTAMP) AS dvce_sent_tstamp, + refr_domain_userid, + CAST(refr_dvce_tstamp AS TIMESTAMP) AS refr_dvce_tstamp, + domain_sessionid, + CAST(derived_tstamp AS TIMESTAMP) AS derived_tstamp, + event_vendor, + event_name, + event_format, + event_version, + event_fingerprint, + CAST(true_tstamp AS TIMESTAMP) AS true_tstamp, + CAST(load_tstamp AS TIMESTAMP) AS load_tstamp, + JSON_FORMAT(CAST(contexts_com_snowplowanalytics_snowplow_web_page_1 AS JSON)) + AS contexts_com_snowplowanalytics_snowplow_web_page_1, + CAST(NULL AS VARCHAR) + AS unstruct_event_com_snowplowanalytics_snowplow_consent_preferences_1, + CAST(NULL AS VARCHAR) + AS unstruct_event_com_snowplowanalytics_snowplow_cmp_visible_1, + CAST(NULL AS VARCHAR) + AS contexts_com_iab_snowplow_spiders_and_robots_1, + JSON_FORMAT(CAST(contexts_com_snowplowanalytics_snowplow_ua_parser_context_1 AS JSON)) + AS contexts_com_snowplowanalytics_snowplow_ua_parser_context_1, + JSON_FORMAT(CAST(contexts_nl_basjes_yauaa_context_1 AS JSON)) + AS contexts_nl_basjes_yauaa_context_1, + CAST(NULL AS VARCHAR) + AS unstruct_event_com_snowplowanalytics_snowplow_web_vitals_1 +-- Filter: only post-cutover data (generator realism fix landed 19:47 UTC) +FROM "awsdatacatalog"."analytics_glue"."hooli_events_0417_v2" +WHERE load_tstamp >= TIMESTAMP '2026-04-17 19:47:00 UTC' diff --git a/scripts/load_from_glue.py b/scripts/load_from_glue.py new file mode 100644 index 0000000..445dc16 --- /dev/null +++ b/scripts/load_from_glue.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python3 +"""Load the Snowplow Glue Iceberg source into Embucket's S3 Tables bucket. + +Runs an Athena CTAS that reads the Glue-managed Iceberg source and writes a +new Iceberg table into the S3 Tables bucket Embucket is volumed to. Embucket +and dbt-snowplow-web then read that table directly. + +Usage: + uv run python scripts/load_from_glue.py \\ + --query-output-location s3://athena-query-results-us-east-2-767397688925/ +""" + +from __future__ import annotations + +import argparse +import time +from pathlib import Path + +import boto3 + + +TARGET_CATALOG = "s3tablescatalog/snowplow" +TARGET_SCHEMA = "atomic" +TARGET_TABLE = "events_0416" +DEFAULT_QUERY_OUTPUT = "s3://athena-query-results-us-east-2-767397688925/embucket-snowplow/" +DEFAULT_WORKGROUP = "primary" +DEFAULT_REGION = "us-east-2" + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--query-output-location", + default=DEFAULT_QUERY_OUTPUT, + help=f"S3 URI for Athena query result metadata (default: {DEFAULT_QUERY_OUTPUT})", + ) + parser.add_argument( + "--workgroup", + default=DEFAULT_WORKGROUP, + help=f"Athena workgroup (default: {DEFAULT_WORKGROUP})", + ) + parser.add_argument( + "--region", + default=DEFAULT_REGION, + help=f"AWS region (default: {DEFAULT_REGION})", + ) + parser.add_argument( + "--skip-drop", + action="store_true", + help="Do not drop the target table before CTAS (will fail if it exists)", + ) + parser.add_argument( + "--row-limit", + type=int, + default=None, + help="Optional LIMIT on the CTAS SELECT (for smoke-testing on smaller slices)", + ) + return parser.parse_args() + + +def run_query( + client, + sql: str, + output_location: str, + workgroup: str, + catalog: str | None = None, + database: str | None = None, +) -> str: + query_context = {} + if catalog: + query_context["Catalog"] = catalog + if database: + query_context["Database"] = database + kwargs = { + "QueryString": sql, + "ResultConfiguration": {"OutputLocation": output_location}, + "WorkGroup": workgroup, + } + if query_context: + kwargs["QueryExecutionContext"] = query_context + start = client.start_query_execution(**kwargs) + query_id = start["QueryExecutionId"] + print(f" athena query id: {query_id}") + + while True: + resp = client.get_query_execution(QueryExecutionId=query_id) + state = resp["QueryExecution"]["Status"]["State"] + if state in {"SUCCEEDED", "FAILED", "CANCELLED"}: + break + time.sleep(2) + + if state != "SUCCEEDED": + reason = resp["QueryExecution"]["Status"].get("StateChangeReason", "") + raise RuntimeError(f"Athena query {state}: {reason}") + + return query_id + + +def main() -> None: + args = parse_args() + scripts_dir = Path(__file__).parent + ctas_sql = (scripts_dir / "ctas_from_glue.sql").read_text() + if args.row_limit: + # Strip the trailing semicolon and wrap with LIMIT. + ctas_sql = ctas_sql.rstrip().rstrip(";") + f"\nLIMIT {args.row_limit};" + + client = boto3.client("athena", region_name=args.region) + + target_fqn = f"{TARGET_CATALOG}.{TARGET_SCHEMA}.{TARGET_TABLE}" + + if not args.skip_drop: + print(f"Dropping {target_fqn} if it exists...") + run_query( + client, + f"DROP TABLE IF EXISTS {TARGET_TABLE}", + args.query_output_location, + args.workgroup, + catalog=TARGET_CATALOG, + database=TARGET_SCHEMA, + ) + + print(f"Running CTAS into {target_fqn}...") + run_query( + client, + ctas_sql, + args.query_output_location, + args.workgroup, + catalog=TARGET_CATALOG, + database=TARGET_SCHEMA, + ) + + print(f"Counting rows in {target_fqn}...") + count_qid = run_query( + client, + f"SELECT COUNT(*) FROM {TARGET_TABLE}", + args.query_output_location, + args.workgroup, + catalog=TARGET_CATALOG, + database=TARGET_SCHEMA, + ) + result = client.get_query_results(QueryExecutionId=count_qid) + rows = result["ResultSet"]["Rows"] + # row 0 is the header, row 1 is the single count value + count = rows[1]["Data"][0]["VarCharValue"] + print(f"Done. events_0416 row count: {count}") + + +if __name__ == "__main__": + main() From f4edfec2badb0529b5bf56c35db798a24343acfb Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 14:00:09 -0500 Subject: [PATCH 03/30] docs: design for Embucket vs Snowflake dbt-snowplow-web parity harness Both engines read the same S3 Tables Iceberg source; Athena loads it in two 30-minute batches; parity script rowcount- and MD5-diffs the three headline derived tables after each dbt run to surface semantic drift. --- ...-04-22-embucket-snowflake-parity-design.md | 303 ++++++++++++++++++ 1 file changed, 303 insertions(+) create mode 100644 specs/2026-04-22-embucket-snowflake-parity-design.md diff --git a/specs/2026-04-22-embucket-snowflake-parity-design.md b/specs/2026-04-22-embucket-snowflake-parity-design.md new file mode 100644 index 0000000..cba01e2 --- /dev/null +++ b/specs/2026-04-22-embucket-snowflake-parity-design.md @@ -0,0 +1,303 @@ +# Embucket vs Snowflake parity for dbt-snowplow-web + +## Goal + +Prove that dbt-snowplow-web produces the same derived tables on Embucket and on +Snowflake when run against the same real Snowplow atomic events. The harness +exercises the incremental path by loading data in two batches and re-running +dbt on both engines after each batch; a parity script diffs the three headline +derived tables and exits non-zero on any mismatch. + +## Context + +Prior work (`specs/2026-04-17-athena-to-s3tables-source-design.md`) loads the +Glue Iceberg source `analytics_glue.hooli_events_0417_v2` into the Embucket +S3 Tables bucket as `demo.atomic.events_0416` via Athena CTAS, and wires +dbt-snowplow-web against it. That path is working on the Embucket side. + +This spec adds the parallel Snowflake path so the two engines can be compared. +Scope is limited to making the comparison work for a small, real data sample; +runtime benchmarking is out of scope. + +## Constraints + +- Both engines must read the same physical Iceberg table. Re-deriving the + source per engine would re-introduce the type-reconciliation problem the + Athena CTAS already solved. +- Snowflake reads from an S3 Tables bucket in a different account context than + the default Snowflake-managed storage. An external volume + catalog + integration is required; account-level objects may already exist. +- Athena's `CREATE TABLE AS SELECT` can only create; for a batched load flow + we need `CREATE TABLE` + parametrized `INSERT INTO` instead. +- Snowflake-managed Iceberg tables do not auto-refresh on external writes; a + manual `ALTER ICEBERG TABLE ... REFRESH` is needed after each Athena write. +- The existing Embucket flow must keep working unchanged. + +## Approach + +Single dbt project, two targets (`embucket` default, `snowflake` new) under +the existing `embucket_demo` profile. Both read `atomic.events_0416` — on +Embucket natively; on Snowflake via a Snowflake-managed Iceberg table +(`CATALOG_SOURCE = 'ICEBERG_REST'`, S3 Tables as the catalog source; +Glue-federated catalog is the fallback if S3 Tables integration is not +available on the account). + +The Athena CTAS is split into an idempotent `CREATE TABLE` and a parametrized +`INSERT INTO ... WHERE load_tstamp >= AND load_tstamp < ` so the +source table can be emptied and filled batch by batch. Parity is verified +after each batch's dbt run by a Python script that rowcount-diffs and +hash-diffs the three headline derived tables. + +### Data flow + +``` +Glue Iceberg source (analytics_glue.hooli_events_0417_v2) + │ Athena INSERT (WHERE load_tstamp in [start, end)) + ▼ + S3 Tables: demo.atomic.events_0416 ◀── single source of truth + │ │ + ▼ ▼ + Embucket Lambda Snowflake (REFRESH then SELECT) + │ │ + ▼ ▼ + dbt run --target embucket dbt run --target snowflake + │ │ + └──────────► scripts/parity.py ◀─┘ +``` + +### Batch plan + +Source `load_tstamp` spans `2026-04-17 17:00` to `2026-04-22 18:00`, with +~1.8M–6.4M rows per hour. The harness uses the freshest complete hour split +into two 30-minute batches: + +- **Batch 1**: `2026-04-22 15:00:00` → `2026-04-22 15:30:00` (~3.1M rows) +- **Batch 2**: `2026-04-22 15:30:00` → `2026-04-22 16:00:00` (~3.1M rows) + +`snowplow__start_date` bumps from `2026-04-13` to `2026-04-22` so both +engines' incremental state starts at the same cursor. + +### Snowflake connection + +Credentials come from the user's existing `~/.snowflake/connections.toml` +`default` connection: account `aa06228.us-east-2.aws`, user `rampage644`, +role `ACCOUNTADMIN`, warehouse `compute_wh`. Target lands in database +`sturukin`, schema `atomic`. + +`profiles.yml.example` carries placeholders; the real `profiles.yml` (already +gitignored) holds the values. Both `embucket` and `snowflake` outputs live +under the single `embucket_demo` profile so `sources.yml` stays unchanged and +`source('atomic','events')` resolves to `atomic.events_0416` on both sides. + +### Type and schema reconciliation + +Already handled by the Athena CTAS: both engines read the same Iceberg files, +so there is nothing to reconcile here beyond confirming Snowflake's +Iceberg reader surfaces the same column types Embucket does. A +`DESCRIBE TABLE` diff is part of the harness self-check. + +## Components + +### `scripts/create_events_0416.sql` (new) + +`CREATE TABLE events_0416 (...empty with target schema...)` with +`partitioning = ARRAY['day(load_tstamp)', 'event_name']`. Column list and +order mirror `scripts/create_table.sql`. + +### `scripts/insert_events_0416.sql` (new) + +The projection/cast `SELECT` body from the existing `ctas_from_glue.sql`, +wrapped in: +``` +INSERT INTO events_0416 +SELECT ... +FROM awsdatacatalog.analytics_glue.hooli_events_0417_v2 +WHERE load_tstamp >= TIMESTAMP '{{start}}' + AND load_tstamp < TIMESTAMP '{{end}}'; +``` +`{{start}}` and `{{end}}` are placeholders rendered by `load_from_glue.py`. + +### `scripts/ctas_from_glue.sql` (deleted) + +Replaced by the pair above. The existing one-shot load is reproducible by +running `init` then one `insert` spanning the full source range. + +### `scripts/load_from_glue.py` (refactored) + +Two subcommands: +- `init` — drops `s3tablescatalog/snowplow.atomic.events_0416` if it exists; + runs `create_events_0416.sql`. +- `insert --start --end ` — renders and runs + `insert_events_0416.sql`; polls Athena to completion; prints inserted + row count on success. + +Takes the existing `--athena-workgroup` and `--query-output-location` args. + +### `scripts/snowflake_setup.py` (new) + +Idempotent one-shot Snowflake setup. +1. `SHOW EXTERNAL VOLUMES` — if an existing volume covers the `snowplow` + S3 Tables bucket, reuse it; else `CREATE EXTERNAL VOLUME snowplow_vol ...`. +2. `SHOW CATALOG INTEGRATIONS` — if an S3 Tables or matching Glue integration + already exists, reuse it; else `CREATE CATALOG INTEGRATION snowplow_s3t + CATALOG_SOURCE = 'ICEBERG_REST' TABLE_FORMAT = 'ICEBERG' + REST_CONFIG = (CATALOG_URI = ..., CATALOG_NAME = 'snowplow') + REST_AUTHENTICATION = (TYPE = 'SIGV4' ...)`. +3. `CREATE DATABASE IF NOT EXISTS sturukin; CREATE SCHEMA IF NOT EXISTS + sturukin.atomic;`. +4. `CREATE OR REPLACE ICEBERG TABLE sturukin.atomic.events_0416 + CATALOG = 'snowplow_s3t' CATALOG_TABLE_NAME = 'events_0416' + EXTERNAL_VOLUME = 'snowplow_vol'`. +5. Grants: `GRANT USAGE ON DATABASE sturukin TO ROLE ACCOUNTADMIN` + (no-op under ACCOUNTADMIN but kept for least-privilege future tightening). + +Connects via `snowflake.connector.connect(connection_name='default')` so it +uses the existing `~/.snowflake/connections.toml` entry. + +Fallback (controlled by `--catalog glue`): creates a Glue-federated catalog +integration against `s3tablescatalog/snowplow` instead. + +### `scripts/snowflake_refresh.py` (new) + +Runs `ALTER ICEBERG TABLE sturukin.atomic.events_0416 REFRESH;` and prints +the resulting row count. Invoked between each Athena load and the +corresponding Snowflake dbt run. + +### `scripts/parity.py` (new) + +For each of `snowplow_web_page_views`, `snowplow_web_sessions`, +`snowplow_web_users`: +- `COUNT(*)` on both sides; must be equal. +- Natural-key set diff: + - `page_views.page_view_id` + - `sessions.session_identifier` + - `users.user_identifier` +- Row hash: `SELECT , MD5(CONCAT_WS('|', + COALESCE(CAST(c AS STRING), '∅'), ...))` over the columns declared in the + model's yaml schema (excludes engine-specific metadata columns). + +Prints per-table summary (matched / mismatched / only-in-embucket / +only-in-snowflake counts) and up to 10 example natural keys from each +mismatch bucket. Exits non-zero on any mismatch. + +`--source-only` mode skips derived tables and just diffs +`events_0416` rowcount + `DESCRIBE TABLE`; used as the harness self-check +before any dbt run. + +Connects to Embucket via `scripts/embucket_client.py`, to Snowflake via +`snowflake.connector.connect(connection_name='default')`. + +### `profiles.yml.example` (edit) + +Add `snowflake` output under `embucket_demo`: +```yaml +snowflake: + type: snowflake + account: YOUR_SNOWFLAKE_ACCOUNT + user: YOUR_SNOWFLAKE_USER + password: YOUR_SNOWFLAKE_PASSWORD + role: YOUR_SNOWFLAKE_ROLE + warehouse: YOUR_SNOWFLAKE_WAREHOUSE + database: sturukin + schema: atomic + threads: 4 +``` +Local `profiles.yml` gets the real values from +`~/.snowflake/connections.toml`. + +### `pyproject.toml` (edit) + +Add `snowflake-connector-python` and `dbt-snowflake` (matching the existing +dbt version range) as dependencies. + +### `dbt_project.yml` (edit) + +`snowplow__start_date: '2026-04-22'`. + +### `packages.yml` / `patch_snowplow.sh` + +No change. `patch_snowplow.sh` is Embucket-specific; dbt-snowplow-web is +Snowflake-first upstream, so no patch is needed on the Snowflake target. + +### `README.md` (addendum) + +New "Comparing Embucket vs Snowflake" section documenting the 7-step loop +(setup, init, batch 1 insert, dbt run × 2 + refresh, parity, batch 2 insert, +dbt run × 2 + refresh, parity). + +## End-to-end run flow + +All commands from repo root. + +1. `uv run python scripts/snowflake_setup.py` — one-time idempotent setup. +2. `uv run python scripts/load_from_glue.py init` — empty source. +3. `uv run python scripts/load_from_glue.py insert --start '2026-04-22 15:00:00' --end '2026-04-22 15:30:00'` +4. `uv run dbt run --profiles-dir . --target embucket` + `uv run python scripts/snowflake_refresh.py` + `uv run dbt run --profiles-dir . --target snowflake` +5. `uv run python scripts/parity.py` — must exit 0. +6. `uv run python scripts/load_from_glue.py insert --start '2026-04-22 15:30:00' --end '2026-04-22 16:00:00'` +7. Repeat step 4, then `parity.py` again. + +Success: parity.py exits 0 both times. + +## Verification + +**Harness self-checks (before trusting a run):** + +1. `parity.py --source-only` on an empty `events_0416`: exits 0 + (rowcounts both 0, schemas match). Confirms no false positives. +2. `DESCRIBE TABLE events_0416` on both engines: same column names, same + types. A mismatch here invalidates every downstream diff and is a bug in + this spec's setup, not a finding. +3. First `load_from_glue.py insert` returns a row count matching the Athena + query stats. + +**Parity assertions (the findings):** + +- Zero diffs after batch 1 dbt run on all three headline tables. +- Zero diffs after batch 2 dbt run on all three headline tables. + +**What success means:** the two engines produce byte-for-byte equivalent +headline derived tables on the same input, through both the initial-build +and the incremental-update paths. + +**What failure means (and how to read it):** +- Rowcount matches, hashes don't → semantic drift (cast, NULL handling, + window ordering). This is the signal the harness exists to surface. +- Rowcount differs → incremental-window or JOIN-semantics divergence. +- Batch 1 passes, batch 2 fails → incremental merge logic diverges. +- Snowflake empty after an Athena write → `snowflake_refresh.py` was + skipped. + +## Risks / open issues + +1. **S3 Tables catalog integration support**: Snowflake's native S3 Tables + integration is the preferred path; if unavailable on this account, fall + back to Glue-federated catalog integration (`--catalog glue` on + `snowflake_setup.py`). Both are implemented. +2. **Iceberg type drift between Embucket and Snowflake readers**: both read + the same files but may surface columns differently (e.g. `TIMESTAMP_NTZ` + vs `TIMESTAMP_LTZ`). The `--source-only` parity check catches this + before dbt runs are wasted. +3. **`MD5(CONCAT_WS(...))` column ordering**: must match column-by-column + between engines. Parity script derives the column list from the model's + yaml schema, not `SELECT *`, so ordering is deterministic. +4. **Snowflake-managed Iceberg refresh lag**: `ALTER ICEBERG TABLE ... + REFRESH` is synchronous but metadata refresh can still miss very recent + Athena commits if the Glue commit propagation hasn't landed. If + rowcounts disagree between Embucket and Snowflake on the source, + re-running `snowflake_refresh.py` is the first thing to try. +5. **dbt-snowflake version compatibility**: must satisfy the existing + `[">=1.6.0", "<2.0.0"]` constraint in `dbt_project.yml`. Verified at + dependency-add time. + +## Out of scope + +- Runtime / performance benchmarking between engines. +- Parity on non-headline derived tables or intermediate scratch models. +- Tables gated off by disabled context flags (iab, ua, yauaa, consent, cwv). +- CI integration; the harness runs locally for now. +- `dbt test` suite runs. +- Cross-account Snowflake setup; everything lives in the account already + configured in `~/.snowflake/connections.toml`. From aa3ea4392120668ee616dda616cd0d4ccc0b6ff7 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 14:06:37 -0500 Subject: [PATCH 04/30] docs: implementation plan for Embucket vs Snowflake parity harness 10 tasks: deps + schema split + load refactor + profile + parity script + snowflake setup/refresh + README + end-to-end smoke. Unit-tested hash and diff logic; integration steps exercise real Athena and Snowflake. --- ...26-04-22-embucket-snowflake-parity-plan.md | 1239 +++++++++++++++++ 1 file changed, 1239 insertions(+) create mode 100644 specs/2026-04-22-embucket-snowflake-parity-plan.md diff --git a/specs/2026-04-22-embucket-snowflake-parity-plan.md b/specs/2026-04-22-embucket-snowflake-parity-plan.md new file mode 100644 index 0000000..66b4822 --- /dev/null +++ b/specs/2026-04-22-embucket-snowflake-parity-plan.md @@ -0,0 +1,1239 @@ +# Embucket vs Snowflake parity harness — Implementation Plan + +> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** Run dbt-snowplow-web on both Embucket and Snowflake against the same S3 Tables Iceberg source, loaded in two 30-minute Athena batches, and diff the three headline derived tables after each `dbt run` to surface semantic drift. + +**Architecture:** Single dbt project, two targets (`embucket` default, `snowflake` added) under the existing `embucket_demo` profile. Both read `atomic.events_0416` — Embucket natively; Snowflake via a Snowflake-managed Iceberg table over S3 Tables. Athena CTAS is split into idempotent `init` (empty CREATE TABLE via CTAS-with-WHERE-1=0) + parametrized `insert` (INSERT INTO with load_tstamp range) so the source can be batch-loaded. Parity script rowcount- and MD5-diffs `snowplow_web_page_views`, `snowplow_web_sessions`, `snowplow_web_users` on both sides. + +**Tech Stack:** Python 3.10+ (uv), boto3 (Athena), snowflake-connector-python, dbt-core, dbt-embucket, dbt-snowflake, Athena SQL (Iceberg/Trino), Snowflake SQL. + +**Reference spec:** `specs/2026-04-22-embucket-snowflake-parity-design.md`. + +--- + +## Task 1: Bump `snowplow__start_date` to match the fresh data window + +**Files:** +- Modify: `dbt_project.yml` + +- [ ] **Step 1: Edit `dbt_project.yml`** + +Replace the line ` snowplow__start_date: '2026-04-13'` with: + +```yaml + snowplow__start_date: '2026-04-22' +``` + +- [ ] **Step 2: Verify** + +Run: `grep snowplow__start_date dbt_project.yml` +Expected: ` snowplow__start_date: '2026-04-22'` + +- [ ] **Step 3: Commit** + +```bash +git add dbt_project.yml +git commit -m "chore: bump snowplow__start_date to 2026-04-22 for parity batches" +``` + +--- + +## Task 2: Add `dbt-snowflake` and `snowflake-connector-python` dependencies + +**Files:** +- Modify: `pyproject.toml` + +- [ ] **Step 1: Edit `pyproject.toml`** + +Replace the `dependencies = [...]` block with: + +```toml +dependencies = [ + "boto3>=1.34.0", + "dbt-core>=1.11.0,<2.0", + "dbt-embucket>=0.1.2", + "dbt-snowflake>=1.8.0,<2.0", + "fastavro>=1.9.0", + "pyarrow>=20.0.0", + "s3fs>=2025.3.0", + "snowflake-connector-python>=3.7.0", +] +``` + +- [ ] **Step 2: Sync dependencies** + +Run: `uv sync` +Expected: exits 0; `uv.lock` updated; `dbt-snowflake` and `snowflake-connector-python` present in `uv.lock`. + +- [ ] **Step 3: Verify dbt-snowflake is importable** + +Run: `uv run python -c "import dbt.adapters.snowflake; print(dbt.adapters.snowflake.__version__)"` +Expected: a version string (e.g. `1.8.x` or later). No errors. + +- [ ] **Step 4: Verify snowflake-connector-python is importable** + +Run: `uv run python -c "import snowflake.connector; print(snowflake.connector.__version__)"` +Expected: a version string. No errors. + +- [ ] **Step 5: Commit** + +```bash +git add pyproject.toml uv.lock +git commit -m "build: add dbt-snowflake and snowflake-connector-python deps" +``` + +--- + +## Task 3: Extract the projection SELECT into a shared SQL file + +The current `scripts/ctas_from_glue.sql` couples `CREATE TABLE AS` with the projection and a hardcoded filter. Split so `init` and `insert` reuse one SELECT body. + +**Files:** +- Create: `scripts/events_0416_select.sql` + +- [ ] **Step 1: Create `scripts/events_0416_select.sql`** + +Copy the body of the existing `scripts/ctas_from_glue.sql` **starting at the `SELECT` keyword** (line 30 in the current file) through the `FROM "awsdatacatalog"."analytics_glue"."hooli_events_0417_v2"` line. Do **not** include the `CREATE TABLE events_0416 WITH (...) AS` header or the existing `WHERE load_tstamp >= TIMESTAMP '2026-04-17 19:47:00 UTC'` filter. After the `FROM` line, append a single placeholder line: + +```sql +WHERE {{where}} +``` + +The resulting file starts with `SELECT` and ends with `WHERE {{where}}`. Preserve all casts exactly as in `ctas_from_glue.sql`. This is the canonical projection Python will wrap at render time. + +- [ ] **Step 2: Verify line count and boundaries** + +Run: +```bash +head -1 scripts/events_0416_select.sql +tail -2 scripts/events_0416_select.sql +wc -l scripts/events_0416_select.sql +``` +Expected: first line starts with `SELECT`; last two lines are the `FROM "awsdatacatalog"...` line and `WHERE {{where}}`; line count roughly 145 (the current CTAS body minus the 4-line CREATE TABLE header and the filter lines, plus the 1-line placeholder). + +- [ ] **Step 3: Commit** + +```bash +git add scripts/events_0416_select.sql +git commit -m "refactor: extract events_0416 projection SELECT into reusable file" +``` + +--- + +## Task 4: Refactor `scripts/load_from_glue.py` into `init` / `insert` subcommands + +Replace the single CTAS flow with two idempotent subcommands that share the projection from Task 3. + +**Files:** +- Replace: `scripts/load_from_glue.py` +- Delete: `scripts/ctas_from_glue.sql` + +- [ ] **Step 1: Replace `scripts/load_from_glue.py` with the subcommand version** + +Overwrite the file with: + +```python +#!/usr/bin/env python3 +"""Load the Snowplow Glue Iceberg source into Embucket's S3 Tables bucket. + +Two subcommands: + init drop the target table, CREATE TABLE with the correct schema + partitioning + (via CTAS WHERE 1=0 so the SELECT drives column types) but zero rows. + insert INSERT INTO the target using the same projection filtered to a + [start, end) load_tstamp window. + +Both subcommands share scripts/events_0416_select.sql as the projection body. + +Usage: + uv run python scripts/load_from_glue.py init + uv run python scripts/load_from_glue.py insert \\ + --start '2026-04-22 15:00:00' --end '2026-04-22 15:30:00' +""" + +from __future__ import annotations + +import argparse +import time +from pathlib import Path + +import boto3 + +TARGET_CATALOG = "s3tablescatalog/snowplow" +TARGET_SCHEMA = "atomic" +TARGET_TABLE = "events_0416" +DEFAULT_QUERY_OUTPUT = "s3://athena-query-results-us-east-2-767397688925/embucket-snowplow/" +DEFAULT_WORKGROUP = "primary" +DEFAULT_REGION = "us-east-2" + +ICEBERG_PROPS = """WITH ( + table_type = 'ICEBERG', + is_external = false, + format = 'PARQUET', + write_compression = 'ZSTD', + partitioning = ARRAY['day(load_tstamp)', 'event_name'] +)""" + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--query-output-location", default=DEFAULT_QUERY_OUTPUT) + parser.add_argument("--workgroup", default=DEFAULT_WORKGROUP) + parser.add_argument("--region", default=DEFAULT_REGION) + + sub = parser.add_subparsers(dest="cmd", required=True) + sub.add_parser("init", help="Drop and create empty events_0416") + ins = sub.add_parser("insert", help="Insert a [start, end) load_tstamp window") + ins.add_argument("--start", required=True, help="ISO timestamp, e.g. '2026-04-22 15:00:00'") + ins.add_argument("--end", required=True, help="ISO timestamp, e.g. '2026-04-22 15:30:00'") + return parser.parse_args() + + +def render_select(where_clause: str) -> str: + body = (Path(__file__).parent / "events_0416_select.sql").read_text() + return body.replace("{{where}}", where_clause) + + +def run_query(client, sql: str, output_location: str, workgroup: str, + catalog: str | None = None, database: str | None = None) -> str: + ctx = {} + if catalog: + ctx["Catalog"] = catalog + if database: + ctx["Database"] = database + kwargs = { + "QueryString": sql, + "ResultConfiguration": {"OutputLocation": output_location}, + "WorkGroup": workgroup, + } + if ctx: + kwargs["QueryExecutionContext"] = ctx + qid = client.start_query_execution(**kwargs)["QueryExecutionId"] + print(f" athena query id: {qid}") + while True: + resp = client.get_query_execution(QueryExecutionId=qid) + state = resp["QueryExecution"]["Status"]["State"] + if state in {"SUCCEEDED", "FAILED", "CANCELLED"}: + break + time.sleep(2) + if state != "SUCCEEDED": + reason = resp["QueryExecution"]["Status"].get("StateChangeReason", "") + raise RuntimeError(f"Athena query {state}: {reason}") + return qid + + +def count_rows(client, output_location: str, workgroup: str) -> str: + qid = run_query( + client, + f"SELECT COUNT(*) FROM {TARGET_TABLE}", + output_location, + workgroup, + catalog=TARGET_CATALOG, + database=TARGET_SCHEMA, + ) + result = client.get_query_results(QueryExecutionId=qid) + return result["ResultSet"]["Rows"][1]["Data"][0]["VarCharValue"] + + +def cmd_init(args, client) -> None: + target_fqn = f"{TARGET_CATALOG}.{TARGET_SCHEMA}.{TARGET_TABLE}" + print(f"Dropping {target_fqn} if it exists...") + run_query( + client, + f"DROP TABLE IF EXISTS {TARGET_TABLE}", + args.query_output_location, + args.workgroup, + catalog=TARGET_CATALOG, + database=TARGET_SCHEMA, + ) + select_body = render_select("1=0") + sql = f"CREATE TABLE {TARGET_TABLE}\n{ICEBERG_PROPS} AS\n{select_body}" + print(f"Creating empty {target_fqn}...") + run_query( + client, sql, args.query_output_location, args.workgroup, + catalog=TARGET_CATALOG, database=TARGET_SCHEMA, + ) + print(f"Done. {target_fqn} row count: {count_rows(client, args.query_output_location, args.workgroup)}") + + +def cmd_insert(args, client) -> None: + target_fqn = f"{TARGET_CATALOG}.{TARGET_SCHEMA}.{TARGET_TABLE}" + where = ( + f"load_tstamp >= TIMESTAMP '{args.start}' " + f"AND load_tstamp < TIMESTAMP '{args.end}'" + ) + select_body = render_select(where) + sql = f"INSERT INTO {TARGET_TABLE}\n{select_body}" + print(f"Inserting into {target_fqn} for [{args.start}, {args.end})...") + run_query( + client, sql, args.query_output_location, args.workgroup, + catalog=TARGET_CATALOG, database=TARGET_SCHEMA, + ) + print(f"Done. {target_fqn} row count: {count_rows(client, args.query_output_location, args.workgroup)}") + + +def main() -> None: + args = parse_args() + client = boto3.client("athena", region_name=args.region) + if args.cmd == "init": + cmd_init(args, client) + elif args.cmd == "insert": + cmd_insert(args, client) + else: + raise SystemExit(f"unknown subcommand {args.cmd}") + + +if __name__ == "__main__": + main() +``` + +- [ ] **Step 2: Delete the old `ctas_from_glue.sql`** + +Run: `git rm scripts/ctas_from_glue.sql` + +- [ ] **Step 3: Smoke-test `--help`** + +Run: `uv run python scripts/load_from_glue.py --help` +Expected: usage text showing `init` and `insert` subcommands. + +Run: `uv run python scripts/load_from_glue.py insert --help` +Expected: shows required `--start` and `--end` args. + +- [ ] **Step 4: Smoke-test `init` against the real Athena** + +Run: `uv run python scripts/load_from_glue.py init` +Expected: prints "Dropping ... / Creating empty ... / Done. ... row count: 0". Takes 30-90s. On failure, inspect the printed Athena query id in the Athena console. + +- [ ] **Step 5: Verify table exists and is empty** + +Run: +```bash +aws athena start-query-execution \ + --query-string "SELECT COUNT(*) FROM \"s3tablescatalog/snowplow\".\"atomic\".\"events_0416\"" \ + --work-group primary \ + --result-configuration OutputLocation=s3://athena-query-results-us-east-2-767397688925/embucket-snowplow/ \ + --query-execution-context Database=atomic \ + --query 'QueryExecutionId' --output text +``` +Wait a few seconds, then `aws athena get-query-results --query-execution-id `. Expected: count = 0. + +- [ ] **Step 6: Smoke-test `insert` with a tiny window (5 minutes)** + +Run: +```bash +uv run python scripts/load_from_glue.py insert \ + --start '2026-04-22 15:00:00' --end '2026-04-22 15:05:00' +``` +Expected: prints a non-zero row count on completion (roughly 500K rows for 5 min). If it fails with a schema-mismatch error, the SELECT projection in `events_0416_select.sql` is out of sync with the CREATE's inferred schema — re-run Task 3 and confirm only the `CREATE TABLE ... AS` header and the WHERE line were modified. + +- [ ] **Step 7: Re-run `init` to reset** + +Run: `uv run python scripts/load_from_glue.py init` +Expected: row count 0. + +- [ ] **Step 8: Commit** + +```bash +git add scripts/load_from_glue.py scripts/ctas_from_glue.sql +git commit -m "refactor: split load_from_glue into init/insert subcommands" +``` + +--- + +## Task 5: Add `snowflake` output to `profiles.yml.example` and local `profiles.yml` + +**Files:** +- Modify: `profiles.yml.example` +- Modify: `profiles.yml` (gitignored, not committed) + +- [ ] **Step 1: Edit `profiles.yml.example`** + +Replace its contents with: + +```yaml +embucket_demo: + target: dev + outputs: + dev: + type: embucket + function_arn: "YOUR_LAMBDA_ARN_HERE" + account: "embucket" + user: "demo_user" + password: "demo_password_2026" + database: "demo" + schema: "atomic" + threads: 1 + snowflake: + type: snowflake + account: YOUR_SNOWFLAKE_ACCOUNT + user: YOUR_SNOWFLAKE_USER + password: YOUR_SNOWFLAKE_PASSWORD + role: YOUR_SNOWFLAKE_ROLE + warehouse: YOUR_SNOWFLAKE_WAREHOUSE + database: sturukin + schema: atomic + threads: 4 +``` + +Note: the previous `dev` output is kept; the new `snowflake` output is added as a sibling so `dbt run --target snowflake` works alongside the default `dev` (Embucket) target. + +- [ ] **Step 2: Edit local `profiles.yml` with real values** + +Append a `snowflake:` block under `outputs:` in the gitignored `profiles.yml` so it reads: + +```yaml +embucket_demo: + target: dev + outputs: + dev: + type: embucket + function_arn: "arn:aws:lambda:us-east-2:767397688925:function:embucket-demo-embucket-demo-ramp-1775514830" + account: "embucket" + user: "demo_user" + password: "demo_password_2026" + database: "demo" + schema: "atomic" + threads: 1 + snowflake: + type: snowflake + account: aa06228.us-east-2.aws + user: rampage644 + password: "9i8u7y6T?" + role: ACCOUNTADMIN + warehouse: compute_wh + database: sturukin + schema: atomic + threads: 4 +``` + +(Existing `dev` block's `function_arn` is preserved — only the `snowflake` block is added.) + +- [ ] **Step 3: Verify `profiles.yml` is gitignored** + +Run: `git check-ignore profiles.yml` +Expected: `profiles.yml` (the file is ignored — no accidental credential commit). + +- [ ] **Step 4: Verify dbt sees both targets** + +Run: `uv run dbt debug --profiles-dir . --target snowflake` +Expected: "All checks passed!" including connection to Snowflake. (If the Snowflake `sturukin` database does not exist yet, dbt debug may still pass the auth check but fail the "database exists" check — that is fine; Task 7 creates it.) + +- [ ] **Step 5: Commit only the example file** + +```bash +git add profiles.yml.example +git commit -m "chore: add snowflake output template to profiles.yml.example" +``` + +--- + +## Task 6: Write `scripts/parity.py` — the diff harness (TDD) + +Pure hash/diff logic is unit-tested; the DB-connection layer is a thin wrapper exercised in the end-to-end task. + +**Files:** +- Create: `scripts/parity.py` +- Create: `tests/test_parity.py` +- Create: `tests/__init__.py` + +- [ ] **Step 1: Create empty test package marker** + +Run: `touch tests/__init__.py` + +- [ ] **Step 2: Write the failing test for `diff_sides`** + +Create `tests/test_parity.py`: + +```python +"""Unit tests for the pure diff logic in scripts/parity.py.""" + +from __future__ import annotations + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts")) + +from parity import DiffResult, diff_sides, row_hash + + +def test_diff_sides_all_match(): + left = {"k1": "h1", "k2": "h2"} + right = {"k1": "h1", "k2": "h2"} + result = diff_sides(left, right) + assert result == DiffResult( + matched=2, mismatched=[], only_left=[], only_right=[] + ) + + +def test_diff_sides_hash_mismatch(): + left = {"k1": "h1", "k2": "h2"} + right = {"k1": "h1", "k2": "DIFFERENT"} + result = diff_sides(left, right) + assert result.matched == 1 + assert result.mismatched == ["k2"] + assert result.only_left == [] + assert result.only_right == [] + + +def test_diff_sides_only_in_left(): + left = {"k1": "h1", "k2": "h2"} + right = {"k1": "h1"} + result = diff_sides(left, right) + assert result.matched == 1 + assert result.only_left == ["k2"] + assert result.only_right == [] + + +def test_diff_sides_only_in_right(): + left = {"k1": "h1"} + right = {"k1": "h1", "k2": "h2"} + result = diff_sides(left, right) + assert result.matched == 1 + assert result.only_left == [] + assert result.only_right == ["k2"] + + +def test_diff_sides_empty_both_sides(): + result = diff_sides({}, {}) + assert result == DiffResult(matched=0, mismatched=[], only_left=[], only_right=[]) + + +def test_row_hash_deterministic_and_null_handling(): + h1 = row_hash(["a", None, 1, 2.5]) + h2 = row_hash(["a", None, 1, 2.5]) + assert h1 == h2 + # None is distinguishable from the string 'None' + assert row_hash([None]) != row_hash(["None"]) + # Order matters + assert row_hash(["a", "b"]) != row_hash(["b", "a"]) +``` + +- [ ] **Step 3: Run the test — it must fail with an import error** + +Run: `uv run python -m pytest tests/test_parity.py -v` +Expected: FAILS with `ModuleNotFoundError: No module named 'parity'` (the file doesn't exist yet). + +- [ ] **Step 4: Create `scripts/parity.py` with the pure logic** + +```python +#!/usr/bin/env python3 +"""Compare Embucket and Snowflake outputs of dbt-snowplow-web. + +For each of snowplow_web_page_views, snowplow_web_sessions, snowplow_web_users: + * rowcount on both sides must match + * MD5 hash of every row (by the model's declared column list) must match + when keyed by the table's natural key + +Exits non-zero on any mismatch. Prints up to 10 example natural keys per +mismatch bucket. + +Usage: + uv run python scripts/parity.py # full check + uv run python scripts/parity.py --source-only # just events_0416 rowcount + schema +""" + +from __future__ import annotations + +import argparse +import hashlib +import sys +from dataclasses import dataclass, field +from pathlib import Path + +import snowflake.connector + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import embucket_client # noqa: E402 + +# dbt default schemas: _derived +EMBUCKET_DERIVED = "demo.atomic_derived" +SNOWFLAKE_DERIVED = "sturukin.atomic_derived" + +EMBUCKET_LAMBDA_ARN = ( + "arn:aws:lambda:us-east-2:767397688925:function:" + "embucket-demo-embucket-demo-ramp-1775514830" +) + + +@dataclass +class TableSpec: + name: str + natural_key: str + columns: list[str] + + +# Column lists come from the dbt-snowplow-web model yaml files. +# Keep in sync with dbt_packages/snowplow_web/models//.yml. +# (Comparison uses the intersection of columns present on both engines at +# runtime — see fetch_rows — so extra engine-specific metadata columns like +# _dbt_inserted_at do not break the diff.) +TABLES = [ + TableSpec( + name="snowplow_web_page_views", + natural_key="page_view_id", + columns=[ + "page_view_id", "event_id", "app_id", "user_id", "domain_userid", + "stitched_user_id", "network_userid", "domain_sessionid", + "domain_sessionidx", "page_view_in_session_index", + "page_views_in_session", "dvce_created_tstamp", "collector_tstamp", + "derived_tstamp", "start_tstamp", "end_tstamp", "model_tstamp", + "engaged_time_in_s", "absolute_time_in_s", + "horizontal_pixels_scrolled", "vertical_pixels_scrolled", + "horizontal_percentage_scrolled", "vertical_percentage_scrolled", + "doc_width", "doc_height", "page_title", "page_url", + "page_urlscheme", "page_urlhost", "page_urlpath", "page_urlquery", + "page_urlfragment", "mkt_medium", "mkt_source", "mkt_term", + "mkt_content", "mkt_campaign", "mkt_clickid", "mkt_network", + "page_referrer", "refr_urlscheme", "refr_urlhost", "refr_urlpath", + "refr_urlquery", "refr_urlfragment", "refr_medium", "refr_source", + "refr_term", "geo_country", "geo_region", "geo_region_name", + "geo_city", "geo_zipcode", "geo_latitude", "geo_longitude", + "geo_timezone", "user_ipaddress", "useragent", "br_lang", + "br_viewwidth", "br_viewheight", "br_colordepth", "br_renderengine", + "os_timezone", + ], + ), + TableSpec( + name="snowplow_web_sessions", + natural_key="domain_sessionid", + columns=[ + "app_id", "domain_sessionid", "domain_sessionidx", "start_tstamp", + "end_tstamp", "model_tstamp", "user_id", "domain_userid", + "stitched_user_id", "network_userid", "page_views", + "engaged_time_in_s", "total_events", "is_engaged", + "absolute_time_in_s", "first_page_title", "first_page_url", + "last_page_title", "last_page_url", "referrer", + "geo_country", "geo_region", "geo_city", "geo_timezone", + "user_ipaddress", "useragent", "br_lang", + ], + ), + TableSpec( + name="snowplow_web_users", + natural_key="domain_userid", + columns=[ + "user_id", "domain_userid", "network_userid", "start_tstamp", + "end_tstamp", "model_tstamp", "page_views", "sessions", + "engaged_time_in_s", "first_page_title", "first_page_url", + "first_geo_country", "first_geo_city", + "last_page_title", "last_page_url", "last_geo_country", + "last_geo_city", "referrer", + ], + ), +] + + +@dataclass +class DiffResult: + matched: int + mismatched: list[str] = field(default_factory=list) + only_left: list[str] = field(default_factory=list) + only_right: list[str] = field(default_factory=list) + + +def row_hash(values: list) -> str: + """Hash a row's values deterministically, distinguishing NULL from string.""" + parts = [] + for v in values: + if v is None: + parts.append("\x00NULL\x00") + else: + parts.append(str(v)) + joined = "\x01".join(parts) + return hashlib.md5(joined.encode("utf-8")).hexdigest() + + +def diff_sides(left: dict, right: dict) -> DiffResult: + matched = 0 + mismatched: list[str] = [] + only_left: list[str] = [] + only_right: list[str] = [] + for key, lhash in left.items(): + if key not in right: + only_left.append(key) + elif right[key] != lhash: + mismatched.append(key) + else: + matched += 1 + for key in right: + if key not in left: + only_right.append(key) + return DiffResult(matched=matched, mismatched=mismatched, + only_left=only_left, only_right=only_right) + + +# --- DB access (integration-level, not unit-tested) -------------------------- + +def sf_connect(): + return snowflake.connector.connect(connection_name="default") + + +def emb_session(): + client = embucket_client.lambda_client(EMBUCKET_LAMBDA_ARN) + token = embucket_client.login(client, EMBUCKET_LAMBDA_ARN) + return client, token + + +def sf_rowcount(conn, fqn: str) -> int: + cur = conn.cursor() + try: + cur.execute(f"SELECT COUNT(*) FROM {fqn}") + return int(cur.fetchone()[0]) + finally: + cur.close() + + +def emb_rowcount(client, token, fqn: str) -> int: + body = embucket_client.run_sql(client, EMBUCKET_LAMBDA_ARN, token, + f"SELECT COUNT(*) FROM {fqn}") + return int(body["data"]["rowset"][0][0]) + + +def sf_hashes(conn, fqn: str, spec: TableSpec) -> dict: + cols = ", ".join(spec.columns) + cur = conn.cursor() + try: + cur.execute(f"SELECT {spec.natural_key}, {cols} FROM {fqn}") + return {row[0]: row_hash(list(row[1:])) for row in cur.fetchall()} + finally: + cur.close() + + +def emb_hashes(client, token, fqn: str, spec: TableSpec) -> dict: + cols = ", ".join(spec.columns) + body = embucket_client.run_sql( + client, EMBUCKET_LAMBDA_ARN, token, + f"SELECT {spec.natural_key}, {cols} FROM {fqn}", + ) + out = {} + for row in body["data"]["rowset"]: + out[row[0]] = row_hash(list(row[1:])) + return out + + +# --- Orchestration ---------------------------------------------------------- + +def print_diff(spec: TableSpec, diff: DiffResult) -> None: + print(f" matched: {diff.matched}") + print(f" mismatched: {len(diff.mismatched)}") + print(f" only_embucket: {len(diff.only_left)}") + print(f" only_snowflake: {len(diff.only_right)}") + for bucket_name, keys in [ + ("mismatched", diff.mismatched), + ("only_embucket", diff.only_left), + ("only_snowflake", diff.only_right), + ]: + if keys: + sample = keys[:10] + print(f" first {len(sample)} {bucket_name} {spec.natural_key}:") + for k in sample: + print(f" {k}") + + +def run(source_only: bool) -> int: + any_fail = False + sf_conn = sf_connect() + emb_client, emb_token = emb_session() + + # Source-level check: events_0416 rowcount and (implicit) presence. + emb_src = emb_rowcount(emb_client, emb_token, "demo.atomic.events_0416") + sf_src = sf_rowcount(sf_conn, "sturukin.atomic.events_0416") + print(f"source events_0416: embucket={emb_src} snowflake={sf_src}") + if emb_src != sf_src: + print(" FAIL: source rowcount mismatch") + any_fail = True + + if source_only: + return 1 if any_fail else 0 + + for spec in TABLES: + emb_fqn = f"{EMBUCKET_DERIVED}.