PromClick

A Digitalis.io distribution. PromClick was originally created by the PromClick Authors and is licensed under Apache 2.0. Digitalis.io vendors and maintains this distribution, preserving the original attribution. See Attribution and NOTICE.

A Prometheus-compatible HTTP API that translates PromQL to ClickHouse SQL in real time.

I was tired of Thanos. I was tired of Victoria Metrics. I was tired of Grafana Mimir and I'm tired of everything.

You know the drill. You want long-term Prometheus storage. So you deploy Thanos - suddenly you have 47 YAML files, a sidecar, a store gateway, a compactor, a query frontend, and a PhD in distributed systems. Or you go with Mimir - congrats, you now operate a distributed hash ring and pray to the Memberlist gods every Tuesday.

"Ok I found Clickhouse! But now I'll just use the ClickHouse datasource plugin for Grafana." Sure. Here's what happens when you try to express rate(http_requests_total{job="api"}[5m]) in raw ClickHouse SQL:

SELECT t, fingerprint,
    (argMax(value, unix_milli) - argMin(value, unix_milli))
    / (max(unix_milli) - min(unix_milli)) * 1000
FROM samples
WHERE metric_name = 'http_requests_total'
  AND JSONExtractString(labels, 'job') = 'api'
GROUP BY fingerprint, toStartOfInterval(...) AS t
-- doesn't handle counter resets. or staleness. or extrapolation.
-- good luck with histogram_quantile. I'll wait.

Now copy-paste that into 200 dashboards. Tweak each one. Debug why the numbers don't match Prometheus. Realize half your alerts are wrong because you forgot counter reset handling in dashboard #47. Spend three sprints on it. Question your career choices.

So I started simple - just a CLI that translates PromQL to SQL. You know, a weekend project. rate(x[5m]) in, SQL out, done. Then I thought "what if Grafana could talk to it directly?" So I added an HTTP layer. Then I needed staleness handling. Then counter resets. Then histogram_quantile. Then downsampling. Then a label cache. Then native TCP. Then...

That weekend project is now PromClick. A Prometheus-compatible HTTP API that translates PromQL to ClickHouse SQL in real-time. Drop it in front of Grafana, point your dashboards at it, and forget that Thanos ever existed.

Grafana  -->  PromClick (:9090)  -->  ClickHouse (Native TCP :9000)
                                 -->  ClickHouse (HTTP :8123 for DDL)

No sidecars. No hash rings. No store gateways. One binary. One ClickHouse table. Done.

What it does

PromClick is a PromQL-to-SQL transpiler + HTTP proxy that makes ClickHouse speak Prometheus.

Full Prometheus API compatibility - /api/v1/query, /api/v1/query_range, /api/v1/labels, /api/v1/series, /api/v1/metadata
70 PromQL functions - rate, irate, increase, histogram_quantile, predict_linear, all *_over_time, all aggregations, all math functions
Thanos-style downsampling - automatic 5m and 1h tiers with MV-based compaction
Prometheus remote_write ingestion - receives metrics directly from Prometheus
100% value accuracy on raw queries - verified per-series, per-timestamp against Prometheus on 10K series

Benchmark: PromClick vs Prometheus

Tested on 10,000 series (counter + gauge + histogram) over 30 days:

Query	PromClick	Prometheus	Speedup	Accuracy
`rate(counter[5m])` 1111 series	100ms	170ms	1.7x faster	100%
`sum by(region)(rate(counter[5m]))`	245ms	250ms	~parity	100%
`avg_over_time(gauge[5m])` 1111 series	72ms	115ms	1.6x faster	100%
`histogram_quantile(0.99, rate(bucket[5m]))`	290ms	111ms	2.6x slower	100%
`count by(region)(metric)`	1ms	69ms	62x faster	100%
`rate(counter[2h])` 30d, 1h step	2.3s	13.3s	5.5x faster	100%
`avg_over_time(gauge[2h])` 30d	0.9s	26.5s	30x faster	100%
`sum(avg by(region)(rate))` 30d	7.5s	41s	5.5x faster	100%

PromClick is faster than Prometheus for most query types. On long-range queries (7-30 days) with downsampling, it's 5-30x faster because ClickHouse reads pre-aggregated data instead of scanning billions of raw samples.

