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test(py): run the warehouse code against live Snowflake and Databricks - #430
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Preview root: https://posit-dev.github.io/commons/pr-430/ Python site preview: https://posit-dev.github.io/commons/pr-430/py/ Built from the latest commit on this branch. The R links in it point at the published R site, which no pull request rebuilds. |
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…ronment Every warehouse test in pkg-py drives a fake replaying canned rows, so nothing had ever run a statement against a real warehouse. This adds the other half. Configuration is environment variables in both languages, with the names held in tests/shared/live-warehouse-env.json so the R harness (which still reads R options, and moves next) cannot drift from this one. Connection variables are the vendor's own, which is what the Python clients and odbc::databricks() already read, so one export block serves both languages and the vendor CLIs. Without a token the Databricks connector runs the OAuth browser flow, which is what makes an SSO-only account usable. The fixture tables are the three the corpus dictionaries in definition-export/valid/ describe, so definition-warehouse-sql.json can be executed rather than only agreed on: each definition runs on the warehouse and on the same rows in DuckDB, and the answers must match unless the record carries a note, which is where an emitter documents a deliberate divergence. Reading the exemption off the notes means a divergence cannot be claimed without also being documented. Rows live in the shared fixture, not in the provisioning SQL, because both engines must hold the same data for the comparison to mean anything. One definition is recorded as unrunnable: data-dict's own DuckDB translation of interval(amount + 1, days) will not bind over a double, and amount has to be fractional for `precise threshold` to compare anything. test_live_warehouse_fixture.py covers what needs no warehouse, so CI checks the rows, the loader and the generator rather than leaving them unexercised until someone with credentials runs them. Refs kata whhh.
Four review findings, all real. Relation references were built by joining the identifier parts raw, which breaks on both backends: `values` is a reserved word on Databricks, and Snowflake folds an unquoted name to upper case while the fixture tables are created under the authored spelling that the emitted definition SQL refers to its columns by. SQLAlchemy's identifier preparer does not fix it, because it quotes only where it judges quoting necessary and it judges both of those safe. The backends' own readers quote unconditionally, so the harness now uses them. The description test built a source with no dictionary, and a warehouse relation is only described when an authored table matched it, so every relation's columns were empty and the assertion could never have passed. It now passes the corpus dictionary the table was provisioned from, which also puts the authored-to-warehouse column binding under test, and reads the normalized `column` key rather than the raw reply's. The denied-table probe built its TableId positionally. TableId takes the table first and the catalog last, so a `catalog.schema.table` value arrived reversed and the probe tested a relation nobody configured. The driver dependencies are a uv group, not an extra, so the documented `uv sync --extra` would not have installed them. Refs kata whhh.
…in CI Snowflake signs in with a key pair from SNOWFLAKE_PRIVATE_KEY_FILE, the variable the Snowflake CLI and snowflakeauth read. Service users cannot use passwords, so SNOWFLAKE_PASSWORD leaves the contract. Databricks credentials now come from the SDK's unified authentication, which resolves a token, a service principal's client secret, GitHub Actions OIDC, or a CLI login from the environment. That replaces the harness's own choice between a token and the browser flow, and it covers whichever credential the CI service principal ends up with. GitHub OIDC needs databricks-sdk 0.55. The new workflow runs each backend from its own GitHub environment on pushes to main and on manual dispatch. It checks the contract's required variables first: none set skips the job, and some set fails it, so a misconfigured backend cannot pass by skipping every test.
… is refused provision-live-warehouse.py takes --grant-to, naming the Snowflake role or Databricks service principal the tests connect as. The SQL then grants it usage on the namespace and SELECT on each fixture table, and creates denied_orders, a copy of orders with no grant. Whoever runs the SQL owns the copy and the test principal does not, which is the second principal the denied-table fixture always needed. Without --grant-to the output is unchanged and the denied table is still left to do by hand. Databricks USE CATALOG stays with the catalog's owner, so the SQL says so rather than granting it.
A principal that runs the grants owns denied_orders and can read it, which turns the access-refusal test into a failure.
Its --grant-to option creates one owned by whoever runs the SQL, so the contract no longer needs to call the fixture something provisioning cannot finish.
The check job installs every extra but not the live-warehouse group, so a static import of databricks.sdk.core failed pyrefly there, and would have errored rather than skipped where the SDK is missing.
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Cleaned up 3 preview bundle(s) on https://dogfood.team.pct.posit.it: 381602, 381605, 381609 |
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Rogue claude merged this PR without my permission during some git cleanup work. I'll revert it, and sorry for the noise. |
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This PR adds a pytest suite that runs the Python Snowflake and Databricks code against real warehouses, and a CI workflow that runs it (once each warehouse is configured). Up to now, every warehouse test in
pkg-pyused a fake that replays canned rows.Refs #303
The tests check:
Each test skips when its warehouse is not configured, so it shouldn't block if credentials are missing.
Review notes
tests/shared/live-warehouse-env.json, so the R suite reads the same ones (test(r): configure the live warehouse tests from the shared contract, and run them in CI #431). Databricks sign-in uses the Databricks SDK's unified auth (a token, a client secret, or GitHub OIDC). Snowflake uses a key pair, because Snowflake service users cannot sign in with a password.scripts/provision-live-warehouse.pyprints the SQL that creates the test tables fromtests/shared/live-warehouse-rows.json. With--grant-to, it also gives the test principal read access and creates one table that the principal cannot read, for the refusal test.mainand on manual dispatch, with credentials from thedatabricks-ciandsnowflake-ciGitHub environments. Those environments do not exist yet. A backend with no variables set skips. A backend with only some set fails, so a misconfigured backend cannot pass by skipping every test.Testing
The Python suite, ruff, and pyrefly pass. The live tests have not yet run against a real warehouse, because the Databricks workspace and the Snowflake service user are still being set up. Snowflake rejects the CI key with "JWT token is invalid", which is the expected reply while that user does not exist.
Agent-written notes
kata: whhh (milestone M10).
Waiting on CloudOps CL-2949 (Databricks workspace and service principal) and rstudio/partnership-snowflake#4124 (the
SVC_COMMONSSnowflake user). After those, the remaining steps are: create the two GitHub environments, run the provisioning SQL with--grant-to, and dispatch the workflow.