{spec.name}" + sf_fqn = f"{SNOWFLAKE_DERIVED}.{spec.name}" + emb_count = emb_rowcount(emb_client, emb_token, emb_fqn) + sf_count = sf_rowcount(sf_conn, sf_fqn) + print(f"\n{spec.name}: embucket={emb_count} snowflake={sf_count}") + if emb_count != sf_count: + any_fail = True + emb = emb_hashes(emb_client, emb_token, emb_fqn, spec) + sf = sf_hashes(sf_conn, sf_fqn, spec) + diff = diff_sides(emb, sf) + print_diff(spec, diff) + if diff.mismatched or diff.only_left or diff.only_right: + any_fail = True + + return 1 if any_fail else 0 + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--source-only", action="store_true", + help="Only diff events_0416 source rowcount, skip derived tables") + args = parser.parse_args() + sys.exit(run(args.source_only)) + + +if __name__ == "__main__": + main() +``` + +- [ ] **Step 5: Run the tests — they must pass** + +Run: `uv run python -m pytest tests/test_parity.py -v` +Expected: 6 passed. + +- [ ] **Step 6: Smoke-test `--help`** + +Run: `uv run python scripts/parity.py --help` +Expected: usage text including `--source-only`. No import errors. + +- [ ] **Step 7: Commit** + +```bash +git add scripts/parity.py tests/__init__.py tests/test_parity.py +git commit -m "feat: add parity.py — rowcount + MD5 diff across Embucket and Snowflake" +``` + +--- + +## Task 7: Write `scripts/snowflake_setup.py` + +One-shot idempotent setup of the Snowflake external volume, catalog integration, database/schema, and the Iceberg table reading `events_0416` from the S3 Tables bucket. + +**Files:** +- Create: `scripts/snowflake_setup.py` + +- [ ] **Step 1: Create `scripts/snowflake_setup.py`** + +```python +#!/usr/bin/env python3 +"""Idempotent Snowflake setup for the parity harness. + +Probes for existing external volume and catalog integration; creates them if +missing; ensures sturukin.atomic exists; (re)creates the managed Iceberg +table pointing at the S3 Tables bucket's atomic.events_0416 table. + +Usage: + uv run python scripts/snowflake_setup.py +""" + +from __future__ import annotations + +import argparse +import sys + +import snowflake.connector + +DATABASE = "sturukin" +SCHEMA = "atomic" +ICEBERG_TABLE = "events_0416" + +# S3 Tables bucket Embucket is volumed to. +S3_TABLES_ARN = "arn:aws:s3tables:us-east-2:767397688925:bucket/snowplow" +NAMESPACE = "atomic" # S3 Tables namespace inside the bucket +EXTERNAL_VOLUME_NAME = "snowplow_vol" +CATALOG_INTEGRATION_NAME = "snowplow_s3t" + + +def exec_one(conn, sql: str): + cur = conn.cursor() + try: + cur.execute(sql) + return cur.fetchall() if cur.description else [] + finally: + cur.close() + + +def volume_exists(conn) -> bool: + rows = exec_one(conn, "SHOW EXTERNAL VOLUMES") + names = [r[0] for r in rows] # column 0 is the volume name + return any(n.upper() == EXTERNAL_VOLUME_NAME.upper() for n in names) + + +def integration_exists(conn) -> bool: + rows = exec_one(conn, "SHOW CATALOG INTEGRATIONS") + names = [r[0] for r in rows] + return any(n.upper() == CATALOG_INTEGRATION_NAME.upper() for n in names) + + +def ensure_volume(conn): + if volume_exists(conn): + print(f" external volume {EXTERNAL_VOLUME_NAME} already exists — reusing") + return + print(f" creating external volume {EXTERNAL_VOLUME_NAME}...") + exec_one(conn, f""" + CREATE EXTERNAL VOLUME {EXTERNAL_VOLUME_NAME} + STORAGE_LOCATIONS = ( + ( + NAME = 's3tables_snowplow' + STORAGE_PROVIDER = 'S3TABLES' + STORAGE_BASE_URL = 's3tables://{S3_TABLES_ARN}' + STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::767397688925:role/SnowflakeS3TablesAccessRole' + ) + ) + ALLOW_WRITES = FALSE + """) + # After creation, fetch DESC output so the operator can copy the IAM user + # Snowflake wants trusted; if the trust policy is already in place this is a + # no-op, but the operator may need to paste STORAGE_AWS_IAM_USER_ARN and + # STORAGE_AWS_EXTERNAL_ID into the role's trust policy. + desc = exec_one(conn, f"DESC EXTERNAL VOLUME {EXTERNAL_VOLUME_NAME}") + print(" DESC EXTERNAL VOLUME output (copy STORAGE_AWS_IAM_USER_ARN / " + "STORAGE_AWS_EXTERNAL_ID into the target role's trust policy if not already):") + for row in desc: + print(f" {row}") + + +def ensure_integration(conn): + if integration_exists(conn): + print(f" catalog integration {CATALOG_INTEGRATION_NAME} already exists — reusing") + return + print(f" creating catalog integration {CATALOG_INTEGRATION_NAME}...") + exec_one(conn, f""" + CREATE CATALOG INTEGRATION {CATALOG_INTEGRATION_NAME} + CATALOG_SOURCE = ICEBERG_REST + TABLE_FORMAT = ICEBERG + CATALOG_NAMESPACE = '{NAMESPACE}' + REST_CONFIG = ( + CATALOG_URI = 'https://s3tables.us-east-2.amazonaws.com/iceberg' + CATALOG_NAME = 's3tablescatalog/snowplow' + ) + REST_AUTHENTICATION = ( + TYPE = SIGV4 + SIGV4_IAM_ROLE = 'arn:aws:iam::767397688925:role/SnowflakeS3TablesAccessRole' + SIGV4_SIGNING_REGION = 'us-east-2' + ) + ENABLED = TRUE + """) + + +def ensure_db_schema(conn): + exec_one(conn, f"CREATE DATABASE IF NOT EXISTS {DATABASE}") + exec_one(conn, f"CREATE SCHEMA IF NOT EXISTS {DATABASE}.{SCHEMA}") + # dbt creates its derived/scratch/manifest schemas on first run; no-op here. + + +def recreate_iceberg_table(conn): + fqn = f"{DATABASE}.{SCHEMA}.{ICEBERG_TABLE}" + print(f" (re)creating iceberg table {fqn}...") + exec_one(conn, f""" + CREATE OR REPLACE ICEBERG TABLE {fqn} + EXTERNAL_VOLUME = '{EXTERNAL_VOLUME_NAME}' + CATALOG = '{CATALOG_INTEGRATION_NAME}' + CATALOG_TABLE_NAME = '{ICEBERG_TABLE}' + AUTO_REFRESH = FALSE + """) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--connection", default="default", + help="snow CLI connection name (default: default)") + args = parser.parse_args() + + conn = snowflake.connector.connect(connection_name=args.connection) + try: + print("== Snowflake parity setup ==") + ensure_volume(conn) + ensure_integration(conn) + ensure_db_schema(conn) + recreate_iceberg_table(conn) + + count = exec_one(conn, f"SELECT COUNT(*) FROM {DATABASE}.{SCHEMA}.{ICEBERG_TABLE}")[0][0] + print(f"\n{DATABASE}.{SCHEMA}.{ICEBERG_TABLE} row count after setup: {count}") + finally: + conn.close() + + +if __name__ == "__main__": + main() +``` + +- [ ] **Step 2: Smoke-test `--help`** + +Run: `uv run python scripts/snowflake_setup.py --help` +Expected: usage text. No import errors. + +- [ ] **Step 3: Run against the real Snowflake account** + +Run: `uv run python scripts/snowflake_setup.py` + +Expected outcomes, in order of likelihood: + 1. **Success on first run**: volume + integration get created (or reused), iceberg table is created, row count prints (matches current `events_0416` state — 0 if Task 4 Step 7 reset it). + 2. **Failure at `ensure_volume` with a trust-policy error**: the `SnowflakeS3TablesAccessRole` IAM role does not exist or does not trust Snowflake. Two fallbacks: + - If an external volume already exists under a different name for this bucket, rename `EXTERNAL_VOLUME_NAME` in the script to match (reuses it) and re-run. `SHOW EXTERNAL VOLUMES` output from the failure lists candidates. + - If no existing volume or role, stop — IAM role creation is outside the scope of this harness. Document as a prerequisite and revisit. + 3. **Failure at `ensure_integration` with `CATALOG_SOURCE` / `TABLE_FORMAT` unsupported**: the Snowflake account does not have S3 Tables REST integration enabled. Fall back to Glue-federated integration by replacing the `ensure_integration` body with: + ```python + exec_one(conn, f""" + CREATE CATALOG INTEGRATION {CATALOG_INTEGRATION_NAME} + CATALOG_SOURCE = GLUE + CATALOG_NAMESPACE = 's3tablescatalog/snowplow.atomic' + TABLE_FORMAT = ICEBERG + GLUE_AWS_ROLE_ARN = 'arn:aws:iam::767397688925:role/SnowflakeS3TablesAccessRole' + GLUE_CATALOG_ID = '767397688925' + GLUE_REGION = 'us-east-2' + ENABLED = TRUE + """) + ``` + and re-run. + +On success, verify by running: +```bash +uv run python -c " +import snowflake.connector +c = snowflake.connector.connect(connection_name='default') +cur = c.cursor() +cur.execute('SELECT COUNT(*) FROM sturukin.atomic.events_0416') +print(cur.fetchone()) +" +``` +Expected: the same count printed by the setup script. + +- [ ] **Step 4: Commit** + +```bash +git add scripts/snowflake_setup.py +git commit -m "feat: add snowflake_setup.py — external volume, catalog integration, iceberg table" +``` + +--- + +## Task 8: Write `scripts/snowflake_refresh.py` + +**Files:** +- Create: `scripts/snowflake_refresh.py` + +- [ ] **Step 1: Create `scripts/snowflake_refresh.py`** + +```python +#!/usr/bin/env python3 +"""Refresh the Snowflake Iceberg view of atomic.events_0416 after an Athena write. + +Snowflake-managed Iceberg tables over externally-written data do not +auto-refresh; this script issues ALTER ICEBERG TABLE ... REFRESH and prints +the post-refresh row count. + +Usage: + uv run python scripts/snowflake_refresh.py +""" + +from __future__ import annotations + +import argparse + +import snowflake.connector + +FQN = "sturukin.atomic.events_0416" + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--connection", default="default") + args = parser.parse_args() + + conn = snowflake.connector.connect(connection_name=args.connection) + try: + cur = conn.cursor() + print(f"Refreshing {FQN}...") + cur.execute(f"ALTER ICEBERG TABLE {FQN} REFRESH") + cur.execute(f"SELECT COUNT(*) FROM {FQN}") + count = cur.fetchone()[0] + print(f"{FQN} row count: {count}") + finally: + conn.close() + + +if __name__ == "__main__": + main() +``` + +- [ ] **Step 2: Smoke-test `--help`** + +Run: `uv run python scripts/snowflake_refresh.py --help` +Expected: usage text. + +- [ ] **Step 3: Run against Snowflake** + +Run: `uv run python scripts/snowflake_refresh.py` +Expected: prints a row count (0 if still reset from Task 4, otherwise whatever rows are present). + +- [ ] **Step 4: Commit** + +```bash +git add scripts/snowflake_refresh.py +git commit -m "feat: add snowflake_refresh.py — ALTER ICEBERG TABLE REFRESH wrapper" +``` + +--- + +## Task 9: README addendum for the parity flow + +**Files:** +- Modify: `README.md` + +- [ ] **Step 1: Read current README end** + +Run: `tail -30 README.md` + +- [ ] **Step 2: Append a new section** + +Append to `README.md`: + +````markdown +## Comparing Embucket vs Snowflake + +This harness runs the same dbt-snowplow-web models on both engines against +the same S3 Tables Iceberg source, loaded in two 30-minute batches, and +diffs the three headline derived tables after each run. + +Prerequisites: +- A Snowflake account with access to `~/.snowflake/connections.toml` + `[connections.default]` (database `sturukin` will be created if missing). +- A pre-existing IAM role `SnowflakeS3TablesAccessRole` with permissions + to the S3 Tables bucket, trusted by Snowflake. If you don't have one, + `snowflake_setup.py` will print setup details from `DESC EXTERNAL VOLUME`. +- `profiles.yml` contains a `snowflake` output under `embucket_demo.outputs` + (see `profiles.yml.example`). + +Run flow: + +```bash +# 1. One-time Snowflake setup (external volume, catalog integration, iceberg table) +uv run python scripts/snowflake_setup.py + +# 2. Reset the shared Athena-managed source table +uv run python scripts/load_from_glue.py init + +# 3. Load batch 1 +uv run python scripts/load_from_glue.py insert \ + --start '2026-04-22 15:00:00' --end '2026-04-22 15:30:00' + +# 4. Run dbt on both engines (Snowflake needs a metadata refresh first) +uv run dbt run --profiles-dir . --target dev +uv run python scripts/snowflake_refresh.py +uv run dbt run --profiles-dir . --target snowflake + +# 5. Parity check +uv run python scripts/parity.py # exits 0 on zero diffs + +# 6. Load batch 2 (append, do not re-init) +uv run python scripts/load_from_glue.py insert \ + --start '2026-04-22 15:30:00' --end '2026-04-22 16:00:00' + +# 7. Second dbt run exercises the incremental path +uv run dbt run --profiles-dir . --target dev +uv run python scripts/snowflake_refresh.py +uv run dbt run --profiles-dir . --target snowflake + +# 8. Parity check again +uv run python scripts/parity.py +``` + +Success: `parity.py` exits 0 after both batches. + +A non-zero exit means the engines produced different output on the same +input — that's the interesting signal the harness exists to surface. +Rowcount-only mismatches hint at incremental-window or JOIN semantics +divergence; hash mismatches with matching rowcounts hint at cast, NULL, or +ordering divergence. +```` + +- [ ] **Step 3: Verify the section renders** + +Run: `grep -A2 "## Comparing Embucket vs Snowflake" README.md` +Expected: the header plus the first lines of the new section. + +- [ ] **Step 4: Commit** + +```bash +git add README.md +git commit -m "docs: README section for Embucket vs Snowflake parity flow" +``` + +--- + +## Task 10: End-to-end smoke run + +Nothing to edit — this task just executes the documented flow and verifies the harness works end-to-end on both batches. + +- [ ] **Step 1: Reset source** + +Run: `uv run python scripts/load_from_glue.py init` +Expected: prints row count 0. Takes ~60s. + +- [ ] **Step 2: Snowflake setup (idempotent)** + +Run: `uv run python scripts/snowflake_setup.py` +Expected: prints `sturukin.atomic.events_0416 row count after setup: 0`. + +- [ ] **Step 3: Parity sanity-check on empty state** + +Run: `uv run python scripts/parity.py --source-only` +Expected: prints `source events_0416: embucket=0 snowflake=0`, exits 0. + +- [ ] **Step 4: Load batch 1** + +Run: +```bash +uv run python scripts/load_from_glue.py insert \ + --start '2026-04-22 15:00:00' --end '2026-04-22 15:30:00' +``` +Expected: prints a row count around 3.1M. Takes 2-5 min. + +- [ ] **Step 5: Refresh Snowflake source** + +Run: `uv run python scripts/snowflake_refresh.py` +Expected: prints the same row count as step 4. + +- [ ] **Step 6: dbt run on Embucket (batch 1)** + +Run: `uv run dbt run --profiles-dir . --target dev` +Expected: all 18 models succeed. Takes several minutes. If any model fails, stop and diagnose before proceeding. + +- [ ] **Step 7: dbt run on Snowflake (batch 1)** + +Run: `uv run dbt run --profiles-dir . --target snowflake` +Expected: all 18 models succeed. + +- [ ] **Step 8: Parity after batch 1** + +Run: `uv run python scripts/parity.py` +Expected: rowcount and hash diffs print; the harness exits 0 if parity is clean, non-zero if there are diffs. A non-zero exit here is a **finding**, not a harness bug — capture the output. + +- [ ] **Step 9: Load batch 2** + +Run: +```bash +uv run python scripts/load_from_glue.py insert \ + --start '2026-04-22 15:30:00' --end '2026-04-22 16:00:00' +``` +Expected: prints a row count roughly double the batch 1 total (~6.2M). + +- [ ] **Step 10: Refresh Snowflake and dbt run on both** + +Run: +```bash +uv run python scripts/snowflake_refresh.py +uv run dbt run --profiles-dir . --target dev +uv run dbt run --profiles-dir . --target snowflake +``` +Expected: each command succeeds; dbt run output shows incremental models taking a faster path than on the first run. + +- [ ] **Step 11: Parity after batch 2** + +Run: `uv run python scripts/parity.py` +Expected: diffs printed; exit code recorded. Same interpretation as step 8. + +- [ ] **Step 12: Record results** + +No code change. Record in the PR description or a follow-up note: +- Did parity pass on batch 1? On batch 2? +- If not, which tables diverged, how many rows, and a sample of natural keys from each bucket (the parity output already contains this). + +--- + +## Self-review + +- **Spec coverage**: batch plan (Task 4, Task 10 steps 4 & 9) ✓; load subcommands (Task 4) ✓; split SQL (Task 3, Task 4) ✓; Snowflake setup (Task 7) ✓; refresh (Task 8) ✓; parity script (Task 6) ✓; profiles (Task 5) ✓; dependencies (Task 2) ✓; start_date bump (Task 1) ✓; README (Task 9) ✓; end-to-end verification (Task 10) ✓; `--source-only` harness self-check (Task 6, Task 10 step 3) ✓. +- **Placeholders**: none. SQL bodies reference the existing `events_0416_select.sql` (Task 3) rather than reproducing 140 columns inline, but Task 3 specifies exactly what lines to take from the pre-existing `ctas_from_glue.sql`. +- **Type consistency**: `TableSpec`, `DiffResult`, `row_hash`, and `diff_sides` are consistent between the test (Task 6 Step 2) and implementation (Task 6 Step 4). The target name in Python (`TARGET_TABLE = "events_0416"`), SQL, and Snowflake setup (`ICEBERG_TABLE = "events_0416"`) all agree. Embucket target schema (`demo.atomic_derived`) matches the embucket dbt profile's `schema: atomic`; Snowflake target schema (`sturukin.atomic_derived`) matches the snowflake profile's `schema: atomic`. From 7b4f15b7cda7e85dbc1e88373ac95df19926bd01 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 15:02:52 -0500 Subject: [PATCH 05/30] chore: bump snowplow__start_date to 2026-04-22 for parity batches --- dbt_project.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dbt_project.yml b/dbt_project.yml index c6cf48a..e0e30fe 100644 --- a/dbt_project.yml +++ b/dbt_project.yml @@ -10,7 +10,7 @@ dispatch: search_order: ['snowplow_utils', 'dbt'] vars: - snowplow__start_date: '2026-04-13' + snowplow__start_date: '2026-04-22' snowplow__atomic_schema: 'atomic' snowplow__events_table: 'events_0416' # Context flags — set to true once the corresponding context column is From 7bb2d493884c06dd4d2b18379edddc3329ac48aa Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 15:03:26 -0500 Subject: [PATCH 06/30] build: add dbt-snowflake and snowflake-connector-python deps --- pyproject.toml | 2 ++ uv.lock | 4 ++++ 2 files changed, 6 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 7a2a777..a8fefb0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,9 @@ dependencies = [ "boto3>=1.34.0", "dbt-core>=1.11.0,<2.0", "dbt-embucket>=0.1.2", + "dbt-snowflake>=1.8.0,<2.0", "fastavro>=1.9.0", "pyarrow>=20.0.0", "s3fs>=2025.3.0", + "snowflake-connector-python>=3.7.0", ] diff --git a/uv.lock b/uv.lock index f536332..56c89dc 100644 --- a/uv.lock +++ b/uv.lock @@ -743,9 +743,11 @@ dependencies = [ { name = "boto3" }, { name = "dbt-core" }, { name = "dbt-embucket" }, + { name = "dbt-snowflake" }, { name = "fastavro" }, { name = "pyarrow" }, { name = "s3fs" }, + { name = "snowflake-connector-python" }, ] [package.metadata] @@ -753,9 +755,11 @@ requires-dist = [ { name = "boto3", specifier = ">=1.34.0" }, { name = "dbt-core", specifier = ">=1.11.0,<2.0" }, { name = "dbt-embucket", specifier = ">=0.1.2" }, + { name = "dbt-snowflake", specifier = ">=1.8.0,<2.0" }, { name = "fastavro", specifier = ">=1.9.0" }, { name = "pyarrow", specifier = ">=20.0.0" }, { name = "s3fs", specifier = ">=2025.3.0" }, + { name = "snowflake-connector-python", specifier = ">=3.7.0" }, ] [[package]] From 572ba1ac4dba674481415c32989b2c06c958077a Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 15:04:02 -0500 Subject: [PATCH 07/30] refactor: extract events_0416 projection SELECT into reusable file --- scripts/events_0416_select.sql | 146 +++++++++++++++++++++++++++++++++ 1 file changed, 146 insertions(+) create mode 100644 scripts/events_0416_select.sql diff --git a/scripts/events_0416_select.sql b/scripts/events_0416_select.sql new file mode 100644 index 0000000..5dd8ba8 --- /dev/null +++ b/scripts/events_0416_select.sql @@ -0,0 +1,146 @@ +SELECT + app_id, + platform, + CAST(etl_tstamp AS TIMESTAMP) AS etl_tstamp, + CAST(collector_tstamp AS TIMESTAMP) AS collector_tstamp, + CAST(dvce_created_tstamp AS TIMESTAMP) AS dvce_created_tstamp, + event, + event_id, + CAST(txn_id AS VARCHAR) AS txn_id, + name_tracker, + v_tracker, + v_collector, + v_etl, + user_id, + user_ipaddress, + user_fingerprint, + domain_userid, + domain_sessionidx, + network_userid, + geo_country, + geo_region, + geo_city, + geo_zipcode, + geo_latitude, + geo_longitude, + geo_region_name, + ip_isp, + ip_organization, + ip_domain, + ip_netspeed, + page_url, + page_title, + page_referrer, + page_urlscheme, + page_urlhost, + page_urlport, + page_urlpath, + page_urlquery, + page_urlfragment, + refr_urlscheme, + refr_urlhost, + refr_urlport, + refr_urlpath, + refr_urlquery, + refr_urlfragment, + refr_medium, + refr_source, + refr_term, + mkt_medium, + mkt_source, + mkt_term, + mkt_content, + mkt_campaign, + se_category, + se_action, + se_label, + se_property, + CAST(se_value AS VARCHAR) AS se_value, + tr_orderid, + tr_affiliation, + CAST(tr_total AS DOUBLE) AS tr_total, + CAST(tr_tax AS DOUBLE) AS tr_tax, + CAST(tr_shipping AS DOUBLE) AS tr_shipping, + tr_city, + tr_state, + tr_country, + ti_orderid, + ti_sku, + ti_name, + ti_category, + CAST(ti_price AS DOUBLE) AS ti_price, + ti_quantity, + pp_xoffset_min, + pp_xoffset_max, + pp_yoffset_min, + pp_yoffset_max, + useragent, + br_name, + br_family, + br_version, + br_type, + br_renderengine, + br_lang, + br_features_pdf, + br_features_flash, + br_features_java, + br_features_director, + br_features_quicktime, + br_features_realplayer, + br_features_windowsmedia, + br_features_gears, + br_features_silverlight, + br_cookies, + TRY_CAST(br_colordepth AS INTEGER) AS br_colordepth, + br_viewwidth, + br_viewheight, + os_name, + os_family, + os_manufacturer, + os_timezone, + dvce_type, + dvce_ismobile, + dvce_screenwidth, + dvce_screenheight, + doc_charset, + doc_width, + doc_height, + tr_currency, + CAST(tr_total_base AS DOUBLE) AS tr_total_base, + CAST(tr_tax_base AS DOUBLE) AS tr_tax_base, + CAST(tr_shipping_base AS DOUBLE) AS tr_shipping_base, + ti_currency, + CAST(ti_price_base AS DOUBLE) AS ti_price_base, + base_currency, + geo_timezone, + mkt_clickid, + mkt_network, + etl_tags, + CAST(dvce_sent_tstamp AS TIMESTAMP) AS dvce_sent_tstamp, + refr_domain_userid, + CAST(refr_dvce_tstamp AS TIMESTAMP) AS refr_dvce_tstamp, + domain_sessionid, + CAST(derived_tstamp AS TIMESTAMP) AS derived_tstamp, + event_vendor, + event_name, + event_format, + event_version, + event_fingerprint, + CAST(true_tstamp AS TIMESTAMP) AS true_tstamp, + CAST(load_tstamp AS TIMESTAMP) AS load_tstamp, + JSON_FORMAT(CAST(contexts_com_snowplowanalytics_snowplow_web_page_1 AS JSON)) + AS contexts_com_snowplowanalytics_snowplow_web_page_1, + CAST(NULL AS VARCHAR) + AS unstruct_event_com_snowplowanalytics_snowplow_consent_preferences_1, + CAST(NULL AS VARCHAR) + AS unstruct_event_com_snowplowanalytics_snowplow_cmp_visible_1, + CAST(NULL AS VARCHAR) + AS contexts_com_iab_snowplow_spiders_and_robots_1, + JSON_FORMAT(CAST(contexts_com_snowplowanalytics_snowplow_ua_parser_context_1 AS JSON)) + AS contexts_com_snowplowanalytics_snowplow_ua_parser_context_1, + JSON_FORMAT(CAST(contexts_nl_basjes_yauaa_context_1 AS JSON)) + AS contexts_nl_basjes_yauaa_context_1, + CAST(NULL AS VARCHAR) + AS unstruct_event_com_snowplowanalytics_snowplow_web_vitals_1 +FROM "awsdatacatalog"."analytics_glue"."hooli_events_0417_v2" +WHERE {{where}} From 7629f33f45819809a91d10d4840bc25f92aea149 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 15:05:22 -0500 Subject: [PATCH 08/30] refactor: split load_from_glue into init/insert subcommands --- scripts/ctas_from_glue.sql | 176 ------------------------------------ scripts/load_from_glue.py | 178 +++++++++++++++++++------------------ 2 files changed, 90 insertions(+), 264 deletions(-) delete mode 100644 scripts/ctas_from_glue.sql diff --git a/scripts/ctas_from_glue.sql b/scripts/ctas_from_glue.sql deleted file mode 100644 index f56cf16..0000000 --- a/scripts/ctas_from_glue.sql +++ /dev/null @@ -1,176 +0,0 @@ --- Athena CTAS: copy Glue Iceberg source into the Embucket S3 Tables bucket. --- --- Source: awsdatacatalog.analytics_glue.hooli_events_0417_v2 (Iceberg v2 in Glue). --- Target: "s3tablescatalog/snowplow"."atomic"."events_0416" (new Iceberg table --- in the bucket Embucket Lambda has volumed as database `demo`). --- --- Column list and ordering mirror scripts/create_table.sql so the existing --- dbt-snowplow-web wiring works unchanged. Type casts reconcile differences --- between the source's Iceberg types and the target schema: --- timestamptz -> timestamp (strip tz) --- decimal(18,2) -> double (tr_*, ti_* monetary cols) --- int txn_id -> varchar --- double se_value -> varchar --- string br_colordepth -> integer (via TRY_CAST) --- list/struct context cols -> varchar (JSON) (via JSON_FORMAT) --- --- Only the 3 context columns that exist in the source are populated; the 4 --- absent ones (consent_preferences, cmp_visible, iab_spiders_and_robots, --- web_vitals) are emitted as NULL so the column set matches the synthetic --- schema. - -CREATE TABLE events_0416 -WITH ( - table_type = 'ICEBERG', - is_external = false, - format = 'PARQUET', - write_compression = 'ZSTD', - partitioning = ARRAY['day(load_tstamp)', 'event_name'] -) AS -SELECT - app_id, - platform, - CAST(etl_tstamp AS TIMESTAMP) AS etl_tstamp, - CAST(collector_tstamp AS TIMESTAMP) AS collector_tstamp, - CAST(dvce_created_tstamp AS TIMESTAMP) AS dvce_created_tstamp, - event, - event_id, - CAST(txn_id AS VARCHAR) AS txn_id, - name_tracker, - v_tracker, - v_collector, - v_etl, - user_id, - user_ipaddress, - user_fingerprint, - domain_userid, - domain_sessionidx, - network_userid, - geo_country, - geo_region, - geo_city, - geo_zipcode, - geo_latitude, - geo_longitude, - geo_region_name, - ip_isp, - ip_organization, - ip_domain, - ip_netspeed, - page_url, - page_title, - page_referrer, - page_urlscheme, - page_urlhost, - page_urlport, - page_urlpath, - page_urlquery, - page_urlfragment, - refr_urlscheme, - refr_urlhost, - refr_urlport, - refr_urlpath, - refr_urlquery, - refr_urlfragment, - refr_medium, - refr_source, - refr_term, - mkt_medium, - mkt_source, - mkt_term, - mkt_content, - mkt_campaign, - se_category, - se_action, - se_label, - se_property, - CAST(se_value AS VARCHAR) AS se_value, - tr_orderid, - tr_affiliation, - CAST(tr_total AS DOUBLE) AS tr_total, - CAST(tr_tax AS DOUBLE) AS tr_tax, - CAST(tr_shipping AS DOUBLE) AS tr_shipping, - tr_city, - tr_state, - tr_country, - ti_orderid, - ti_sku, - ti_name, - ti_category, - CAST(ti_price AS DOUBLE) AS ti_price, - ti_quantity, - pp_xoffset_min, - pp_xoffset_max, - pp_yoffset_min, - pp_yoffset_max, - useragent, - br_name, - br_family, - br_version, - br_type, - br_renderengine, - br_lang, - br_features_pdf, - br_features_flash, - br_features_java, - br_features_director, - br_features_quicktime, - br_features_realplayer, - br_features_windowsmedia, - br_features_gears, - br_features_silverlight, - br_cookies, - TRY_CAST(br_colordepth AS INTEGER) AS br_colordepth, - br_viewwidth, - br_viewheight, - os_name, - os_family, - os_manufacturer, - os_timezone, - dvce_type, - dvce_ismobile, - dvce_screenwidth, - dvce_screenheight, - doc_charset, - doc_width, - doc_height, - tr_currency, - CAST(tr_total_base AS DOUBLE) AS tr_total_base, - CAST(tr_tax_base AS DOUBLE) AS tr_tax_base, - CAST(tr_shipping_base AS DOUBLE) AS tr_shipping_base, - ti_currency, - CAST(ti_price_base AS DOUBLE) AS ti_price_base, - base_currency, - geo_timezone, - mkt_clickid, - mkt_network, - etl_tags, - CAST(dvce_sent_tstamp AS TIMESTAMP) AS dvce_sent_tstamp, - refr_domain_userid, - CAST(refr_dvce_tstamp AS TIMESTAMP) AS refr_dvce_tstamp, - domain_sessionid, - CAST(derived_tstamp AS TIMESTAMP) AS derived_tstamp, - event_vendor, - event_name, - event_format, - event_version, - event_fingerprint, - CAST(true_tstamp AS TIMESTAMP) AS true_tstamp, - CAST(load_tstamp AS TIMESTAMP) AS load_tstamp, - JSON_FORMAT(CAST(contexts_com_snowplowanalytics_snowplow_web_page_1 AS JSON)) - AS contexts_com_snowplowanalytics_snowplow_web_page_1, - CAST(NULL AS VARCHAR) - AS unstruct_event_com_snowplowanalytics_snowplow_consent_preferences_1, - CAST(NULL AS VARCHAR) - AS unstruct_event_com_snowplowanalytics_snowplow_cmp_visible_1, - CAST(NULL AS VARCHAR) - AS contexts_com_iab_snowplow_spiders_and_robots_1, - JSON_FORMAT(CAST(contexts_com_snowplowanalytics_snowplow_ua_parser_context_1 AS JSON)) - AS contexts_com_snowplowanalytics_snowplow_ua_parser_context_1, - JSON_FORMAT(CAST(contexts_nl_basjes_yauaa_context_1 AS JSON)) - AS contexts_nl_basjes_yauaa_context_1, - CAST(NULL AS VARCHAR) - AS unstruct_event_com_snowplowanalytics_snowplow_web_vitals_1 --- Filter: only post-cutover data (generator realism fix landed 19:47 UTC) -FROM "awsdatacatalog"."analytics_glue"."hooli_events_0417_v2" -WHERE load_tstamp >= TIMESTAMP '2026-04-17 19:47:00 UTC' diff --git a/scripts/load_from_glue.py b/scripts/load_from_glue.py index 445dc16..d5f3bf8 100644 --- a/scripts/load_from_glue.py +++ b/scripts/load_from_glue.py @@ -1,13 +1,18 @@ #!/usr/bin/env python3 """Load the Snowplow Glue Iceberg source into Embucket's S3 Tables bucket. -Runs an Athena CTAS that reads the Glue-managed Iceberg source and writes a -new Iceberg table into the S3 Tables bucket Embucket is volumed to. Embucket -and dbt-snowplow-web then read that table directly. +Two subcommands: + init drop the target table, CREATE TABLE with the correct schema + partitioning + (via CTAS WHERE 1=0 so the SELECT drives column types) but zero rows. + insert INSERT INTO the target using the same projection filtered to a + [start, end) load_tstamp window. + +Both subcommands share scripts/events_0416_select.sql as the projection body. Usage: - uv run python scripts/load_from_glue.py \\ - --query-output-location s3://athena-query-results-us-east-2-767397688925/ + uv run python scripts/load_from_glue.py init + uv run python scripts/load_from_glue.py insert \\ + --start '2026-04-22 15:00:00' --end '2026-04-22 15:30:00' """ from __future__ import annotations @@ -18,7 +23,6 @@ import boto3 - TARGET_CATALOG = "s3tablescatalog/snowplow" TARGET_SCHEMA = "atomic" TARGET_TABLE = "events_0416" @@ -26,123 +30,121 @@ DEFAULT_WORKGROUP = "primary" DEFAULT_REGION = "us-east-2" +ICEBERG_PROPS = """WITH ( + table_type = 'ICEBERG', + is_external = false, + format = 'PARQUET', + write_compression = 'ZSTD', + partitioning = ARRAY['day(load_tstamp)', 'event_name'] +)""" + def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - "--query-output-location", - default=DEFAULT_QUERY_OUTPUT, - help=f"S3 URI for Athena query result metadata (default: {DEFAULT_QUERY_OUTPUT})", - ) - parser.add_argument( - "--workgroup", - default=DEFAULT_WORKGROUP, - help=f"Athena workgroup (default: {DEFAULT_WORKGROUP})", - ) - parser.add_argument( - "--region", - default=DEFAULT_REGION, - help=f"AWS region (default: {DEFAULT_REGION})", - ) - parser.add_argument( - "--skip-drop", - action="store_true", - help="Do not drop the target table before CTAS (will fail if it exists)", - ) - parser.add_argument( - "--row-limit", - type=int, - default=None, - help="Optional LIMIT on the CTAS SELECT (for smoke-testing on smaller slices)", - ) + parser.add_argument("--query-output-location", default=DEFAULT_QUERY_OUTPUT) + parser.add_argument("--workgroup", default=DEFAULT_WORKGROUP) + parser.add_argument("--region", default=DEFAULT_REGION) + + sub = parser.add_subparsers(dest="cmd", required=True) + sub.add_parser("init", help="Drop and create empty events_0416") + ins = sub.add_parser("insert", help="Insert a [start, end) load_tstamp window") + ins.add_argument("--start", required=True, help="ISO timestamp, e.g. '2026-04-22 15:00:00'") + ins.add_argument("--end", required=True, help="ISO timestamp, e.g. '2026-04-22 15:30:00'") return parser.parse_args() -def run_query( - client, - sql: str, - output_location: str, - workgroup: str, - catalog: str | None = None, - database: str | None = None, -) -> str: - query_context = {} +def render_select(where_clause: str) -> str: + body = (Path(__file__).parent / "events_0416_select.sql").read_text() + return body.replace("{{where}}", where_clause) + + +def run_query(client, sql: str, output_location: str, workgroup: str, + catalog: str | None = None, database: str | None = None) -> str: + ctx = {} if catalog: - query_context["Catalog"] = catalog + ctx["Catalog"] = catalog if database: - query_context["Database"] = database + ctx["Database"] = database kwargs = { "QueryString": sql, "ResultConfiguration": {"OutputLocation": output_location}, "WorkGroup": workgroup, } - if query_context: - kwargs["QueryExecutionContext"] = query_context - start = client.start_query_execution(**kwargs) - query_id = start["QueryExecutionId"] - print(f" athena query id: {query_id}") - + if ctx: + kwargs["QueryExecutionContext"] = ctx + qid = client.start_query_execution(**kwargs)["QueryExecutionId"] + print(f" athena query id: {qid}") while True: - resp = client.get_query_execution(QueryExecutionId=query_id) + resp = client.get_query_execution(QueryExecutionId=qid) state = resp["QueryExecution"]["Status"]["State"] if state in {"SUCCEEDED", "FAILED", "CANCELLED"}: break time.sleep(2) - if state != "SUCCEEDED": reason = resp["QueryExecution"]["Status"].get("StateChangeReason", "") raise RuntimeError(f"Athena query {state}: {reason}") + return qid - return query_id +def count_rows(client, output_location: str, workgroup: str) -> str: + qid = run_query( + client, + f"SELECT COUNT(*) FROM {TARGET_TABLE}", + output_location, + workgroup, + catalog=TARGET_CATALOG, + database=TARGET_SCHEMA, + ) + result = client.get_query_results(QueryExecutionId=qid) + return result["ResultSet"]["Rows"][1]["Data"][0]["VarCharValue"] -def main() -> None: - args = parse_args() - scripts_dir = Path(__file__).parent - ctas_sql = (scripts_dir / "ctas_from_glue.sql").read_text() - if args.row_limit: - # Strip the trailing semicolon and wrap with LIMIT. - ctas_sql = ctas_sql.rstrip().rstrip(";") + f"\nLIMIT {args.row_limit};" - - client = boto3.client("athena", region_name=args.region) +def cmd_init(args, client) -> None: target_fqn = f"{TARGET_CATALOG}.{TARGET_SCHEMA}.{TARGET_TABLE}" - - if not args.skip_drop: - print(f"Dropping {target_fqn} if it exists...") - run_query( - client, - f"DROP TABLE IF EXISTS {TARGET_TABLE}", - args.query_output_location, - args.workgroup, - catalog=TARGET_CATALOG, - database=TARGET_SCHEMA, - ) - - print(f"Running CTAS into {target_fqn}...") + print(f"Dropping {target_fqn} if it exists...") run_query( client, - ctas_sql, + f"DROP TABLE IF EXISTS {TARGET_TABLE}", args.query_output_location, args.workgroup, catalog=TARGET_CATALOG, database=TARGET_SCHEMA, ) + select_body = render_select("1=0") + sql = f"CREATE TABLE {TARGET_TABLE}\n{ICEBERG_PROPS} AS\n{select_body}" + print(f"Creating empty {target_fqn}...") + run_query( + client, sql, args.query_output_location, args.workgroup, + catalog=TARGET_CATALOG, database=TARGET_SCHEMA, + ) + print(f"Done. {target_fqn} row count: {count_rows(client, args.query_output_location, args.workgroup)}") - print(f"Counting rows in {target_fqn}...") - count_qid = run_query( - client, - f"SELECT COUNT(*) FROM {TARGET_TABLE}", - args.query_output_location, - args.workgroup, - catalog=TARGET_CATALOG, - database=TARGET_SCHEMA, + +def cmd_insert(args, client) -> None: + target_fqn = f"{TARGET_CATALOG}.{TARGET_SCHEMA}.{TARGET_TABLE}" + where = ( + f"load_tstamp >= TIMESTAMP '{args.start}' " + f"AND load_tstamp < TIMESTAMP '{args.end}'" ) - result = client.get_query_results(QueryExecutionId=count_qid) - rows = result["ResultSet"]["Rows"] - # row 0 is the header, row 1 is the single count value - count = rows[1]["Data"][0]["VarCharValue"] - print(f"Done. events_0416 row count: {count}") + select_body = render_select(where) + sql = f"INSERT INTO {TARGET_TABLE}\n{select_body}" + print(f"Inserting into {target_fqn} for [{args.start}, {args.end})...") + run_query( + client, sql, args.query_output_location, args.workgroup, + catalog=TARGET_CATALOG, database=TARGET_SCHEMA, + ) + print(f"Done. {target_fqn} row count: {count_rows(client, args.query_output_location, args.workgroup)}") + + +def main() -> None: + args = parse_args() + client = boto3.client("athena", region_name=args.region) + if args.cmd == "init": + cmd_init(args, client) + elif args.cmd == "insert": + cmd_insert(args, client) + else: + raise SystemExit(f"unknown subcommand {args.cmd}") if __name__ == "__main__": From ca9220b13d836eb46ae07c2033fce7fadebaba17 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 15:05:54 -0500 Subject: [PATCH 09/30] chore: add snowflake output template to profiles.yml.example --- profiles.yml.example | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/profiles.yml.example b/profiles.yml.example index 7e907f5..2134e95 100644 --- a/profiles.yml.example +++ b/profiles.yml.example @@ -10,3 +10,13 @@ embucket_demo: database: "demo" schema: "atomic" threads: 1 + snowflake: + type: snowflake + account: YOUR_SNOWFLAKE_ACCOUNT + user: YOUR_SNOWFLAKE_USER + password: YOUR_SNOWFLAKE_PASSWORD + role: YOUR_SNOWFLAKE_ROLE + warehouse: YOUR_SNOWFLAKE_WAREHOUSE + database: sturukin + schema: atomic + threads: 4 From 2f2c7ddfad80696a9c9b9a33e8f34a9b953db56e Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 15:07:10 -0500 Subject: [PATCH 10/30] feat: add parity.py with MD5 diff across Embucket and Snowflake Adds pytest as a dev dependency. Pure hash/diff logic is unit tested; DB wrappers are exercised by the end-to-end smoke run. --- pyproject.toml | 5 + scripts/parity.py | 250 +++++++++++++++++++++++++++++++++++++++++++ tests/__init__.py | 0 tests/test_parity.py | 62 +++++++++++ uv.lock | 119 ++++++++++++++++++++ 5 files changed, 436 insertions(+) create mode 100644 scripts/parity.py create mode 100644 tests/__init__.py create mode 100644 tests/test_parity.py diff --git a/pyproject.toml b/pyproject.toml index a8fefb0..1e52fed 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,3 +13,8 @@ dependencies = [ "s3fs>=2025.3.0", "snowflake-connector-python>=3.7.0", ] + +[dependency-groups] +dev = [ + "pytest>=9.0.3", +] diff --git a/scripts/parity.py b/scripts/parity.py new file mode 100644 index 0000000..f710047 --- /dev/null +++ b/scripts/parity.py @@ -0,0 +1,250 @@ +#!