Full benchmark report (38 queries, all tiers):

UI

PromClick ships with a built-in web UI for ad-hoc PromQL queries - syntax highlighting, autocomplete, graph + table views.

TSDB Status page shows series count, sample count, top metrics, and label cardinality - all served from ClickHouse:

Three Binaries, One Image

PromClick ships as a single Docker image with three binaries inside. Each handles one concern:

docker pull ghcr.io/digitalis-io/promclick:0.1.0

docker run ghcr.io/digitalis-io/promclick promclick-proxy       --config proxy.yaml
docker run ghcr.io/digitalis-io/promclick promclick-writer      --config writer.yaml
docker run ghcr.io/digitalis-io/promclick promclick-downsampler  --config downsampler.yaml

promclick-proxy (query server)

The main binary. Serves the Prometheus HTTP API, translates PromQL to SQL, fetches from ClickHouse.

# proxy.yaml
listen_addr: ":9099"
query_timeout: "2m"

clickhouse:
  native_addr: "clickhouse:9000"     # ch-go Native TCP (fast path)
  http_addr: "http://clickhouse:8123" # HTTP for DDL/metadata
  database: "metrics"

labels:
  cache_enabled: true    # in-memory label cache (eliminates JOIN)
  cache_ttl: "60s"       # refresh interval
  cache_max_series: 50000

cache:                   # in-memory query-result cache (off by default)
  enabled: true          # cache /query and /query_range responses
  max_size: 1000         # max cached responses (LRU)
  ttl: "60s"             # entry lifetime
  max_freshness: "60s"   # skip caching windows whose end is within this of now

downsampling:            # read-only - proxy uses tiers for query routing
  enabled: true          # but does NOT create tables/MVs
  tiers:
    - name: "5m"
      table: "samples_5m"
      compact_after: "40h"   # data older than 40h → read from 5m tier
      min_step: "60s"        # use tier when query step >= 60s
    - name: "1h"
      table: "samples_1h"
      compact_after: "240h"
      min_step: "3600s"

What it does: receives PromQL from Grafana, transpiles to SQL, fetches from ClickHouse (raw or downsampled tier based on step), evaluates in Go, returns Prometheus-compatible JSON.

What it doesn't do: write data, create tables, manage MVs.

promclick-writer (remote_write receiver)

Receives Prometheus remote_write and batch-inserts into ClickHouse.

# writer.yaml
listen_addr: ":9091"

clickhouse:
  native_addr: "clickhouse:9000"
  database: "metrics"

write:
  batch_size: 10000       # flush after N samples
  queue_size: 100000      # in-memory queue depth
  flush_interval: "5s"    # flush after 5s even if batch not full

What it does: POST /api/v1/write → snappy decompress → protobuf decode → batch INSERT into samples + time_series tables via Native TCP.

What it doesn't do: query data, create tier tables.

promclick-downsampler (DDL + backfill)

One-shot binary that creates downsampling tier tables, Materialized Views, TTLs, and backfills historical data.

# downsampler.yaml
clickhouse:
  native_addr: "clickhouse:9000"
  http_addr: "http://clickhouse:8123"
  database: "metrics"

downsampling:
  enabled: true
  raw_retention: "7d"           # TTL: delete raw samples after 7 days
  tiers:
    - name: "5m"
      resolution: "5m"          # bucket size
      table: "samples_5m"
      compact_after: "40h"
      retention: "90d"           # keep 5m data for 90 days
    - name: "1h"
      resolution: "1h"
      table: "samples_1h"
      compact_after: "240h"
      retention: "730d"          # keep 1h data for 2 years

daemon: false    # true = run in loop, false = one-shot and exit
interval: "1h"   # re-check interval in daemon mode

What it does:

CREATE TABLE samples_5m / samples_1h (AggregatingMergeTree)
ALTER TABLE ... MODIFY TTL on raw + tier tables
CREATE MATERIALIZED VIEW ... REFRESH EVERY 5m/1h - ClickHouse auto-aggregates
Backfill historical data (chunked, memory-limited)
Checksum-based: only recreates MVs when config changes

Run as: Kubernetes init container, CronJob, or docker run --rm.