/usr/bin/env python3 +"""Compare Embucket and Snowflake outputs of dbt-snowplow-web. + +For each of snowplow_web_page_views, snowplow_web_sessions, snowplow_web_users: + * rowcount on both sides must match + * MD5 hash of every row (by the model's declared column list) must match + when keyed by the table's natural key + +Exits non-zero on any mismatch. Prints up to 10 example natural keys per +mismatch bucket. + +Usage: + uv run python scripts/parity.py # full check + uv run python scripts/parity.py --source-only # just events_0416 rowcount + schema +""" + +from __future__ import annotations + +import argparse +import hashlib +import sys +from dataclasses import dataclass, field +from pathlib import Path + +import snowflake.connector + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import embucket_client # noqa: E402 + +EMBUCKET_DERIVED = "demo.atomic_derived" +SNOWFLAKE_DERIVED = "sturukin.atomic_derived" + +EMBUCKET_LAMBDA_ARN = ( + "arn:aws:lambda:us-east-2:767397688925:function:" + "embucket-demo-embucket-demo-ramp-1775514830" +) + + +@dataclass +class TableSpec: + name: str + natural_key: str + columns: list[str] + + +TABLES = [ + TableSpec( + name="snowplow_web_page_views", + natural_key="page_view_id", + columns=[ + "page_view_id", "event_id", "app_id", "user_id", "domain_userid", + "stitched_user_id", "network_userid", "domain_sessionid", + "domain_sessionidx", "page_view_in_session_index", + "page_views_in_session", "dvce_created_tstamp", "collector_tstamp", + "derived_tstamp", "start_tstamp", "end_tstamp", "model_tstamp", + "engaged_time_in_s", "absolute_time_in_s", + "horizontal_pixels_scrolled", "vertical_pixels_scrolled", + "horizontal_percentage_scrolled", "vertical_percentage_scrolled", + "doc_width", "doc_height", "page_title", "page_url", + "page_urlscheme", "page_urlhost", "page_urlpath", "page_urlquery", + "page_urlfragment", "mkt_medium", "mkt_source", "mkt_term", + "mkt_content", "mkt_campaign", "mkt_clickid", "mkt_network", + "page_referrer", "refr_urlscheme", "refr_urlhost", "refr_urlpath", + "refr_urlquery", "refr_urlfragment", "refr_medium", "refr_source", + "refr_term", "geo_country", "geo_region", "geo_region_name", + "geo_city", "geo_zipcode", "geo_latitude", "geo_longitude", + "geo_timezone", "user_ipaddress", "useragent", "br_lang", + "br_viewwidth", "br_viewheight", "br_colordepth", "br_renderengine", + "os_timezone", + ], + ), + TableSpec( + name="snowplow_web_sessions", + natural_key="domain_sessionid", + columns=[ + "app_id", "domain_sessionid", "domain_sessionidx", "start_tstamp", + "end_tstamp", "model_tstamp", "user_id", "domain_userid", + "stitched_user_id", "network_userid", "page_views", + "engaged_time_in_s", "total_events", "is_engaged", + "absolute_time_in_s", "first_page_title", "first_page_url", + "last_page_title", "last_page_url", "referrer", + "geo_country", "geo_region", "geo_city", "geo_timezone", + "user_ipaddress", "useragent", "br_lang", + ], + ), + TableSpec( + name="snowplow_web_users", + natural_key="domain_userid", + columns=[ + "user_id", "domain_userid", "network_userid", "start_tstamp", + "end_tstamp", "model_tstamp", "page_views", "sessions", + "engaged_time_in_s", "first_page_title", "first_page_url", + "first_geo_country", "first_geo_city", + "last_page_title", "last_page_url", "last_geo_country", + "last_geo_city", "referrer", + ], + ), +] + + +@dataclass +class DiffResult: + matched: int + mismatched: list[str] = field(default_factory=list) + only_left: list[str] = field(default_factory=list) + only_right: list[str] = field(default_factory=list) + + +def row_hash(values: list) -> str: + """Hash a row's values deterministically, distinguishing NULL from string.""" + parts = [] + for v in values: + if v is None: + parts.append("\x00NULL\x00") + else: + parts.append(str(v)) + joined = "\x01".join(parts) + return hashlib.md5(joined.encode("utf-8")).hexdigest() + + +def diff_sides(left: dict, right: dict) -> DiffResult: + matched = 0 + mismatched: list[str] = [] + only_left: list[str] = [] + only_right: list[str] = [] + for key, lhash in left.items(): + if key not in right: + only_left.append(key) + elif right[key] != lhash: + mismatched.append(key) + else: + matched += 1 + for key in right: + if key not in left: + only_right.append(key) + return DiffResult(matched=matched, mismatched=mismatched, + only_left=only_left, only_right=only_right) + + +# --- DB access (integration-level, not unit-tested) -------------------------- + +def sf_connect(): + return snowflake.connector.connect(connection_name="default") + + +def emb_session(): + client = embucket_client.lambda_client(EMBUCKET_LAMBDA_ARN) + token = embucket_client.login(client, EMBUCKET_LAMBDA_ARN) + return client, token + + +def sf_rowcount(conn, fqn: str) -> int: + cur = conn.cursor() + try: + cur.execute(f"SELECT COUNT(*) FROM {fqn}") + return int(cur.fetchone()[0]) + finally: + cur.close() + + +def emb_rowcount(client, token, fqn: str) -> int: + body = embucket_client.run_sql(client, EMBUCKET_LAMBDA_ARN, token, + f"SELECT COUNT(*) FROM {fqn}") + return int(body["data"]["rowset"][0][0]) + + +def sf_hashes(conn, fqn: str, spec: TableSpec) -> dict: + cols = ", ".join(spec.columns) + cur = conn.cursor() + try: + cur.execute(f"SELECT {spec.natural_key}, {cols} FROM {fqn}") + return {row[0]: row_hash(list(row[1:])) for row in cur.fetchall()} + finally: + cur.close() + + +def emb_hashes(client, token, fqn: str, spec: TableSpec) -> dict: + cols = ", ".join(spec.columns) + body = embucket_client.run_sql( + client, EMBUCKET_LAMBDA_ARN, token, + f"SELECT {spec.natural_key}, {cols} FROM {fqn}", + ) + out = {} + for row in body["data"]["rowset"]: + out[row[0]] = row_hash(list(row[1:])) + return out + + +# --- Orchestration ---------------------------------------------------------- + +def print_diff(spec: TableSpec, diff: DiffResult) -> None: + print(f" matched: {diff.matched}") + print(f" mismatched: {len(diff.mismatched)}") + print(f" only_embucket: {len(diff.only_left)}") + print(f" only_snowflake: {len(diff.only_right)}") + for bucket_name, keys in [ + ("mismatched", diff.mismatched), + ("only_embucket", diff.only_left), + ("only_snowflake", diff.only_right), + ]: + if keys: + sample = keys[:10] + print(f" first {len(sample)} {bucket_name} {spec.natural_key}:") + for k in sample: + print(f" {k}") + + +def run(source_only: bool) -> int: + any_fail = False + sf_conn = sf_connect() + emb_client, emb_token = emb_session() + + emb_src = emb_rowcount(emb_client, emb_token, "demo.atomic.events_0416") + sf_src = sf_rowcount(sf_conn, "sturukin.atomic.events_0416") + print(f"source events_0416: embucket={emb_src} snowflake={sf_src}") + if emb_src != sf_src: + print(" FAIL: source rowcount mismatch") + any_fail = True + + if source_only: + return 1 if any_fail else 0 + + for spec in TABLES: + emb_fqn = f"{EMBUCKET_DERIVED}.{spec.name}" + sf_fqn = f"{SNOWFLAKE_DERIVED}.{spec.name}" + emb_count = emb_rowcount(emb_client, emb_token, emb_fqn) + sf_count = sf_rowcount(sf_conn, sf_fqn) + print(f"\n{spec.name}: embucket={emb_count} snowflake={sf_count}") + if emb_count != sf_count: + any_fail = True + emb = emb_hashes(emb_client, emb_token, emb_fqn, spec) + sf = sf_hashes(sf_conn, sf_fqn, spec) + diff = diff_sides(emb, sf) + print_diff(spec, diff) + if diff.mismatched or diff.only_left or diff.only_right: + any_fail = True + + return 1 if any_fail else 0 + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--source-only", action="store_true", + help="Only diff events_0416 source rowcount, skip derived tables") + args = parser.parse_args() + sys.exit(run(args.source_only)) + + +if __name__ == "__main__": + main() diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_parity.py b/tests/test_parity.py new file mode 100644 index 0000000..8ea6b53 --- /dev/null +++ b/tests/test_parity.py @@ -0,0 +1,62 @@ +"""Unit tests for the pure diff logic in scripts/parity.py.""" + +from __future__ import annotations + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts")) + +from parity import DiffResult, diff_sides, row_hash + + +def test_diff_sides_all_match(): + left = {"k1": "h1", "k2": "h2"} + right = {"k1": "h1", "k2": "h2"} + result = diff_sides(left, right) + assert result == DiffResult( + matched=2, mismatched=[], only_left=[], only_right=[] + ) + + +def test_diff_sides_hash_mismatch(): + left = {"k1": "h1", "k2": "h2"} + right = {"k1": "h1", "k2": "DIFFERENT"} + result = diff_sides(left, right) + assert result.matched == 1 + assert result.mismatched == ["k2"] + assert result.only_left == [] + assert result.only_right == [] + + +def test_diff_sides_only_in_left(): + left = {"k1": "h1", "k2": "h2"} + right = {"k1": "h1"} + result = diff_sides(left, right) + assert result.matched == 1 + assert result.only_left == ["k2"] + assert result.only_right == [] + + +def test_diff_sides_only_in_right(): + left = {"k1": "h1"} + right = {"k1": "h1", "k2": "h2"} + result = diff_sides(left, right) + assert result.matched == 1 + assert result.only_left == [] + assert result.only_right == ["k2"] + + +def test_diff_sides_empty_both_sides(): + result = diff_sides({}, {}) + assert result == DiffResult(matched=0, mismatched=[], only_left=[], only_right=[]) + + +def test_row_hash_deterministic_and_null_handling(): + h1 = row_hash(["a", None, 1, 2.5]) + h2 = row_hash(["a", None, 1, 2.5]) + assert h1 == h2 + # None is distinguishable from the string 'None' + assert row_hash([None]) != row_hash(["None"]) + # Order matters + assert row_hash(["a", "b"]) != row_hash(["b", "a"]) diff --git a/uv.lock b/uv.lock index 56c89dc..d57fbd6 100644 --- a/uv.lock +++ b/uv.lock @@ -750,6 +750,11 @@ dependencies = [ { name = "snowflake-connector-python" }, ] +[package.dev-dependencies] +dev = [ + { name = "pytest" }, +] + [package.metadata] requires-dist = [ { name = "boto3", specifier = ">=1.34.0" }, @@ -762,6 +767,21 @@ requires-dist = [ { name = "snowflake-connector-python", specifier = ">=3.7.0" }, ] +[package.metadata.requires-dev] +dev = [{ name = "pytest", specifier = ">=9.0.3" }] + +[[package]] +name = "exceptiongroup" +version = "1.3.1" +source = { registry = "https://pypi.org/simple" 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pointing at the snowplow bucket. The IAM policy was extended out-of-band to include the snowplow catalog ARNs, and Lake Formation DESCRIBE+SELECT was granted to the role on atomic.events_0416. --- profiles.yml.example | 2 +- scripts/snowflake_setup.py | 116 +++++++++++++++++++++++++++++++++++++ 2 files changed, 117 insertions(+), 1 deletion(-) create mode 100644 scripts/snowflake_setup.py diff --git a/profiles.yml.example b/profiles.yml.example index 2134e95..e6d3765 100644 --- a/profiles.yml.example +++ b/profiles.yml.example @@ -17,6 +17,6 @@ embucket_demo: password: YOUR_SNOWFLAKE_PASSWORD role: YOUR_SNOWFLAKE_ROLE warehouse: YOUR_SNOWFLAKE_WAREHOUSE - database: sturukin + database: sturukin_db schema: atomic threads: 4 diff --git a/scripts/snowflake_setup.py b/scripts/snowflake_setup.py new file mode 100644 index 0000000..24516e4 --- /dev/null +++ b/scripts/snowflake_setup.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +"""Idempotent Snowflake setup for the parity harness. + +Prereqs (already on this account): + * IAM role 'snowflake-table-bucket-access' with glue:Get* permissions on + the 'snowplow' federated catalog and s3tables:* on the bucket. Versioned + inline in specs/2026-04-22-embucket-snowflake-parity-design.md. + +This script: + 1. Creates catalog integration SNOWPLOW_S3T (REST/Glue federated) if missing. + 2. Creates database sturukin_db / schema atomic if missing. + 3. (Re)creates the managed Iceberg table sturukin_db.atomic.events_0416 + pointing at s3tablescatalog/snowplow.atomic.events_0416. + +The Lake Formation DESCRIBE+SELECT grant on the source table is assumed to be +in place (granted manually to snowflake-table-bucket-access). If the CREATE +ICEBERG TABLE step fails with a Lake Formation permission error, run: + + aws lakeformation grant-permissions \\ + --principal DataLakePrincipalIdentifier=arn:aws:iam::767397688925:role/snowflake-table-bucket-access \\ + --resource '{"Table":{"CatalogId":"767397688925:s3tablescatalog/snowplow","DatabaseName":"atomic","Name":"events_0416"}}' \\ + --permissions DESCRIBE SELECT + +Usage: + uv run python scripts/snowflake_setup.py +""" + +from __future__ import annotations + +import argparse + +import snowflake.connector + +DATABASE = "sturukin_db" +SCHEMA = "atomic" +ICEBERG_TABLE = "events_0416" +CATALOG_INTEGRATION_NAME = "SNOWPLOW_S3T" +CATALOG_NAME = "767397688925:s3tablescatalog/snowplow" +SIGV4_ROLE = "arn:aws:iam::767397688925:role/snowflake-table-bucket-access" + + +def exec_fetchall(conn, sql: str): + cur = conn.cursor() + try: + cur.execute(sql) + return cur.fetchall() if cur.description else [] + finally: + cur.close() + + +def integration_exists(conn) -> bool: + rows = exec_fetchall(conn, "SHOW CATALOG INTEGRATIONS") + return any(r[0].upper() == CATALOG_INTEGRATION_NAME for r in rows) + + +def ensure_integration(conn): + if integration_exists(conn): + print(f" catalog integration {CATALOG_INTEGRATION_NAME} already exists -- reusing") + return + print(f" creating catalog integration {CATALOG_INTEGRATION_NAME}...") + exec_fetchall(conn, f""" + CREATE CATALOG INTEGRATION {CATALOG_INTEGRATION_NAME} + CATALOG_SOURCE = ICEBERG_REST + CATALOG_NAMESPACE = '{SCHEMA}' + TABLE_FORMAT = ICEBERG + REST_CONFIG = ( + CATALOG_URI = 'https://glue.us-east-2.amazonaws.com/iceberg' + CATALOG_API_TYPE = AWS_GLUE + CATALOG_NAME = '{CATALOG_NAME}' + ACCESS_DELEGATION_MODE = VENDED_CREDENTIALS + ) + REST_AUTHENTICATION = ( + TYPE = SIGV4 + SIGV4_IAM_ROLE = '{SIGV4_ROLE}' + SIGV4_SIGNING_REGION = 'us-east-2' + ) + ENABLED = TRUE + """) + + +def ensure_db_schema(conn): + exec_fetchall(conn, f"CREATE DATABASE IF NOT EXISTS {DATABASE}") + exec_fetchall(conn, f"CREATE SCHEMA IF NOT EXISTS {DATABASE}.{SCHEMA}") + + +def recreate_iceberg_table(conn): + fqn = f"{DATABASE}.{SCHEMA}.{ICEBERG_TABLE}" + print(f" (re)creating iceberg table {fqn}...") + exec_fetchall(conn, f""" + CREATE OR REPLACE ICEBERG TABLE {fqn} + CATALOG = '{CATALOG_INTEGRATION_NAME}' + CATALOG_TABLE_NAME = '{ICEBERG_TABLE}' + AUTO_REFRESH = FALSE + """) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--connection", default="default", + help="snow CLI connection name (default: default)") + args = parser.parse_args() + + conn = snowflake.connector.connect(connection_name=args.connection) + try: + print("== Snowflake parity setup ==") + ensure_integration(conn) + ensure_db_schema(conn) + recreate_iceberg_table(conn) + count = exec_fetchall(conn, f"SELECT COUNT(*) FROM {DATABASE}.{SCHEMA}.{ICEBERG_TABLE}")[0][0] + print(f"\n{DATABASE}.{SCHEMA}.{ICEBERG_TABLE} row count after setup: {count}") + finally: + conn.close() + + +if __name__ == "__main__": + main() From 36fbaca85fe446bdd1b8d660cf09b18beaea7b2f Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 15:32:42 -0500 Subject: [PATCH 12/30] fix: use sturukin_db (not sturukin) in parity.py --- scripts/parity.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scripts/parity.py b/scripts/parity.py index f710047..af8e5f1 100644 --- a/scripts/parity.py +++ b/scripts/parity.py @@ -28,7 +28,7 @@ import embucket_client # noqa: E402 EMBUCKET_DERIVED = "demo.atomic_derived" -SNOWFLAKE_DERIVED = "sturukin.atomic_derived" +SNOWFLAKE_DERIVED = "sturukin_db.atomic_derived" EMBUCKET_LAMBDA_ARN = ( "arn:aws:lambda:us-east-2:767397688925:function:" @@ -211,7 +211,7 @@ def run(source_only: bool) -> int: emb_client, emb_token = emb_session() emb_src = emb_rowcount(emb_client, emb_token, "demo.atomic.events_0416") - sf_src = sf_rowcount(sf_conn, "sturukin.atomic.events_0416") + sf_src = sf_rowcount(sf_conn, "sturukin_db.atomic.events_0416") print(f"source events_0416: embucket={emb_src} snowflake={sf_src}") if emb_src != sf_src: print(" FAIL: source rowcount mismatch") From 6d3c4de70659d89bcfb77fa410746041d1e0e02c Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 15:33:00 -0500 Subject: [PATCH 13/30] feat: add snowflake_refresh.py -- ALTER ICEBERG TABLE REFRESH wrapper --- scripts/snowflake_refresh.py | 39 ++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 scripts/snowflake_refresh.py diff --git a/scripts/snowflake_refresh.py b/scripts/snowflake_refresh.py new file mode 100644 index 0000000..391a0b2 --- /dev/null +++ b/scripts/snowflake_refresh.py @@ -0,0 +1,39 @@ +#!/usr/bin/env python3 +"""Refresh the Snowflake Iceberg view of atomic.events_0416 after an Athena write. + +Snowflake-managed Iceberg tables over externally-written data do not +auto-refresh; this script issues ALTER ICEBERG TABLE ... REFRESH and prints +the post-refresh row count. + +Usage: + uv run python scripts/snowflake_refresh.py +""" + +from __future__ import annotations + +import argparse + +import snowflake.connector + +FQN = "sturukin_db.atomic.events_0416" + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--connection", default="default") + args = parser.parse_args() + + conn = snowflake.connector.connect(connection_name=args.connection) + try: + cur = conn.cursor() + print(f"Refreshing {FQN}...") + cur.execute(f"ALTER ICEBERG TABLE {FQN} REFRESH") + cur.execute(f"SELECT COUNT(*) FROM {FQN}") + count = cur.fetchone()[0] + print(f"{FQN} row count: {count}") + finally: + conn.close() + + +if __name__ == "__main__": + main() From 29241c4e21c09fe78ae8bbac44811dc2f52076d1 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 15:33:33 -0500 Subject: [PATCH 14/30] docs: README section for Embucket vs Snowflake parity flow --- README.md | 65 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) diff --git a/README.md b/README.md index 98a1f70..80e7afb 100644 --- a/README.md +++ b/README.md @@ -164,3 +164,68 @@ AWS S3 Table Bucket - **dbt-embucket** adapter calls Lambda directly via AWS IAM — no public endpoints - **Embucket** is a Snowflake-compatible query engine built on Apache DataFusion + Apache Iceberg - Data is stored as Iceberg tables in your S3 Table Bucket + +## Comparing Embucket vs Snowflake + +Runs dbt-snowplow-web on both engines against the same S3 Tables Iceberg +source (loaded in two 30-minute Athena batches) and diffs the three +headline derived tables after each run to surface semantic drift. + +Prerequisites: +- `~/.snowflake/connections.toml` has a `[connections.default]` entry with + credentials for an account where you have ACCOUNTADMIN (or a role that + can create catalog integrations and iceberg tables). +- IAM role `snowflake-table-bucket-access` has `glue:Get*` on + `arn:aws:glue:us-east-2::catalog/s3tablescatalog/snowplow` and + `s3tables:*` on the snowplow bucket. +- Lake Formation `DESCRIBE` + `SELECT` granted to that role on + `s3tablescatalog/snowplow.atomic.events_0416` (grant once after the + first `load_from_glue.py init`): + ```bash + aws lakeformation grant-permissions \ + --principal DataLakePrincipalIdentifier=arn:aws:iam::767397688925:role/snowflake-table-bucket-access \ + --resource '{"Table":{"CatalogId":"767397688925:s3tablescatalog/snowplow","DatabaseName":"atomic","Name":"events_0416"}}' \ + --permissions DESCRIBE SELECT + ``` +- `profiles.yml` has a `snowflake` output under `embucket_demo.outputs` + (see `profiles.yml.example`). + +Run flow: + +```bash +# 1. One-time Snowflake setup (catalog integration + iceberg table) +uv run python scripts/snowflake_setup.py + +# 2. Reset the shared Athena-managed source table +uv run python scripts/load_from_glue.py init + +# 3. Load batch 1 +uv run python scripts/load_from_glue.py insert \ + --start '2026-04-22 15:00:00' --end '2026-04-22 15:30:00' + +# 4. Run dbt on both engines (Snowflake needs a metadata refresh first) +uv run dbt run --profiles-dir . --target dev +uv run python scripts/snowflake_refresh.py +uv run dbt run --profiles-dir . --target snowflake + +# 5. Parity check +uv run python scripts/parity.py # exits 0 on zero diffs + +# 6. Load batch 2 (append, do not re-init) +uv run python scripts/load_from_glue.py insert \ + --start '2026-04-22 15:30:00' --end '2026-04-22 16:00:00' + +# 7. Second dbt run exercises the incremental path +uv run dbt run --profiles-dir . --target dev +uv run python scripts/snowflake_refresh.py +uv run dbt run --profiles-dir . --target snowflake + +# 8. Parity check again +uv run python scripts/parity.py +``` + +A non-zero exit from `parity.py` means the engines produced different +output on the same input — that's the interesting signal the harness +exists to surface. Rowcount-only mismatches hint at incremental-window or +JOIN semantics divergence; hash mismatches with matching rowcounts hint at +cast, NULL, or ordering divergence. From cd2cf2698e4c2594d42e8564d7ed97b31815c6aa Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 15:47:26 -0500 Subject: [PATCH 15/30] fix: add variant view for Snowflake + graceful rowcount-mismatch handling Snowflake needs VARIANT columns (not VARCHAR) to index JSON contexts; a new sturukin_db.atomic.events_0416_v view TRY_PARSE_JSONs the 7 context columns and the snowplow__events_table var now dispatches on target.type. parity.py no longer attempts to hash-diff when rowcounts already differ (the first-order finding), which also avoids hitting the 6 MB Lambda response limit on 1M+ row tables. --- dbt_project.yml | 2 +- scripts/parity.py | 6 +++++ scripts/snowflake_setup.py | 49 ++++++++++++++++++++++++++++++++++++++ 3 files changed, 56 insertions(+), 1 deletion(-) diff --git a/dbt_project.yml b/dbt_project.yml index e0e30fe..66f64ef 100644 --- a/dbt_project.yml +++ b/dbt_project.yml @@ -12,7 +12,7 @@ dispatch: vars: snowplow__start_date: '2026-04-22' snowplow__atomic_schema: 'atomic' - snowplow__events_table: 'events_0416' + snowplow__events_table: "{{ 'events_0416_v' if target.type == 'snowflake' else 'events_0416' }}" # Context flags — set to true once the corresponding context column is # populated (see scripts/ctas_from_glue.sql). web_page_1 is always on. snowplow__enable_iab: false diff --git a/scripts/parity.py b/scripts/parity.py index af8e5f1..a19b878 100644 --- a/scripts/parity.py +++ b/scripts/parity.py @@ -227,7 +227,13 @@ def run(source_only: bool) -> int: sf_count = sf_rowcount(sf_conn, sf_fqn) print(f"\n{spec.name}: embucket={emb_count} snowflake={sf_count}") if emb_count != sf_count: + print(" ROWCOUNT DIFFERS -- skipping hash diff " + "(rowcount mismatch is the first-order finding)") any_fail = True + continue + if emb_count == 0: + print(" both sides empty; nothing to hash") + continue emb = emb_hashes(emb_client, emb_token, emb_fqn, spec) sf = sf_hashes(sf_conn, sf_fqn, spec) diff = diff_sides(emb, sf) diff --git a/scripts/snowflake_setup.py b/scripts/snowflake_setup.py index 24516e4..f320d81 100644 --- a/scripts/snowflake_setup.py +++ b/scripts/snowflake_setup.py @@ -29,6 +29,7 @@ import argparse +import boto3 import snowflake.connector DATABASE = "sturukin_db" @@ -37,6 +38,8 @@ CATALOG_INTEGRATION_NAME = "SNOWPLOW_S3T" CATALOG_NAME = "767397688925:s3tablescatalog/snowplow" SIGV4_ROLE = "arn:aws:iam::767397688925:role/snowflake-table-bucket-access" +LF_CATALOG_ID = "767397688925:s3tablescatalog/snowplow" +AWS_REGION = "us-east-2" def exec_fetchall(conn, sql: str): @@ -94,6 +97,50 @@ def recreate_iceberg_table(conn): """) +JSON_CONTEXT_COLUMNS = [ + "contexts_com_snowplowanalytics_snowplow_web_page_1", + "contexts_com_snowplowanalytics_snowplow_ua_parser_context_1", + "contexts_nl_basjes_yauaa_context_1", + "unstruct_event_com_snowplowanalytics_snowplow_consent_preferences_1", + "unstruct_event_com_snowplowanalytics_snowplow_cmp_visible_1", + "contexts_com_iab_snowplow_spiders_and_robots_1", + "unstruct_event_com_snowplowanalytics_snowplow_web_vitals_1", +] + + +def recreate_variant_view(conn): + """View that PARSE_JSONs the VARCHAR context columns into VARIANT for dbt.""" + view_fqn = f"{DATABASE}.{SCHEMA}.{ICEBERG_TABLE}_v" + print(f" (re)creating variant view {view_fqn}...") + exclude = ", ".join(JSON_CONTEXT_COLUMNS) + parsed = ",\n ".join( + f"TRY_PARSE_JSON({c}) AS {c}" for c in JSON_CONTEXT_COLUMNS + ) + exec_fetchall(conn, f""" + CREATE OR REPLACE VIEW {view_fqn} AS + SELECT + * EXCLUDE ({exclude}), + {parsed} + FROM {DATABASE}.{SCHEMA}.{ICEBERG_TABLE} + """) + + +def ensure_lf_grant(): + """Re-grant LF DESCRIBE+SELECT; needed after every load_from_glue.py init.""" + print(f" granting Lake Formation DESCRIBE+SELECT on " + f"{LF_CATALOG_ID}.{SCHEMA}.{ICEBERG_TABLE}...") + lf = boto3.client("lakeformation", region_name=AWS_REGION) + lf.grant_permissions( + Principal={"DataLakePrincipalIdentifier": SIGV4_ROLE}, + Resource={"Table": { + "CatalogId": LF_CATALOG_ID, + "DatabaseName": SCHEMA, + "Name": ICEBERG_TABLE, + }}, + Permissions=["DESCRIBE", "SELECT"], + ) + + def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--connection", default="default", @@ -105,7 +152,9 @@ def main() -> None: print("== Snowflake parity setup ==") ensure_integration(conn) ensure_db_schema(conn) + ensure_lf_grant() recreate_iceberg_table(conn) + recreate_variant_view(conn) count = exec_fetchall(conn, f"SELECT COUNT(*) FROM {DATABASE}.{SCHEMA}.{ICEBERG_TABLE}")[0][0] print(f"\n{DATABASE}.{SCHEMA}.{ICEBERG_TABLE} row count after setup: {count}") finally: From 8b3f73457c9f56041a9d5ac4c4f0213d597a0ef1 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 16:01:41 -0500 Subject: [PATCH 16/30] =?UTF-8?q?docs:=20parity=20harness=20run=20results?= =?UTF-8?q?=20=E2=80=94=20source=20parity=20holds,=20derived=20tables=20di?= =?UTF-8?q?verge?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- specs/2026-04-22-parity-results.md | 57 ++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 specs/2026-04-22-parity-results.md diff --git a/specs/2026-04-22-parity-results.md b/specs/2026-04-22-parity-results.md new file mode 100644 index 0000000..d729a3a --- /dev/null +++ b/specs/2026-04-22-parity-results.md @@ -0,0 +1,57 @@ +# Parity harness run results (2026-04-22) + +Commands: `load_from_glue.py init` → `snowflake_setup.py` → batch 1 + dbt + parity → batch 2 + dbt + parity. + +## Source (Iceberg events_0416) + +Both engines read the same S3 Tables Iceberg snapshot. Source parity holds +after each Athena insert + Snowflake refresh. + +| batch | embucket | snowflake | +|-------|----------|-----------| +| 1 (15:00–15:30) | 2,834,944 | 2,834,944 | +| 2 (15:30–16:00) | 6,162,099 | 6,162,099 | + +## Derived tables — findings + +Rowcount-only comparison (hash-diff skipped when rowcounts differ). + +### After batch 1 + +| table | embucket | snowflake | ratio | +|-------|----------|-----------|-------| +| snowplow_web_page_views | 1,338,846 | 814,564 | 1.64× | +| snowplow_web_sessions | 553,795 | 360,999 | 1.53× | +| snowplow_web_users | 233,818 | 122,041 | 1.92× | + +### After batch 2 + +| table | embucket | snowflake | notes | +|-------|----------|-----------|-------| +| snowplow_web_page_views | 2,370,157 | 814,564 | **Snowflake unchanged** — batch 2 incremental did not advance | +| snowplow_web_sessions | 1,099,618 | 360,999 | **Snowflake unchanged** | +| snowplow_web_users | 15,437,123 | 122,041 | Embucket 15.4M >> 6.16M input events — suspicious | + +## Interpretation + +1. Snowflake batch-2 run rebuilt `_this_run` scratch tables but did not + advance `snowplow_web_{page_views,sessions,users}`. The + `snowplow_web_incremental_manifest` rows for those models show + `last_success = 2026-04-22 15:26:32` both before and after the batch-2 + run, while all scratch models advanced to 15:56:33. Likely cause: the + `snowplow_utils.is_run_with_new_events('snowplow_web')` guard evaluated + false despite new data being present. Worth isolating; not a harness bug. + +2. Embucket batch-2 produced 15.4M user rows against 6.16M input events — + more rows than input. Likely unique-key collision or duplicated merge + in the users incremental. Needs engine-side investigation. + +3. Source parity is byte-for-byte between Embucket and Snowflake, so the + divergence is purely in the dbt model execution, not in how the two + engines read the Iceberg data. + +## Harness status + +Working as intended. The comparison flow (init → setup → insert → refresh +→ dbt × 2 → parity) runs end-to-end on fresh infrastructure and surfaces +the divergences cleanly. No blockers. From 5c12048891e1748a0258d4fc130d2404e48a2a9a Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 16:30:42 -0500 Subject: [PATCH 17/30] docs: update parity results -- Embucket MERGE ignores unique_key on dup source Clean batch-1-only investigation: source has 4.7% duplicate event_ids (Glue DQ issue). Scratch tables are essentially identical between engines. The divergence is in the final incremental MERGE: Snowflake errors loudly (ANSI-correct), Embucket silently lets duplicate unique_key rows into the derived tables. --- specs/2026-04-22-parity-results.md | 153 ++++++++++++++++++----------- 1 file changed, 98 insertions(+), 55 deletions(-) diff --git a/specs/2026-04-22-parity-results.md b/specs/2026-04-22-parity-results.md index d729a3a..ab0efef 100644 --- a/specs/2026-04-22-parity-results.md +++ b/specs/2026-04-22-parity-results.md @@ -1,57 +1,100 @@ # Parity harness run results (2026-04-22) -Commands: `load_from_glue.py init` → `snowflake_setup.py` → batch 1 + dbt + parity → batch 2 + dbt + parity. - -## Source (Iceberg events_0416) - -Both engines read the same S3 Tables Iceberg snapshot. Source parity holds -after each Athena insert + Snowflake refresh. - -| batch | embucket | snowflake | -|-------|----------|-----------| -| 1 (15:00–15:30) | 2,834,944 | 2,834,944 | -| 2 (15:30–16:00) | 6,162,099 | 6,162,099 | - -## Derived tables — findings - -Rowcount-only comparison (hash-diff skipped when rowcounts differ). - -### After batch 1 - -| table | embucket | snowflake | ratio | -|-------|----------|-----------|-------| -| snowplow_web_page_views | 1,338,846 | 814,564 | 1.64× | -| snowplow_web_sessions | 553,795 | 360,999 | 1.53× | -| snowplow_web_users | 233,818 | 122,041 | 1.92× | - -### After batch 2 - -| table | embucket | snowflake | notes | -|-------|----------|-----------|-------| -| snowplow_web_page_views | 2,370,157 | 814,564 | **Snowflake unchanged** — batch 2 incremental did not advance | -| snowplow_web_sessions | 1,099,618 | 360,999 | **Snowflake unchanged** | -| snowplow_web_users | 15,437,123 | 122,041 | Embucket 15.4M >> 6.16M input events — suspicious | - -## Interpretation - -1. Snowflake batch-2 run rebuilt `_this_run` scratch tables but did not - advance `snowplow_web_{page_views,sessions,users}`. The - `snowplow_web_incremental_manifest` rows for those models show - `last_success = 2026-04-22 15:26:32` both before and after the batch-2 - run, while all scratch models advanced to 15:56:33. Likely cause: the - `snowplow_utils.is_run_with_new_events('snowplow_web')` guard evaluated - false despite new data being present. Worth isolating; not a harness bug. - -2. Embucket batch-2 produced 15.4M user rows against 6.16M input events — - more rows than input. Likely unique-key collision or duplicated merge - in the users incremental. Needs engine-side investigation. - -3. Source parity is byte-for-byte between Embucket and Snowflake, so the - divergence is purely in the dbt model execution, not in how the two - engines read the Iceberg data. - -## Harness status - -Working as intended. The comparison flow (init → setup → insert → refresh -→ dbt × 2 → parity) runs end-to-end on fresh infrastructure and surfaces -the divergences cleanly. No blockers. +## First full run (batch 1 + batch 2, stale state between) + +Initial run on the first loaded state. Both engines processed 2.83M rows +after batch 1 and 6.16M after batch 2. Rowcount divergence on all three +headline tables. Snowflake batch-2 incremental did not advance headline +models (incremental manifest guard did not trigger). Embucket users table +grew far beyond input (15.4M vs 6.16M events). See git history. + +## Clean batch-1-only investigation (the useful one) + +After this run all downstream state (derived/scratch/manifest schemas on +both engines; dbt seeds re-run on both) was dropped, `load_from_glue.py +init` fresh, only batch 1 loaded, `dbt run` once per target. + +### Source + +| table | rows | distinct event_id | dup ratio | +|-------|------|-------------------|-----------| +| Glue source (hooli_events_0417_v2, 15:00-15:30 window) | 2,834,944 | 2,700,579 | 1.050 | +| demo.atomic.events_0416 (Embucket) | 2,834,944 | 2,700,579 | 1.050 | +| sturukin_db.atomic.events_0416 (Snowflake) | 2,834,944 | 2,700,579 | 1.050 | + +Source parity is exact. Source contains **4.7% duplicate event_ids** -- +an upstream data quality issue inherited from the Glue-managed source. + +### Scratch tables after batch 1 (essentially identical on both engines) + +| table | Embucket rows / distinct key | Snowflake rows / distinct key | dup ratio | +|-------|------------------------------|-------------------------------|-----------| +| snowplow_web_base_events_this_run | 2,834,944 / 2,700,579 event_id | 2,834,944 / 2,700,579 | 1.050 | +| snowplow_web_page_views_this_run | 814,555 / 775,543 page_view_id | 814,570 / 775,543 | 1.050 | +| snowplow_web_sessions_this_run | 360,999 / 317,162 domain_sessionid | 360,999 / 317,162 | 1.138 | + +Pre-merge state is byte-equivalent across engines. + +### Derived tables diverge in MERGE behavior + +- **Snowflake** fails loudly: `100090 (42P18): Duplicate row detected + during DML action` on `snowplow_web_page_views` and + `snowplow_web_sessions` merges. ANSI MERGE cannot match multiple + source rows to one target row when two source rows share the + unique_key. +- **Embucket** succeeds silently. Final derived tables retain the + duplicates from the scratch: + +| table | rows | distinct unique_key | dup ratio | +|-------|------|---------------------|-----------| +| demo.atomic_derived.snowplow_web_page_views | 814,555 | 775,543 page_view_id | 1.050 | +| demo.atomic_derived.snowplow_web_sessions | 360,999 | 317,162 domain_sessionid | 1.138 | + +This contradicts the model config `unique_key='page_view_id'` +(`domain_sessionid` for sessions) -- Embucket's incremental MERGE is +not deduplicating by unique_key. + +## Findings + +1. **Glue source has ~4.7% duplicate event_ids**. Snowplow assumes the + atomic layer is deduplicated upstream; this source violates that + contract. +2. **Embucket adapter bug: incremental MERGE ignores unique_key when + the source has duplicates**. Snowflake errors correctly; Embucket + silently lets duplicate rows through to the derived layer. Worth + filing upstream. +3. **Snowflake strict-MERGE behavior** is surfacing what is really a + source data quality problem. Snowplow Web doesn't have a global + pre-merge `distinct on unique_key` -- the scratch pipelines assume + the atomic-level dedup already happened. + +## Reproducing + +From a clean state (both engines' derived/scratch/manifest schemas +dropped, iceberg source table dropped): + +```bash +uv run python scripts/load_from_glue.py init +uv run python scripts/snowflake_setup.py +uv run python scripts/load_from_glue.py insert \ + --start '2026-04-22 15:00:00' --end '2026-04-22 15:30:00' +uv run python scripts/snowflake_refresh.py +uv run dbt seed --profiles-dir . --target dev +uv run dbt seed --profiles-dir . --target snowflake +uv run dbt run --profiles-dir . --target dev # 31 PASS +uv run dbt run --profiles-dir . --target snowflake # 2 errors on MERGE +``` + +Scratch counts can be read from either engine: +```sql +SELECT COUNT(*), COUNT(DISTINCT page_view_id) +FROM .atomic_scratch.snowplow_web_page_views_this_run; +``` + +Source dup rate from Athena: +```sql +SELECT COUNT(*), COUNT(DISTINCT event_id) +FROM analytics_glue.hooli_events_0417_v2 +WHERE load_tstamp >= TIMESTAMP '2026-04-22 15:00:00 UTC' + AND load_tstamp < TIMESTAMP '2026-04-22 15:30:00 UTC'; +``` From c1afbe6ae3f731b684746764146569121fda791e Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 16:42:09 -0500 Subject: [PATCH 18/30] docs: correct parity findings -- dup source is the root cause, not Embucket MERGE First dbt run on either engine does CTAS (no MERGE) and both engines preserve the source's 4.7% duplicate event_ids into the derived tables identically. The MERGE divergence (Snowflake errors / Embucket appends) only manifests on a second run against the same scratch state. The missing dedup is in snowplow-web's _this_run -> derived step, not in the Embucket adapter. --- specs/2026-04-22-parity-results.md | 68 ++++++++++++++++++------------ 1 file changed, 41 insertions(+), 27 deletions(-) diff --git a/specs/2026-04-22-parity-results.md b/specs/2026-04-22-parity-results.md index ab0efef..cbf47e3 100644 --- a/specs/2026-04-22-parity-results.md +++ b/specs/2026-04-22-parity-results.md @@ -35,38 +35,49 @@ an upstream data quality issue inherited from the Glue-managed source. Pre-merge state is byte-equivalent across engines. -### Derived tables diverge in MERGE behavior +### Derived tables: first `dbt run` CTAS, second+ run MERGE + +On the first `dbt run` with no pre-existing target, dbt's incremental +materialization issues `CREATE TABLE AS` (Snowflake) / equivalent +(Embucket), bypassing MERGE entirely. Both engines produce identical +counts, and **both preserve the source duplicates** -- the snowplow-web +`_this_run → derived` step does not deduplicate on unique_key: + +| table | Embucket rows / distinct | Snowflake rows / distinct | dup ratio | +|-------|--------------------------|---------------------------|-----------| +| snowplow_web_page_views | 814,555 / 775,543 page_view_id | 814,569 / 775,543 | 1.050 | +| snowplow_web_sessions | 360,999 / 317,162 domain_sessionid | 360,999 / 317,162 | 1.138 | +| snowplow_web_users | ? | 122,041 / 71,352 domain_userid | 1.710 | + +On a **second** `dbt run` with the scratch still holding the same +duplicates and the target now populated: - **Snowflake** fails loudly: `100090 (42P18): Duplicate row detected - during DML action` on `snowplow_web_page_views` and - `snowplow_web_sessions` merges. ANSI MERGE cannot match multiple - source rows to one target row when two source rows share the - unique_key. -- **Embucket** succeeds silently. Final derived tables retain the - duplicates from the scratch: - -| table | rows | distinct unique_key | dup ratio | -|-------|------|---------------------|-----------| -| demo.atomic_derived.snowplow_web_page_views | 814,555 | 775,543 page_view_id | 1.050 | -| demo.atomic_derived.snowplow_web_sessions | 360,999 | 317,162 domain_sessionid | 1.138 | - -This contradicts the model config `unique_key='page_view_id'` -(`domain_sessionid` for sessions) -- Embucket's incremental MERGE is -not deduplicating by unique_key. + during DML action`. ANSI MERGE refuses to match multiple source rows + to one target row when they share the unique_key. +- **Embucket** succeeds silently and adds more duplicate rows to the + derived tables (this is how the first full harness run ended up with + Embucket page_views = 1.34M vs Snowflake 814K -- two dbt runs amplified + Embucket's duplicate count). ## Findings 1. **Glue source has ~4.7% duplicate event_ids**. Snowplow assumes the atomic layer is deduplicated upstream; this source violates that - contract. -2. **Embucket adapter bug: incremental MERGE ignores unique_key when - the source has duplicates**. Snowflake errors correctly; Embucket - silently lets duplicate rows through to the derived layer. Worth - filing upstream. -3. **Snowflake strict-MERGE behavior** is surfacing what is really a - source data quality problem. Snowplow Web doesn't have a global - pre-merge `distinct on unique_key` -- the scratch pipelines assume - the atomic-level dedup already happened. + contract. Root cause of everything below. +2. **snowplow-web does not deduplicate on unique_key in the + `_this_run → derived` step**, on either engine. Both Snowflake and + Embucket's first `dbt run` CTAS the scratch content verbatim, so + both derived tables end up with duplicate page_view_id / + domain_sessionid / domain_userid rows. Not an engine bug -- a + package-level assumption that the atomic layer is already clean. +3. **MERGE semantics diverge on re-run.** With duplicates in the + scratch `_this_run` tables and rows in the target, Snowflake's + MERGE errors with `100090 (42P18): Duplicate row detected` while + Embucket's MERGE succeeds and keeps inserting more duplicate rows. + This explains the earlier "Embucket 1.34M page_views vs Snowflake + 814K" numbers -- two dbt runs on Embucket against duplicated + source, each run appending. ## Reproducing @@ -81,8 +92,11 @@ uv run python scripts/load_from_glue.py insert \ uv run python scripts/snowflake_refresh.py uv run dbt seed --profiles-dir . --target dev uv run dbt seed --profiles-dir . --target snowflake -uv run dbt run --profiles-dir . --target dev # 31 PASS -uv run dbt run --profiles-dir . --target snowflake # 2 errors on MERGE +uv run dbt run --profiles-dir . --target dev # 31 PASS on first run (CTAS) +uv run dbt run --profiles-dir . --target snowflake # 31 PASS on first run (CTAS) +# Re-running either without resetting scratch/derived state: +uv run dbt run --profiles-dir . --target snowflake # 2 errors on MERGE (dup source) +uv run dbt run --profiles-dir . --target dev # succeeds silently, appending dups ``` Scratch counts can be read from either engine: From 961a73f93fcd7ad6a0dbd49471428fc07d22d829 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 17:13:17 -0500 Subject: [PATCH 19/30] feat(parity): server-side aggregate parity + document correctness findings parity_deep.py returns one row per table (count, distinct key count, micros-epoch min/max start, a few SUM metrics, 16 MD5 position checksums) -- avoids Lambda payload limits and makes engine-vs-engine divergence directly comparable. Run against a clean first-dbt-run on both engines: distinct natural keys match exactly on every headline table. Divergences are (1) different duplicate-row multiplicity in the scratch-to-derived CTAS path and (2) occasional 1-second rounding on absolute_time_in_s from different timestamp-difference arithmetic between engines. --- scripts/parity_deep.py | 211 +++++++++++++++++++++++++++++ specs/2026-04-22-parity-results.md | 41 ++++++ 2 files changed, 252 insertions(+) create mode 100644 scripts/parity_deep.py diff --git a/scripts/parity_deep.py b/scripts/parity_deep.py new file mode 100644 index 0000000..9373ecd --- /dev/null +++ b/scripts/parity_deep.py @@ -0,0 +1,211 @@ +#!/usr/bin/env python3 +"""Deep parity between Embucket and Snowflake derived tables. + +Compares each headline table with server-side aggregation (1 row returned +per table per engine), so Lambda payload size is not an issue: + + * COUNT(*), COUNT(DISTINCT ) + * MIN/MAX of start_tstamp (time range coverage) + * SUM of a few numeric metrics (engaged_time_in_s, page_views, sessions) + * 16 order-independent checksums: for each of 16 hex positions in the + MD5 of each row's column-concatenated text, SUM(ASCII of that hex + char) across all rows. A single-character divergence in any one + column, on any row, changes the MD5 and perturbs at least one of the + 16 sums. Full agreement across all 16 sums + counts is strong + evidence of byte-for-byte content parity. + +Usage: + uv run python scripts/parity_deep.py + +Exit 0 on full parity, 1 on any divergence. Divergence output pinpoints +which check failed per table. +""" + +from __future__ import annotations + +import sys +from dataclasses import dataclass +from pathlib import Path + +import snowflake.connector + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import embucket_client # noqa: E402 +from parity import TABLES, TableSpec # noqa: E402 + +EMBUCKET_DERIVED = "demo.atomic_derived" +SNOWFLAKE_DERIVED = "sturukin_db.atomic_derived" + +EMBUCKET_LAMBDA_ARN = ( + "arn:aws:lambda:us-east-2:767397688925:function:" + "embucket-demo-embucket-demo-ramp-1775514830" +) + +# Aggregated metrics to check per table. (column, agg_fn) +PER_TABLE_METRICS = { + "snowplow_web_page_views": [ + ("engaged_time_in_s", "SUM"), + ("absolute_time_in_s", "SUM"), + ("page_views_in_session", "SUM"), + ("doc_width", "SUM"), + ("doc_height", "SUM"), + ], + "snowplow_web_sessions": [ + ("engaged_time_in_s", "SUM"), + ("page_views", "SUM"), + ("total_events", "SUM"), + ("absolute_time_in_s", "SUM"), + ], + "snowplow_web_users": [ + ("engaged_time_in_s", "SUM"), + ("page_views", "SUM"), + ("sessions", "SUM"), + ], +} + + +TIMESTAMP_COLS = { + "dvce_created_tstamp", "collector_tstamp", "derived_tstamp", + "start_tstamp", "end_tstamp", "model_tstamp", +} + + +def ts_to_micros(col: str, engine: str) -> str: + """Portable cast of a timestamp column to epoch-microseconds BIGINT.""" + if engine == "snowflake": + return f"CAST(EXTRACT(EPOCH_MICROSECOND FROM {col}) AS BIGINT)" + # embucket / datafusion + return f"CAST(EXTRACT(EPOCH FROM {col}) * 1000000 AS BIGINT)" + + +def col_to_md5_input(col: str, engine: str) -> str: + """Serialize one column into a canonical null-safe VARCHAR for hashing.""" + if col in TIMESTAMP_COLS: + # Convert the timestamp to a fixed integer representation (microseconds + # since epoch) so both engines produce byte-identical hash input. + return f"COALESCE(CAST({ts_to_micros(col, engine)} AS VARCHAR), 'NULL')" + return f"COALESCE(CAST({col} AS VARCHAR), 'NULL')" + + +def build_query(fqn: str, spec: TableSpec, engine: str) -> str: + row_md5 = "MD5(CONCAT_WS('|', " + ", ".join( + col_to_md5_input(c, engine) for c in spec.columns + ) + "))" + + metric_exprs = ", ".join( + f"{agg}({col}) AS {agg.lower()}_{col}" + for col, agg in PER_TABLE_METRICS[spec.name] + ) + + checksum_exprs = ", ".join( + f"SUM(ASCII(SUBSTR(row_md5, {i + 1}, 1))) AS md5_pos{i:02d}" + for i in range(16) + ) + + # Normalize min/max start_tstamp to microseconds so the return values + # are directly comparable as integers across engines. + min_start = ts_to_micros("start_tstamp", engine) + max_start = ts_to_micros("start_tstamp", engine).replace("EXTRACT", "EXTRACT") # no-op, keep shape + + return f""" + WITH hashed AS ( + SELECT *, {row_md5} AS row_md5 + FROM {fqn} + ) + SELECT + COUNT(*) AS n, + COUNT(DISTINCT {spec.natural_key}) AS n_distinct_key, + MIN({min_start}) AS min_start_micros, + MAX({max_start}) AS max_start_micros, + {metric_exprs}, + {checksum_exprs} + FROM hashed + """.strip() + + +def sf_connect(): + return snowflake.connector.connect(connection_name="default") + + +def sf_query(conn, sql: str) -> dict: + cur = conn.cursor() + try: + cur.execute(sql) + cols = [d[0].lower() for d in cur.description] + row = cur.fetchone() + return dict(zip(cols, row)) + finally: + cur.close() + + +def emb_session(): + client = embucket_client.lambda_client(EMBUCKET_LAMBDA_ARN) + token = embucket_client.login(client, EMBUCKET_LAMBDA_ARN) + return client, token + + +def emb_query(client, token, sql: str) -> dict: + body = embucket_client.run_sql(client, EMBUCKET_LAMBDA_ARN, token, sql) + row = body["data"]["rowset"][0] + cols = [c["name"].lower() for c in body["data"]["rowtype"]] + return dict(zip(cols, row)) + + +def diff_dicts(emb: dict, sf: dict) -> list[str]: + """Return list of 'key: emb_val != sf_val' strings for diverging keys.""" + diffs = [] + keys = set(emb) | set(sf) + for k in sorted(keys): + e, s = emb.get(k), sf.get(k) + # numeric coercion where useful + try: + if e is not None: e = str(e) + if s is not None: s = str(s) + except Exception: + pass + if e != s: + diffs.append(f" {k}: embucket={e!r} snowflake={s!r}") + return diffs + + +def main() -> None: + sf_conn = sf_connect() + emb_client, emb_token = emb_session() + + any_fail = False + for spec in TABLES: + emb_fqn = f"{EMBUCKET_DERIVED}.{spec.name}" + sf_fqn = f"{SNOWFLAKE_DERIVED}.{spec.name}" + + print(f"\n=== {spec.name} ===") + emb_sql = build_query(emb_fqn, spec, "embucket") + sf_sql = build_query(sf_fqn, spec, "snowflake") + try: + emb = emb_query(emb_client, emb_token, emb_sql) + except Exception as ex: + print(f" embucket query FAILED: {ex}") + any_fail = True + continue + try: + sf = sf_query(sf_conn, sf_sql) + except Exception as ex: + print(f" snowflake query FAILED: {ex}") + any_fail = True + continue + + diffs = diff_dicts(emb, sf) + if diffs: + print(f" DIVERGE ({len(diffs)} fields):") + for d in diffs: + print(d) + any_fail = True + else: + print(f" PARITY: n={emb.get('n')} " + f"n_distinct_key={emb.get('n_distinct_key')} -- " + "all metrics + 16 md5 checksums match") + + sys.exit(1 if any_fail else 0) + + +if __name__ == "__main__": + main() diff --git a/specs/2026-04-22-parity-results.md b/specs/2026-04-22-parity-results.md index cbf47e3..d4ac35b 100644 --- a/specs/2026-04-22-parity-results.md +++ b/specs/2026-04-22-parity-results.md @@ -60,6 +60,47 @@ duplicates and the target now populated: Embucket page_views = 1.34M vs Snowflake 814K -- two dbt runs amplified Embucket's duplicate count). +## Deep column-level parity (parity_deep.py) + +With seeds loaded on both engines and a single clean `dbt run` each +(31 PASS both), `scripts/parity_deep.py` computes server-side +aggregates per headline table: +row count, distinct natural-key count, MIN/MAX `start_tstamp` (cast to +epoch microseconds so engine timestamp-serialization differences don't +masquerade as content divergence), 4-5 numeric SUMs, and 16 order- +independent row-MD5 checksums (one per hex position). Results: + +| table | rowcount | distinct natural key | min/max start | md5 checksums | selected SUMs | +|-------|----------|----------------------|---------------|---------------|---------------| +| snowplow_web_page_views | **+12 on Snowflake** (814,547 vs 814,559) | **equal** (775,543 both) | equal | all 16 drift ~0.03% | sum_engaged: +180 sec (0.001%); sum_absolute_time: +1.27% on Embucket; sum_doc_height/width: drift < 0.003% | +| snowplow_web_sessions | equal (360,999 both) | equal (317,162 both) | equal | all 16 drift ~0.1% | sum_absolute_time: -0.86% on Snowflake | +| snowplow_web_users | +12,056 on Embucket (134,097 vs 122,041) | **equal** (71,352 both) | equal | drift ~9% | sum_engaged/page_views/sessions scale with the +9.9% row excess on Embucket | + +**Interpretation**: + +1. **Same content, different duplicate multiplicity.** Distinct + natural-key counts match exactly on every table. Distinct + `(key, absolute_time, engaged_time, page_views_in_session)` tuple + count also matches (775,543 on both for page_views). So the two + engines identified the same set of page views / sessions / users, + produced the same attribute values for each, but copied some of + those rows into the derived tables a different number of times + during the scratch→derived CTAS step. This is an artifact of the + non-deduplicating `_this_run → derived` path running over a source + that has duplicate event_ids. + +2. **±1 second rounding on `absolute_time_in_s`** for individual rows. + Spot-checked session `7da3c35f-5565-4903-88df-bb3c19a918f9`: + Embucket 231, Snowflake 230, all other columns identical. + Spot-checked page_view `00005058-c0e9-4e2a-8c2a-24db2e1f8fa4`: + Embucket 40, Snowflake 39, other columns identical. + Consistent with different integer-truncation of second-level + timestamp subtraction between the two engines. + +3. **Tiny md5-position drift with matching distinct-key counts** is the + hash signature of (1)+(2) combined; it is not independent evidence + of more divergence. + ## Findings 1. **Glue source has ~4.7% duplicate event_ids**. Snowplow assumes the From eaecb27f101f6772e20594e4f1f88e5e89dfa6cf Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 17:32:08 -0500 Subject: [PATCH 20/30] docs: record Embucket OOM on snowplow_web_base_events_this_run After fresh dbt deps: unpatched package errors at compile (Unexpected target type embucket). Patched package hits DataFusion memory-pool exhaustion on the 2.83M-row base-events repartition, even at 10 GB function memory with 10 GB disk spill. Snowflake on the same source runs to completion (20 PASS). --- specs/2026-04-22-parity-results.md | 36 ++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/specs/2026-04-22-parity-results.md b/specs/2026-04-22-parity-results.md index d4ac35b..28f74ab 100644 --- a/specs/2026-04-22-parity-results.md +++ b/specs/2026-04-22-parity-results.md @@ -60,6 +60,42 @@ duplicates and the target now populated: Embucket page_views = 1.34M vs Snowflake 814K -- two dbt runs amplified Embucket's duplicate count). +## Fresh-`dbt deps` re-run (no patch, then with patch) + +After running `dbt deps` from scratch (prior modified copy preserved as +`dbt_packages.bak.1776896708/`) on the same batch-1 source: + +**Without `patch_snowplow.sh`** the Embucket target fails immediately at +compile time: +``` +Compilation Error in model snowplow_web_page_views + Snowplow: Unexpected target type embucket + > in macro get_value_by_target_type +``` +Confirms why the patch exists: `snowplow_utils` has explicit +`target.type` branches (snowflake/bigquery/databricks/postgres/redshift/ +spark) and errors out on anything else. + +**With `patch_snowplow.sh`** applied (rewrites `target.type == 'snowflake'` +to `target.type in ['snowflake','embucket']` so Embucket dispatches to +the Snowflake-flavored models), Embucket now hits a **memory OOM** on +the biggest scratch model: +``` +Failure in model snowplow_web_base_events_this_run + Database Error + Resources exhausted: Failed to allocate additional 5.4 MB for + RepartitionExec[2] with 5.1 MB already allocated for this + reservation - 3.1 MB remain available for the total pool +``` +All 12 downstream models SKIP; only the 7 pre-scratch models succeed. +Lambda config at the time: 10 GB function memory, `MEM_POOL_SIZE_MB=9216`, +`DISK_POOL_SIZE_MB=10240` (spill enabled), `MEM_POOL_TYPE=greedy`. 2.83M +rows × ~130 columns (including the 7 JSON-context VARIANT columns) +still exhaust DataFusion's pool during the repartition for the JOIN +against `snowplow_web_base_sessions_this_run`. + +Snowflake ran to completion on the same source: `Done. PASS=20`. + ## Deep column-level parity (parity_deep.py) With seeds loaded on both engines and a single clean `dbt run` each From f2f4f203eed7ad3953a4bf55a5d38184ab7524a1 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 17:39:59 -0500 Subject: [PATCH 21/30] docs: 1/10 source scale results -- tight parity, isolate users.referrer tie-break At 323,559 events, Embucket no longer OOMs and both engines produce matching rowcounts / distinct-key counts / user-table SUMs. Divergence shrinks to (1) 1-second rounding on page_view/session absolute_time_in_s and (2) 239 users where MAX(domain_sessionid) tie-breaks to a different first session whose referrer NULL-state differs. --- specs/2026-04-22-parity-results.md | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/specs/2026-04-22-parity-results.md b/specs/2026-04-22-parity-results.md index 28f74ab..764f081 100644 --- a/specs/2026-04-22-parity-results.md +++ b/specs/2026-04-22-parity-results.md @@ -96,6 +96,35 @@ against `snowplow_web_base_sessions_this_run`. Snowflake ran to completion on the same source: `Done. PASS=20`. +## 1/10 source scale (323,559 events, 3-min window) + +Both engines complete cleanly (20 PASS each) at this size -- Embucket +no longer OOMs. Parity picture at this scale is much tighter: + +- **Rowcounts match exactly** on all three derived tables. +- **Distinct natural-key counts match exactly.** +- **SUMs of all tracked metrics match on users** (page_views, sessions, + engaged_time_in_s identical). +- **Only remaining numeric divergence**: `sum_absolute_time_in_s` off + by 1.24% on page_views and 0.81% on sessions -- the same per-row + ±1-second rounding seen earlier, scaled down. +- **Users table referrer column diverges on NULL-fill**: 12,585 + non-null referrers on Snowflake vs 12,346 on Embucket (18 distinct + values on both). Root-caused in the compiled + `snowplow_web_users_this_run` -- `first_domain_sessionid` is + computed via `MAX(case when start_tstamp = user_start_tstamp then + domain_sessionid end)`. When two sessions for the same user share + `start_tstamp` (tie), `MAX(domain_sessionid)` resolves to a different + UUID ordering on each engine, picking a different first session whose + referrer value may be NULL or populated. 239 users (~2%) affected. + +At 2.83M rows the same effects are magnified (rowcount drifts by +thousands on users, SUMs drift ~10%) and Embucket OOMs the +`snowplow_web_base_events_this_run` repartition. So the 2.83M +divergence is best understood as: same root-cause pipelines, but at +scale the Embucket engine runs out of memory before completion, +and the duplicate-row drift is amplified. + ## Deep column-level parity (parity_deep.py) With seeds loaded on both engines and a single clean `dbt run` each From 28bd39d9ce22a485b8ab83666e6c5e703477ba9e Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 17:55:07 -0500 Subject: [PATCH 22/30] docs: batch-2 MERGE finding -- Embucket loses cross-batch user state 9% of users (3,164 of 35,094) end up with smaller aggregates on Embucket than Snowflake after the second dbt run. Direction is strictly one-sided (sf >= emb, never the reverse). Consistent with Embucket's snowplow_web_users_this_run producing batch-2-only aggregates that then replace the batch-1 row via MERGE UPDATE, while Snowflake's producing cumulative aggregates. --- specs/2026-04-22-parity-results.md | 36 ++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/specs/2026-04-22-parity-results.md b/specs/2026-04-22-parity-results.md index 764f081..e70dd4f 100644 --- a/specs/2026-04-22-parity-results.md +++ b/specs/2026-04-22-parity-results.md @@ -118,6 +118,42 @@ no longer OOMs. Parity picture at this scale is much tighter: UUID ordering on each engine, picking a different first session whose referrer value may be NULL or populated. 239 users (~2%) affected. +### After batch 2 at 1/10 scale (MERGE path engaged) + +Second load: 286,112 rows at 15:15 load_tstamp burst, total source +now 609,671 rows. Both engines: `dbt run` 20 PASS. + +- Rowcounts + distinct keys still match exactly on all three + derived tables (35,094 users / 35,094 distinct domain_userid on + both; page_views and sessions also equal). +- `sum_absolute_time_in_s` drift on page_views / sessions compounds + with the same ±1-sec-per-row pattern. +- **Users table: 3,164 of 35,094 users (9%) have diverging + aggregates, all in the same direction -- Snowflake accumulates + more.** `sum_engaged_time_in_s` +6.4% on Snowflake, + `sum_page_views` +6.4%, `sum_sessions` +6.5%. Direction check: + `sf_aggregate_larger=3164 emb_aggregate_larger=0`. + +Example users where batch 1 activity is visible on Snowflake but not +Embucket: +``` +2696ac4b-...: emb=(1 pv, 1 sess, 15 eng) sf=(2 pv, 2 sess, 15 eng) +eeb8f559-...: emb=(27 pv, 9 sess, 415 eng) sf=(39 pv, 16 sess, 535 eng) +``` + +Snowplow's users incremental model MERGEs via `update set col = source.col` +(row-replace), so the source `snowplow_web_users_this_run` must itself +contain cumulative batch-1+batch-2 aggregates per user for cross-batch +users. Snowflake's scratch pipeline is producing that cumulative row; +Embucket's is producing a batch-2-only row, losing the prior batch's +counts on the target update. Root cause is most likely in how +`snowplow_web_base_sessions_this_run` computes the lookback window +(which sessions from the manifest get re-aggregated against new +events) -- worth drilling into on its own, but outside this +investigation's scope. + +### 2.83M scale comparison + At 2.83M rows the same effects are magnified (rowcount drifts by thousands on users, SUMs drift ~10%) and Embucket OOMs the `snowplow_web_base_events_this_run` repartition. So the 2.83M From 941d33e08931eee72d6c401f7e2ea03b0aa814cb Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 18:06:42 -0500 Subject: [PATCH 23/30] docs: localize batch-2 divergence to Embucket MERGE UPDATE execution Every upstream scratch stage has identical aggregates on both engines (users_aggs SUM(page_views) = 181,900 on both). The divergence appears only in the final snowplow_web_users derived table after MERGE: Embucket's target still carries batch-1 values while its own scratch source has the correct batch-1+batch-2 cumulative values and the compiled MERGE SQL is identical to Snowflake's. Snowflake applies the WHEN MATCHED UPDATE; Embucket does not -- 9% of users affected, all one-sided (sf >= emb). Likely bug in Embucket server-side MERGE UPDATE execution when the ON clause combines a BETWEEN predicate with an equality predicate. --- specs/2026-04-22-parity-results.md | 57 ++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) diff --git a/specs/2026-04-22-parity-results.md b/specs/2026-04-22-parity-results.md index e70dd4f..e10beb6 100644 --- a/specs/2026-04-22-parity-results.md +++ b/specs/2026-04-22-parity-results.md @@ -134,6 +134,63 @@ now 609,671 rows. Both engines: `dbt run` 20 PASS. `sum_page_views` +6.4%, `sum_sessions` +6.5%. Direction check: `sf_aggregate_larger=3164 emb_aggregate_larger=0`. +#### Drill-down: where does the divergence enter? + +Every stage in the lineage before the final MERGE produces +byte-identical aggregates on both engines: + +| stage | rowcount | SUM(page_views) | SUM(engaged_time_in_s) | +|-------|----------|-----------------|------------------------| +| `snowplow_web_base_events_this_run` | 609,671 / 609,671 | -- | -- | +| `snowplow_web_base_sessions_this_run` | 75,642 / 75,642 | -- | -- | +| `snowplow_web_sessions_this_run` | 75,482 / 75,482 | -- | -- | +| `snowplow_web_sessions` (derived) | 75,482 / 75,482 | -- | -- | +| `snowplow_web_users_sessions_this_run` | 75,482 / 75,482 | **181,900 both** | **3,298,530 both** | +| `snowplow_web_users_aggs` | 35,094 / 35,094 | **181,900 both** | **3,298,530 both** | +| `snowplow_web_users_this_run` | 35,094 / 35,094 | -- | -- | +| **`snowplow_web_users` (derived)** | 35,094 / 35,094 | **emb 170,884 / sf 181,900** | **emb 3,100,035 / sf 3,298,530** | + +The scratch source for the MERGE has identical data on both engines; +the divergence enters purely at the MERGE step. + +Per-user spot check for a high-diff user +`eeb8f559-c8d0-4c3b-b50f-e70a11a5d997`: + +| source | Embucket | Snowflake | +|--------|----------|-----------| +| scratch `users_this_run` | (39 pv, 16 sess, 535 eng, end=15:14:44) | (39 pv, 16 sess, 535 eng, end=15:14:44) | +| derived `users` after MERGE | **(27 pv, 9 sess, 415 eng, end=15:02:11)** | (39 pv, 16 sess, 535 eng, end=15:14:44) | + +The scratch `users_this_run` row on Embucket has the correct +cumulative batch-1+batch-2 values, but after the MERGE INTO the +derived target, Embucket's row still carries the batch-1 values +unchanged. Snowflake's MERGE updates correctly. + +The compiled MERGE SQL is **identical** on both engines (the +`patch_snowplow.sh` rewrite makes Embucket dispatch the Snowflake +MERGE macro): + +```sql +MERGE INTO AS DBT_INTERNAL_DEST + USING <__dbt_tmp> AS DBT_INTERNAL_SOURCE + ON (DBT_INTERNAL_DEST.start_tstamp BETWEEN + '2026-04-22 14:52:51.530000' AND '2026-04-22 15:15:39.548000') + AND (DBT_INTERNAL_SOURCE.domain_userid = DBT_INTERNAL_DEST.domain_userid) +WHEN MATCHED THEN UPDATE SET ... +WHEN NOT MATCHED THEN INSERT ... +``` + +For the affected user, the target row's `start_tstamp = 14:58:56.280` +is well inside the BETWEEN range and the `domain_userid` is present +in both sides, so Snowflake fires UPDATE and Embucket does not. +Embucket's server-side MERGE UPDATE is either mis-evaluating the ON +clause (fewer matches than expected) or silently no-op'ing the +WHEN MATCHED UPDATE branch for some matching rows -- ~9% of rows +affected on the users MERGE at 609K source events. Surfaces as +Snowflake aggregates strictly >= Embucket aggregates (never the +reverse), consistent with "batch-1 row left unmodified while it +should have been replaced by cumulative batch-1+batch-2 scratch row." + Example users where batch 1 activity is visible on Snowflake but not Embucket: ``` From fc477ffe660bcf70812295e1dc3ad4249c6e9d18 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 18:10:19 -0500 Subject: [PATCH 24/30] docs: consolidated investigation report Single standalone report covering setup, shared-source mechanics, tooling, checks performed at two data volumes and two dbt run paths (CTAS and MERGE), findings, and conclusions. Puts the localized Embucket MERGE UPDATE correctness issue (9% of users unchanged despite identical source and ON clause satisfied) alongside the upstream findings (source DQ, snowplow-web dedup assumptions, 1-second rounding) for context. --- ...-04-22-embucket-snowflake-investigation.md | 366 ++++++++++++++++++ 1 file changed, 366 insertions(+) create mode 100644 specs/2026-04-22-embucket-snowflake-investigation.md diff --git a/specs/2026-04-22-embucket-snowflake-investigation.md b/specs/2026-04-22-embucket-snowflake-investigation.md new file mode 100644 index 0000000..9e1d1e7 --- /dev/null +++ b/specs/2026-04-22-embucket-snowflake-investigation.md @@ -0,0 +1,366 @@ +# Embucket vs Snowflake correctness investigation — dbt-snowplow-web + +**Date:** 2026-04-22 +**Branch:** `rampage644/real-snowplow-source` +**PR:** https://github.com/Embucket/embucket-snowplow/pull/6 + +## 1. Goal + +Run the same `dbt-snowplow-web` pipeline on Embucket (Snowflake-compatible +engine on AWS Lambda + S3 Tables Iceberg) and on Snowflake, against the same +source data, and determine whether the two engines produce equivalent results. +The Embucket side is the system under investigation; Snowflake is the +reference. + +## 2. Setup + +### 2.1 Source data + +- Glue-managed Iceberg table `analytics_glue.hooli_events_0417_v2` + in account 767397688925, us-east-2. Real Snowplow atomic events, + ~2.1M rows per hour of data, bursty `load_tstamp` distribution within + each hour. +- Source has an upstream data quality issue: **4.7% duplicate `event_id` + values** (2,834,944 rows in the 15:00-15:30 window / 2,700,579 distinct + `event_id`). Snowplow Web expects the atomic layer to be pre-deduped. + +### 2.2 Shared source table + +Both engines read the **same physical Iceberg table**, not two copies: + +- Athena writes the source into the S3 Tables bucket + `arn:aws:s3tables:us-east-2:767397688925:bucket/snowplow` as + `atomic.events_0416` (`scripts/load_from_glue.py`). +- Embucket Lambda is volumed to that bucket and sees + `demo.atomic.events_0416` directly. +- Snowflake reads the same table via a Glue-federated Iceberg REST + catalog integration (`SNOWPLOW_S3T`) as + `sturukin_db.atomic.events_0416`, fronted by a `events_0416_v` + view that `TRY_PARSE_JSON`s the seven context VARCHAR columns + into VARIANT (so snowplow-web's `contexts_...[0]:id` indexing + works). + +Integrity of the shared source verified: both engines return identical +`COUNT(*)`, identical `MIN/MAX(load_tstamp)` after every load. + +### 2.3 Loading mechanics + +- `scripts/load_from_glue.py init` — drops and recreates the empty + `events_0416` with the correct schema (via CTAS `WHERE 1=0`) and + `partitioning = ARRAY['day(load_tstamp)', 'event_name']`. +- `scripts/load_from_glue.py insert --start --end ` — INSERTs + a `[start, end)` window of `load_tstamp` from the Glue source. +- `scripts/snowflake_setup.py` — idempotent: creates catalog integration, + grants Lake Formation DESCRIBE+SELECT on the S3 Tables source table to + the Snowflake IAM role, (re)creates the managed Iceberg table + VARIANT + view. +- `scripts/snowflake_refresh.py` — `ALTER ICEBERG TABLE ... REFRESH` after + each Athena write. + +### 2.4 dbt wiring + +- Single project, single `embucket_demo` profile, two targets: `dev` + (type: embucket) and `snowflake` (type: snowflake). +- `scripts/patch_snowplow.sh` rewrites + `target.type == 'snowflake'` → `target.type in ['snowflake', 'embucket']` + across `dbt_packages/`, so Embucket dispatches the Snowflake-flavored + snowplow-web models and macros. Without this, compilation fails with + `Snowplow: Unexpected target type embucket` from + `snowplow_utils.get_value_by_target_type`. +- `dbt_project.yml` sets `snowplow__events_table` target-aware: + `events_0416` on Embucket, `events_0416_v` on Snowflake. +- 7 of the 7 snowplow-web context flags are at their defaults (only + `snowplow__enable_iab`, `_ua`, `_yauaa`, `_consent`, `_cwv` are + relevant here; all off in both targets). + +### 2.5 Embucket Lambda configuration + +- Function memory: 10240 MB (10 GB) +- `MEM_POOL_SIZE_MB = 9216` +- `MEM_POOL_TYPE = greedy` +- `DISK_POOL_SIZE_MB = 10240` (spill enabled) +- `QUERY_TIMEOUT_SECS = 1200` + +### 2.6 Comparison tooling + +- `scripts/parity.py` — set-based row-level diff. Fetches `(key, md5)` + pairs from each engine and diffs. Abandoned for large tables: 775K + rows × 64 bytes = 50 MB, well above the Lambda 6 MB response cap. +- `scripts/parity_deep.py` — **server-side aggregate parity**. One query + per table per engine returning a single row: + - `COUNT(*)`, `COUNT(DISTINCT )` + - `MIN`/`MAX` `start_tstamp` cast to epoch microseconds (BIGINT) + - 4-5 SUM metrics (page_views, sessions, engaged_time_in_s, etc.) + - 16 position-wise MD5 checksums: for each of 16 hex positions of each + row's `MD5(CONCAT_WS('|', col1, col2, ...))`, `SUM(ASCII(SUBSTR(md5, i, 1)))`. + Order-independent and portable. Timestamp columns are normalized to + epoch-microseconds before MD5 so engine-specific `CAST(ts AS VARCHAR)` + rendering differences don't contaminate the hash. + +## 3. Checks performed and findings + +Four full runs at two data volumes; batch 1 only (CTAS-path) and batch +1 + batch 2 (MERGE-path on the incremental models). + +### 3.1 2.83M rows (30-minute window `15:00-15:30`) + +**Embucket behavior:** +- Without `patch_snowplow.sh`: compile error + (`Unexpected target type embucket`). +- With `patch_snowplow.sh`: **OOM** on + `snowplow_web_base_events_this_run`: + ``` + Resources exhausted: Failed to allocate additional 5.4 MB + for RepartitionExec[2] with 5.1 MB already allocated for + this reservation - 3.1 MB remain available for the total pool + ``` + The repartition for the join against + `snowplow_web_base_sessions_this_run` exhausts DataFusion's 9 GB pool + even with spill enabled. All 12 downstream models `SKIP`. + +**Snowflake behavior:** completes cleanly, 20 PASS (or 31, depending on +the package version). Derived tables populated. + +**Result at this scale:** Embucket cannot complete the pipeline on +2.83M rows × ~130 columns on the current Lambda configuration. + +### 3.2 0.28M rows (1/10 scale, single 15:02 burst, 323,559 events) + +Both engines: `dbt seed` + `dbt run` succeed, 20 PASS each. + +**Parity deep check (batch 1 only — CTAS path for incremental models):** + +| table | rowcount | distinct natural key | MIN/MAX start_tstamp | SUM metrics | 16 md5 checksums | +|-------|----------|----------------------|----------------------|-------------|------------------| +| `snowplow_web_page_views` | **equal** | **equal** | **equal** | `sum_absolute_time_in_s` -1.24% on Snowflake; others match | all 16 drift ~0.03% | +| `snowplow_web_sessions` | **equal** (10,891) | **equal** | **equal** | `sum_absolute_time_in_s` -0.81% on Snowflake | all 16 drift ~0.1% | +| `snowplow_web_users` | **equal** (6,243) | **equal** | **equal** | **all match** | drift driven by 1 column | + +Two real divergences isolated at this scale: + +**Finding A — `absolute_time_in_s` ±1-second rounding.** Spot-checks: +- Session `7da3c35f-5565-4903-88df-bb3c19a918f9`: Embucket 231, Snowflake 230; all other columns identical. +- Page view `00005058-c0e9-4e2a-8c2a-24db2e1f8fa4`: Embucket 40, Snowflake 39; all others identical. + +Consistent with different integer-truncation of second-level timestamp +subtraction between engines. Cumulative effect is ~1% divergence on the +table-level SUM. + +**Finding B — `users.referrer` column NULL drift.** At 1/10 scale, 239 +of 12,346 users (2%) differ on whether `referrer` is NULL. Same 18 +distinct non-null referrer values on both engines. Root cause in +`snowplow_web_users_this_run`: + +```sql +max(case when start_tstamp = user_start_tstamp + then domain_sessionid end) AS first_domain_sessionid +``` + +When a user has two sessions sharing `start_tstamp` (tie), `MAX(uuid)` +resolves to a different UUID on each engine, joining to a different +first-session row whose `referrer` is populated on one engine and NULL +on the other. Deterministic tie-break difference, not a correctness bug +per se. + +### 3.3 0.61M rows — batch 2 added (MERGE path for incremental models) + +Batch 2: +286,112 rows from 15:15 load_tstamp burst. Total source: +609,671 events. + +Both engines: `dbt run` 20 PASS. No errors. + +**Parity deep check:** + +| table | rowcount | distinct key | SUM metrics | +|-------|----------|--------------|-------------| +| `snowplow_web_page_views` | equal | equal | `sum_absolute_time_in_s` -1.25% on Snowflake (per-row rounding compounding); others match | +| `snowplow_web_sessions` | equal | equal | `sum_absolute_time_in_s` -0.85% on Snowflake; others match | +| **`snowplow_web_users`** | **equal (35,094)** | **equal (35,094)** | **`sum_page_views` +6.4% / `sum_sessions` +6.5% / `sum_engaged_time_in_s` +6.4% on Snowflake** | + +**Finding C — Embucket MERGE UPDATE does not apply for ~9% of users on +the second `dbt run`.** 3,164 of 35,094 users have diverging +aggregates, strictly one-sided: + +``` +of mismatches: sf_aggregate_larger=3164 emb_aggregate_larger=0 +``` + +### 3.4 Localization of Finding C + +Instrumented every upstream stage: + +| stage | rowcount | SUM(page_views) | SUM(engaged_time_in_s) | +|-------|----------|-----------------|------------------------| +| `snowplow_web_base_events_this_run` | 609,671 / 609,671 | — | — | +| `snowplow_web_base_sessions_this_run` | 75,642 / 75,642 | — | — | +| `snowplow_web_sessions_this_run` | 75,482 / 75,482 | — | — | +| `snowplow_web_sessions` (derived) | 75,482 / 75,482 | — | — | +| `snowplow_web_users_sessions_this_run` | 75,482 / 75,482 | **181,900 both** | **3,298,530 both** | +| `snowplow_web_users_aggs` | 35,094 / 35,094 | **181,900 both** | **3,298,530 both** | +| `snowplow_web_users_this_run` | 35,094 / 35,094 | — | — | +| **`snowplow_web_users` (derived)** | 35,094 / 35,094 | **emb 170,884 / sf 181,900** | **emb 3,100,035 / sf 3,298,530** | + +The scratch `users_this_run` source for the MERGE has identical +aggregates on both engines. The divergence appears purely at the final +MERGE step. + +**Per-user spot check** (user `eeb8f559-c8d0-4c3b-b50f-e70a11a5d997`): + +| source | Embucket | Snowflake | +|--------|----------|-----------| +| scratch `users_this_run` | 39 pv, 16 sess, 535 eng, end=15:14:44 | 39 pv, 16 sess, 535 eng, end=15:14:44 | +| derived `users` after MERGE | **27 pv, 9 sess, 415 eng, end=15:02:11** | 39 pv, 16 sess, 535 eng, end=15:14:44 | + +Embucket's scratch row has the correct cumulative values; the target +row still carries batch-1 values. Snowflake updates correctly. + +**Compiled MERGE SQL is byte-identical on both engines** (the patch +makes Embucket dispatch the Snowflake MERGE macro): + +```sql +MERGE INTO AS DBT_INTERNAL_DEST + USING <__dbt_tmp> AS DBT_INTERNAL_SOURCE + ON (DBT_INTERNAL_DEST.start_tstamp BETWEEN + '2026-04-22 14:52:51.530000' AND '2026-04-22 15:15:39.548000') + AND (DBT_INTERNAL_SOURCE.domain_userid = DBT_INTERNAL_DEST.domain_userid) +WHEN MATCHED THEN UPDATE SET ... +WHEN NOT MATCHED THEN INSERT ... +``` + +For the affected user: target's `start_tstamp = 14:58:56.280` is +inside the BETWEEN range, `domain_userid` matches the scratch row — +so the ON condition is satisfied and WHEN MATCHED UPDATE should fire. + +## 4. Summary of findings + +| # | Finding | Scope | Attribution | +|---|---------|-------|-------------| +| 0 | Source Glue table has 4.7% duplicate `event_id` | upstream | data quality (not engine) | +| 1 | Embucket OOMs on `snowplow_web_base_events_this_run` at 2.83M rows even with 10 GB pool + 10 GB spill | Embucket engine | memory/spill behavior on wide row with JSON VARIANT columns and repartition-on-join | +| 2 | Unpatched snowplow-web fails on Embucket with `Unexpected target type embucket` | dbt package | hardcoded `target.type` branches; patchable via 1-line regex | +| 3 | `absolute_time_in_s` drifts ±1 second per row | both engines | different integer truncation of timestamp subtraction; visible in ~1% of rows | +| 4 | `users.referrer` NULL-fill differs for 2% of users | snowplow-web package | `MAX(uuid)` tie-break when two sessions share `start_tstamp`; deterministic per engine but not engine-invariant | +| 5 | Snowplow users/page_views/sessions derived tables preserve the source's duplicate-`event_id` rows on first CTAS | snowplow-web package | `_this_run → derived` step doesn't dedup on unique_key; Snowflake errors on second MERGE, Embucket silently appends | +| 6 | **Embucket MERGE UPDATE fails to apply for ~9% of matching target rows on the users derived table** | **Embucket engine** | **compiled MERGE SQL identical; source identical; ~9% of matched rows silently not updated. One-sided (sf ≥ emb always)** | + +## 5. Conclusions + +**1. Source parity is not the issue.** The Iceberg table +`atomic.events_0416` is byte-for-byte identical from both engines' +perspective: same `COUNT(*)`, same `MIN/MAX(load_tstamp)`, same column +types (after the Snowflake-side VARIANT view converts the JSON context +VARCHARs). Every downstream divergence is post-read. + +**2. Most divergence is attributable to the source or to the +snowplow-web package, not to Embucket.** Findings 0, 3, 4, and 5 apply +equally to both engines in the sense that they produce different +outputs from the *same input* because the package's SQL under-specifies +order-insensitive behavior (tie-breaks, dedup) and because the source +has DQ issues the package assumes away. A clean source plus strict-SQL +snowplow-web would remove these. + +**3. Two Embucket-specific engine issues remain.** + + - **Memory**: at 2.83M rows the base-events repartition exceeds 9 GB + pool + 10 GB spill. Either the repartition shuffle is + under-spilling or the per-row footprint is larger than + Snowflake's. Reproducer: `load_from_glue.py insert` with a + full-hour window + `dbt run --target dev`. Probably worth + investigating whether disk spill actually engages for the + RepartitionExec path. + + - **MERGE UPDATE correctness**: Finding 6 is the most concrete + correctness finding. Identical SQL, identical source data, + identical target pre-state (both engines have the same batch-1 + row); Snowflake executes the UPDATE, Embucket leaves ~9% of + matching rows unmodified. Investigation should focus on + Embucket's handling of MERGE when the ON clause combines a + `BETWEEN` predicate on the target with an equality predicate on + the source's unique key. Candidate hypotheses: + - Predicate is evaluated against a pre-filtered subset of the + target rather than all target rows (incorrect index/partition + pruning). + - MATCHED rows are identified but UPDATE is skipped when the + source row and target row differ by columns that aren't in the + ON clause, due to a mis-applied "no-op if unchanged" + optimization. + - The target-vs-source role is inverted for a subset of rows + during parallel execution. + +**4. At scale the two kinds of issues compound.** The OOM at 2.83M +rows and the 9% MERGE UPDATE miss at 609K rows are the practical +blockers for running dbt-snowplow-web on real-volume data through +Embucket. Both are worth isolating into minimal reproducers outside +the snowplow-web package. + +## 6. Reproducers + +**Memory OOM:** + +```bash +./scripts/patch_snowplow.sh +uv run python scripts/load_from_glue.py init +uv run python scripts/load_from_glue.py insert \ + --start '2026-04-22 15:00:00' --end '2026-04-22 15:30:00' +uv run dbt seed --profiles-dir . --target dev +uv run dbt run --profiles-dir . --target dev +# Expect: Failure in snowplow_web_base_events_this_run, "Resources exhausted" +``` + +**MERGE UPDATE correctness:** + +```bash +# 1. Load batch 1 (15:02 burst, 323,559 rows) +uv run python scripts/load_from_glue.py init +uv run python scripts/load_from_glue.py insert \ + --start '2026-04-22 15:00:00' --end '2026-04-22 15:03:00' +uv run python scripts/snowflake_refresh.py +uv run dbt seed --profiles-dir . --target dev +uv run dbt seed --profiles-dir . --target snowflake +uv run dbt run --profiles-dir . --target dev +uv run dbt run --profiles-dir . --target snowflake + +# 2. Load batch 2 (15:15 burst, 286,112 rows) and re-run dbt +uv run python scripts/load_from_glue.py insert \ + --start '2026-04-22 15:15:00' --end '2026-04-22 15:16:00' +uv run python scripts/snowflake_refresh.py +uv run dbt run --profiles-dir . --target dev +uv run dbt run --profiles-dir . --target snowflake + +# 3. Parity check: expect sf_page_views > emb_page_views by ~6.4% +uv run python scripts/parity_deep.py +``` + +Drill-down on a single affected user after the reproducer: + +```python +SELECT page_views, sessions, engaged_time_in_s, end_tstamp +FROM demo.atomic_derived.snowplow_web_users +WHERE domain_userid = 'eeb8f559-c8d0-4c3b-b50f-e70a11a5d997'; +-- Embucket: (27, 9, 415, 15:02:11) + +SELECT page_views, sessions, engaged_time_in_s, end_tstamp +FROM sturukin_db.atomic_derived.snowplow_web_users +WHERE domain_userid = 'eeb8f559-c8d0-4c3b-b50f-e70a11a5d997'; +-- Snowflake: (39, 16, 535, 15:14:44) + +SELECT page_views, sessions, engaged_time_in_s, end_tstamp +FROM demo.atomic_scratch.snowplow_web_users_this_run +WHERE domain_userid = 'eeb8f559-c8d0-4c3b-b50f-e70a11a5d997'; +-- Both: (39, 16, 535, 15:14:44) -- scratch is correct on both engines; +-- the bug is in how Embucket's MERGE applies the UPDATE. +``` + +## 7. Tooling added on this branch + +- `scripts/load_from_glue.py` — Athena loader with `init`/`insert` subcommands. +- `scripts/events_0416_select.sql` — shared projection SELECT body with a + `{{where}}` placeholder. +- `scripts/snowflake_setup.py` — idempotent Snowflake-side setup: catalog + integration, LF grants, iceberg table, VARIANT view. +- `scripts/snowflake_refresh.py` — `ALTER ICEBERG TABLE ... REFRESH`. +- `scripts/parity.py` — set-based row-level diff (abandoned at scale). +- `scripts/parity_deep.py` — server-side aggregate parity (the useful one). +- `tests/test_parity.py` — pure-Python unit tests on the hash/diff logic. +- `patch_snowplow.sh` — 1-line rewrite that makes Embucket dispatch + Snowflake snowplow-web models. From 4f30397b7c69096e2089d58682774e3f7968ad19 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 18:19:49 -0500 Subject: [PATCH 25/30] docs: root-cause Finding 4 -- alias-shadow SQL scoping bug in Embucket Hypothesis that MAX(uuid) tie-break caused the users.referrer drift is rejected (zero users have tied start_tstamp). The real bug: Embucket's planner resolves column references inside aggregate CASE expressions against SELECT-list aliases instead of FROM-clause columns. When snowplow_web_users_aggs aliases user_start_tstamp AS start_tstamp in the SELECT list and then references start_tstamp inside a MAX(CASE ...), Embucket rebinds it to user_start_tstamp, making the condition always-true and degenerating MAX to MAX(domain_sessionid). 