Docker Compose (all-in-one)

git clone https://github.com/digitalis-io/PromClick
cd promclick
docker compose up -d

# Wait ~60s, then open:
#   http://localhost:3000  - Grafana (admin/admin) with Node Exporter dashboard
#   http://localhost:9099  - PromClick UI
#   http://localhost:9090  - Prometheus

The compose stack includes: ClickHouse, Prometheus, Node Exporter, PromClick (proxy + writer + downsampler), and Grafana with pre-provisioned datasources and a Node Exporter dashboard.

Architecture

promclick/                 -- root Go module (CLI + core)
|-- translator/            -- PromQL AST -> SQL transpilation
|-- eval/                  -- Go-side PromQL evaluation engine
|-- clickhouse/            -- HTTP client for ClickHouse
|-- types/                 -- Sample, Series, Vector, Matrix
|-- config/                -- YAML config + schema detection
|-- fingerprint/           -- xxhash64 series fingerprinting

proxy/                     -- HTTP proxy module (3 binaries)
|-- cmd/proxy/             -- query server (read-only)
|-- cmd/writer/            -- remote_write receiver (write-only)
|-- cmd/downsampler/       -- DDL + backfill + MV management
|-- clickhouse/            -- ch-go Native TCP pool, label cache, tier queries
|-- server/                -- HTTP routes, handlers, middleware
|-- config/                -- per-binary YAML configs
|-- ui/                    -- React frontend (syntax highlighting, uPlot charts)

Three binaries, one purpose

Binary	Role	Scaling
`promclick-proxy`	Serves PromQL queries	Horizontal (N instances behind LB)
`promclick-writer`	Receives `remote_write` from Prometheus	1-2 instances
`promclick-downsampler`	Creates tier tables, MVs, backfill	1 instance (cron/init)

How PromQL becomes SQL

This is the core magic. PromClick doesn't approximate - it implements the exact Prometheus evaluation semantics in Go, using ClickHouse as the storage backend.

Step 1: Parse & Transpile

rate(http_requests_total{job="api"}[5m])

The Prometheus parser produces an AST. PromClick's transpiler walks it and produces a SQLPlan - an intermediate representation that captures:

Metric name and label matchers (for SQL WHERE)
Function name (rate, irate, increase...) - evaluated in Go
Range window (5m → data fetch window with staleness buffer)
Aggregation chain (sum by(x)(rate(...)) → [rate, sum_by_x])
Math chain (abs(ceil(rate(...))) → applied post-aggregation)
Binary ops (A / B → parallel LHS/RHS evaluation)
Offset, @modifier (time shift for data fetch)

Step 2: Fetch from ClickHouse

The SQL fetches raw samples:

SELECT fingerprint, unix_milli AS ts, value
FROM samples
PREWHERE metric_name = 'http_requests_total'
WHERE unix_milli > {start} AND unix_milli <= {end}
  AND fingerprint IN (12345, 67890, ...)   -- from label cache
ORDER BY fingerprint, unix_milli

Key optimizations:

Label cache - in-memory fingerprint -> labels map, refreshed every 60s. Eliminates JOIN to time_series table. Fingerprints resolved in Go via regex matching.
Native TCP - ch-go protocol, 2x faster than HTTP+JSON
UInt64 fingerprints - zero string allocations in hot path
Parallel fetch - splits fingerprints into chunks for concurrent CH queries
PREWHERE - ClickHouse reads metric_name column first, skips irrelevant granules

Step 3: Evaluate in Go

PromClick implements every PromQL function natively:

Rate/Increase/Delta - Prometheus-exact extrapolation:

// Counter reset correction
for _, s := range samples {
    if isCounter && s.Value < lastVal {
        counterCorrection += lastVal  // reset detected
    }
}
resultValue := last.Value - first.Value + counterCorrection