10-line standalone reproducer confirmed. Silently wrong result, no error. Promoted to Embucket-specific engine issue in the conclusions. --- ...-04-22-embucket-snowflake-investigation.md | 103 ++++++++++++++---- 1 file changed, 82 insertions(+), 21 deletions(-) diff --git a/specs/2026-04-22-embucket-snowflake-investigation.md b/specs/2026-04-22-embucket-snowflake-investigation.md index 9e1d1e7..4a2f9de 100644 --- a/specs/2026-04-22-embucket-snowflake-investigation.md +++ b/specs/2026-04-22-embucket-snowflake-investigation.md @@ -146,21 +146,73 @@ Consistent with different integer-truncation of second-level timestamp subtraction between engines. Cumulative effect is ~1% divergence on the table-level SUM. -**Finding B — `users.referrer` column NULL drift.** At 1/10 scale, 239 -of 12,346 users (2%) differ on whether `referrer` is NULL. Same 18 -distinct non-null referrer values on both engines. Root cause in -`snowplow_web_users_this_run`: +**Finding B — `users.referrer` column NULL drift, root-caused to an +Embucket SQL scoping bug.** At 1/10 scale, 239 of 12,346 users (2%) +differ on whether `referrer` is NULL; at 2/10 scale (batch 1+2) this +grows to **8,166 of 35,094 users (23%) with different +`first_domain_sessionid` between engines**. Initial hypothesis was +`MAX(uuid)` tie-breaking when two sessions share `start_tstamp`, but +that is **rejected**: zero users have tied `start_tstamp` in either +engine. + +The actual root cause is in `snowplow_web_users_aggs.sql`: + +```sql +SELECT domain_userid, + user_start_tstamp AS start_tstamp, -- alias shadows column! + user_end_tstamp AS end_tstamp, -- alias shadows column! + MAX(CASE WHEN start_tstamp = user_start_tstamp THEN domain_sessionid END) AS first_domain_sessionid, + MAX(CASE WHEN end_tstamp = user_end_tstamp THEN domain_sessionid END) AS last_domain_sessionid, + ... +FROM snowplow_web_users_sessions_this_run +GROUP BY 1, 2, 3 +``` + +The SELECT list aliases `user_start_tstamp AS start_tstamp`, so the +name `start_tstamp` is ambiguous: it can refer to either the FROM +table's column or the SELECT alias. + +- **Snowflake** resolves `start_tstamp` in the CASE to the FROM + table's column (ANSI-correct). MAX picks the session whose actual + `start_tstamp` equals the user's first session start. +- **Embucket** resolves `start_tstamp` in the CASE to the SELECT-list + alias `user_start_tstamp`, making the condition + `user_start_tstamp = user_start_tstamp` (always true). + `MAX(CASE WHEN TRUE THEN domain_sessionid END)` degenerates to + `MAX(domain_sessionid)` over all of the user's sessions — the + lexicographic max UUID. The same collapse happens to + `last_domain_sessionid`, so Embucket's `first_*` and `last_*` fields + degenerate to the same row. + +Minimal standalone reproducer (10 lines, no snowplow): ```sql -max(case when start_tstamp = user_start_tstamp - then domain_sessionid end) AS first_domain_sessionid +WITH s AS ( + SELECT 'S1' AS sid, TIMESTAMP '2020-01-01 00:00:00' AS start_tstamp, + TIMESTAMP '2020-01-01 00:00:00' AS user_start_tstamp + UNION ALL + SELECT 'S2' AS sid, TIMESTAMP '2020-01-01 05:00:00' AS start_tstamp, + TIMESTAMP '2020-01-01 00:00:00' AS user_start_tstamp +) +SELECT user_start_tstamp AS start_tstamp, + MAX(CASE WHEN start_tstamp = user_start_tstamp THEN sid END) AS first_sid +FROM s +GROUP BY user_start_tstamp; +-- Snowflake: first_sid = 'S1' (correct: only S1 has start_tstamp == user_start_tstamp) +-- Embucket: first_sid = 'S2' (alias-shadow bug: CASE collapses to always-true) ``` -When a user has two sessions sharing `start_tstamp` (tie), `MAX(uuid)` -resolves to a different UUID on each engine, joining to a different -first-session row whose `referrer` is populated on one engine and NULL -on the other. Deterministic tie-break difference, not a correctness bug -per se. +Removing the `AS start_tstamp` alias makes both engines return `'S1'`. +This is a **silently wrong-result bug** -- no error, no warning; +Embucket just produces incorrect output when a SELECT alias shadows +an underlying column name that is referenced elsewhere in the same +SELECT. Any aggregation of this shape is affected. + +User-facing impact on dbt-snowplow-web: the `first_*` fields of +`snowplow_web_users` (first_page_url, first_page_title, first_geo_*, +first_br_lang, referrer, etc.) are wrong on Embucket for every user +with >1 session. The final MERGE propagates these wrong values to the +derived table. ### 3.3 0.61M rows — batch 2 added (MERGE path for incremental models) @@ -239,7 +291,7 @@ so the ON condition is satisfied and WHEN MATCHED UPDATE should fire. | 1 | Embucket OOMs on `snowplow_web_base_events_this_run` at 2.83M rows even with 10 GB pool + 10 GB spill | Embucket engine | memory/spill behavior on wide row with JSON VARIANT columns and repartition-on-join | | 2 | Unpatched snowplow-web fails on Embucket with `Unexpected target type embucket` | dbt package | hardcoded `target.type` branches; patchable via 1-line regex | | 3 | `absolute_time_in_s` drifts ±1 second per row | both engines | different integer truncation of timestamp subtraction; visible in ~1% of rows | -| 4 | `users.referrer` NULL-fill differs for 2% of users | snowplow-web package | `MAX(uuid)` tie-break when two sessions share `start_tstamp`; deterministic per engine but not engine-invariant | +| 4 | `users.referrer` NULL-fill differs for 2% / `first_domain_sessionid` differs for 23% of users | **Embucket engine** | **alias-shadow bug: column reference inside aggregate CASE resolves to SELECT alias instead of FROM column; silently wrong result with 10-line standalone reproducer** | | 5 | Snowplow users/page_views/sessions derived tables preserve the source's duplicate-`event_id` rows on first CTAS | snowplow-web package | `_this_run → derived` step doesn't dedup on unique_key; Snowflake errors on second MERGE, Embucket silently appends | | 6 | **Embucket MERGE UPDATE fails to apply for ~9% of matching target rows on the users derived table** | **Embucket engine** | **compiled MERGE SQL identical; source identical; ~9% of matched rows silently not updated. One-sided (sf ≥ emb always)** | @@ -259,7 +311,7 @@ order-insensitive behavior (tie-breaks, dedup) and because the source has DQ issues the package assumes away. A clean source plus strict-SQL snowplow-web would remove these. -**3. Two Embucket-specific engine issues remain.** +**3. Three Embucket-specific engine issues remain.** - **Memory**: at 2.83M rows the base-events repartition exceeds 9 GB pool + 10 GB spill. Either the repartition shuffle is @@ -269,14 +321,23 @@ snowplow-web would remove these. investigating whether disk spill actually engages for the RepartitionExec path. - - **MERGE UPDATE correctness**: Finding 6 is the most concrete - correctness finding. Identical SQL, identical source data, - identical target pre-state (both engines have the same batch-1 - row); Snowflake executes the UPDATE, Embucket leaves ~9% of - matching rows unmodified. Investigation should focus on - Embucket's handling of MERGE when the ON clause combines a - `BETWEEN` predicate on the target with an equality predicate on - the source's unique key. Candidate hypotheses: + - **Alias-shadow correctness bug (Finding 4)**: column references + inside aggregate `CASE` expressions resolve to SELECT-list + aliases instead of the FROM table's columns. Silently wrong + results, no error. 10-line standalone reproducer attached. This + is the root cause of 23% of users getting wrong + `first_domain_sessionid` / `referrer` / first-session fields on + the derived `snowplow_web_users` table. High-severity because + any downstream query with a `SELECT col AS same_name_as_another_col` + pattern is silently compromised. + + - **MERGE UPDATE correctness (Finding 6)**: identical SQL, + identical source data, identical target pre-state (both engines + have the same batch-1 row); Snowflake executes the UPDATE, + Embucket leaves ~9% of matching rows unmodified. Investigation + should focus on Embucket's handling of MERGE when the ON clause + combines a `BETWEEN` predicate on the target with an equality + predicate on the source's unique key. Candidate hypotheses: - Predicate is evaluated against a pre-filtered subset of the target rather than all target rows (incorrect index/partition pruning). From 7b54daab1867c0a4ffcb00c8c4cbd6b5520076c6 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Wed, 22 Apr 2026 18:24:44 -0500 Subject: [PATCH 26/30] docs: confirm absolute_time_in_s hypothesis with refined DATEDIFF formula Snowflake's DATEDIFF('second', a, b) is boundary-count (floor(epoch_seconds(b)) - floor(epoch_seconds(a))). Embucket's is CEIL((b - a) / 1 second). They agree only when both endpoints are on exact second boundaries or the ceil value coincides with boundary count. Seven-case standalone reproducer added; impact is strictly one-sided inflation (+0 to +1 sec per row) on any DATEDIFF('second') output, surfacing as +1.25% on page_views.sum_absolute_time_in_s and +0.85% on sessions.sum_absolute_time_in_s at 609K events. Finding 3 re-attributed from 'both engines / truncation difference' to Embucket-specific non-compliance with documented Snowflake semantics. --- ...-04-22-embucket-snowflake-investigation.md | 51 ++++++++++++++++--- 1 file changed, 43 insertions(+), 8 deletions(-) diff --git a/specs/2026-04-22-embucket-snowflake-investigation.md b/specs/2026-04-22-embucket-snowflake-investigation.md index 4a2f9de..a1f375e 100644 --- a/specs/2026-04-22-embucket-snowflake-investigation.md +++ b/specs/2026-04-22-embucket-snowflake-investigation.md @@ -138,13 +138,48 @@ Both engines: `dbt seed` + `dbt run` succeed, 20 PASS each. Two real divergences isolated at this scale: -**Finding A — `absolute_time_in_s` ±1-second rounding.** Spot-checks: -- Session `7da3c35f-5565-4903-88df-bb3c19a918f9`: Embucket 231, Snowflake 230; all other columns identical. -- Page view `00005058-c0e9-4e2a-8c2a-24db2e1f8fa4`: Embucket 40, Snowflake 39; all others identical. - -Consistent with different integer-truncation of second-level timestamp -subtraction between engines. Cumulative effect is ~1% divergence on the -table-level SUM. +**Finding A — `DATEDIFF('second', a, b)` semantics diverge.** Initial +hypothesis was "different integer truncation"; the real finding is +that the two engines implement fundamentally different semantics for +`DATEDIFF('second', ...)`: + +- **Snowflake**: boundary-count. `DATEDIFF('second', a, b) = + floor(epoch_seconds(b)) - floor(epoch_seconds(a))`. Matches the + Snowflake documentation. +- **Embucket**: `CEIL((b - a) / 1 second)`. Any positive sub-second + difference rounds up to 1. + +Test matrix (10-line standalone reproducer): + +| duration a → b | true sec | Embucket | Snowflake | +|---|---|---|---| +| `00:00:00.100` → `00:00:00.900` (same second) | 0.8 | **1** | 0 | +| `00:00:00.900` → `00:00:01.100` (straddles boundary) | 0.2 | 1 | 1 | +| `00:00:00.500` → `00:00:01.400` (straddles) | 0.9 | 1 | 1 | +| `00:00:00.250` → `00:00:01.750` | 1.5 | **2** | 1 | +| `00:00:00.500` → `00:00:02.900` | 2.4 | **3** | 2 | +| `00:00:00.000` → `00:00:02.500` | 2.5 | **3** | 2 | +| `00:00:00.999` → `00:01:00.000` | 59.001 | 60 | 60 | + +The engines agree only when both endpoints sit on exact second +boundaries, or when the ceil-of-duration happens to equal the +boundary-count. + +Impact on snowplow-web: +- `page_views.absolute_time_in_s = DATEDIFF('second', p.derived_tstamp, + COALESCE(t.end_tstamp, p.derived_tstamp))` +- `sessions.absolute_time_in_s = DATEDIFF('second', MIN(derived_tstamp), + MAX(derived_tstamp))` + +Embucket inflates each row by 0 or +1 seconds. Spot-checks confirmed: +- Session `7da3c35f-...`: Embucket 231, Snowflake 230; all other + columns identical. +- Page view `00005058-...`: Embucket 40, Snowflake 39; all else equal. + +Aggregate inflation at 609K-event scale: +1.25% on +`page_views.sum_absolute_time_in_s`, +0.85% on +`sessions.sum_absolute_time_in_s`. Never the other direction. +**Hypothesis confirmed, with refined formula.** **Finding B — `users.referrer` column NULL drift, root-caused to an Embucket SQL scoping bug.** At 1/10 scale, 239 of 12,346 users (2%) @@ -290,7 +325,7 @@ so the ON condition is satisfied and WHEN MATCHED UPDATE should fire. | 0 | Source Glue table has 4.7% duplicate `event_id` | upstream | data quality (not engine) | | 1 | Embucket OOMs on `snowplow_web_base_events_this_run` at 2.83M rows even with 10 GB pool + 10 GB spill | Embucket engine | memory/spill behavior on wide row with JSON VARIANT columns and repartition-on-join | | 2 | Unpatched snowplow-web fails on Embucket with `Unexpected target type embucket` | dbt package | hardcoded `target.type` branches; patchable via 1-line regex | -| 3 | `absolute_time_in_s` drifts ±1 second per row | both engines | different integer truncation of timestamp subtraction; visible in ~1% of rows | +| 3 | `absolute_time_in_s` inflated 0-1 sec per row on Embucket, never on Snowflake | **Embucket engine** | **`DATEDIFF('second', a, b)` = CEIL((b-a)/1s) on Embucket vs boundary-count on Snowflake; documented Snowflake semantics is boundary-count, Embucket is non-compliant** | | 4 | `users.referrer` NULL-fill differs for 2% / `first_domain_sessionid` differs for 23% of users | **Embucket engine** | **alias-shadow bug: column reference inside aggregate CASE resolves to SELECT alias instead of FROM column; silently wrong result with 10-line standalone reproducer** | | 5 | Snowplow users/page_views/sessions derived tables preserve the source's duplicate-`event_id` rows on first CTAS | snowplow-web package | `_this_run → derived` step doesn't dedup on unique_key; Snowflake errors on second MERGE, Embucket silently appends | | 6 | **Embucket MERGE UPDATE fails to apply for ~9% of matching target rows on the users derived table** | **Embucket engine** | **compiled MERGE SQL identical; source identical; ~9% of matched rows silently not updated. One-sided (sf ≥ emb always)** | From 702002f4ee7bb184e6f1277fa47bc483999d97b6 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Fri, 24 Apr 2026 11:48:08 -0500 Subject: [PATCH 27/30] test: verify OOM-mitigation dbt patch on Snowflake; NOT semantically equivalent Add a Snowflake-only parity harness that runs the Snowplow dbt pipeline twice on identical input -- once with upstream models, once with the Apr-10 decomposed stash -- and diffs golden tables row-hash by row-hash. Result: all three golden tables inflate under the patch (users +71%, sessions +13.8%, page_views +5.0%). Root cause is the removed event_id QUALIFY in base_create_snowplow_events_this_run.sql; ~134k duplicate event_ids in the source propagate into every downstream count-based aggregate. See specs/2026-04-24-*-results.md for the fix sketch. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../base_create_snowplow_events_this_run.sql | 251 ++++++++++ .../snowplow_web_page_views_this_run.sql | 429 ++++++++++++++++++ .../snowplow_web_pv_dedup_this_run.sql | 21 + .../snowplow_web_first_event_ids_this_run.sql | 18 + .../snowplow_web_last_event_ids_this_run.sql | 18 + .../snowplow_web_session_aggs_this_run.sql | 33 ++ ...nowplow_web_session_base_aggs_this_run.sql | 39 ++ .../snowplow_web_session_firsts_this_run.sql | 95 ++++ .../snowplow_web_session_lasts_this_run.sql | 38 ++ ...low_web_session_pp_bucket_set_this_run.sql | 25 + ...nowplow_web_session_pp_counts_this_run.sql | 14 + ...nowplow_web_session_pv_counts_this_run.sql | 15 + .../snowplow_web_session_pv_set_this_run.sql | 24 + .../snowplow_web_sessions_this_run.sql | 210 +++++++++ .../snowplow_web_user_mapping.sql | 39 ++ scripts/apply_oom_patch.sh | 26 ++ scripts/parity_self.py | 201 ++++++++ scripts/snowflake_clone_schema.py | 36 ++ scripts/verify_oom_patch.sh | 99 ++++ ...-snowflake-oom-patch-equivalence-design.md | 256 +++++++++++ ...snowflake-oom-patch-equivalence-results.md | 166 +++++++ 21 files changed, 2053 insertions(+) create mode 100644 patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql create mode 100644 patches/snowplow_web/models/page_views/scratch/snowflake/snowplow_web_page_views_this_run.sql create mode 100644 patches/snowplow_web/models/page_views/scratch/snowflake/snowplow_web_pv_dedup_this_run.sql create mode 100644 patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_first_event_ids_this_run.sql create mode 100644 patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_last_event_ids_this_run.sql create mode 100644 patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_aggs_this_run.sql create mode 100644 patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_base_aggs_this_run.sql create mode 100644 patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_firsts_this_run.sql create mode 100644 patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_lasts_this_run.sql create mode 100644 patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pp_bucket_set_this_run.sql create mode 100644 patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pp_counts_this_run.sql create mode 100644 patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pv_counts_this_run.sql create mode 100644 patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pv_set_this_run.sql create mode 100644 patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_sessions_this_run.sql create mode 100644 patches/snowplow_web/models/user_mapping/snowplow_web_user_mapping.sql create mode 100755 scripts/apply_oom_patch.sh create mode 100755 scripts/parity_self.py create mode 100755 scripts/snowflake_clone_schema.py create mode 100755 scripts/verify_oom_patch.sh create mode 100644 specs/2026-04-24-snowflake-oom-patch-equivalence-design.md create mode 100644 specs/2026-04-24-snowflake-oom-patch-equivalence-results.md diff --git a/patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql b/patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql new file mode 100644 index 0000000..3a07818 --- /dev/null +++ b/patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql @@ -0,0 +1,251 @@ +{# +Copyright (c) 2021-present Snowplow Analytics Ltd. All rights reserved. +This program is licensed to you under the Snowplow Community License Version 1.0, +and you may not use this file except in compliance with the Snowplow Community License Version 1.0. +You may obtain a copy of the Snowplow Community License Version 1.0 at https://docs.snowplow.io/community-license-1.0 +#} + + +{% macro base_create_snowplow_events_this_run(sessions_this_run_table='snowplow_base_sessions_this_run', session_identifiers=[{"schema" : "atomic", "field" : "domain_sessionid"}], session_sql=none, session_timestamp='load_tstamp', derived_tstamp_partitioned=true, days_late_allowed=3, max_session_days=3, app_ids=[], snowplow_events_database=none, snowplow_events_schema='atomic', snowplow_events_table='events', entities_or_sdes=none, custom_sql=none) %} + {{ return(adapter.dispatch('base_create_snowplow_events_this_run', 'snowplow_utils')(sessions_this_run_table, session_identifiers, session_sql, session_timestamp, derived_tstamp_partitioned, days_late_allowed, max_session_days, app_ids, snowplow_events_database, snowplow_events_schema, snowplow_events_table, entities_or_sdes, custom_sql)) }} +{% endmacro %} + +{% macro default__base_create_snowplow_events_this_run(sessions_this_run_table, session_identifiers, session_sql, session_timestamp, derived_tstamp_partitioned, days_late_allowed, max_session_days, app_ids, snowplow_events_database, snowplow_events_schema, snowplow_events_table, entities_or_sdes, custom_sql) %} + {%- set lower_limit, upper_limit = snowplow_utils.return_limits_from_model(ref(sessions_this_run_table), + 'start_tstamp', + 'end_tstamp') %} + {% set sessions_this_run = ref(sessions_this_run_table) %} + {% set snowplow_events = api.Relation.create(database=snowplow_events_database, schema=snowplow_events_schema, identifier=snowplow_events_table) %} + + {# event_id dedup removed: this macro reads from the raw source events table (all time). + A CTE-based narrow-projection dedup would double-scan the full table (DataFusion + inlines CTEs), exceeding the Lambda memory budget. The original QUALIFY also OOMs + because it buffers 137+ columns. Dedup is handled by downstream models which + each dedup by their own key (page_view_id, domain_sessionid, domain_userid) + on the much smaller batch-sized base_events_this_run table. #} + {% set events_this_run_query %} + with identified_events AS ( + select + {% if session_sql %} + {{ session_sql }} as session_identifier, + {% else -%} + COALESCE( + {% for identifier in session_identifiers %} + {%- if identifier['schema']|lower != 'atomic' -%} + {{ snowplow_utils.get_field(identifier['schema'], identifier['field'], 'e', dbt.type_string(), 0, snowplow_events) }} + {%- else -%} + e.{{identifier['field']}} + {%- endif -%} + , + {%- endfor -%} + NULL + ) as session_identifier, + {%- endif %} + e.* + {% if custom_sql %} + , {{ custom_sql }} + {% endif %} + + from {{ snowplow_events }} e + + ) + + select + a.*, + b.user_identifier -- take user_identifier from manifest. This ensures only 1 domain_userid per session. + + from identified_events as a + inner join {{ sessions_this_run }} as b + on a.session_identifier = b.session_identifier + + where a.{{ session_timestamp }} <= {{ snowplow_utils.timestamp_add('day', max_session_days, 'b.start_tstamp') }} + and a.dvce_sent_tstamp <= {{ snowplow_utils.timestamp_add('day', days_late_allowed, 'a.dvce_created_tstamp') }} + and a.{{ session_timestamp }} >= {{ lower_limit }} + and a.{{ session_timestamp }} <= {{ upper_limit }} + and a.{{ session_timestamp }} >= b.start_tstamp -- deal with late loading events + + {% if derived_tstamp_partitioned and target.type == 'bigquery' | as_bool() %} + and a.derived_tstamp >= {{ snowplow_utils.timestamp_add('hour', -1, lower_limit) }} + and a.derived_tstamp <= {{ upper_limit }} + {% endif %} + + and {{ snowplow_utils.app_id_filter(app_ids) }} + {% endset %} + + {{ return(events_this_run_query) }} + +{% endmacro %} + +{% macro postgres__base_create_snowplow_events_this_run(sessions_this_run_table, session_identifiers, session_sql, session_timestamp, derived_tstamp_partitioned, days_late_allowed, max_session_days, app_ids, snowplow_events_database, snowplow_events_schema, snowplow_events_table, entities_or_sdes, custom_sql) %} + {%- set lower_limit, upper_limit = snowplow_utils.return_limits_from_model(ref(sessions_this_run_table), + 'start_tstamp', + 'end_tstamp') %} + + + {# Get all the session and user contexts extracted and ready to join later #} + {% set unique_session_identifiers = dict() %} {# need to avoid duplicate contexts when values come from the same one, so just use the first of that context #} + + {% if session_identifiers %} + {% for identifier in session_identifiers %} + {% if identifier['schema']|lower != 'atomic' and identifier['schema'] not in unique_session_identifiers %} + {% do unique_session_identifiers.update({identifier['schema']: identifier}) %} + {%- endif -%} + {% if identifier['schema'] in unique_session_identifiers.keys() %} + {% if identifier['alias'] != unique_session_identifiers[identifier['schema']]['alias'] or identifier['prefix'] != unique_session_identifiers[identifier['schema']]['prefix'] %} + {% do exceptions.warn("Snowplow Warning: Duplicate context ( " ~ identifier['schema'] ~" ) detected for session identifiers, using first alias and prefix provided ( " ~ unique_session_identifiers[identifier['schema']] ~ " ) in base events this run.") %} + {% endif %} + {% endif %} + {% endfor %} + {% endif %} + + {# check uniqueness of entity/sde names provided, warn those also in session identifiers #} + {% if entities_or_sdes %} + {% set ent_sde_names = [] %} + {% for ent_or_sde in entities_or_sdes %} + {% do ent_sde_names.append(ent_or_sde['schema']) %} + {% if ent_or_sde['schema'] in unique_session_identifiers.keys() %} + {% if ent_or_sde['alias'] != unique_session_identifiers[ent_or_sde['schema']]['alias'] or ent_or_sde['prefix'] != unique_session_identifiers[ent_or_sde['schema']]['prefix'] %} + {% do exceptions.warn("Snowplow Warning: Context or SDE ( " ~ ent_or_sde['schema'] ~ " ) used for session_identifier is being included, using alias and prefix from session_identifier ( " ~ unique_session_identifiers[ent_or_sde['schema']] ~ " ).") %} + {% endif %} + {% endif %} + {% endfor %} + {% if ent_sde_names | unique | list | length != entities_or_sdes | length %} + {% do exceptions.raise_compiler_error("There are duplicate schema names in your provided `entities_or_sdes` list. Please correct this before proceeding.")%} + {% endif %} + {% endif %} + + {% set sessions_this_run = ref(sessions_this_run_table) %} + {% set snowplow_events = api.Relation.create(database=snowplow_events_database, schema=snowplow_events_schema, identifier=snowplow_events_table) %} + + {% set events_this_run_query %} + with + + {# Extract the session identifier contexts into CTEs #} + {% if unique_session_identifiers -%} + {% for identifier in unique_session_identifiers.values() %} + {% if identifier['schema']|lower != 'atomic' %} + {{ snowplow_utils.get_sde_or_context(snowplow_events_schema, identifier['schema'], lower_limit, upper_limit, identifier['prefix']) }}, + {%- endif -%} + {% endfor %} + {% endif %} + + {# Extract the entitity/sde contexts into CTEs UNLESS they are in the session already #} + {%- if entities_or_sdes -%} + {%- for ent_or_sde in entities_or_sdes -%} + {%- set name = none -%} + {%- set prefix = none -%} + {%- set single_entity = true -%} + {%- if ent_or_sde['schema'] -%} + {%- set name = ent_or_sde['schema'] -%} + {%- else -%} + {%- do exceptions.raise_compiler_error("Need to specify the schema name of your Entity or SDE using the {'schema'} attribute in a key-value map.") -%} + {%- endif -%} + {%- if ent_or_sde['prefix'] -%} + {%- set prefix = ent_or_sde['prefix'] -%} + {%- else -%} + {%- set prefix = name -%} + {%- endif -%} + {%- if ent_or_sde['single_entity'] and ent_or_sde['single_entity'] is boolean -%} + {%- set single_entity = ent_or_sde['single_entity'] -%} + {%- endif %} + {% if ent_or_sde['schema'] not in unique_session_identifiers.keys() %} {# Exclude any that we have already made above #} + {{ snowplow_utils.get_sde_or_context(snowplow_events_schema, name, lower_limit, upper_limit, prefix, single_entity) }}, + {% endif %} + {% endfor -%} + {%- endif %} + + identified_events AS ( + select + {% if session_sql -%} + {{ session_sql }} as session_identifier, + {% else -%} + COALESCE( + {% for identifier in session_identifiers %} + {%- if identifier['schema']|lower != 'atomic' %} + {# Use the parsed version of the context to ensure we have the right alias and prefix #} + {% set uniq_iden = unique_session_identifiers[identifier['schema']] %} + {% if uniq_iden['alias'] %}{{uniq_iden['alias']}}{% else %}{{uniq_iden['schema']}}{% endif %}.{% if uniq_iden['prefix'] %}{{ uniq_iden['prefix'] }}{% else %}{{ uniq_iden['schema']}}{% endif %}_{{identifier['field']}} + {%- else %} + e.{{identifier['field']}} + {%- endif -%} + , + {%- endfor -%} + NULL + ) as session_identifier, + {%- endif %} + e.* + {% if custom_sql %} + , {{ custom_sql }} + {%- endif %} + + from {{ snowplow_events }} e + {% if unique_session_identifiers|length > 0 %} + {% for identifier in unique_session_identifiers.values() %} + {%- if identifier['schema']|lower != 'atomic' -%} + left join {{ identifier['schema'] }} {% if identifier['alias'] %}as {{ identifier['alias'] }}{% endif %} on e.event_id = {% if identifier['alias'] %}{{ identifier['alias']}}{% else %}{{ identifier['schema'] }}{% endif %}.{{identifier['prefix']}}__id and e.collector_tstamp = {% if identifier['alias'] %}{{ identifier['alias']}}{% else %}{{ identifier['schema'] }}{% endif %}.{{ identifier['prefix'] }}__tstamp + {% endif -%} + {% endfor %} + {% endif %} + + ), events_this_run as ( + + select + a.*, + b.user_identifier, -- take user_identifier from manifest. This ensures only 1 domain_userid per session. + row_number() over (partition by a.event_id order by a.{{ session_timestamp }}, a.dvce_created_tstamp ) as event_id_dedupe_index, + count(*) over (partition by a.event_id) as event_id_dedupe_count + + from identified_events as a + inner join {{ sessions_this_run }} as b + on a.session_identifier = b.session_identifier + + where a.{{ session_timestamp }} <= {{ snowplow_utils.timestamp_add('day', max_session_days, 'b.start_tstamp') }} + and a.dvce_sent_tstamp <= {{ snowplow_utils.timestamp_add('day', days_late_allowed, 'a.dvce_created_tstamp') }} + and a.{{ session_timestamp }} >= {{ lower_limit }} + and a.{{ session_timestamp }} <= {{ upper_limit }} + and a.{{ session_timestamp }} >= b.start_tstamp -- deal with late loading events + and {{ snowplow_utils.app_id_filter(app_ids) }} + + ) + + select * + + from events_this_run as e + {%- if entities_or_sdes -%} + {% for ent_or_sde in entities_or_sdes -%} + {%- set name = none -%} + {%- set prefix = none -%} + {%- set single_entity = true -%} + {%- set alias = none -%} + {%- if ent_or_sde['schema'] -%} + {%- set name = ent_or_sde['schema'] -%} + {%- else -%} + {%- do exceptions.raise_compiler_error("Need to specify the schema name of your Entity or SDE using the {'schema'} attribute in a key-value map.") -%} + {%- endif -%} + {%- if ent_or_sde['prefix'] and name not in unique_session_identifiers.keys() -%} + {%- set prefix = ent_or_sde['prefix'] -%} + {%- elif name in unique_session_identifiers.keys() and unique_session_identifiers.get(name, {}).get('prefix') -%} + {%- set prefix = unique_session_identifiers[name]['prefix'] -%} + {%- else -%} + {%- set prefix = name -%} + {%- endif -%} + {%- if ent_or_sde['single_entity'] and ent_or_sde['single_entity'] is boolean -%} + {%- set single_entity = ent_or_sde['single_entity'] -%} + {%- endif -%} + {%- if ent_or_sde['alias'] and name not in unique_session_identifiers.keys() -%} + {%- set alias = ent_or_sde['alias'] -%} + {%- elif name in unique_session_identifiers.keys() and unique_session_identifiers.get(name, {}).get('alias') -%} + {%- set alias = unique_session_identifiers[name] -%} + {%- endif %} + left join {{name}} {% if alias -%} as {{ alias }} {%- endif %} on e.event_id = {% if alias -%} {{ alias }} {%- else -%}{{name}}{%- endif %}.{{prefix}}__id + and e.collector_tstamp = {% if alias -%} {{ alias }} {%- else -%}{{name}}{%- endif %}.{{prefix}}__tstamp + {% if not single_entity -%} and mod({% if alias -%} {{ alias }} {%- else -%}{{name}}{%- endif %}.{{prefix}}__index, e.event_id_dedupe_count) = 0{%- endif -%} + {% endfor %} + {% endif %} + where event_id_dedupe_index = 1 + + {% endset %} + + {{ return(events_this_run_query) }} + +{% endmacro %} diff --git a/patches/snowplow_web/models/page_views/scratch/snowflake/snowplow_web_page_views_this_run.sql b/patches/snowplow_web/models/page_views/scratch/snowflake/snowplow_web_page_views_this_run.sql new file mode 100644 index 0000000..4ed1fe8 --- /dev/null +++ b/patches/snowplow_web/models/page_views/scratch/snowflake/snowplow_web_page_views_this_run.sql @@ -0,0 +1,429 @@ +{# +Copyright (c) 2020-present Snowplow Analytics Ltd. All rights reserved. +This program is licensed to you under the Snowplow Community License Version 1.0, +and you may not use this file except in compliance with the Snowplow Community License Version 1.0. +You may obtain a copy of the Snowplow Community License Version 1.0 at https://docs.snowplow.io/community-license-1.0 +#} + +{{ + config( + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +{# Narrow-projection QUALIFY moved to snowplow_web_pv_dedup_this_run for memory isolation #} +with prep as ( +select + ev.page_view_id, + ev.event_id, + + ev.app_id, + ev.platform, + + -- user fields + ev.user_id, + ev.domain_userid, + ev.original_domain_userid, + {% if var('snowplow__page_view_stitching') %} + -- updated with mapping as part of post hook on derived page_views table + cast(domain_userid as {{ type_string() }}) as stitched_user_id, + {% else %} + cast(null as {{ type_string() }}) as stitched_user_id, + {% endif %} + ev.network_userid, + + -- session fields + ev.domain_sessionid, + ev.original_domain_sessionid, + ev.domain_sessionidx, + + -- timestamp fields + ev.dvce_created_tstamp, + ev.collector_tstamp, + ev.derived_tstamp, + ev.derived_tstamp as start_tstamp, + + ev.doc_width, + ev.doc_height, + + ev.page_title, + {{ content_group_query() }} as content_group, + ev.page_url, + ev.page_urlscheme, + ev.page_urlhost, + ev.page_urlpath, + ev.page_urlquery, + ev.page_urlfragment, + + -- marketing fields + ev.mkt_medium, + ev.mkt_source, + ev.mkt_term, + ev.mkt_content, + ev.mkt_campaign, + ev.mkt_clickid, + ev.mkt_network, + {{ channel_group_query() }} as default_channel_group, + + -- referrer fields + ev.page_referrer, + ev.refr_urlscheme, + ev.refr_urlhost, + ev.refr_urlpath, + ev.refr_urlquery, + ev.refr_urlfragment, + ev.refr_medium, + ev.refr_source, + ev.refr_term, + + -- geo fields + ev.geo_country, + ev.geo_region, + ev.geo_region_name, + ev.geo_city, + ev.geo_zipcode, + ev.geo_latitude, + ev.geo_longitude, + ev.geo_timezone , + + ev.user_ipaddress, + + ev.useragent, + + ev.dvce_screenwidth || 'x' || ev.dvce_screenheight as screen_resolution, + + ev.br_lang, + ev.br_viewwidth, + ev.br_viewheight, + ev.br_colordepth, + ev.br_renderengine, + ev.os_timezone, + + -- optional fields, only populated if enabled. + + -- iab enrichment fields: set iab variable to true to enable + {{snowplow_web.get_iab_context_fields()}}, + + -- ua parser enrichment fields + {{snowplow_web.get_ua_context_fields()}}, + + -- yauaa enrichment fields + {{snowplow_web.get_yauaa_context_fields()}} + + {%- if var('snowplow__page_view_passthroughs', []) -%} + {%- set passthrough_names = [] -%} + {%- for identifier in var('snowplow__page_view_passthroughs', []) %} + {# Check if it's a simple column or a sql+alias #} + {%- if identifier is mapping -%} + ,{{identifier['sql']}} as {{identifier['alias']}} + {%- do passthrough_names.append(identifier['alias']) -%} + {%- else -%} + ,ev.{{identifier}} + {%- do passthrough_names.append(identifier) -%} + {%- endif -%} + {% endfor -%} + {%- endif %} + + from {{ ref('snowplow_web_base_events_this_run') }} as ev + + inner join {{ ref('snowplow_web_pv_dedup_this_run') }} d on ev.event_id = d.event_id + + left join {{ ref(var('snowplow__ga4_categories_seed')) }} c on lower(trim(ev.mkt_source)) = lower(c.source) + + where ev.event_name = 'page_view' + and ev.page_view_id is not null + + {% if var("snowplow__ua_bot_filter", true) %} + {{ filter_bots('ev') }} + {% endif %} +) + +, page_view_events as ( + select + p.page_view_id, + p.event_id, + + p.app_id, + p.platform, + + -- user fields + p.user_id, + p.domain_userid, + p.original_domain_userid, + p.stitched_user_id, + p.network_userid, + + -- session fields + p.domain_sessionid, + p.original_domain_sessionid, + p.domain_sessionidx, + + row_number() over (partition by p.domain_sessionid order by p.derived_tstamp, p.dvce_created_tstamp, p.event_id) AS page_view_in_session_index, + + -- timestamp fields + p.dvce_created_tstamp, + p.collector_tstamp, + p.derived_tstamp, + p.start_tstamp, + coalesce(t.end_tstamp, p.derived_tstamp) as end_tstamp, -- only page views with pings will have a row in table t + {{ snowplow_utils.current_timestamp_in_utc() }} as model_tstamp, + + coalesce(t.engaged_time_in_s, 0) as engaged_time_in_s, -- where there are no pings, engaged time is 0. + {{ datediff('p.derived_tstamp', 'coalesce(t.end_tstamp, p.derived_tstamp)', 'second') }} as absolute_time_in_s, + + sd.hmax as horizontal_pixels_scrolled, + sd.vmax as vertical_pixels_scrolled, + + sd.relative_hmax as horizontal_percentage_scrolled, + sd.relative_vmax as vertical_percentage_scrolled, + + p.doc_width, + p.doc_height, + + p.content_group, + + p.page_title, + p.page_url, + p.page_urlscheme, + p.page_urlhost, + p.page_urlpath, + p.page_urlquery, + p.page_urlfragment, + + p.mkt_medium, + p.mkt_source, + p.mkt_term, + p.mkt_content, + p.mkt_campaign, + p.mkt_clickid, + p.mkt_network, + p.default_channel_group, + + p.page_referrer, + p.refr_urlscheme, + p.refr_urlhost, + p.refr_urlpath, + p.refr_urlquery, + p.refr_urlfragment, + p.refr_medium, + p.refr_source, + p.refr_term, + + p.geo_country, + p.geo_region, + p.geo_region_name, + p.geo_city, + p.geo_zipcode, + p.geo_latitude, + p.geo_longitude, + p.geo_timezone, + + p.user_ipaddress, + + p.useragent, + + p.screen_resolution, + + p.br_lang, + p.br_viewwidth, + p.br_viewheight, + p.br_colordepth, + p.br_renderengine, + + p.os_timezone, + + p.category, + p.primary_impact, + p.reason, + p.spider_or_robot, + + p.useragent_family, + p.useragent_major, + p.useragent_minor, + p.useragent_patch, + p.useragent_version, + p.os_family, + p.os_major, + p.os_minor, + p.os_patch, + p.os_patch_minor, + p.os_version, + p.device_family, + + p.device_class, + p.agent_class, + p.agent_name, + p.agent_name_version, + p.agent_name_version_major, + p.agent_version, + p.agent_version_major, + p.device_brand, + p.device_name, + p.device_version, + p.layout_engine_class, + p.layout_engine_name, + p.layout_engine_name_version, + p.layout_engine_name_version_major, + p.layout_engine_version, + p.layout_engine_version_major, + p.operating_system_class, + p.operating_system_name, + p.operating_system_name_version, + p.operating_system_version + {%- if var('snowplow__page_view_passthroughs', []) -%} + {%- for col in passthrough_names %} + , p.{{col}} + {%- endfor -%} + {%- endif %} + + from prep p + + left join {{ ref('snowplow_web_pv_engaged_time') }} t + on p.page_view_id = t.page_view_id {% if var('snowplow__limit_page_views_to_session', true) %} and p.domain_sessionid = t.domain_sessionid {% endif %} + + left join {{ ref('snowplow_web_pv_scroll_depth') }} sd + on p.page_view_id = sd.page_view_id {% if var('snowplow__limit_page_views_to_session', true) %} and p.domain_sessionid = sd.domain_sessionid {% endif %} +) + +select + pve.page_view_id, + pve.event_id, + + pve.app_id, + pve.platform, + + -- user fields + pve.user_id, + pve.domain_userid, + pve.original_domain_userid, + pve.stitched_user_id, + pve.network_userid, + + -- session fields + pve.domain_sessionid, + pve.original_domain_sessionid, + pve.domain_sessionidx, + + pve.page_view_in_session_index, + max(pve.page_view_in_session_index) over (partition by pve.domain_sessionid) as page_views_in_session, + + -- timestamp fields + pve.dvce_created_tstamp, + pve.collector_tstamp, + pve.derived_tstamp, + pve.start_tstamp, + pve.end_tstamp, + pve.model_tstamp, + + pve.engaged_time_in_s, + pve.absolute_time_in_s, + + pve.horizontal_pixels_scrolled, + pve.vertical_pixels_scrolled, + + pve.horizontal_percentage_scrolled, + pve.vertical_percentage_scrolled, + + pve.doc_width, + pve.doc_height, + pve.content_group, + + pve.page_title, + pve.page_url, + pve.page_urlscheme, + pve.page_urlhost, + pve.page_urlpath, + pve.page_urlquery, + pve.page_urlfragment, + + pve.mkt_medium, + pve.mkt_source, + pve.mkt_term, + pve.mkt_content, + pve.mkt_campaign, + pve.mkt_clickid, + pve.mkt_network, + pve.default_channel_group, + + pve.page_referrer, + pve.refr_urlscheme, + pve.refr_urlhost, + pve.refr_urlpath, + pve.refr_urlquery, + pve.refr_urlfragment, + pve.refr_medium, + pve.refr_source, + pve.refr_term, + + pve.geo_country, + pve.geo_region, + pve.geo_region_name, + pve.geo_city, + pve.geo_zipcode, + pve.geo_latitude, + pve.geo_longitude, + pve.geo_timezone, + + pve.user_ipaddress, + + pve.useragent, + + pve.br_lang, + pve.br_viewwidth, + pve.br_viewheight, + pve.br_colordepth, + pve.br_renderengine, + + pve.os_timezone, + + pve.category, + pve.primary_impact, + pve.reason, + pve.spider_or_robot, + + pve.useragent_family, + pve.useragent_major, + pve.useragent_minor, + pve.useragent_patch, + pve.useragent_version, + pve.os_family, + pve.os_major, + pve.os_minor, + pve.os_patch, + pve.os_patch_minor, + pve.os_version, + pve.device_family, + + pve.device_class, + case when pve.device_class = 'Desktop' then 'Desktop' + when pve.device_class = 'Phone' then 'Mobile' + when pve.device_class = 'Tablet' then 'Tablet' + else 'Other' end as device_category, + pve.screen_resolution, + pve.agent_class, + pve.agent_name, + pve.agent_name_version, + pve.agent_name_version_major, + pve.agent_version, + pve.agent_version_major, + pve.device_brand, + pve.device_name, + pve.device_version, + pve.layout_engine_class, + pve.layout_engine_name, + pve.layout_engine_name_version, + pve.layout_engine_name_version_major, + pve.layout_engine_version, + pve.layout_engine_version_major, + pve.operating_system_class, + pve.operating_system_name, + pve.operating_system_name_version, + pve.operating_system_version + {%- if var('snowplow__page_view_passthroughs', []) -%} + {%- for col in passthrough_names %} + , pve.{{col}} + {%- endfor -%} + {%- endif %} + +from page_view_events pve diff --git a/patches/snowplow_web/models/page_views/scratch/snowflake/snowplow_web_pv_dedup_this_run.sql b/patches/snowplow_web/models/page_views/scratch/snowflake/snowplow_web_pv_dedup_this_run.sql new file mode 100644 index 0000000..ca2b8e0 --- /dev/null +++ b/patches/snowplow_web/models/page_views/scratch/snowflake/snowplow_web_pv_dedup_this_run.sql @@ -0,0 +1,21 @@ +{# +Narrow-projection QUALIFY isolated in its own model for memory safety at scale. +row_number() runs over 4 columns instead of 80+ in page_views_this_run. +#} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +select event_id +from ( + select event_id, page_view_id, derived_tstamp, dvce_created_tstamp + from {{ ref('snowplow_web_base_events_this_run') }} + where event_name = 'page_view' + and page_view_id is not null +) +qualify row_number() over (partition by page_view_id order by derived_tstamp, dvce_created_tstamp) = 1 diff --git a/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_first_event_ids_this_run.sql b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_first_event_ids_this_run.sql new file mode 100644 index 0000000..0881a98 --- /dev/null +++ b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_first_event_ids_this_run.sql @@ -0,0 +1,18 @@ +{# Narrow-projection QUALIFY: first event per session on 4 columns. #} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +select event_id +from ( + select event_id, domain_sessionid, derived_tstamp, dvce_created_tstamp + from {{ ref('snowplow_web_base_events_this_run') }} + where event_name in ('page_ping', 'page_view') + and page_view_id is not null +) +qualify row_number() over (partition by domain_sessionid order by derived_tstamp, dvce_created_tstamp, event_id) = 1 diff --git a/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_last_event_ids_this_run.sql b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_last_event_ids_this_run.sql new file mode 100644 index 0000000..98821a1 --- /dev/null +++ b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_last_event_ids_this_run.sql @@ -0,0 +1,18 @@ +{# Narrow-projection QUALIFY: last event per session on 4 columns. #} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +select event_id +from ( + select event_id, domain_sessionid, derived_tstamp, dvce_created_tstamp + from {{ ref('snowplow_web_base_events_this_run') }} + where event_name = 'page_view' + and page_view_id is not null +) +qualify row_number() over (partition by domain_sessionid order by derived_tstamp desc, dvce_created_tstamp desc, event_id) = 1 diff --git a/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_aggs_this_run.sql b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_aggs_this_run.sql new file mode 100644 index 0000000..39e4e5c --- /dev/null +++ b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_aggs_this_run.sql @@ -0,0 +1,33 @@ +{# +Final session aggregates: 3-way join of pre-materialized small tables. +All GROUP BYs run in isolated models to keep hash join memory low. +#} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +select + a.domain_sessionid + , a.start_tstamp + , a.end_tstamp + {%- if var('snowplow__list_event_counts', false) %} + , a.event_counts_string + {%- endif %} + , a.total_events + , coalesce(pv.page_views, 0) as page_views + , ({{ var("snowplow__heartbeat", 10) }} * (coalesce(pp.total_ping_buckets, 0) - coalesce(pv.pv_with_ping, 0))) + + (coalesce(pv.pv_with_ping, 0) * {{ var("snowplow__min_visit_length", 5) }}) as engaged_time_in_s + , a.absolute_time_in_s +{%- if var('snowplow__conversion_events', none) %} + {%- for conv_def in var('snowplow__conversion_events') %} + {{ snowplow_web.get_conversion_columns(conv_def, names_only = true)}} + {%- endfor %} +{%- endif %} +from {{ ref('snowplow_web_session_base_aggs_this_run') }} a +left join {{ ref('snowplow_web_session_pv_counts_this_run') }} pv on a.domain_sessionid = pv.domain_sessionid +left join {{ ref('snowplow_web_session_pp_counts_this_run') }} pp on a.domain_sessionid = pp.domain_sessionid diff --git a/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_base_aggs_this_run.sql b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_base_aggs_this_run.sql new file mode 100644 index 0000000..fabca9a --- /dev/null +++ b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_base_aggs_this_run.sql @@ -0,0 +1,39 @@ +{# +Heavy GROUP BY over base_events_this_run isolated in its own model so it doesn't +share a memory pool with the downstream hash joins in session_aggs_this_run. +#} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +select + domain_sessionid + , min(derived_tstamp) as start_tstamp + , max(derived_tstamp) as end_tstamp + {%- if var('snowplow__list_event_counts', false) %} + {% set event_names = dbt_utils.get_column_values(ref('snowplow_web_base_events_this_run'), 'event_name', order_by = 'event_name') %} + , '{' || rtrim( + {%- for event_name in event_names %} + case when sum(case when event_name = '{{event_name}}' then 1 else 0 end) > 0 then '"{{event_name}}" :' || sum(case when event_name = '{{event_name}}' then 1 else 0 end) || ', ' else '' end || + {%- endfor -%} + '', ', ') || '}' as event_counts_string + {%- endif %} + , count(*) as total_events + , {{ snowplow_utils.timestamp_diff('min(derived_tstamp)', 'max(derived_tstamp)', 'second') }} as absolute_time_in_s +{%- if var('snowplow__conversion_events', none) %} + {%- for conv_def in var('snowplow__conversion_events') %} + {{ snowplow_web.get_conversion_columns(conv_def)}} + {%- endfor %} +{%- endif %} +from {{ ref('snowplow_web_base_events_this_run') }} +where + 1 = 1 + {% if var("snowplow__ua_bot_filter", true) %} + {{ filter_bots() }} + {% endif %} +group by domain_sessionid diff --git a/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_firsts_this_run.sql b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_firsts_this_run.sql new file mode 100644 index 0000000..3cc6940 --- /dev/null +++ b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_firsts_this_run.sql @@ -0,0 +1,95 @@ +{# Wide projection for first event per session, isolated for memory safety. #} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +select + ev.app_id as app_id, + ev.platform, + ev.domain_sessionid, + ev.original_domain_sessionid, + ev.domain_sessionidx, + {{ snowplow_utils.current_timestamp_in_utc() }} as model_tstamp, + ev.user_id, + ev.domain_userid, + ev.original_domain_userid, + {% if var('snowplow__session_stitching') %} + cast(ev.domain_userid as {{ type_string() }}) as stitched_user_id, + {% else %} + cast(null as {{ type_string() }}) as stitched_user_id, + {% endif %} + ev.network_userid as network_userid, + ev.page_title as first_page_title, + ev.page_url as first_page_url, + ev.page_urlscheme as first_page_urlscheme, + ev.page_urlhost as first_page_urlhost, + ev.page_urlpath as first_page_urlpath, + ev.page_urlquery as first_page_urlquery, + ev.page_urlfragment as first_page_urlfragment, + ev.page_referrer as referrer, + ev.refr_urlscheme as refr_urlscheme, + ev.refr_urlhost as refr_urlhost, + ev.refr_urlpath as refr_urlpath, + ev.refr_urlquery as refr_urlquery, + ev.refr_urlfragment as refr_urlfragment, + ev.refr_medium as refr_medium, + ev.refr_source as refr_source, + ev.refr_term as refr_term, + ev.mkt_medium as mkt_medium, + ev.mkt_source as mkt_source, + ev.mkt_term as mkt_term, + ev.mkt_content as mkt_content, + ev.mkt_campaign as mkt_campaign, + ev.mkt_clickid as mkt_clickid, + ev.mkt_network as mkt_network, + regexp_substr(ev.page_urlquery, 'utm_source_platform=([^?&#]*)', 1, 1, 'e') as mkt_source_platform, + {{ channel_group_query() }} as default_channel_group, + ev.geo_country as geo_country, + ev.geo_region as geo_region, + ev.geo_region_name as geo_region_name, + ev.geo_city as geo_city, + ev.geo_zipcode as geo_zipcode, + ev.geo_latitude as geo_latitude, + ev.geo_longitude as geo_longitude, + ev.geo_timezone as geo_timezone, + g.name as geo_country_name, + g.region as geo_continent, + ev.user_ipaddress as user_ipaddress, + ev.useragent as useragent, + ev.dvce_screenwidth || 'x' || ev.dvce_screenheight as screen_resolution, + ev.br_renderengine as br_renderengine, + ev.br_lang as br_lang, + l.name as br_lang_name, + ev.os_timezone as os_timezone, + {{snowplow_web.get_iab_context_fields()}}, + {{snowplow_web.get_ua_context_fields()}}, + {{snowplow_web.get_yauaa_context_fields()}}, + ev.event_name + {%- if var('snowplow__session_passthroughs', []) -%} + {%- for identifier in var('snowplow__session_passthroughs', []) %} + {%- if identifier is mapping -%} + ,{{identifier['sql']}} as {{identifier['alias']}} + {%- else -%} + ,ev.{{identifier}} + {%- endif -%} + {% endfor -%} + {%- endif %} +from {{ ref('snowplow_web_base_events_this_run') }} ev +inner join {{ ref('snowplow_web_first_event_ids_this_run') }} fe on ev.event_id = fe.event_id +left join + {{ ref(var('snowplow__ga4_categories_seed')) }} c on lower(trim(ev.mkt_source)) = lower(c.source) +left join + {{ ref(var('snowplow__rfc_5646_seed')) }} l on lower(ev.br_lang) = lower(l.lang_tag) +left join + {{ ref(var('snowplow__geo_mapping_seed')) }} g on lower(ev.geo_country) = lower(g.alpha_2) +where + ev.event_name in ('page_ping', 'page_view') + and ev.page_view_id is not null + {% if var("snowplow__ua_bot_filter", true) %} + {{ filter_bots() }} + {% endif %} diff --git a/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_lasts_this_run.sql b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_lasts_this_run.sql new file mode 100644 index 0000000..7d0938f --- /dev/null +++ b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_lasts_this_run.sql @@ -0,0 +1,38 @@ +{# Wide projection for last event per session, isolated for memory safety. #} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +select + ev.domain_sessionid, + ev.page_title as last_page_title, + ev.page_url as last_page_url, + ev.page_urlscheme as last_page_urlscheme, + ev.page_urlhost as last_page_urlhost, + ev.page_urlpath as last_page_urlpath, + ev.page_urlquery as last_page_urlquery, + ev.page_urlfragment as last_page_urlfragment, + ev.geo_country as last_geo_country, + ev.geo_city as last_geo_city, + ev.geo_region_name as last_geo_region_name, + g.name as last_geo_country_name, + g.region as last_geo_continent, + ev.br_lang as last_br_lang, + l.name as last_br_lang_name +from {{ ref('snowplow_web_base_events_this_run') }} ev +inner join {{ ref('snowplow_web_last_event_ids_this_run') }} le on ev.event_id = le.event_id +left join + {{ ref(var('snowplow__rfc_5646_seed')) }} l on lower(ev.br_lang) = lower(l.lang_tag) +left join + {{ ref(var('snowplow__geo_mapping_seed')) }} g on lower(ev.geo_country) = lower(g.alpha_2) +where + ev.event_name = 'page_view' + and ev.page_view_id is not null + {% if var("snowplow__ua_bot_filter", true) %} + {{ filter_bots() }} + {% endif %} diff --git a/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pp_bucket_set_this_run.sql b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pp_bucket_set_this_run.sql new file mode 100644 index 0000000..4a17de3 --- /dev/null +++ b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pp_bucket_set_this_run.sql @@ -0,0 +1,25 @@ +{# +Spillable GROUP BY replacement for count(distinct page_view_id || ping_bucket) +which is non-spillable in DataFusion's hash aggregate. +Each row = one unique (session, page_view, heartbeat-bucket) combination. +#} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +select + domain_sessionid, + page_view_id, + cast(floor({{ snowplow_utils.to_unixtstamp('dvce_created_tstamp') }} / {{ var('snowplow__heartbeat', 10) }}) as {{ dbt.type_string() }}) as ping_bucket +from {{ ref('snowplow_web_base_events_this_run') }} +where event_name = 'page_ping' + and page_view_id is not null + {% if var("snowplow__ua_bot_filter", true) %} + {{ filter_bots() }} + {% endif %} +group by domain_sessionid, page_view_id, ping_bucket diff --git a/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pp_counts_this_run.sql b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pp_counts_this_run.sql new file mode 100644 index 0000000..06ed8de --- /dev/null +++ b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pp_counts_this_run.sql @@ -0,0 +1,14 @@ +{# Pre-materialized ping bucket counts per session to keep session_aggs hash joins small. #} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +select domain_sessionid, + count(*) as total_ping_buckets +from {{ ref('snowplow_web_session_pp_bucket_set_this_run') }} +group by domain_sessionid diff --git a/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pv_counts_this_run.sql b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pv_counts_this_run.sql new file mode 100644 index 0000000..123787b --- /dev/null +++ b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pv_counts_this_run.sql @@ -0,0 +1,15 @@ +{# Pre-materialized page_view counts per session to keep session_aggs hash joins small. #} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +select domain_sessionid, + count(*) as page_views, + count(case when has_ping = 1 then 1 end) as pv_with_ping +from {{ ref('snowplow_web_session_pv_set_this_run') }} +group by domain_sessionid diff --git a/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pv_set_this_run.sql b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pv_set_this_run.sql new file mode 100644 index 0000000..937294a --- /dev/null +++ b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_session_pv_set_this_run.sql @@ -0,0 +1,24 @@ +{# +Spillable GROUP BY replacement for count(distinct page_view_id) which is +non-spillable in DataFusion's hash aggregate. +#} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +select + domain_sessionid, + page_view_id, + max(case when event_name = 'page_ping' then 1 else 0 end) as has_ping +from {{ ref('snowplow_web_base_events_this_run') }} +where event_name in ('page_ping', 'page_view') + and page_view_id is not null + {% if var("snowplow__ua_bot_filter", true) %} + {{ filter_bots() }} + {% endif %} +group by domain_sessionid, page_view_id diff --git a/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_sessions_this_run.sql b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_sessions_this_run.sql new file mode 100644 index 0000000..0461abe --- /dev/null +++ b/patches/snowplow_web/models/sessions/scratch/snowflake/snowplow_web_sessions_this_run.sql @@ -0,0 +1,210 @@ +{# +Copyright (c) 2020-present Snowplow Analytics Ltd. All rights reserved. +This program is licensed to you under the Snowplow Community License Version 1.0, +and you may not use this file except in compliance with the Snowplow Community License Version 1.0. +You may obtain a copy of the Snowplow Community License Version 1.0 at https://docs.snowplow.io/community-license-1.0 + +NOTE (Embucket / DataFusion): +QUALIFY dedup + wide projections moved to materialized models for memory isolation: + - snowplow_web_first_event_ids_this_run (QUALIFY on 4 narrow cols) + - snowplow_web_last_event_ids_this_run (QUALIFY on 4 narrow cols) + - snowplow_web_session_firsts_this_run (wide projection + lookup joins) + - snowplow_web_session_lasts_this_run (wide projection + lookup joins) +This model is now just the final 3-way LEFT JOIN. +#} + +{{ + config( + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +{%- if var('snowplow__session_passthroughs', []) -%} + {%- set passthrough_names = [] -%} + {%- for identifier in var('snowplow__session_passthroughs', []) -%} + {%- if identifier is mapping -%} + {%- do passthrough_names.append(identifier['alias']) -%} + {%- else -%} + {%- do passthrough_names.append(identifier) -%} + {%- endif -%} + {%- endfor -%} +{%- endif %} + +select + -- app id + a.app_id, + + a.platform, + + -- session fields + a.domain_sessionid, + a.original_domain_sessionid, + a.domain_sessionidx, + + -- when the session starts with a ping we need to add the min visit length to get when the session actually started + case when a.event_name = 'page_ping' then + {{ snowplow_utils.timestamp_add(datepart="second", interval=-var("snowplow__min_visit_length", 5), tstamp="c.start_tstamp") }} + else c.start_tstamp end as start_tstamp, + c.end_tstamp, + a.model_tstamp, + + -- user fields + a.user_id, + a.domain_userid, + a.original_domain_userid, + a.stitched_user_id, + a.network_userid, + + -- engagement fields + c.page_views, + c.engaged_time_in_s, + {%- if var('snowplow__list_event_counts', false) %} + try_parse_json(c.event_counts_string) as event_counts, + {%- endif %} + c.total_events, + {{ engaged_session() }} as is_engaged, + -- when the session starts with a ping we need to add the min visit length to get when the session actually started + c.absolute_time_in_s + case when a.event_name = 'page_ping' then {{ var("snowplow__min_visit_length", 5) }} else 0 end as absolute_time_in_s, + + -- first page fields + a.first_page_title, + a.first_page_url, + a.first_page_urlscheme, + a.first_page_urlhost, + a.first_page_urlpath, + a.first_page_urlquery, + a.first_page_urlfragment, + + -- only take the first value when the last is genuinely missing (base on url as has to always be populated) + case when b.last_page_url is null then coalesce(b.last_page_title, a.first_page_title) else b.last_page_title end as last_page_title, + case when b.last_page_url is null then coalesce(b.last_page_url, a.first_page_url) else b.last_page_url end as last_page_url, + case when b.last_page_url is null then coalesce(b.last_page_urlscheme, a.first_page_urlscheme) else b.last_page_urlscheme end as last_page_urlscheme, + case when b.last_page_url is null then coalesce(b.last_page_urlhost, a.first_page_urlhost) else b.last_page_urlhost end as last_page_urlhost, + case when b.last_page_url is null then coalesce(b.last_page_urlpath, a.first_page_urlpath) else b.last_page_urlpath end as last_page_urlpath, + case when b.last_page_url is null then coalesce(b.last_page_urlquery, a.first_page_urlquery) else b.last_page_urlquery end as last_page_urlquery, + case when b.last_page_url is null then coalesce(b.last_page_urlfragment, a.first_page_urlfragment) else b.last_page_urlfragment end as last_page_urlfragment, + + -- referrer fields + a.referrer, + a.refr_urlscheme, + a.refr_urlhost, + a.refr_urlpath, + a.refr_urlquery, + a.refr_urlfragment, + a.refr_medium, + a.refr_source, + a.refr_term, + + -- marketing fields + a.mkt_medium, + a.mkt_source, + a.mkt_term, + a.mkt_content, + a.mkt_campaign, + a.mkt_clickid, + a.mkt_network, + a.mkt_source_platform, + a.default_channel_group, + + -- geo fields + a.geo_country, + a.geo_region, + a.geo_region_name, + a.geo_city, + a.geo_zipcode, + a.geo_latitude, + a.geo_longitude, + a.geo_timezone, + a.geo_country_name, + a.geo_continent, + case when b.last_geo_country is null then coalesce(b.last_geo_country, a.geo_country) else b.last_geo_country end as last_geo_country, + case when b.last_geo_country is null then coalesce(b.last_geo_region_name, a.geo_region_name) else b.last_geo_region_name end as last_geo_region_name, + case when b.last_geo_country is null then coalesce(b.last_geo_city, a.geo_city) else b.last_geo_city end as last_geo_city, + case when b.last_geo_country is null then coalesce(b.last_geo_country_name, a.geo_country_name) else b.last_geo_country_name end as last_geo_country_name, + case when b.last_geo_country is null then coalesce(b.last_geo_continent, a.geo_continent) else b.last_geo_continent end as last_geo_continent, + + -- ip address + a.user_ipaddress, + + -- user agent + a.useragent, + + a.br_renderengine, + a.br_lang, + a.br_lang_name, + case when b.last_br_lang is null then coalesce(b.last_br_lang, a.br_lang) else b.last_br_lang end as last_br_lang, + case when b.last_br_lang is null then coalesce(b.last_br_lang_name, a.br_lang_name) else b.last_br_lang_name end as last_br_lang_name, + + a.os_timezone, + + -- iab enrichment fields + a.category, + a.primary_impact, + a.reason, + a.spider_or_robot, + + -- ua parser enrichment fields + a.useragent_family, + a.useragent_major, + a.useragent_minor, + a.useragent_patch, + a.useragent_version, + a.os_family, + a.os_major, + a.os_minor, + a.os_patch, + a.os_patch_minor, + a.os_version, + a.device_family, + + -- yauaa enrichment fields + a.device_class, + case when a.device_class = 'Desktop' THEN 'Desktop' + when a.device_class = 'Phone' then 'Mobile' + when a.device_class = 'Tablet' then 'Tablet' + else 'Other' end as device_category, + a.screen_resolution, + a.agent_class, + a.agent_name, + a.agent_name_version, + a.agent_name_version_major, + a.agent_version, + a.agent_version_major, + a.device_brand, + a.device_name, + a.device_version, + a.layout_engine_class, + a.layout_engine_name, + a.layout_engine_name_version, + a.layout_engine_name_version_major, + a.layout_engine_version, + a.layout_engine_version_major, + a.operating_system_class, + a.operating_system_name, + a.operating_system_name_version, + a.operating_system_version + + -- conversion fields + {%- if var('snowplow__conversion_events', none) %} + {%- for conv_def in var('snowplow__conversion_events') %} + {{ snowplow_web.get_conversion_columns(conv_def, names_only = true)}} + {%- endfor %} + {% if var('snowplow__total_all_conversions', false) %} + ,{%- for conv_def in var('snowplow__conversion_events') %}{{'cv_' ~ conv_def['name'] ~ '_volume'}}{%- if not loop.last %} + {% endif -%}{%- endfor %} as cv__all_volume + ,0 {%- for conv_def in var('snowplow__conversion_events') %}{%- if conv_def.get('value') %} + {{'cv_' ~ conv_def['name'] ~ '_total'}}{% endif -%}{%- endfor %} as cv__all_total + {% endif %} + {%- endif %} + + -- passthrough fields + {%- if var('snowplow__session_passthroughs', []) -%} + {%- for col in passthrough_names %} + , a.{{col}} + {%- endfor -%} + {%- endif %} +from + {{ ref('snowplow_web_session_firsts_this_run') }} a +left join + {{ ref('snowplow_web_session_lasts_this_run') }} b on a.domain_sessionid = b.domain_sessionid +left join + {{ ref('snowplow_web_session_aggs_this_run') }} c on a.domain_sessionid = c.domain_sessionid diff --git a/patches/snowplow_web/models/user_mapping/snowplow_web_user_mapping.sql b/patches/snowplow_web/models/user_mapping/snowplow_web_user_mapping.sql new file mode 100644 index 0000000..c497a61 --- /dev/null +++ b/patches/snowplow_web/models/user_mapping/snowplow_web_user_mapping.sql @@ -0,0 +1,39 @@ +{# +Copyright (c) 2020-present Snowplow Analytics Ltd. All rights reserved. +This program is licensed to you under the Snowplow Community License Version 1.0, +and you may not use this file except in compliance with the Snowplow Community License Version 1.0. +You may obtain a copy of the Snowplow Community License Version 1.0 at https://docs.snowplow.io/community-license-1.0 +#} + +{{ + config( + materialized='incremental', + unique_key='domain_userid', + sort='end_tstamp', + dist='domain_userid', + partition_by = snowplow_utils.get_value_by_target_type(bigquery_val={ + "field": "end_tstamp", + "data_type": "timestamp" + }), + tags=["derived"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + + +{# Narrow projection + spillable QUALIFY instead of non-spillable UNBOUNDED window. + row_number() uses a sort (spillable), while last_value() with UNBOUNDED buffers + entire partitions in memory (non-spillable) — OOMs on large datasets. + Semantically equivalent: the user_id and end_tstamp of the last event per user. #} +select domain_userid, user_id, end_tstamp +from ( + select domain_userid, user_id, collector_tstamp as end_tstamp + from ( + select domain_userid, {{ var('snowplow__user_stitching_id', 'user_id') }} as user_id, collector_tstamp + from {{ ref('snowplow_web_base_events_this_run') }} + where {{ snowplow_utils.is_run_with_new_events('snowplow_web') }} + and {{ var('snowplow__user_stitching_id', 'user_id') }} is not null + and domain_userid is not null + ) + qualify row_number() over (partition by domain_userid order by collector_tstamp desc) = 1 +) diff --git a/scripts/apply_oom_patch.sh b/scripts/apply_oom_patch.sh new file mode 100755 index 0000000..17d7886 --- /dev/null +++ b/scripts/apply_oom_patch.sh @@ -0,0 +1,26 @@ +#!/usr/bin/env bash +# Apply the OOM-mitigation decomposed models on top of a fresh dbt_packages/. +# Survives `dbt deps` (patches/ is in the repo; this script is re-runnable). +set -euo pipefail + +if [[ ! -d dbt_packages ]]; then + echo "ERROR: dbt_packages/ missing. Run 'uv run dbt deps --profiles-dir .' first." >&2 + exit 1 +fi + +# 1) Existing embucket-target-type sed patch (safe to layer on snowflake target too). +# It uses `grep | while read` under pipefail; grep exits 1 when already-patched, +# which is not an error for us. Tolerate exit 1 only. +set +e +./scripts/patch_snowplow.sh +rc=$? +set -e +if (( rc != 0 && rc != 1 )); then + echo "patch_snowplow.sh failed with rc=$rc" >&2 + exit $rc +fi + +# 2) Overlay decomposed models. --checksum compares content, not mtime -> idempotent. +rsync -a --checksum patches/ dbt_packages/ + +echo "Applied OOM patch to dbt_packages/" diff --git a/scripts/parity_self.py b/scripts/parity_self.py new file mode 100755 index 0000000..6894187 --- /dev/null +++ b/scripts/parity_self.py @@ -0,0 +1,201 @@ +#!/usr/bin/env python3 +"""Snowflake-only two-schema parity check. + +Diffs two Snowflake schemas containing the three Snowplow golden tables. +Same rowcount + MD5-row-hash methodology as parity.py, but both sides are +Snowflake, and `model_tstamp` is excluded from every hash (it is +current_timestamp() at dbt run time and will differ by design). + +Usage: + uv run python scripts/parity_self.py \\ + --left sturukin_db.atomic_derived_baseline_fr \\ + --right sturukin_db.atomic_derived_patched_fr +""" + +from __future__ import annotations + +import argparse +import hashlib +import sys +from dataclasses import dataclass, field + +import snowflake.connector + + +@dataclass +class TableSpec: + name: str + natural_key: str + columns: list[str] + + +# Columns mirror parity.py but with model_tstamp removed. +TABLES = [ + TableSpec( + name="snowplow_web_page_views", + natural_key="page_view_id", + columns=[ + "page_view_id", "event_id", "app_id", "user_id", "domain_userid", + "stitched_user_id", "network_userid", "domain_sessionid", + "domain_sessionidx", "page_view_in_session_index", + "page_views_in_session", "dvce_created_tstamp", "collector_tstamp", + "derived_tstamp", "start_tstamp", "end_tstamp", + "engaged_time_in_s", "absolute_time_in_s", + "horizontal_pixels_scrolled", "vertical_pixels_scrolled", + "horizontal_percentage_scrolled", "vertical_percentage_scrolled", + "doc_width", "doc_height", "page_title", "page_url", + "page_urlscheme", "page_urlhost", "page_urlpath", "page_urlquery", + "page_urlfragment", "mkt_medium", "mkt_source", "mkt_term", + "mkt_content", "mkt_campaign", "mkt_clickid", "mkt_network", + "page_referrer", "refr_urlscheme", "refr_urlhost", "refr_urlpath", + "refr_urlquery", "refr_urlfragment", "refr_medium", "refr_source", + "refr_term", "geo_country", "geo_region", "geo_region_name", + "geo_city", "geo_zipcode", "geo_latitude", "geo_longitude", + "geo_timezone", "user_ipaddress", "useragent", "br_lang", + "br_viewwidth", "br_viewheight", "br_colordepth", "br_renderengine", + "os_timezone", + ], + ), + TableSpec( + name="snowplow_web_sessions", + natural_key="domain_sessionid", + columns=[ + "app_id", "domain_sessionid", "domain_sessionidx", "start_tstamp", + "end_tstamp", "user_id", "domain_userid", + "stitched_user_id", "network_userid", "page_views", + "engaged_time_in_s", "total_events", "is_engaged", + "absolute_time_in_s", "first_page_title", "first_page_url", + "last_page_title", "last_page_url", "referrer", + "geo_country", "geo_region", "geo_city", "geo_timezone", + "user_ipaddress", "useragent", "br_lang", + ], + ), + TableSpec( + name="snowplow_web_users", + natural_key="domain_userid", + columns=[ + "user_id", "domain_userid", "network_userid", "start_tstamp", + "end_tstamp", "page_views", "sessions", + "engaged_time_in_s", "first_page_title", "first_page_url", + "first_geo_country", "first_geo_city", + "last_page_title", "last_page_url", "last_geo_country", + "last_geo_city", "referrer", + ], + ), +] + + +@dataclass +class DiffResult: + matched: int + mismatched: list[str] = field(default_factory=list) + only_left: list[str] = field(default_factory=list) + only_right: list[str] = field(default_factory=list) + + +def row_hash(values: list) -> str: + parts = [] + for v in values: + if v is None: + parts.append("\x00NULL\x00") + else: + parts.append(str(v)) + return hashlib.md5("\x01".join(parts).encode("utf-8")).hexdigest() + + +def fetch_hashes(conn, fqn: str, spec: TableSpec) -> dict: + cols = ", ".join(spec.columns) + cur = conn.cursor() + try: + cur.execute(f"SELECT {spec.natural_key}, {cols} FROM {fqn}") + return {row[0]: row_hash(list(row[1:])) for row in cur.fetchall()} + finally: + cur.close() + + +def rowcount(conn, fqn: str) -> int: + cur = conn.cursor() + try: + cur.execute(f"SELECT COUNT(*) FROM {fqn}") + return int(cur.fetchone()[0]) + finally: + cur.close() + + +def diff_sides(left: dict, right: dict) -> DiffResult: + matched = 0 + mismatched: list[str] = [] + only_left: list[str] = [] + only_right: list[str] = [] + for key, lhash in left.items(): + if key not in right: + only_left.append(key) + elif right[key] != lhash: + mismatched.append(key) + else: + matched += 1 + for key in right: + if key not in left: + only_right.append(key) + return DiffResult(matched=matched, mismatched=mismatched, + only_left=only_left, only_right=only_right) + + +def print_diff(spec: TableSpec, diff: DiffResult, left_label: str, + right_label: str) -> None: + print(f" matched: {diff.matched}") + print(f" mismatched: {len(diff.mismatched)}") + print(f" only_{left_label}: {len(diff.only_left)}") + print(f" only_{right_label}: {len(diff.only_right)}") + for bucket_name, keys in [ + ("mismatched", diff.mismatched), + (f"only_{left_label}", diff.only_left), + (f"only_{right_label}", diff.only_right), + ]: + if keys: + sample = keys[:10] + print(f" first {len(sample)} {bucket_name} {spec.natural_key}:") + for k in sample: + print(f" {k}") + + +def run(left_schema: str, right_schema: str) -> int: + any_fail = False + conn = snowflake.connector.connect(connection_name="default") + try: + for spec in TABLES: + left_fqn = f"{left_schema}.{spec.name}" + right_fqn = f"{right_schema}.{spec.name}" + lc = rowcount(conn, left_fqn) + rc = rowcount(conn, right_fqn) + print(f"\n{spec.name}: left={lc} right={rc}") + if lc != rc: + print(" ROWCOUNT DIFFERS -- skipping hash diff") + any_fail = True + continue + if lc == 0: + print(" both sides empty; nothing to hash") + continue + lh = fetch_hashes(conn, left_fqn, spec) + rh = fetch_hashes(conn, right_fqn, spec) + diff = diff_sides(lh, rh) + print_diff(spec, diff, "left", "right") + if diff.mismatched or diff.only_left or diff.only_right: + any_fail = True + finally: + conn.close() + return 1 if any_fail else 0 + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--left", required=True, + help="Left-side database.schema") + parser.add_argument("--right", required=True, + help="Right-side database.schema") + args = parser.parse_args() + sys.exit(run(args.left, args.right)) + + +if __name__ == "__main__": + main() diff --git a/scripts/snowflake_clone_schema.py b/scripts/snowflake_clone_schema.py new file mode 100755 index 0000000..ac67700 --- /dev/null +++ b/scripts/snowflake_clone_schema.py @@ -0,0 +1,36 @@ +#!/usr/bin/env python3 +"""Zero-copy snapshot a Snowflake schema via CREATE OR REPLACE SCHEMA ... CLONE. + +Usage: + uv run python scripts/snowflake_clone_schema.py \\ + --src sturukin_db.atomic_derived \\ + --dst sturukin_db.atomic_derived_baseline_fr +""" + +from __future__ import annotations + +import argparse + +import snowflake.connector + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--src", required=True, help="database.schema") + parser.add_argument("--dst", required=True, help="database.schema") + parser.add_argument("--connection", default="default") + args = parser.parse_args() + + conn = snowflake.connector.connect(connection_name=args.connection) + try: + cur = conn.cursor() + sql = f"CREATE OR REPLACE SCHEMA {args.dst} CLONE {args.src}" + print(sql) + cur.execute(sql) + print(f"cloned {args.src} -> {args.dst}") + finally: + conn.close() + + +if __name__ == "__main__": + main() diff --git a/scripts/verify_oom_patch.sh b/scripts/verify_oom_patch.sh new file mode 100755 index 0000000..83dc7fd --- /dev/null +++ b/scripts/verify_oom_patch.sh @@ -0,0 +1,99 @@ +#!/usr/bin/env bash +# End-to-end verification that the OOM-mitigation patch to snowplow-web dbt +# models produces semantically identical golden tables on Snowflake. +# +# baseline = upstream snowplow-web models (as installed by dbt deps) +# patched = baseline + patches/ overlay via scripts/apply_oom_patch.sh +# +# Flow: +# 1. dbt deps + embucket-target-type patch (baseline state). +# 2. init source, load batch 1, snowflake refresh. +# 3. dbt run --full-refresh, clone output -> atomic_derived_baseline_fr. +# 4. load batch 2, snowflake refresh. +# 5. dbt run (incremental), clone output -> atomic_derived_baseline_inc. +# 6. Apply OOM patch. +# 7. Repeat 2-5 with clones -> atomic_derived_patched_{fr,inc}. +# 8. parity_self.py on both (fr, inc) pairs. +# +# Source data: hooli_events_0417_v2 in Glue (READ-ONLY, never written). +# Athena-managed atomic.events_0416 on S3 Tables is recreated between variants. +set -euo pipefail + +BATCH1_START='2026-04-22 15:00:00' +BATCH1_END='2026-04-22 15:30:00' +BATCH2_START='2026-04-22 15:30:00' +BATCH2_END='2026-04-22 16:00:00' + +DERIVED_SRC='sturukin_db.atomic_derived' + +dbt_run_stage() { + local full_refresh=$1 + if [[ $full_refresh == 1 ]]; then + uv run dbt run --profiles-dir . --target snowflake --full-refresh + else + uv run dbt run --profiles-dir . --target snowflake + fi +} + +clone_to() { + local dst=$1 + uv run python scripts/snowflake_clone_schema.py \ + --src "$DERIVED_SRC" --dst "$dst" +} + +load_batch() { + local start=$1 end=$2 + uv run python scripts/load_from_glue.py insert --start "$start" --end "$end" + uv run python scripts/snowflake_refresh.py +} + +reset_source() { + uv run python scripts/load_from_glue.py init + # After Athena drops + recreates events_0416 its S3 Tables location changes, + # so we must re-register the Snowflake iceberg table and re-grant LF perms. + uv run python scripts/snowflake_setup.py + load_batch "$BATCH1_START" "$BATCH1_END" +} + +# --- BASELINE --------------------------------------------------------- +echo "=== baseline: fresh dbt_packages ===" +rm -rf dbt_packages +uv run dbt deps --profiles-dir . +./scripts/patch_snowplow.sh # embucket target-type only + +echo "=== baseline: init source + batch 1 + full-refresh ===" +reset_source +dbt_run_stage 1 +clone_to "sturukin_db.atomic_derived_baseline_fr" + +echo "=== baseline: batch 2 + incremental ===" +load_batch "$BATCH2_START" "$BATCH2_END" +dbt_run_stage 0 +clone_to "sturukin_db.atomic_derived_baseline_inc" + +# --- PATCHED ---------------------------------------------------------- +echo "=== patched: apply OOM patch ===" +./scripts/apply_oom_patch.sh + +echo "=== patched: reset source + full-refresh ===" +reset_source +dbt_run_stage 1 +clone_to "sturukin_db.atomic_derived_patched_fr" + +echo "=== patched: batch 2 + incremental ===" +load_batch "$BATCH2_START" "$BATCH2_END" +dbt_run_stage 0 +clone_to "sturukin_db.atomic_derived_patched_inc" + +# --- DIFF ------------------------------------------------------------- +echo "=== parity: full-refresh ===" +uv run python scripts/parity_self.py \ + --left sturukin_db.atomic_derived_baseline_fr \ + --right sturukin_db.atomic_derived_patched_fr + +echo "=== parity: incremental ===" +uv run python scripts/parity_self.py \ + --left sturukin_db.atomic_derived_baseline_inc \ + --right sturukin_db.atomic_derived_patched_inc + +echo "=== ALL CHECKS PASSED ===" diff --git a/specs/2026-04-24-snowflake-oom-patch-equivalence-design.md b/specs/2026-04-24-snowflake-oom-patch-equivalence-design.md new file mode 100644 index 0000000..9c5701e --- /dev/null +++ b/specs/2026-04-24-snowflake-oom-patch-equivalence-design.md @@ -0,0 +1,256 @@ +# Verifying semantic equivalence of the OOM-mitigation dbt patch on Snowflake + +## Problem + +`dbt_packages.bak.1776896708/` contains a hand-patched copy of the +Snowplow dbt models (`snowplow_web`, `snowplow_utils`) that rewrites +four memory-heavy queries into eleven narrow, materialized steps so +they run on Embucket/DataFusion without OOM. The rewrite preserves +upstream semantics only if the chained scratch models compute the same +final aggregates as the original monolithic queries. + +The patch is ephemeral: it lives only in `.bak`, and `dbt deps` +restores the unmodified upstream `dbt_packages/`. + +Goal: prove, on Snowflake, that the patched pipeline and the upstream +pipeline produce byte-identical golden tables +(`snowplow_web_page_views`, `snowplow_web_sessions`, +`snowplow_web_users`) across both a full-refresh run and an +incremental run, using the same source data. + +## Strategy + +Snowflake is the oracle: it runs both variants successfully and has +no memory pressure, so any diff comes from SQL semantics, not engine +behaviour. Run the full pipeline four times in a single ordered flow. + +``` +init source + batch 1 + baseline --full-refresh → clone atomic_derived to atomic_derived_baseline_fr +batch 2 + baseline (incremental) → clone atomic_derived to atomic_derived_baseline_inc + +init source + batch 1 (reset to identical starting state) +apply OOM patch + patched --full-refresh → clone atomic_derived to atomic_derived_patched_fr +batch 2 + patched (incremental) → clone atomic_derived to atomic_derived_patched_inc + +diff (baseline_fr, patched_fr) +diff (baseline_inc, patched_inc) +``` + +The source Iceberg table is Athena-managed and safe to recreate; data +comes from `awsdatacatalog.analytics_glue.hooli_events_0417_v2` via +`scripts/events_0416_select.sql`. `load_from_glue.py init` is +deterministic against that Glue table for a fixed `[start, end)` +window. + +Batches reuse the canonical README windows: + +- batch 1: `2026-04-22 15:00:00` to `2026-04-22 15:30:00` +- batch 2: `2026-04-22 15:30:00` to `2026-04-22 16:00:00` + +Snapshotting via `CREATE SCHEMA … CLONE` is a Snowflake zero-copy +metadata op; it costs nothing and lets all four output sets coexist +for diffing. + +## Components + +### 1. `patches/` — git-tracked patched model tree + +Mirror structure of `dbt_packages/`. Holds the exact files from +`dbt_packages.bak.1776896708/` that differ from upstream: + +``` +patches/ + snowplow_web/models/sessions/scratch/snowflake/ + snowplow_web_first_event_ids_this_run.sql (new) + snowplow_web_last_event_ids_this_run.sql (new) + snowplow_web_session_firsts_this_run.sql (new) + snowplow_web_session_lasts_this_run.sql (new) + snowplow_web_session_base_aggs_this_run.sql (new) + snowplow_web_session_pv_set_this_run.sql (new) + snowplow_web_session_pv_counts_this_run.sql (new) + snowplow_web_session_pp_bucket_set_this_run.sql (new) + snowplow_web_session_pp_counts_this_run.sql (new) + snowplow_web_session_aggs_this_run.sql (rewritten) + snowplow_web_sessions_this_run.sql (rewritten) + snowplow_web/models/page_views/scratch/snowflake/ + snowplow_web_pv_dedup_this_run.sql (new) + snowplow_web_page_views_this_run.sql (rewritten) + snowplow_web/models/user_mapping/ + snowplow_web_user_mapping.sql (rewritten) + snowplow_utils/macros/base/ + base_create_snowplow_events_this_run.sql (rewritten) +``` + +Files are copied verbatim from `.bak`. The dirty diffs noted in the +earlier analysis (the `get_value_by_target_type.sql` and optional- +module `target.type` edits) are NOT in this patch — they belong to a +different concern (embucket adapter recognition) and are handled by +the existing `patch_snowplow.sh`. + +### 2. `scripts/apply_oom_patch.sh` — patch applier + +``` +#!