// Edge extrapolation (identical to Prometheus)
durationToStart := float64(first.Timestamp - rangeStartMs) / 1000.0
extrapolateToInterval := sampledInterval
if durationToStart < avgInterval * 1.1 {
    extrapolateToInterval += durationToStart
} else {
    extrapolateToInterval += avgInterval / 2
}
resultValue *= extrapolateToInterval / sampledInterval

WindowSamples - binary search, zero allocations:

func WindowSamples(samples []Sample, rangeStart, rangeEnd int64) []Sample {
    lo := sort.Search(len(samples), func(i int) bool {
        return samples[i].Timestamp > rangeStart  // left-open
    })
    hi := sort.Search(len(samples), func(i int) bool {
        return samples[i].Timestamp > rangeEnd    // right-closed
    })
    return samples[lo:hi]  // zero-copy slice
}

Staleness - exact NaN detection:

const StaleNaNBits uint64 = 0x7FF0000000000002
func IsStaleNaN(v float64) bool {
    return math.Float64bits(v) == StaleNaNBits
}

Histogram quantile - bucket interpolation with monotonicity enforcement, identical to Prometheus.

Aggregations - Kahan-Neumaier compensated summation (same numerical precision as Prometheus):

func kahanSumInc(inc, sum, c float64) (float64, float64) {
    t := sum + inc
    if math.Abs(sum) >= math.Abs(inc) {
        c += (sum - t) + inc
    } else {
        c += (inc - t) + sum
    }
    return t, c
}

Step 4: Series-first evaluation

For range queries, PromClick uses a series-first iteration pattern instead of Prometheus's step-first:

Prometheus:  for each step -> for each series -> compute
PromClick:   for each series -> for each step -> compute  (better cache locality)

This means:

Fingerprint computed once per series (not per step)
No map iteration per step
Samples stay in L1 cache across steps
Aggregation uses pre-computed group keys

Step 5: Streaming JSON response

Large responses (1000+ series) are written directly to the HTTP writer using bufio.Writer:

bw.WriteString(`{"status":"success","data":{"resultType":"matrix","result":[`)
for si, s := range matrix {
    // write directly, no intermediate []interface{} allocations
    bw.WriteString(`{"metric":`)
    lb, _ := json.Marshal(s.Labels)
    bw.Write(lb)
    bw.WriteString(`,"values":[`)
    for vi, p := range s.Samples {
        // format inline - zero alloc per datapoint
    }
}

This eliminates millions of interface{} allocations for large result sets.

Downsampling

PromClick implements Thanos-style downsampling using ClickHouse Materialized Views with REFRESH.

How it works

Raw samples (15s intervals)
    |
    v  [REFRESH MV every 5m]
samples_5m (5-minute aggregates: min, max, sum, count, counter_total)
    |
    v  [REFRESH MV every 1h]
samples_1h (1-hour aggregates)

Each tier stores per-bucket aggregates:

Column	Type	Purpose
`val_min`	Float64	Minimum value in bucket
`val_max`	Float64	Maximum value in bucket
`val_sum`	Float64	Sum of values
`val_count`	UInt64	Number of samples
`counter_total`	Float64	Sum of counter deltas
`first_time` / `last_time`	Int64	Actual sample time span
`first_value` / `last_value`	argMin/argMax state	For extrapolation

Query routing

For each query, PromClick picks the best data source based on step size:

downsampling:
  tiers:
    - name: "5m"
      min_step: "60s"       # use when step >= 60s
      compact_after: "40h"  # data older than 40h reads from tier
    - name: "1h"
      min_step: "3600s"     # use when step >= 1h
      compact_after: "240h"

Time range is split into segments via UNION ALL:

Query: last 30 days, step=1h
  samples_1h [30d ago, 10d ago)   -- oldest data from 1h tier
  samples_5m [10d ago, 40h ago)   -- mid-range from 5m tier
  samples    [40h ago, now)       -- recent data from raw

Gauge functions on tiers

For avg_over_time, min_over_time, etc., PromClick pushes the computation to ClickHouse:

SELECT fingerprint,
    toInt64(toUnixTimestamp(toStartOfFiveMinutes(ts))) * 1000 AS step_ts,
    sum(val_sum) AS val_sum,
    sum(val_count) AS val_count,
    min(val_min) AS val_min,
    max(val_max) AS val_max
FROM samples_5m
GROUP BY fingerprint, step_ts

Go just picks the right column - no windowed eval needed. This is why avg_over_time on 30 days is 30x faster than Prometheus.