/usr/bin/env bash +set -euo pipefail +# 1. Apply the pre-existing embucket-target-type sed patch. +./scripts/patch_snowplow.sh +# 2. Overlay the OOM-mitigation decomposed models. +rsync -a --checksum patches/ dbt_packages/ +echo "Applied OOM patch to dbt_packages/" +``` + +Idempotent. `rsync -a --checksum` compares content not mtime, so +repeat runs are no-ops. Survives `dbt deps` because `patches/` is in +the repo; re-run after any `dbt deps`. + +### 3. `scripts/parity_self.py` — Snowflake-only two-schema diff + +Fork of `scripts/parity.py` with the following changes: + +- Drop the Embucket path entirely; only uses + `snowflake.connector.connect(connection_name="default")`. +- Takes `--left` and `--right` arguments naming two schemas in + `sturukin_db` (e.g. `atomic_derived_baseline_fr` and + `atomic_derived_patched_fr`). +- Keeps the three `TableSpec` entries from `parity.py` unchanged + except that `model_tstamp` is removed from each `columns` list. + `model_tstamp` is `current_timestamp()` at dbt run time and will + always differ between the two pipelines; its presence would produce + 100% row mismatches and drown the signal. +- Same methodology: per-table rowcount check, then per-row MD5 hash + keyed on the natural key, with a summary of matched / mismatched / + only-left / only-right rows. +- Exits non-zero on any mismatch. + +### 4. `scripts/verify_oom_patch.sh` — orchestrator + +``` +#!/usr/bin/env bash +set -euo pipefail + +BATCH1_START='2026-04-22 15:00:00' +BATCH1_END='2026-04-22 15:30:00' +BATCH2_START='2026-04-22 15:30:00' +BATCH2_END='2026-04-22 16:00:00' + +run_pipeline_stage() { + local label=$1 refresh_flag=$2 suffix=$3 + if [[ -n $refresh_flag ]]; then + uv run dbt run --profiles-dir . --target snowflake --full-refresh + else + uv run dbt run --profiles-dir . --target snowflake + fi + uv run python scripts/snowflake_clone_schema.py \ + --src sturukin_db.atomic_derived \ + --dst sturukin_db.atomic_derived_${label}_${suffix} +} + +# --- BASELINE --------------------------------------------------------- +rm -rf dbt_packages && uv run dbt deps --profiles-dir . +./scripts/patch_snowplow.sh # embucket target-type only; no OOM patch + +uv run python scripts/load_from_glue.py init +uv run python scripts/load_from_glue.py insert \ + --start "$BATCH1_START" --end "$BATCH1_END" +uv run python scripts/snowflake_refresh.py +run_pipeline_stage baseline --full-refresh fr + +uv run python scripts/load_from_glue.py insert \ + --start "$BATCH2_START" --end "$BATCH2_END" +uv run python scripts/snowflake_refresh.py +run_pipeline_stage baseline "" inc + +# --- PATCHED ---------------------------------------------------------- +./scripts/apply_oom_patch.sh + +uv run python scripts/load_from_glue.py init +uv run python scripts/load_from_glue.py insert \ + --start "$BATCH1_START" --end "$BATCH1_END" +uv run python scripts/snowflake_refresh.py +run_pipeline_stage patched --full-refresh fr + +uv run python scripts/load_from_glue.py insert \ + --start "$BATCH2_START" --end "$BATCH2_END" +uv run python scripts/snowflake_refresh.py +run_pipeline_stage patched "" inc + +# --- DIFF ------------------------------------------------------------- +uv run python scripts/parity_self.py \ + --left sturukin_db.atomic_derived_baseline_fr \ + --right sturukin_db.atomic_derived_patched_fr +uv run python scripts/parity_self.py \ + --left sturukin_db.atomic_derived_baseline_inc \ + --right sturukin_db.atomic_derived_patched_inc +``` + +Before baseline, we force-delete `dbt_packages/` and re-run `dbt deps` +to guarantee upstream state (a previous `apply_oom_patch.sh` run would +otherwise persist). + +### 5. `scripts/snowflake_clone_schema.py` — zero-copy snapshot helper + +``` +CREATE OR REPLACE SCHEMA CLONE ; +``` + +One `snowflake.connector` call, parameters `--src` and `--dst` as +fully-qualified `database.schema`. Zero-copy, metadata-only. + +## Data flow + +``` +Glue: hooli_events_0417_v2 (read-only; NEVER written) + ↓ Athena projection +S3 Tables Iceberg: atomic.events_0416 (shared, recreated per variant) + ↓ Snowflake iceberg external ref +sturukin_db.atomic.events_0416 + ↓ dbt (baseline or patched) +sturukin_db.atomic_derived.{snowplow_web_page_views,sessions,users} + ↓ CLONE +sturukin_db.atomic_derived__.* + ↓ parity_self.py +diff report +``` + +## Error handling + +- `apply_oom_patch.sh` aborts if `dbt_packages/` is missing (caller + must `dbt deps` first). +- `snowflake_clone_schema.py` uses `CREATE OR REPLACE`; reruns are + idempotent. Caller responsible for cleanup if they want to reclaim + catalog entries. +- `parity_self.py` exits 1 on any diff. `verify_oom_patch.sh` runs + under `set -e`, so the first diff halts the run; this is correct — + once baseline_fr vs patched_fr differs, we want to investigate + before the incremental run is judged on top of divergent state. + +## Explicit non-goals + +- No automated cleanup of `atomic_derived_*` schemas. Zero-copy clones + are cheap; leaving them aids post-mortem investigation. +- No column-level diff tooling beyond natural-key row hashes. + If diffs appear, follow-up is manual SQL. +- No cross-variant interleaving (e.g. running both pipelines on batch + 1, comparing, then both on batch 2). The sequential approach above + is simpler and produces the same equivalence signal. +- No change to `parity.py`. The existing Embucket-vs-Snowflake harness + stays untouched. +- The existing `.bak.1776896708/` is left in place as a reference + snapshot and will not be moved or deleted. + +## Success criterion + +Both `parity_self.py` invocations exit 0: + +- `atomic_derived_baseline_fr` matches `atomic_derived_patched_fr` on + rowcount and per-row hash for all three golden tables, ignoring + `model_tstamp`. +- Same for `_inc`. + +If either fails, the mismatch summary (per-table matched / +mismatched / only-left / only-right counts plus up to ten example +natural keys per bucket) identifies where semantic divergence exists. diff --git a/specs/2026-04-24-snowflake-oom-patch-equivalence-results.md b/specs/2026-04-24-snowflake-oom-patch-equivalence-results.md new file mode 100644 index 0000000..9e4b606 --- /dev/null +++ b/specs/2026-04-24-snowflake-oom-patch-equivalence-results.md @@ -0,0 +1,166 @@ +# OOM-mitigation dbt patch is NOT semantically equivalent to upstream + +## Verdict + +The decomposed `snowplow_web` / `snowplow_utils` models in +`dbt_packages.bak.1776896708/` (snapshot Apr 10) do NOT produce the +same golden tables as the upstream models when run on the same input. +The patched pipeline produces more rows in all three golden tables on +Snowflake where memory is not a constraint. + +Full-refresh parity on identical input (6,162,099 source events): + +| table | baseline | patched | Δ rows | Δ % | +|--------------------------|-----------|-----------|---------|--------| +| snowplow_web_page_views | 775,543 | 814,570 | +39,027 | +5.0% | +| snowplow_web_sessions | 317,162 | 360,999 | +43,837 | +13.8% | +| snowplow_web_users | 71,352 | 122,041 | +50,689 | +71.0% | + +Incremental parity is not informative in this experiment (see +"Incremental run caveat" below); the full-refresh numbers carry the +full signal. + +## Root cause + +`patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql` +removes the upstream event_id dedup: + +``` +# upstream (baseline) +qualify row_number() over (partition by a.event_id + order by a.{{ session_timestamp }}, + a.dvce_created_tstamp) = 1 + +# patched +(removed; comment: "Dedup is handled by downstream models which + each dedup by their own key (page_view_id, domain_sessionid, + domain_userid) on the much smaller batch-sized + base_events_this_run table.") +``` + +The source data has duplicate event_ids. On our 6.16M-row batch: + +``` +events_0416 total rows : 6,162,099 +events_0416 distinct event_ids: 6,027,734 +duplicate event_ids : 134,365 (2.18% of input) +``` + +These duplicates are Snowplow collector retries or client replays — +two rows with the same `event_id` but potentially different +`collector_tstamp`, `derived_tstamp`, or enrichment columns. + +The patch's rationale ("downstream models dedup by their own key") is +only partially true. It holds for `snowplow_web_pv_dedup_this_run` +(deduplicates on `page_view_id`) and for the session `_firsts`/`_lasts` +narrow QUALIFY variants (deduplicate on `domain_sessionid`). It does +NOT hold for everything downstream of the base model — in particular: + +- `snowplow_web_session_aggs_this_run` does `COUNT(*)` and + `COUNT(DISTINCT page_view_id || bucket)` grouped by + `domain_sessionid`. Duplicate event rows inflate `total_events` + and, crucially, add spurious `(page_view, heartbeat_bucket)` + combinations that inflate `engaged_time_in_s`. +- `snowplow_web_users_this_run` is driven from + `snowplow_web_users_sessions_this_run`, which aggregates over + `snowplow_web_sessions_this_run`. Each duplicate session row + propagates into extra users rows (users table has 1.71 rows per + distinct domain_userid in the patched output). +- `snowplow_web_users_lasts` uses `qualify row_number() partition by + domain_userid` but with ties on `collector_tstamp` caused by + duplicate events, the tie-break picks one row per (user, tstamp), + not per user — leaving ~25k extra rows. + +## Not a root cause + +The other three rewrites (page_views narrow-QUALIFY, session +`_firsts`/`_lasts` narrow-QUALIFY, user_mapping `last_value` → +`row_number`) are semantically equivalent to upstream. The whole +row-count divergence traces back to the single decision to drop +event_id dedup in `base_create_snowplow_events_this_run.sql`. + +## What the patch should have done + +Preserve event_id dedup, but do it in a memory-safe way. The same +"extract a narrow projection, inner-join back" pattern the patch +already uses for page_view dedup and session firsts/lasts applies +cleanly here: + +```sql +-- snowplow_web_base_event_ids_this_run (new, narrow, materialized=table): +select a.event_id +from {{ source('atomic', 'events') }} a +inner join {{ ref('snowplow_web_base_sessions_this_run') }} b + on a.domain_sessionid = b.session_identifier +where a.{{ session_timestamp }} <= b.end_tstamp + ...other upstream filters... +qualify row_number() over ( + partition by a.event_id + order by a.{{ session_timestamp }}, a.dvce_created_tstamp +) = 1 +``` + +Then the rewritten `base_create_snowplow_events_this_run.sql` adds an +`INNER JOIN snowplow_web_base_event_ids_this_run USING (event_id)`. +The QUALIFY runs on 4 narrow columns (event_id, session_timestamp, +dvce_created_tstamp, domain_sessionid) — same idea as +`snowplow_web_pv_dedup_this_run`. + +This is a two-line change once the scratch model is added, and +restores row-count parity without bringing back the OOM. + +## Incremental run caveat + +The full-refresh numbers above are directly comparable. The +incremental numbers collected in this experiment are NOT, because +the Snowplow dbt package stores its own state in +`sturukin_db.atomic_snowplow_manifest.snowplow_web_incremental_manifest` +(a cross-schema singleton), and `dbt run --full-refresh` does not +reset it for every model — in practice the manifest carries state +across pipeline variants. The patched full-refresh run therefore +inherited the baseline's "last-processed timestamp" and processed +almost nothing on the second (incremental) dbt invocation: + +| table | baseline_inc | patched_inc | +|--------------------------|-------------:|------------:| +| snowplow_web_page_views | 1,731,722 | 814,570 | +| snowplow_web_sessions | 704,107 | 360,999 | +| snowplow_web_users | 148,357 | 122,041 | + +`patched_inc` equals `patched_fr` exactly — the patched incremental +dbt run did not process batch 2. This is an artifact of the +experimental harness, not a property of the patch. For a clean +incremental comparison the manifest table would need to be truncated +between variants. + +## Evidence artifacts + +All four snapshots remain in Snowflake as zero-copy clones, available +for any further investigation: + +``` +sturukin_db.atomic_derived_baseline_fr -- upstream, full-refresh on batch 1 +sturukin_db.atomic_derived_baseline_inc -- upstream, incremental after batch 2 +sturukin_db.atomic_derived_patched_fr -- patched, full-refresh on batch 1 +sturukin_db.atomic_derived_patched_inc -- patched, incremental after batch 2 + (see caveat above — near-identical to _fr) +``` + +The patch mechanism itself works cleanly: + +- `patches/` (git-tracked) holds the 15 patched files. +- `scripts/apply_oom_patch.sh` overlays them via rsync onto a fresh + `dbt_packages/` and is idempotent. +- `scripts/parity_self.py` performs Snowflake-to-Snowflake diffs with + the same rowcount + row-hash methodology as the existing + Embucket-vs-Snowflake `parity.py`, with `model_tstamp` excluded. +- `scripts/verify_oom_patch.sh` orchestrates the four-run flow + end-to-end. + +## Recommendation + +Do NOT merge the Apr 10 stash as-is. Before landing the decomposed +models, add a narrow-projection `snowplow_web_base_event_ids_this_run` +scratch model to restore event_id dedup, and re-run this same parity +harness. Expected outcome: all three golden tables match byte-for-byte +with model_tstamp excluded. From 0fd1d13e32bda314d0507b7551b8751076e9b568 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Fri, 24 Apr 2026 13:26:25 -0500 Subject: [PATCH 28/30] test: patch now byte-equivalent to upstream on Snowflake (fr + inc) Removes the base_create_snowplow_events_this_run.sql macro patch (which had silently dropped event_id dedup), keeping upstream's QUALIFY active. Adds manifest reset between variants so incremental comparisons aren't poisoned by shared Snowplow state; parity_self now excludes event_id from the page_view hash because row_number()=1 tiebreak over identical (derived_tstamp, dvce_created_tstamp) is non-deterministic in both pipelines (every non-event_id column matches). All three golden tables match exactly: 775,543 / 317,162 / 71,352 on full-refresh, 1,731,722 / 704,107 / 148,357 on incremental, 0 diffs. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../base_create_snowplow_events_this_run.sql | 251 ---------------- scripts/parity_self.py | 9 +- scripts/snowflake_reset_manifest.py | 50 ++++ scripts/verify_oom_patch.sh | 4 + ...snowflake-oom-patch-equivalence-results.md | 274 ++++++++---------- 5 files changed, 187 insertions(+), 401 deletions(-) delete mode 100644 patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql create mode 100755 scripts/snowflake_reset_manifest.py diff --git a/patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql b/patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql deleted file mode 100644 index 3a07818..0000000 --- a/patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql +++ /dev/null @@ -1,251 +0,0 @@ -{# -Copyright (c) 2021-present Snowplow Analytics Ltd. All rights reserved. -This program is licensed to you under the Snowplow Community License Version 1.0, -and you may not use this file except in compliance with the Snowplow Community License Version 1.0. -You may obtain a copy of the Snowplow Community License Version 1.0 at https://docs.snowplow.io/community-license-1.0 -#} - - -{% macro base_create_snowplow_events_this_run(sessions_this_run_table='snowplow_base_sessions_this_run', session_identifiers=[{"schema" : "atomic", "field" : "domain_sessionid"}], session_sql=none, session_timestamp='load_tstamp', derived_tstamp_partitioned=true, days_late_allowed=3, max_session_days=3, app_ids=[], snowplow_events_database=none, snowplow_events_schema='atomic', snowplow_events_table='events', entities_or_sdes=none, custom_sql=none) %} - {{ return(adapter.dispatch('base_create_snowplow_events_this_run', 'snowplow_utils')(sessions_this_run_table, session_identifiers, session_sql, session_timestamp, derived_tstamp_partitioned, days_late_allowed, max_session_days, app_ids, snowplow_events_database, snowplow_events_schema, snowplow_events_table, entities_or_sdes, custom_sql)) }} -{% endmacro %} - -{% macro default__base_create_snowplow_events_this_run(sessions_this_run_table, session_identifiers, session_sql, session_timestamp, derived_tstamp_partitioned, days_late_allowed, max_session_days, app_ids, snowplow_events_database, snowplow_events_schema, snowplow_events_table, entities_or_sdes, custom_sql) %} - {%- set lower_limit, upper_limit = snowplow_utils.return_limits_from_model(ref(sessions_this_run_table), - 'start_tstamp', - 'end_tstamp') %} - {% set sessions_this_run = ref(sessions_this_run_table) %} - {% set snowplow_events = api.Relation.create(database=snowplow_events_database, schema=snowplow_events_schema, identifier=snowplow_events_table) %} - - {# event_id dedup removed: this macro reads from the raw source events table (all time). - A CTE-based narrow-projection dedup would double-scan the full table (DataFusion - inlines CTEs), exceeding the Lambda memory budget. The original QUALIFY also OOMs - because it buffers 137+ columns. Dedup is handled by downstream models which - each dedup by their own key (page_view_id, domain_sessionid, domain_userid) - on the much smaller batch-sized base_events_this_run table. #} - {% set events_this_run_query %} - with identified_events AS ( - select - {% if session_sql %} - {{ session_sql }} as session_identifier, - {% else -%} - COALESCE( - {% for identifier in session_identifiers %} - {%- if identifier['schema']|lower != 'atomic' -%} - {{ snowplow_utils.get_field(identifier['schema'], identifier['field'], 'e', dbt.type_string(), 0, snowplow_events) }} - {%- else -%} - e.{{identifier['field']}} - {%- endif -%} - , - {%- endfor -%} - NULL - ) as session_identifier, - {%- endif %} - e.* - {% if custom_sql %} - , {{ custom_sql }} - {% endif %} - - from {{ snowplow_events }} e - - ) - - select - a.*, - b.user_identifier -- take user_identifier from manifest. This ensures only 1 domain_userid per session. - - from identified_events as a - inner join {{ sessions_this_run }} as b - on a.session_identifier = b.session_identifier - - where a.{{ session_timestamp }} <= {{ snowplow_utils.timestamp_add('day', max_session_days, 'b.start_tstamp') }} - and a.dvce_sent_tstamp <= {{ snowplow_utils.timestamp_add('day', days_late_allowed, 'a.dvce_created_tstamp') }} - and a.{{ session_timestamp }} >= {{ lower_limit }} - and a.{{ session_timestamp }} <= {{ upper_limit }} - and a.{{ session_timestamp }} >= b.start_tstamp -- deal with late loading events - - {% if derived_tstamp_partitioned and target.type == 'bigquery' | as_bool() %} - and a.derived_tstamp >= {{ snowplow_utils.timestamp_add('hour', -1, lower_limit) }} - and a.derived_tstamp <= {{ upper_limit }} - {% endif %} - - and {{ snowplow_utils.app_id_filter(app_ids) }} - {% endset %} - - {{ return(events_this_run_query) }} - -{% endmacro %} - -{% macro postgres__base_create_snowplow_events_this_run(sessions_this_run_table, session_identifiers, session_sql, session_timestamp, derived_tstamp_partitioned, days_late_allowed, max_session_days, app_ids, snowplow_events_database, snowplow_events_schema, snowplow_events_table, entities_or_sdes, custom_sql) %} - {%- set lower_limit, upper_limit = snowplow_utils.return_limits_from_model(ref(sessions_this_run_table), - 'start_tstamp', - 'end_tstamp') %} - - - {# Get all the session and user contexts extracted and ready to join later #} - {% set unique_session_identifiers = dict() %} {# need to avoid duplicate contexts when values come from the same one, so just use the first of that context #} - - {% if session_identifiers %} - {% for identifier in session_identifiers %} - {% if identifier['schema']|lower != 'atomic' and identifier['schema'] not in unique_session_identifiers %} - {% do unique_session_identifiers.update({identifier['schema']: identifier}) %} - {%- endif -%} - {% if identifier['schema'] in unique_session_identifiers.keys() %} - {% if identifier['alias'] != unique_session_identifiers[identifier['schema']]['alias'] or identifier['prefix'] != unique_session_identifiers[identifier['schema']]['prefix'] %} - {% do exceptions.warn("Snowplow Warning: Duplicate context ( " ~ identifier['schema'] ~" ) detected for session identifiers, using first alias and prefix provided ( " ~ unique_session_identifiers[identifier['schema']] ~ " ) in base events this run.") %} - {% endif %} - {% endif %} - {% endfor %} - {% endif %} - - {# check uniqueness of entity/sde names provided, warn those also in session identifiers #} - {% if entities_or_sdes %} - {% set ent_sde_names = [] %} - {% for ent_or_sde in entities_or_sdes %} - {% do ent_sde_names.append(ent_or_sde['schema']) %} - {% if ent_or_sde['schema'] in unique_session_identifiers.keys() %} - {% if ent_or_sde['alias'] != unique_session_identifiers[ent_or_sde['schema']]['alias'] or ent_or_sde['prefix'] != unique_session_identifiers[ent_or_sde['schema']]['prefix'] %} - {% do exceptions.warn("Snowplow Warning: Context or SDE ( " ~ ent_or_sde['schema'] ~ " ) used for session_identifier is being included, using alias and prefix from session_identifier ( " ~ unique_session_identifiers[ent_or_sde['schema']] ~ " ).") %} - {% endif %} - {% endif %} - {% endfor %} - {% if ent_sde_names | unique | list | length != entities_or_sdes | length %} - {% do exceptions.raise_compiler_error("There are duplicate schema names in your provided `entities_or_sdes` list. Please correct this before proceeding.")%} - {% endif %} - {% endif %} - - {% set sessions_this_run = ref(sessions_this_run_table) %} - {% set snowplow_events = api.Relation.create(database=snowplow_events_database, schema=snowplow_events_schema, identifier=snowplow_events_table) %} - - {% set events_this_run_query %} - with - - {# Extract the session identifier contexts into CTEs #} - {% if unique_session_identifiers -%} - {% for identifier in unique_session_identifiers.values() %} - {% if identifier['schema']|lower != 'atomic' %} - {{ snowplow_utils.get_sde_or_context(snowplow_events_schema, identifier['schema'], lower_limit, upper_limit, identifier['prefix']) }}, - {%- endif -%} - {% endfor %} - {% endif %} - - {# Extract the entitity/sde contexts into CTEs UNLESS they are in the session already #} - {%- if entities_or_sdes -%} - {%- for ent_or_sde in entities_or_sdes -%} - {%- set name = none -%} - {%- set prefix = none -%} - {%- set single_entity = true -%} - {%- if ent_or_sde['schema'] -%} - {%- set name = ent_or_sde['schema'] -%} - {%- else -%} - {%- do exceptions.raise_compiler_error("Need to specify the schema name of your Entity or SDE using the {'schema'} attribute in a key-value map.") -%} - {%- endif -%} - {%- if ent_or_sde['prefix'] -%} - {%- set prefix = ent_or_sde['prefix'] -%} - {%- else -%} - {%- set prefix = name -%} - {%- endif -%} - {%- if ent_or_sde['single_entity'] and ent_or_sde['single_entity'] is boolean -%} - {%- set single_entity = ent_or_sde['single_entity'] -%} - {%- endif %} - {% if ent_or_sde['schema'] not in unique_session_identifiers.keys() %} {# Exclude any that we have already made above #} - {{ snowplow_utils.get_sde_or_context(snowplow_events_schema, name, lower_limit, upper_limit, prefix, single_entity) }}, - {% endif %} - {% endfor -%} - {%- endif %} - - identified_events AS ( - select - {% if session_sql -%} - {{ session_sql }} as session_identifier, - {% else -%} - COALESCE( - {% for identifier in session_identifiers %} - {%- if identifier['schema']|lower != 'atomic' %} - {# Use the parsed version of the context to ensure we have the right alias and prefix #} - {% set uniq_iden = unique_session_identifiers[identifier['schema']] %} - {% if uniq_iden['alias'] %}{{uniq_iden['alias']}}{% else %}{{uniq_iden['schema']}}{% endif %}.{% if uniq_iden['prefix'] %}{{ uniq_iden['prefix'] }}{% else %}{{ uniq_iden['schema']}}{% endif %}_{{identifier['field']}} - {%- else %} - e.{{identifier['field']}} - {%- endif -%} - , - {%- endfor -%} - NULL - ) as session_identifier, - {%- endif %} - e.* - {% if custom_sql %} - , {{ custom_sql }} - {%- endif %} - - from {{ snowplow_events }} e - {% if unique_session_identifiers|length > 0 %} - {% for identifier in unique_session_identifiers.values() %} - {%- if identifier['schema']|lower != 'atomic' -%} - left join {{ identifier['schema'] }} {% if identifier['alias'] %}as {{ identifier['alias'] }}{% endif %} on e.event_id = {% if identifier['alias'] %}{{ identifier['alias']}}{% else %}{{ identifier['schema'] }}{% endif %}.{{identifier['prefix']}}__id and e.collector_tstamp = {% if identifier['alias'] %}{{ identifier['alias']}}{% else %}{{ identifier['schema'] }}{% endif %}.{{ identifier['prefix'] }}__tstamp - {% endif -%} - {% endfor %} - {% endif %} - - ), events_this_run as ( - - select - a.*, - b.user_identifier, -- take user_identifier from manifest. This ensures only 1 domain_userid per session. - row_number() over (partition by a.event_id order by a.{{ session_timestamp }}, a.dvce_created_tstamp ) as event_id_dedupe_index, - count(*) over (partition by a.event_id) as event_id_dedupe_count - - from identified_events as a - inner join {{ sessions_this_run }} as b - on a.session_identifier = b.session_identifier - - where a.{{ session_timestamp }} <= {{ snowplow_utils.timestamp_add('day', max_session_days, 'b.start_tstamp') }} - and a.dvce_sent_tstamp <= {{ snowplow_utils.timestamp_add('day', days_late_allowed, 'a.dvce_created_tstamp') }} - and a.{{ session_timestamp }} >= {{ lower_limit }} - and a.{{ session_timestamp }} <= {{ upper_limit }} - and a.{{ session_timestamp }} >= b.start_tstamp -- deal with late loading events - and {{ snowplow_utils.app_id_filter(app_ids) }} - - ) - - select * - - from events_this_run as e - {%- if entities_or_sdes -%} - {% for ent_or_sde in entities_or_sdes -%} - {%- set name = none -%} - {%- set prefix = none -%} - {%- set single_entity = true -%} - {%- set alias = none -%} - {%- if ent_or_sde['schema'] -%} - {%- set name = ent_or_sde['schema'] -%} - {%- else -%} - {%- do exceptions.raise_compiler_error("Need to specify the schema name of your Entity or SDE using the {'schema'} attribute in a key-value map.") -%} - {%- endif -%} - {%- if ent_or_sde['prefix'] and name not in unique_session_identifiers.keys() -%} - {%- set prefix = ent_or_sde['prefix'] -%} - {%- elif name in unique_session_identifiers.keys() and unique_session_identifiers.get(name, {}).get('prefix') -%} - {%- set prefix = unique_session_identifiers[name]['prefix'] -%} - {%- else -%} - {%- set prefix = name -%} - {%- endif -%} - {%- if ent_or_sde['single_entity'] and ent_or_sde['single_entity'] is boolean -%} - {%- set single_entity = ent_or_sde['single_entity'] -%} - {%- endif -%} - {%- if ent_or_sde['alias'] and name not in unique_session_identifiers.keys() -%} - {%- set alias = ent_or_sde['alias'] -%} - {%- elif name in unique_session_identifiers.keys() and unique_session_identifiers.get(name, {}).get('alias') -%} - {%- set alias = unique_session_identifiers[name] -%} - {%- endif %} - left join {{name}} {% if alias -%} as {{ alias }} {%- endif %} on e.event_id = {% if alias -%} {{ alias }} {%- else -%}{{name}}{%- endif %}.{{prefix}}__id - and e.collector_tstamp = {% if alias -%} {{ alias }} {%- else -%}{{name}}{%- endif %}.{{prefix}}__tstamp - {% if not single_entity -%} and mod({% if alias -%} {{ alias }} {%- else -%}{{name}}{%- endif %}.{{prefix}}__index, e.event_id_dedupe_count) = 0{%- endif -%} - {% endfor %} - {% endif %} - where event_id_dedupe_index = 1 - - {% endset %} - - {{ return(events_this_run_query) }} - -{% endmacro %} diff --git a/scripts/parity_self.py b/scripts/parity_self.py index 6894187..79fa0b9 100755 --- a/scripts/parity_self.py +++ b/scripts/parity_self.py @@ -31,11 +31,18 @@ class TableSpec: # Columns mirror parity.py but with model_tstamp removed. TABLES = [ + # event_id is excluded: when the source has two events with identical + # (derived_tstamp, dvce_created_tstamp) for the same page_view_id, the + # row_number()=1 tiebreak is non-deterministic. Upstream's wide QUALIFY and + # the patched narrow-scratch QUALIFY may pick different tied event_ids, but + # every other column (metrics, scroll, URL, ...) is identical. Excluding + # event_id makes the parity statement "same aggregate output per + # page_view_id" rather than "same label for the representative event". TableSpec( name="snowplow_web_page_views", natural_key="page_view_id", columns=[ - "page_view_id", "event_id", "app_id", "user_id", "domain_userid", + "page_view_id", "app_id", "user_id", "domain_userid", "stitched_user_id", "network_userid", "domain_sessionid", "domain_sessionidx", "page_view_in_session_index", "page_views_in_session", "dvce_created_tstamp", "collector_tstamp", diff --git a/scripts/snowflake_reset_manifest.py b/scripts/snowflake_reset_manifest.py new file mode 100755 index 0000000..a545dd5 --- /dev/null +++ b/scripts/snowflake_reset_manifest.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python3 +"""Reset Snowplow web package state so the next dbt run starts from scratch. + +Snowplow tracks processed event windows in three tables under +atomic_snowplow_manifest. `dbt run --full-refresh` does NOT truncate them +(incremental manifest has full_refresh=allow_refresh() which defaults to +false), so state leaks between pipeline variants and prevents apples-to- +apples incremental comparisons. + +This script DROPs the three state tables. They are recreated empty on the +next dbt run. Seed-backed dim tables in the same schema are left alone. + +Usage: + uv run python scripts/snowflake_reset_manifest.py +""" + +from __future__ import annotations + +import argparse + +import snowflake.connector + +DATABASE = "sturukin_db" +MANIFEST_SCHEMA = "atomic_snowplow_manifest" +STATE_TABLES = [ + "snowplow_web_incremental_manifest", + "snowplow_web_base_sessions_lifecycle_manifest", + "snowplow_web_base_quarantined_sessions", +] + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--connection", default="default") + args = parser.parse_args() + + conn = snowflake.connector.connect(connection_name=args.connection) + try: + cur = conn.cursor() + for table in STATE_TABLES: + fqn = f"{DATABASE}.{MANIFEST_SCHEMA}.{table}" + print(f"DROP TABLE IF EXISTS {fqn}") + cur.execute(f"DROP TABLE IF EXISTS {fqn}") + print("manifest state cleared") + finally: + conn.close() + + +if __name__ == "__main__": + main() diff --git a/scripts/verify_oom_patch.sh b/scripts/verify_oom_patch.sh index 83dc7fd..485f91a 100755 --- a/scripts/verify_oom_patch.sh +++ b/scripts/verify_oom_patch.sh @@ -52,6 +52,10 @@ reset_source() { # After Athena drops + recreates events_0416 its S3 Tables location changes, # so we must re-register the Snowflake iceberg table and re-grant LF perms. uv run python scripts/snowflake_setup.py + # Reset Snowplow package state (incremental manifest + session lifecycle) + # so the next run processes events from scratch. --full-refresh on dbt does + # not touch these tables by default. + uv run python scripts/snowflake_reset_manifest.py load_batch "$BATCH1_START" "$BATCH1_END" } diff --git a/specs/2026-04-24-snowflake-oom-patch-equivalence-results.md b/specs/2026-04-24-snowflake-oom-patch-equivalence-results.md index 9e4b606..c7b57e6 100644 --- a/specs/2026-04-24-snowflake-oom-patch-equivalence-results.md +++ b/specs/2026-04-24-snowflake-oom-patch-equivalence-results.md @@ -1,166 +1,142 @@ -# OOM-mitigation dbt patch is NOT semantically equivalent to upstream +# OOM-mitigation dbt patch is semantically equivalent to upstream on Snowflake ## Verdict -The decomposed `snowplow_web` / `snowplow_utils` models in -`dbt_packages.bak.1776896708/` (snapshot Apr 10) do NOT produce the -same golden tables as the upstream models when run on the same input. -The patched pipeline produces more rows in all three golden tables on -Snowflake where memory is not a constraint. - -Full-refresh parity on identical input (6,162,099 source events): - -| table | baseline | patched | Δ rows | Δ % | -|--------------------------|-----------|-----------|---------|--------| -| snowplow_web_page_views | 775,543 | 814,570 | +39,027 | +5.0% | -| snowplow_web_sessions | 317,162 | 360,999 | +43,837 | +13.8% | -| snowplow_web_users | 71,352 | 122,041 | +50,689 | +71.0% | - -Incremental parity is not informative in this experiment (see -"Incremental run caveat" below); the full-refresh numbers carry the -full signal. - -## Root cause - -`patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql` -removes the upstream event_id dedup: +The decomposed `snowplow_web` models in `patches/` produce byte-identical +golden tables to the upstream (unpatched) models on Snowflake across +both a full-refresh run and an incremental run on the same source data. + +Parity on 6,162,099 source events (batch 1 = 2,834,944 rows; batch 1+2 += 6,162,099 rows), comparing hashes of the three golden tables keyed +on their natural keys with `model_tstamp` and `event_id` excluded: + +| table | rows (fr) | rows (inc) | mismatched | only_left | only_right | +|--------------------------|----------:|-----------:|-----------:|----------:|-----------:| +| snowplow_web_page_views | 775,543 | 1,731,722 | 0 | 0 | 0 | +| snowplow_web_sessions | 317,162 | 704,107 | 0 | 0 | 0 | +| snowplow_web_users | 71,352 | 148,357 | 0 | 0 | 0 | + +Both `parity_self.py` invocations exit 0. + +## What was wrong in the earlier Apr-10 stash + +The first iteration of this verification found massive row-count +inflation in the patched variant (users +71%, sessions +13.8%, +page_views +5.0%). Root cause: the stash had removed upstream's +event-level dedup in `snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql` +without a replacement, so duplicate event_ids in the source (134,365 +out of 6.16M rows, i.e. 2.18% — collector retries and tracker replays) +propagated into every downstream aggregate. + +## Fix + +Removed the patch to +`snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql`. +The upstream macro already performs event_id dedup via a `QUALIFY +row_number() over (partition by event_id order by collector_tstamp, +dvce_created_tstamp) = 1`; the stash removed that QUALIFY with a +comment arguing it was too memory-heavy. Restoring it gives correct +semantics on Snowflake. + +On Embucket/DataFusion the wide-projection QUALIFY in that macro may +still OOM — that is a separate, open issue. It should be addressed by +extracting event_id dedup into a narrow-projection scratch model, but +NOT via the naive wrapper-level INNER JOIN attempted in an earlier +iteration of this work. That approach fails because an INNER JOIN on +`event_id = event_id` filters but does not deduplicate: if `base_query` +contains two rows with the same event_id, both rows match the single +dedup row. A correct narrow-dedup on Embucket needs either (a) a +deterministic row_id in the join predicate, or (b) materialising the +dedup as the driving table and projecting `SELECT * FROM +dedup_winners_wide` — both of which are follow-up work. + +## Harness improvements over iteration 1 + +Iteration 1 showed `patched_inc` rowcounts equal to `patched_fr` — +the patched pipeline's second dbt invocation processed no new events. +Root cause: the Snowplow dbt package stores its state (last-processed +run window) in `sturukin_db.atomic_snowplow_manifest.*`, and +`dbt run --full-refresh` does NOT reset it (the manifest model has +`full_refresh=allow_refresh()` which defaults to false). The harness +now calls `scripts/snowflake_reset_manifest.py` between variants, +which `DROP`s three state tables so each variant starts with a clean +Snowplow state. Iteration 2's `inc` numbers are real. + +## Non-deterministic tie-break in `event_id` + +Earlier runs flagged 194 (fr) / 198 (inc) mismatched page_view_ids. +Investigation showed every such row differed ONLY in `event_id`, with +every aggregate / scroll / engagement / URL column matching. In every +case both chosen event_ids had identical `(derived_tstamp, +dvce_created_tstamp)` — the natural ORDER BY of the QUALIFY — so +`row_number() = 1` was non-deterministic. + +Both upstream and patched pipelines are non-deterministic in this +respect, though each is internally consistent at a given execution. +The pipelines' semantic output (the aggregates) is identical. The +parity check was tightened to exclude `event_id` from the page_view +hash for this reason — same reason `model_tstamp` is excluded. + +The equivalence claim the parity harness now proves: **for every +page_view_id, session, and user the upstream and patched pipelines +compute the same aggregate values.** + +## What the patch set now contains ``` -# upstream (baseline) -qualify row_number() over (partition by a.event_id - order by a.{{ session_timestamp }}, - a.dvce_created_tstamp) = 1 - -# patched -(removed; comment: "Dedup is handled by downstream models which - each dedup by their own key (page_view_id, domain_sessionid, - domain_userid) on the much smaller batch-sized - base_events_this_run table.") +patches/ + snowplow_web/models/sessions/scratch/snowflake/ + snowplow_web_first_event_ids_this_run.sql (new, narrow-QUALIFY on 4 cols) + snowplow_web_last_event_ids_this_run.sql (new) + snowplow_web_session_firsts_this_run.sql (new, wide projection, inner-join fe) + snowplow_web_session_lasts_this_run.sql (new, wide projection, inner-join le) + snowplow_web_session_base_aggs_this_run.sql (new, isolated heavy GROUP BY) + snowplow_web_session_pv_set_this_run.sql (new, spillable replacement for + count(distinct page_view_id)) + snowplow_web_session_pv_counts_this_run.sql (new) + snowplow_web_session_pp_bucket_set_this_run.sql (new, spillable replacement for + count(distinct pv_id || bucket)) + snowplow_web_session_pp_counts_this_run.sql (new) + snowplow_web_session_aggs_this_run.sql (rewritten, 3-way join of + pre-materialized aggregates) + snowplow_web_sessions_this_run.sql (rewritten, 3-way left join only) + snowplow_web/models/page_views/scratch/snowflake/ + snowplow_web_pv_dedup_this_run.sql (new, narrow-QUALIFY on 4 cols) + snowplow_web_page_views_this_run.sql (rewritten, inner-join d on event_id) + snowplow_web/models/user_mapping/ + snowplow_web_user_mapping.sql (rewritten, UNBOUNDED window → + QUALIFY row_number = 1 DESC) ``` -The source data has duplicate event_ids. On our 6.16M-row batch: +14 files — 10 new, 4 rewritten. The earlier `snowplow_utils` macro +patch was removed (upstream event_id QUALIFY is kept). -``` -events_0416 total rows : 6,162,099 -events_0416 distinct event_ids: 6,027,734 -duplicate event_ids : 134,365 (2.18% of input) -``` - -These duplicates are Snowplow collector retries or client replays — -two rows with the same `event_id` but potentially different -`collector_tstamp`, `derived_tstamp`, or enrichment columns. - -The patch's rationale ("downstream models dedup by their own key") is -only partially true. It holds for `snowplow_web_pv_dedup_this_run` -(deduplicates on `page_view_id`) and for the session `_firsts`/`_lasts` -narrow QUALIFY variants (deduplicate on `domain_sessionid`). It does -NOT hold for everything downstream of the base model — in particular: - -- `snowplow_web_session_aggs_this_run` does `COUNT(*)` and - `COUNT(DISTINCT page_view_id || bucket)` grouped by - `domain_sessionid`. Duplicate event rows inflate `total_events` - and, crucially, add spurious `(page_view, heartbeat_bucket)` - combinations that inflate `engaged_time_in_s`. -- `snowplow_web_users_this_run` is driven from - `snowplow_web_users_sessions_this_run`, which aggregates over - `snowplow_web_sessions_this_run`. Each duplicate session row - propagates into extra users rows (users table has 1.71 rows per - distinct domain_userid in the patched output). -- `snowplow_web_users_lasts` uses `qualify row_number() partition by - domain_userid` but with ties on `collector_tstamp` caused by - duplicate events, the tie-break picks one row per (user, tstamp), - not per user — leaving ~25k extra rows. - -## Not a root cause - -The other three rewrites (page_views narrow-QUALIFY, session -`_firsts`/`_lasts` narrow-QUALIFY, user_mapping `last_value` → -`row_number`) are semantically equivalent to upstream. The whole -row-count divergence traces back to the single decision to drop -event_id dedup in `base_create_snowplow_events_this_run.sql`. - -## What the patch should have done - -Preserve event_id dedup, but do it in a memory-safe way. The same -"extract a narrow projection, inner-join back" pattern the patch -already uses for page_view dedup and session firsts/lasts applies -cleanly here: - -```sql --- snowplow_web_base_event_ids_this_run (new, narrow, materialized=table): -select a.event_id -from {{ source('atomic', 'events') }} a -inner join {{ ref('snowplow_web_base_sessions_this_run') }} b - on a.domain_sessionid = b.session_identifier -where a.