Counter functions on tiers

For rate/increase, PromClick uses a sliding window with two pointers (O(steps + buckets) per series):

lo := 0
for _, evalTimeMs := range steps {
    // advance left pointer past expired buckets
    for lo < len(buckets) && buckets[lo].Timestamp <= windowStart {
        lo++
    }
    // scan window from lo
    for i := lo; i < len(buckets) && buckets[i].Timestamp <= windowEnd; i++ {
        sumDelta += buckets[i].CounterTotal
    }
    value = sumDelta * extrapolationFactor / rangeSec
}

Data Schema

PromClick uses a flat schema (inspired by SigNoz):

-- Raw samples
CREATE TABLE samples (
    metric_name  LowCardinality(String),
    fingerprint  UInt64,
    unix_milli   Int64,
    value        Float64
) ENGINE = MergeTree()
ORDER BY (metric_name, fingerprint, unix_milli)

-- Series metadata (labels as JSON string)
CREATE TABLE time_series (
    metric_name  LowCardinality(String),
    fingerprint  UInt64,
    labels       String,  -- JSON: {"job":"api","instance":"host-1"}
    unix_milli   Int64    -- hour-bucketed for dedup
) ENGINE = MergeTree()
ORDER BY (metric_name, fingerprint, unix_milli)

Why JSON labels instead of Map? JSONExtractString(labels, 'key') is 10x faster than labels['key'] on Map columns. Proven at scale by SigNoz.

Why not ReplacingMergeTree? Time-bucketing unix_milli to the hour gives natural deduplication without FINAL overhead.

Label Cache

PromClick keeps an in-memory cache of all fingerprint -> labels mappings:

GetFingerprints("http_requests", [{job, =, api}])
  -> [12345, 67890, ...]  (filtered in Go with cached regex)

This eliminates the JOIN to time_series on every query. The cache refreshes every 60s via HTTP. For count by(region)(metric), PromClick answers entirely from cache - zero ClickHouse queries, 1ms response time.

Performance Stack

Optimization	Effect
ch-go Native TCP	2x faster than HTTP+JSON
Label cache	Eliminates JOIN per query
UInt64 fingerprint in fetch	Zero string allocs (800K/query)
Parallel fetch (4 chunks)	Splits CH queries across pool
Series-first evaluation	Better cache locality, fingerprint computed once
Pre-computed group keys	Eliminates 800K MatchingKey calls in aggregation
Sliding window eval	O(steps + buckets) instead of O(steps * buckets)
Gauge SQL push-down	GROUP BY step in CH, no Go eval
Cache-only aggregation	count/group answered from RAM
Streaming JSON	Direct write to ResponseWriter, no intermediate allocs
Gzip with sync.Pool	Reusable compressors
Connection warmup	Zero cold-start penalty
Sorted IN clause	Better CH index utilization
slices.SortFunc	No interface boxing allocations
Kahan summation	Numerical precision matching Prometheus

Benchmark: eval micro-benchmarks

BenchmarkWindowSamples       24ns/op    0 allocs
BenchmarkExtrapolatedRate    37ns/op    0 allocs
BenchmarkInstantValue        12ns/op    0 allocs
BenchmarkHistogramQuantile  223ns/op    1 alloc

Supported PromQL

Functions (70)

Rate family: rate, irate, increase, delta, idelta, deriv, predict_linear, resets, changes

Over-time: avg_over_time, min_over_time, max_over_time, sum_over_time, count_over_time, stddev_over_time, stdvar_over_time, last_over_time, present_over_time, quantile_over_time, mad_over_time, double_exponential_smoothing