{{ session_timestamp }} <= b.end_tstamp - ...other upstream filters... -qualify row_number() over ( - partition by a.event_id - order by a.{{ session_timestamp }}, a.dvce_created_tstamp -) = 1 -``` +## Verification how-to -Then the rewritten `base_create_snowplow_events_this_run.sql` adds an -`INNER JOIN snowplow_web_base_event_ids_this_run USING (event_id)`. -The QUALIFY runs on 4 narrow columns (event_id, session_timestamp, -dvce_created_tstamp, domain_sessionid) — same idea as -`snowplow_web_pv_dedup_this_run`. +All in `scripts/`: -This is a two-line change once the scratch model is added, and -restores row-count parity without bringing back the OOM. - -## Incremental run caveat - -The full-refresh numbers above are directly comparable. The -incremental numbers collected in this experiment are NOT, because -the Snowplow dbt package stores its own state in -`sturukin_db.atomic_snowplow_manifest.snowplow_web_incremental_manifest` -(a cross-schema singleton), and `dbt run --full-refresh` does not -reset it for every model — in practice the manifest carries state -across pipeline variants. The patched full-refresh run therefore -inherited the baseline's "last-processed timestamp" and processed -almost nothing on the second (incremental) dbt invocation: - -| table | baseline_inc | patched_inc | -|--------------------------|-------------:|------------:| -| snowplow_web_page_views | 1,731,722 | 814,570 | -| snowplow_web_sessions | 704,107 | 360,999 | -| snowplow_web_users | 148,357 | 122,041 | +``` +scripts/apply_oom_patch.sh # overlays patches/ into dbt_packages/ +scripts/snowflake_reset_manifest.py # DROPs Snowplow state tables +scripts/snowflake_clone_schema.py # zero-copy schema snapshot +scripts/parity_self.py # Snowflake-to-Snowflake row-hash diff +scripts/verify_oom_patch.sh # end-to-end orchestrator +``` -`patched_inc` equals `patched_fr` exactly — the patched incremental -dbt run did not process batch 2. This is an artifact of the -experimental harness, not a property of the patch. For a clean -incremental comparison the manifest table would need to be truncated -between variants. +`verify_oom_patch.sh` does it all in one invocation: reset to baseline +dbt packages, run baseline fr + inc with manifest reset, snapshot; +apply patch, run patched fr + inc with manifest reset, snapshot; +parity diff both pairs; exit 0 on full agreement. -## Evidence artifacts +## Live evidence -All four snapshots remain in Snowflake as zero-copy clones, available -for any further investigation: +All four output-schema snapshots are in Snowflake as zero-copy clones: ``` -sturukin_db.atomic_derived_baseline_fr -- upstream, full-refresh on batch 1 -sturukin_db.atomic_derived_baseline_inc -- upstream, incremental after batch 2 -sturukin_db.atomic_derived_patched_fr -- patched, full-refresh on batch 1 -sturukin_db.atomic_derived_patched_inc -- patched, incremental after batch 2 - (see caveat above — near-identical to _fr) +sturukin_db.atomic_derived_baseline_fr +sturukin_db.atomic_derived_baseline_inc +sturukin_db.atomic_derived_patched_fr +sturukin_db.atomic_derived_patched_inc ``` -The patch mechanism itself works cleanly: - -- `patches/` (git-tracked) holds the 15 patched files. -- `scripts/apply_oom_patch.sh` overlays them via rsync onto a fresh - `dbt_packages/` and is idempotent. -- `scripts/parity_self.py` performs Snowflake-to-Snowflake diffs with - the same rowcount + row-hash methodology as the existing - Embucket-vs-Snowflake `parity.py`, with `model_tstamp` excluded. -- `scripts/verify_oom_patch.sh` orchestrates the four-run flow - end-to-end. - -## Recommendation - -Do NOT merge the Apr 10 stash as-is. Before landing the decomposed -models, add a narrow-projection `snowplow_web_base_event_ids_this_run` -scratch model to restore event_id dedup, and re-run this same parity -harness. Expected outcome: all three golden tables match byte-for-byte -with model_tstamp excluded. +Drop at your convenience — clones are free to keep. From 36cf1dc91e82658bf24fafe2ee04af0aa4b3a383 Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Fri, 24 Apr 2026 13:49:40 -0500 Subject: [PATCH 29/30] test: iteration-2 patch OOMs on Embucket at base_events; log finding + harness Ran the same verification flow on Embucket Lambda. Both the upstream baseline and the iteration-2 patched build fail at snowplow_web_base_events_this_run with DataFusion RepartitionExec resource exhaustion: the Snowflake-equivalence fix kept upstream's wide-column QUALIFY in the base-events macro, which is exactly the OOM site. Adds Embucket-side harness (manifest reset, CTAS snapshot, row-hash parity) so the verification is ready to re-run once the base-events dedup is decomposed into the three-model narrow-scratch pattern sketched in the results doc. Co-Authored-By: Claude Opus 4.7 (1M context) --- scripts/embucket_reset_manifest.py | 43 ++++ scripts/embucket_snapshot_derived.py | 58 +++++ scripts/parity_self_embucket.py | 198 ++++++++++++++++++ .../2026-04-24-embucket-patch-run-results.md | 102 +++++++++ 4 files changed, 401 insertions(+) create mode 100755 scripts/embucket_reset_manifest.py create mode 100755 scripts/embucket_snapshot_derived.py create mode 100755 scripts/parity_self_embucket.py create mode 100644 specs/2026-04-24-embucket-patch-run-results.md diff --git a/scripts/embucket_reset_manifest.py b/scripts/embucket_reset_manifest.py new file mode 100755 index 0000000..f4b49d0 --- /dev/null +++ b/scripts/embucket_reset_manifest.py @@ -0,0 +1,43 @@ +#!/usr/bin/env python3 +"""Reset Snowplow state tables on Embucket before a parity run. + +Counterpart to snowflake_reset_manifest.py. Drops the three tables +dbt-snowplow-web uses to track processed windows so the next dbt run +starts clean. + +Usage: + uv run python scripts/embucket_reset_manifest.py +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import embucket_client + +LAMBDA_ARN = ( + "arn:aws:lambda:us-east-2:767397688925:function:" + "embucket-demo-embucket-demo-ramp-1775514830" +) + +STATE_TABLES = [ + "snowplow_web_incremental_manifest", + "snowplow_web_base_sessions_lifecycle_manifest", + "snowplow_web_base_quarantined_sessions", +] + + +def main() -> None: + client = embucket_client.lambda_client(LAMBDA_ARN) + token = embucket_client.login(client, LAMBDA_ARN) + for table in STATE_TABLES: + sql = f"DROP TABLE IF EXISTS demo.atomic_snowplow_manifest.{table}" + print(sql) + embucket_client.run_sql(client, LAMBDA_ARN, token, sql) + print("manifest state cleared") + + +if __name__ == "__main__": + main() diff --git a/scripts/embucket_snapshot_derived.py b/scripts/embucket_snapshot_derived.py new file mode 100755 index 0000000..5108859 --- /dev/null +++ b/scripts/embucket_snapshot_derived.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +"""Snapshot the three Snowplow golden tables from demo.atomic_derived into +a dedicated destination schema on Embucket via CTAS. + +Embucket (S3 Tables / Iceberg) has no CLONE; CTAS is the portable path. +Cost is another write of each table - the three golden tables total a +few million rows, so this is acceptable for a post-run snapshot. + +Usage: + uv run python scripts/embucket_snapshot_derived.py --dst atomic_derived_baseline_fr +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import embucket_client + +LAMBDA_ARN = ( + "arn:aws:lambda:us-east-2:767397688925:function:" + "embucket-demo-embucket-demo-ramp-1775514830" +) + +TABLES = [ + "snowplow_web_page_views", + "snowplow_web_sessions", + "snowplow_web_users", +] + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--src", default="atomic_derived") + parser.add_argument("--dst", required=True) + args = parser.parse_args() + + client = embucket_client.lambda_client(LAMBDA_ARN) + token = embucket_client.login(client, LAMBDA_ARN) + + embucket_client.run_sql(client, LAMBDA_ARN, token, + f"CREATE SCHEMA IF NOT EXISTS demo.{args.dst}") + + for tbl in TABLES: + src_fqn = f"demo.{args.src}.{tbl}" + dst_fqn = f"demo.{args.dst}.{tbl}" + print(f"snapshotting {src_fqn} -> {dst_fqn}") + embucket_client.run_sql(client, LAMBDA_ARN, token, + f"DROP TABLE IF EXISTS {dst_fqn}") + embucket_client.run_sql(client, LAMBDA_ARN, token, + f"CREATE TABLE {dst_fqn} AS SELECT * FROM {src_fqn}") + print("done") + + +if __name__ == "__main__": + main() diff --git a/scripts/parity_self_embucket.py b/scripts/parity_self_embucket.py new file mode 100755 index 0000000..801c0ca --- /dev/null +++ b/scripts/parity_self_embucket.py @@ -0,0 +1,198 @@ +#!/usr/bin/env python3 +"""Embucket-only two-schema parity check. + +Counterpart to parity_self.py but runs against Embucket via the +Lambda client. Same methodology: rowcount + MD5 row-hash on the three +Snowplow golden tables, keyed on natural keys, with model_tstamp and +event_id excluded from the hash. + +Usage: + uv run python scripts/parity_self_embucket.py \\ + --left atomic_derived_baseline_fr \\ + --right atomic_derived_patched_fr +""" + +from __future__ import annotations + +import argparse +import hashlib +import sys +from dataclasses import dataclass, field +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import embucket_client + +LAMBDA_ARN = ( + "arn:aws:lambda:us-east-2:767397688925:function:" + "embucket-demo-embucket-demo-ramp-1775514830" +) + + +@dataclass +class TableSpec: + name: str + natural_key: str + columns: list[str] + + +# Columns mirror parity_self.py (Snowflake variant): model_tstamp and +# event_id are excluded (non-deterministic between pipeline variants). +TABLES = [ + TableSpec( + name="snowplow_web_page_views", + natural_key="page_view_id", + columns=[ + "page_view_id", "app_id", "user_id", "domain_userid", + "stitched_user_id", "network_userid", "domain_sessionid", + "domain_sessionidx", "page_view_in_session_index", + "page_views_in_session", "dvce_created_tstamp", "collector_tstamp", + "derived_tstamp", "start_tstamp", "end_tstamp", + "engaged_time_in_s", "absolute_time_in_s", + "horizontal_pixels_scrolled", "vertical_pixels_scrolled", + "horizontal_percentage_scrolled", "vertical_percentage_scrolled", + "doc_width", "doc_height", "page_title", "page_url", + "page_urlscheme", "page_urlhost", "page_urlpath", "page_urlquery", + "page_urlfragment", "mkt_medium", "mkt_source", "mkt_term", + "mkt_content", "mkt_campaign", "mkt_clickid", "mkt_network", + "page_referrer", "refr_urlscheme", "refr_urlhost", "refr_urlpath", + "refr_urlquery", "refr_urlfragment", "refr_medium", "refr_source", + "refr_term", "geo_country", "geo_region", "geo_region_name", + "geo_city", "geo_zipcode", "geo_latitude", "geo_longitude", + "geo_timezone", "user_ipaddress", "useragent", "br_lang", + "br_viewwidth", "br_viewheight", "br_colordepth", "br_renderengine", + "os_timezone", + ], + ), + TableSpec( + name="snowplow_web_sessions", + natural_key="domain_sessionid", + columns=[ + "app_id", "domain_sessionid", "domain_sessionidx", "start_tstamp", + "end_tstamp", "user_id", "domain_userid", + "stitched_user_id", "network_userid", "page_views", + "engaged_time_in_s", "total_events", "is_engaged", + "absolute_time_in_s", "first_page_title", "first_page_url", + "last_page_title", "last_page_url", "referrer", + "geo_country", "geo_region", "geo_city", "geo_timezone", + "user_ipaddress", "useragent", "br_lang", + ], + ), + TableSpec( + name="snowplow_web_users", + natural_key="domain_userid", + columns=[ + "user_id", "domain_userid", "network_userid", "start_tstamp", + "end_tstamp", "page_views", "sessions", + "engaged_time_in_s", "first_page_title", "first_page_url", + "first_geo_country", "first_geo_city", + "last_page_title", "last_page_url", "last_geo_country", + "last_geo_city", "referrer", + ], + ), +] + + +@dataclass +class DiffResult: + matched: int + mismatched: list[str] = field(default_factory=list) + only_left: list[str] = field(default_factory=list) + only_right: list[str] = field(default_factory=list) + + +def row_hash(values: list) -> str: + parts = [] + for v in values: + if v is None: + parts.append("\x00NULL\x00") + else: + parts.append(str(v)) + return hashlib.md5("\x01".join(parts).encode("utf-8")).hexdigest() + + +def fetch_rowcount(client, token, fqn: str) -> int: + body = embucket_client.run_sql(client, LAMBDA_ARN, token, + f"SELECT COUNT(*) FROM {fqn}") + return int(body["data"]["rowset"][0][0]) + + +def fetch_hashes(client, token, fqn: str, spec: TableSpec) -> dict: + cols = ", ".join(spec.columns) + body = embucket_client.run_sql(client, LAMBDA_ARN, token, + f"SELECT {spec.natural_key}, {cols} FROM {fqn}") + return {row[0]: row_hash(list(row[1:])) for row in body["data"]["rowset"]} + + +def diff_sides(left: dict, right: dict) -> DiffResult: + matched = 0 + mismatched: list[str] = [] + only_left: list[str] = [] + only_right: list[str] = [] + for key, lhash in left.items(): + if key not in right: + only_left.append(key) + elif right[key] != lhash: + mismatched.append(key) + else: + matched += 1 + for key in right: + if key not in left: + only_right.append(key) + return DiffResult(matched=matched, mismatched=mismatched, + only_left=only_left, only_right=only_right) + + +def print_diff(spec: TableSpec, diff: DiffResult) -> None: + print(f" matched: {diff.matched}") + print(f" mismatched: {len(diff.mismatched)}") + print(f" only_left: {len(diff.only_left)}") + print(f" only_right: {len(diff.only_right)}") + for bucket_name, keys in [ + ("mismatched", diff.mismatched), + ("only_left", diff.only_left), + ("only_right", diff.only_right), + ]: + if keys: + sample = keys[:10] + print(f" first {len(sample)} {bucket_name} {spec.natural_key}:") + for k in sample: + print(f" {k}") + + +def run(left_schema: str, right_schema: str) -> int: + any_fail = False + client = embucket_client.lambda_client(LAMBDA_ARN) + token = embucket_client.login(client, LAMBDA_ARN) + for spec in TABLES: + left_fqn = f"demo.{left_schema}.{spec.name}" + right_fqn = f"demo.{right_schema}.{spec.name}" + lc = fetch_rowcount(client, token, left_fqn) + rc = fetch_rowcount(client, token, right_fqn) + print(f"\n{spec.name}: left={lc} right={rc}") + if lc != rc: + print(" ROWCOUNT DIFFERS -- skipping hash diff") + any_fail = True + continue + if lc == 0: + print(" both sides empty") + continue + lh = fetch_hashes(client, token, left_fqn, spec) + rh = fetch_hashes(client, token, right_fqn, spec) + diff = diff_sides(lh, rh) + print_diff(spec, diff) + if diff.mismatched or diff.only_left or diff.only_right: + any_fail = True + return 1 if any_fail else 0 + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--left", required=True) + parser.add_argument("--right", required=True) + args = parser.parse_args() + sys.exit(run(args.left, args.right)) + + +if __name__ == "__main__": + main() diff --git a/specs/2026-04-24-embucket-patch-run-results.md b/specs/2026-04-24-embucket-patch-run-results.md new file mode 100644 index 0000000..f235ecc --- /dev/null +++ b/specs/2026-04-24-embucket-patch-run-results.md @@ -0,0 +1,102 @@ +# OOM-mitigation patch on Embucket: neither upstream nor current patch runs + +## Verdict + +Both variants fail on Embucket at the same model, +`snowplow_web_base_events_this_run`, with the same DataFusion resource +exhaustion. The Snowflake-verified patch set (iteration 2) is +**semantically correct but not Embucket-viable** — making it +Embucket-viable requires additional work on the event_id dedup step +that iteration 2 deliberately avoided. + +| variant | result | +|---------------------------|------------------------------------------| +| upstream (no patch) | FAIL — OOM in `snowplow_web_base_events_this_run` | +| iteration-2 patched | FAIL — OOM in the SAME model, same cause | + +Error on both (abbreviated): + +``` +Resources exhausted: Failed to allocate additional 5.0 MB for RepartitionExec[4] +with 13.5 MB already allocated for this reservation +- 1348.8 KB remain available for the total pool +``` + +Source: `demo.atomic.events_0416` with 2,834,944 rows (batch 1 only). +Embucket Lambda config: `MEM_POOL_SIZE_MB=2048`, greedy pool. + +## Why both failed + +The upstream macro +`snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql` +ends with: + +``` +qualify row_number() over (partition by a.event_id + order by a.collector_tstamp, + a.dvce_created_tstamp) = 1 +``` + +`a.*` expands to the full 137+ column row inside the QUALIFY scope, so +the row_number() window buffers 137 columns per partition. On a 2.8M +input it exceeds DataFusion's 2 GB memory pool via `RepartitionExec` +during the qualify's shuffle. + +Iteration 1 of the Apr-10 stash removed this QUALIFY entirely, which +avoided the OOM but broke semantics (the 2.18% duplicate event_ids in +the source started double-counting downstream, inflating users by ++71%). Iteration 2 reverted that macro patch — restoring upstream +semantics at the cost of restoring the Embucket OOM. + +The iteration-2 patch set relieves memory pressure in the +sessions/page_views/user_mapping layers (decomposed into +narrow-projection scratch models + inner-joins), but it leaves the +base-events macro untouched. The OOM site is therefore reached before +any of those improvements matter. + +## What's needed to make the patch Embucket-viable + +The narrow-scratch pattern the stash already uses for page-view and +session dedup needs to be extended to event_id dedup. The shape that +WOULD work (a three-model decomposition): + +1. `snowplow_web_base_events_raw_this_run` - wide, materialized=table, + emitted by the macro with the QUALIFY removed. May contain + duplicate event_ids. +2. `snowplow_web_base_events_winners_this_run` - narrow, + materialized=table. `SELECT event_id, collector_tstamp, + dvce_created_tstamp FROM raw QUALIFY row_number() OVER (...) = 1`. + Spillable because projection is 3 columns. +3. `snowplow_web_base_events_this_run` - wide, materialized=table. + Inner-joins `_raw` to `_winners`. **Caveat**: a naive inner join on + `event_id` alone is NOT a dedup - if two raw rows share an event_id + they both still match. A correct join needs a row-uniquely- + determining key. Options: + + - **Hash-based**: add `HASH(event_id, collector_tstamp, + dvce_created_tstamp, load_tstamp, ...)` to both sides; join on + `(event_id, row_hash)`. Cheap, narrow, deterministic. + - **Aggregated winner**: in `_winners` pick one full set of + metadata per event_id via `MIN(hash) GROUP BY event_id`; join on + `(event_id, hash)`. + + Materialisation between each step gives DataFusion a fresh memory + pool and keeps the QUALIFY on a narrow projection. + +An earlier attempt in this branch tried a simpler wrapper-level +`INNER JOIN event_id_dedup ON event_id = event_id`; that was wrong for +the reason above (it filters but doesn't dedup) and was reverted. The +correct solution is the three-model decomposition described here. + +## What to report back + +- The verification infrastructure for Embucket now exists: + `scripts/embucket_reset_manifest.py`, `scripts/embucket_snapshot_derived.py`, + `scripts/parity_self_embucket.py`. These are useful as soon as the + base-events dedup is re-engineered. +- Iteration 2's patches are correct and merged for the Snowflake + story. Landing Embucket requires one more iteration specifically on + the base-events model, following the design sketch above. +- The existing `scripts/parity.py` is still the right tool for cross- + engine comparison (Embucket patched vs Snowflake upstream) once the + Embucket side actually runs. From 80b9be5cfb77788e65ad66ca37280f063e3ae1ea Mon Sep 17 00:00:00 2001 From: Sergei Turukin Date: Fri, 24 Apr 2026 14:25:13 -0500 Subject: [PATCH 30/30] feat: three-model base-events decomposition - patch now runs on Embucket Adds a narrow-scratch dedup chain for snowplow_web_base_events_this_run: _raw_this_run (wide, dups allowed) -> _winners_this_run (narrow: event_id + md5 of order-by tuple) -> _this_run (INNER JOIN on event_id+winner_hash) Each hop materialises so DataFusion's memory pool resets, keeping the QUALIFY on a 3-column projection instead of 137. MD5 replaces Snowflake HASH() for portability (DataFusion does not implement HASH yet). Embucket: full-refresh + incremental both complete, 33/33 pass, no OOM. Snowflake self-parity (baseline vs patched): 0 diffs across all three golden tables in both fr and inc. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../base_create_snowplow_events_this_run.sql | 84 +++++++++ .../snowplow_web_base_events_raw_this_run.sql | 50 ++++++ .../snowplow_web_base_events_this_run.sql | 25 +++ ...wplow_web_base_events_winners_this_run.sql | 31 ++++ .../2026-04-24-embucket-patch-run-results.md | 170 +++++++++--------- 5 files changed, 274 insertions(+), 86 deletions(-) create mode 100644 patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql create mode 100644 patches/snowplow_web/models/base/scratch/snowflake/snowplow_web_base_events_raw_this_run.sql create mode 100644 patches/snowplow_web/models/base/scratch/snowflake/snowplow_web_base_events_this_run.sql create mode 100644 patches/snowplow_web/models/base/scratch/snowflake/snowplow_web_base_events_winners_this_run.sql diff --git a/patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql b/patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql new file mode 100644 index 0000000..6ce609a --- /dev/null +++ b/patches/snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql @@ -0,0 +1,84 @@ +{# +Copyright (c) 2021-present Snowplow Analytics Ltd. All rights reserved. +This program is licensed to you under the Snowplow Community License Version 1.0, +and you may not use this file except in compliance with the Snowplow Community License Version 1.0. +You may obtain a copy of the Snowplow Community License Version 1.0 at https://docs.snowplow.io/community-license-1.0 + +Patched: the final `qualify row_number() over (partition by event_id ...)` is +removed. The original QUALIFY operates on the 137-column join output and +OOMs DataFusion's RepartitionExec on Embucket. Event_id dedup is restored +via a narrow-scratch pipeline downstream: + + snowplow_web_base_events_raw_this_run (wide, materialised, may have event_id duplicates) + snowplow_web_base_events_winners_this_run (narrow: 2 columns, one row per event_id) + snowplow_web_base_events_this_run (raw INNER JOIN winners ON (event_id, winner_hash)) + +Each step materialises as a table, so DataFusion's memory pool resets +between them; the QUALIFY that picks the winner runs on a 3-column +projection instead of 137. +#} + + +{% macro base_create_snowplow_events_this_run(sessions_this_run_table='snowplow_base_sessions_this_run', session_identifiers=[{"schema" : "atomic", "field" : "domain_sessionid"}], session_sql=none, session_timestamp='load_tstamp', derived_tstamp_partitioned=true, days_late_allowed=3, max_session_days=3, app_ids=[], snowplow_events_database=none, snowplow_events_schema='atomic', snowplow_events_table='events', entities_or_sdes=none, custom_sql=none) %} + {{ return(adapter.dispatch('base_create_snowplow_events_this_run', 'snowplow_utils')(sessions_this_run_table, session_identifiers, session_sql, session_timestamp, derived_tstamp_partitioned, days_late_allowed, max_session_days, app_ids, snowplow_events_database, snowplow_events_schema, snowplow_events_table, entities_or_sdes, custom_sql)) }} +{% endmacro %} + +{% macro default__base_create_snowplow_events_this_run(sessions_this_run_table, session_identifiers, session_sql, session_timestamp, derived_tstamp_partitioned, days_late_allowed, max_session_days, app_ids, snowplow_events_database, snowplow_events_schema, snowplow_events_table, entities_or_sdes, custom_sql) %} + {%- set lower_limit, upper_limit = snowplow_utils.return_limits_from_model(ref(sessions_this_run_table), + 'start_tstamp', + 'end_tstamp') %} + {% set sessions_this_run = ref(sessions_this_run_table) %} + {% set snowplow_events = api.Relation.create(database=snowplow_events_database, schema=snowplow_events_schema, identifier=snowplow_events_table) %} + + {% set events_this_run_query %} + with identified_events AS ( + select + {% if session_sql %} + {{ session_sql }} as session_identifier, + {% else -%} + COALESCE( + {% for identifier in session_identifiers %} + {%- if identifier['schema']|lower != 'atomic' -%} + {{ snowplow_utils.get_field(identifier['schema'], identifier['field'], 'e', dbt.type_string(), 0, snowplow_events) }} + {%- else -%} + e.{{identifier['field']}} + {%- endif -%} + , + {%- endfor -%} + NULL + ) as session_identifier, + {%- endif %} + e.* + {% if custom_sql %} + , {{ custom_sql }} + {% endif %} + + from {{ snowplow_events }} e + + ) + + select + a.*, + b.user_identifier -- take user_identifier from manifest. This ensures only 1 domain_userid per session. + + from identified_events as a + inner join {{ sessions_this_run }} as b + on a.session_identifier = b.session_identifier + + where a.{{ session_timestamp }} <= {{ snowplow_utils.timestamp_add('day', max_session_days, 'b.start_tstamp') }} + and a.dvce_sent_tstamp <= {{ snowplow_utils.timestamp_add('day', days_late_allowed, 'a.dvce_created_tstamp') }} + and a.{{ session_timestamp }} >= {{ lower_limit }} + and a.{{ session_timestamp }} <= {{ upper_limit }} + and a.{{ session_timestamp }} >= b.start_tstamp -- deal with late loading events + + {% if derived_tstamp_partitioned and target.type == 'bigquery' | as_bool() %} + and a.derived_tstamp >= {{ snowplow_utils.timestamp_add('hour', -1, lower_limit) }} + and a.derived_tstamp <= {{ upper_limit }} + {% endif %} + + and {{ snowplow_utils.app_id_filter(app_ids) }} + {% endset %} + + {{ return(events_this_run_query) }} + +{% endmacro %} diff --git a/patches/snowplow_web/models/base/scratch/snowflake/snowplow_web_base_events_raw_this_run.sql b/patches/snowplow_web/models/base/scratch/snowflake/snowplow_web_base_events_raw_this_run.sql new file mode 100644 index 0000000..8623e62 --- /dev/null +++ b/patches/snowplow_web/models/base/scratch/snowflake/snowplow_web_base_events_raw_this_run.sql @@ -0,0 +1,50 @@ +{# +Copyright (c) 2020-present Snowplow Analytics Ltd. All rights reserved. +This program is licensed to you under the Snowplow Community License Version 1.0, +and you may not use this file except in compliance with the Snowplow Community License Version 1.0. +You may obtain a copy of the Snowplow Community License Version 1.0 at https://docs.snowplow.io/community-license-1.0 + +Patched new model: upstream's wrapper content (wide projection + derived +page_view_id / domain_userid columns) but WITHOUT the event_id QUALIFY. +May contain duplicate event_ids. Deduped downstream via: + + snowplow_web_base_events_winners_this_run (narrow: event_id + hash) + snowplow_web_base_events_this_run (raw INNER JOIN winners) + +Each hop is materialised so DataFusion's memory pool resets between them. +#} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +{% set base_events_query = snowplow_utils.base_create_snowplow_events_this_run( + sessions_this_run_table='snowplow_web_base_sessions_this_run', + session_identifiers=var('snowplow__session_identifiers', [{"schema" : "atomic", "field" : "domain_sessionid"}]), + session_sql=var('snowplow__session_sql', none), + session_timestamp=var('snowplow__session_timestamp', 'collector_tstamp'), + derived_tstamp_partitioned=var('snowplow__derived_tstamp_partitioned', true), + days_late_allowed=var('snowplow__days_late_allowed', 3), + max_session_days=var('snowplow__max_session_days', 3), + app_ids=var('snowplow__app_id', []), + snowplow_events_database=var('snowplow__database', target.database) if target.type not in ['databricks', 'spark'] else var('snowplow__databricks_catalog', 'hive_metastore') if target.type in ['databricks'] else var('snowplow__atomic_schema', 'atomic'), + snowplow_events_schema=var('snowplow__atomic_schema', 'atomic'), + snowplow_events_table=var('snowplow__events_table', 'events')) %} + +with base_query as ( + {{ base_events_query }} +) + +select + a.contexts_com_snowplowanalytics_snowplow_web_page_1[0]:id::varchar as page_view_id, + a.session_identifier as domain_sessionid, + a.domain_sessionid as original_domain_sessionid, + a.user_identifier as domain_userid, + a.domain_userid as original_domain_userid, + a.* exclude(contexts_com_snowplowanalytics_snowplow_web_page_1, domain_sessionid, domain_userid) + +from base_query a diff --git a/patches/snowplow_web/models/base/scratch/snowflake/snowplow_web_base_events_this_run.sql b/patches/snowplow_web/models/base/scratch/snowflake/snowplow_web_base_events_this_run.sql new file mode 100644 index 0000000..0626228 --- /dev/null +++ b/patches/snowplow_web/models/base/scratch/snowflake/snowplow_web_base_events_this_run.sql @@ -0,0 +1,25 @@ +{# +Copyright (c) 2020-present Snowplow Analytics Ltd. All rights reserved. +This program is licensed to you under the Snowplow Community License Version 1.0, +and you may not use this file except in compliance with the Snowplow Community License Version 1.0. +You may obtain a copy of the Snowplow Community License Version 1.0 at https://docs.snowplow.io/community-license-1.0 + +Patched: replaces the upstream monolithic base_events_this_run with a +3-model narrow-scratch dedup chain. This model is now just the final +INNER JOIN of the wide raw table against the winners table, producing +one wide row per event_id. +#} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +select r.* +from {{ ref('snowplow_web_base_events_raw_this_run') }} r +inner join {{ ref('snowplow_web_base_events_winners_this_run') }} w + on r.event_id = w.event_id + and md5(concat_ws('|', r.event_id, cast(r.collector_tstamp as varchar), cast(r.dvce_created_tstamp as varchar), cast(r.load_tstamp as varchar))) = w.winner_hash diff --git a/patches/snowplow_web/models/base/scratch/snowflake/snowplow_web_base_events_winners_this_run.sql b/patches/snowplow_web/models/base/scratch/snowflake/snowplow_web_base_events_winners_this_run.sql new file mode 100644 index 0000000..6ea4bd2 --- /dev/null +++ b/patches/snowplow_web/models/base/scratch/snowflake/snowplow_web_base_events_winners_this_run.sql @@ -0,0 +1,31 @@ +{# +Narrow-projection event_id dedup. Two columns buffered per partition +(event_id + winner_hash); the QUALIFY is spillable. + +winner_hash is computed as HASH(event_id, collector_tstamp, +dvce_created_tstamp, load_tstamp) so the raw table's inner join back on +(event_id, winner_hash) picks exactly one source row per event_id, even +when two events share the same (collector_tstamp, dvce_created_tstamp). +#} + +{{ + config( + materialized='table', + tags=["this_run"], + sql_header=snowplow_utils.set_query_tag(var('snowplow__query_tag', 'snowplow_dbt')) + ) +}} + +select event_id, winner_hash +from ( + select + event_id, + md5(concat_ws('|', event_id, cast(collector_tstamp as varchar), cast(dvce_created_tstamp as varchar), cast(load_tstamp as varchar))) as winner_hash, + collector_tstamp, + dvce_created_tstamp + from {{ ref('snowplow_web_base_events_raw_this_run') }} +) +qualify row_number() over ( + partition by event_id + order by collector_tstamp, dvce_created_tstamp, winner_hash +) = 1 diff --git a/specs/2026-04-24-embucket-patch-run-results.md b/specs/2026-04-24-embucket-patch-run-results.md index f235ecc..d1b324f 100644 --- a/specs/2026-04-24-embucket-patch-run-results.md +++ b/specs/2026-04-24-embucket-patch-run-results.md @@ -1,102 +1,100 @@ -# OOM-mitigation patch on Embucket: neither upstream nor current patch runs +# OOM-mitigation patch: runs on both Embucket and Snowflake, Snowflake-equivalent ## Verdict -Both variants fail on Embucket at the same model, -`snowplow_web_base_events_this_run`, with the same DataFusion resource -exhaustion. The Snowflake-verified patch set (iteration 2) is -**semantically correct but not Embucket-viable** — making it -Embucket-viable requires additional work on the event_id dedup step -that iteration 2 deliberately avoided. +After a third iteration of the patch set the pipeline runs to +completion on Embucket Lambda (2 GB memory pool, DataFusion engine) +AND remains byte-equivalent to upstream on Snowflake. -| variant | result | -|---------------------------|------------------------------------------| -| upstream (no patch) | FAIL — OOM in `snowplow_web_base_events_this_run` | -| iteration-2 patched | FAIL — OOM in the SAME model, same cause | +### Embucket +Both full-refresh and incremental dbt runs complete successfully: -Error on both (abbreviated): +| phase | result | duration | +|-----------------------|----------------------------|----------| +| full-refresh, batch 1 | PASS 33/33, ERROR 0 | 3m 5s | +| incremental, batch 2 | PASS 33/33, ERROR 0 | 8m 52s | -``` -Resources exhausted: Failed to allocate additional 5.0 MB for RepartitionExec[4] -with 13.5 MB already allocated for this reservation -- 1348.8 KB remain available for the total pool -``` - -Source: `demo.atomic.events_0416` with 2,834,944 rows (batch 1 only). -Embucket Lambda config: `MEM_POOL_SIZE_MB=2048`, greedy pool. +Rowcounts (Embucket patched): -## Why both failed +| table | rows (fr) | rows (inc) | +|--------------------------|-----------:|-----------:| +| snowplow_web_page_views | 775,543 | 1,728,842 | +| snowplow_web_sessions | 317,162 | 704,037 | +| snowplow_web_users | 71,352 | 148,357 | -The upstream macro -`snowplow_utils/macros/base/base_create_snowplow_events_this_run.sql` -ends with: +### Snowflake self-parity (baseline vs patched, new patch set) -``` -qualify row_number() over (partition by a.event_id - order by a.collector_tstamp, - a.dvce_created_tstamp) = 1 -``` +Both `parity_self.py` invocations exit 0. -`a.*` expands to the full 137+ column row inside the QUALIFY scope, so -the row_number() window buffers 137 columns per partition. On a 2.8M -input it exceeds DataFusion's 2 GB memory pool via `RepartitionExec` -during the qualify's shuffle. +| table | rows (fr) | rows (inc) | fr diffs | inc diffs | +|--------------------------|-----------:|-----------:|---------:|----------:| +| snowplow_web_page_views | 775,543 | 1,731,722 | 0 | 0 | +| snowplow_web_sessions | 317,162 | 704,107 | 0 | 0 | +| snowplow_web_users | 71,352 | 148,357 | 0 | 0 | -Iteration 1 of the Apr-10 stash removed this QUALIFY entirely, which -avoided the OOM but broke semantics (the 2.18% duplicate event_ids in -the source started double-counting downstream, inflating users by -+71%). Iteration 2 reverted that macro patch — restoring upstream -semantics at the cost of restoring the Embucket OOM. +### Cross-engine rowcount comparison (Embucket patched vs Snowflake patched) -The iteration-2 patch set relieves memory pressure in the -sessions/page_views/user_mapping layers (decomposed into -narrow-projection scratch models + inner-joins), but it leaves the -base-events macro untouched. The OOM site is therefore reached before -any of those improvements matter. +Full-refresh: exact match on all three tables. +Incremental: small drift on page_views (-2,880, 0.17%) and sessions +(-70, 0.01%); users exact. This is a cross-engine incremental-window +timing artefact (the Snowplow package's `current_timestamp()`-bounded +upper_limit differed between the two invocations run minutes apart), +not a patch semantics issue — the Snowflake self-parity remains 0 diffs. -## What's needed to make the patch Embucket-viable +## What changed from iteration 2 -The narrow-scratch pattern the stash already uses for page-view and -session dedup needs to be extended to event_id dedup. The shape that -WOULD work (a three-model decomposition): +Iteration 2's patch set kept upstream's event_id QUALIFY in the +base-events macro (to preserve Snowflake semantics) and failed with +`RepartitionExec` resource exhaustion on Embucket. Iteration 3 replaces +that single wide-column QUALIFY with a three-model narrow-scratch chain: -1. `snowplow_web_base_events_raw_this_run` - wide, materialized=table, - emitted by the macro with the QUALIFY removed. May contain +1. `snowplow_web_base_events_raw_this_run` - materialized=table, wide + projection from the macro with the QUALIFY removed. May contain duplicate event_ids. -2. `snowplow_web_base_events_winners_this_run` - narrow, - materialized=table. `SELECT event_id, collector_tstamp, - dvce_created_tstamp FROM raw QUALIFY row_number() OVER (...) = 1`. - Spillable because projection is 3 columns. -3. `snowplow_web_base_events_this_run` - wide, materialized=table. - Inner-joins `_raw` to `_winners`. **Caveat**: a naive inner join on - `event_id` alone is NOT a dedup - if two raw rows share an event_id - they both still match. A correct join needs a row-uniquely- - determining key. Options: - - - **Hash-based**: add `HASH(event_id, collector_tstamp, - dvce_created_tstamp, load_tstamp, ...)` to both sides; join on - `(event_id, row_hash)`. Cheap, narrow, deterministic. - - **Aggregated winner**: in `_winners` pick one full set of - metadata per event_id via `MIN(hash) GROUP BY event_id`; join on - `(event_id, hash)`. - - Materialisation between each step gives DataFusion a fresh memory - pool and keeps the QUALIFY on a narrow projection. - -An earlier attempt in this branch tried a simpler wrapper-level -`INNER JOIN event_id_dedup ON event_id = event_id`; that was wrong for -the reason above (it filters but doesn't dedup) and was reverted. The -correct solution is the three-model decomposition described here. - -## What to report back - -- The verification infrastructure for Embucket now exists: - `scripts/embucket_reset_manifest.py`, `scripts/embucket_snapshot_derived.py`, - `scripts/parity_self_embucket.py`. These are useful as soon as the - base-events dedup is re-engineered. -- Iteration 2's patches are correct and merged for the Snowflake - story. Landing Embucket requires one more iteration specifically on - the base-events model, following the design sketch above. -- The existing `scripts/parity.py` is still the right tool for cross- - engine comparison (Embucket patched vs Snowflake upstream) once the - Embucket side actually runs. +2. `snowplow_web_base_events_winners_this_run` - materialized=table, + narrow (2 columns): `(event_id, winner_hash)`. One row per event_id. + `winner_hash = MD5(concat_ws('|', event_id, collector_tstamp, + dvce_created_tstamp, load_tstamp))`. +3. `snowplow_web_base_events_this_run` - materialized=table, the final + INNER JOIN of `_raw` against `_winners` on `(event_id, winner_hash)`, + producing exactly one wide row per event_id. + +Each hop materialises, so DataFusion's memory pool resets between +steps; the memory-bounded QUALIFY runs on a 3-column projection rather +than 137. + +Why MD5 and not `HASH()`: Embucket (DataFusion) rejects Snowflake's +`HASH()` function with "Function 'hash' is not implemented yet". +`MD5(CONCAT_WS('|', ...))` is portable across both engines. + +Why the inner join on `(event_id, winner_hash)` and not on `event_id` +alone: if `_raw` has two rows with the same event_id, a join on +`event_id` alone matches both (filter, not dedup). Matching on the +hash of the full ORDER BY tuple uniquely identifies the chosen +"winner" row. + +## Harness state (all committed on this branch) + +- `patches/` - 18 patch files (14 from iteration 2 + 4 base-events + files added in iteration 3). +- `scripts/apply_oom_patch.sh` - idempotent rsync, survives `dbt deps`. +- Snowflake verification: `scripts/verify_oom_patch.sh`, + `scripts/parity_self.py`, `scripts/snowflake_clone_schema.py`, + `scripts/snowflake_reset_manifest.py`. +- Embucket verification: `scripts/parity_self_embucket.py`, + `scripts/embucket_snapshot_derived.py` (CTAS; Iceberg has no CLONE), + `scripts/embucket_reset_manifest.py`. + +## Live evidence + +Snapshots live in both engines; zero-copy on Snowflake, CTAS copies +on Embucket: + +``` +sturukin_db.atomic_derived_{baseline,patched}_{fr,inc} -- Snowflake +demo.atomic_derived_patched_{fr,inc} -- Embucket +``` + +The baseline snapshots on Snowflake cover the upstream (un-patched) +side; Embucket does not have a baseline snapshot because upstream +does not run there.