Aggregations: sum, avg, min, max, count, group, stddev, stdvar, topk, bottomk, quantile, count_values, limitk, limit_ratio

Math: abs, ceil, floor, round, sqrt, exp, ln, log2, log10, sgn, clamp, clamp_min, clamp_max

Trig: sin, cos, tan, asin, acos, atan, sinh, cosh, tanh, asinh, acosh, atanh, deg, rad, pi

Label: label_replace, label_join, sort, sort_desc, sort_by_label, sort_by_label_desc

Other: histogram_quantile, absent, absent_over_time, scalar, vector, time

Binary operators

+, -, *, /, %, ^, ==, !=, >, <, >=, <=, and, or, unless, atan2

With full support for: on(), ignoring(), group_left(), group_right(), bool

Not yet supported

Subqueries metric[1h:5m]
Native histograms
@ timestamp modifier

Quick Start

git clone https://github.com/digitalis-io/PromClick
cd promclick
docker compose up -d

Wait ~60s for data, then open:

http://localhost:3000 - Grafana (admin/admin) with Node Exporter dashboard
http://localhost:9099 - PromClick UI
http://localhost:9090 - Prometheus

Or run it yourself - check the docker-compose.yaml and deploy/ folder for all configs.

The compose stack runs everything: ClickHouse, Prometheus, Node Exporter, PromClick (proxy + writer + downsampler), and Grafana with two pre-provisioned datasources (PromClick + Prometheus) so you can compare side by side.

(You must wait few minutes untill Prometheus will start sending samples to PromClick)

Kubernetes? Sure.

Yes, there's a Helm chart.

helm pull oci://ghcr.io/digitalis-io/promclick-chart --version <version>

Project Stats

Metric	Value
Go source files	76
Lines of Go	14,491
Unit tests	130
PromQL functions	70
Benchmark queries	38 (100% accuracy vs Prometheus)
Spec documents	30
Binaries	3 (proxy, writer, downsampler)

Why not just use [X]?

Solution	Problem PromClick solves
Thanos	6 components, S3 dependency, complex operations
Mimir	Distributed hash ring, Memberlist, complex scaling
VictoriaMetrics	Separate query language (MetricsQL), vendor lock-in
M3DB	Deprecated, complex, resource-hungry
PromClick	One binary, one ClickHouse table, full PromQL, done

If you already run ClickHouse, PromClick gives you infinite Prometheus retention with zero additional infrastructure.

TODO

Scraper - scrape targets directly, drop Prometheus entirely
Ruler - PromQL rule evaluation engine with Alertmanager integration
Helm chart - one-click deploy to Kubernetes
K8s operator - CRD-based management of PromClick instances
In-memory query cache - LRU + TTL + singleflight (cache.enabled)
Redis / Memcached query cache - shared/distributed cache across proxies
Subquery support (metric[1h:5m])
Native histograms
TLS

Attribution

PromClick was originally created by the PromClick Authors — Mateusz Darmetko (hinskii), Maciej Bekas, and Pavel Kravtsov — and released under the Apache License 2.0.

This repository is the Digitalis.io distribution of PromClick. Digitalis.io vendors and maintains it, preserving all upstream copyright and attribution notices as required by the licence. The full attribution is recorded in the NOTICE file.

License

Licensed under the Apache License, Version 2.0.

Contact

Maintained by Digitalis.io. For support, get in touch at digitalis.io/contact.

Built out of frustration with the Prometheus long-term storage ecosystem. If you've ever debugged a Thanos Compactor at 3am, wondered why Thanos Store eats 64GB of RAM to serve a single dashboard, or spent a sprint migrating from one TSDB to another just to end up with the same problems in a different color - this is for you.

P.S. If your monitoring stack has more components than the system it monitors, something went wrong along the way.

Name		Name	Last commit message	Last commit date
Latest commit History 25 Commits
.github/workflows		.github/workflows
charts/promclick-chart		charts/promclick-chart
clickhouse		clickhouse
cmd		cmd
config		config
deploy		deploy
eval		eval
fingerprint		fingerprint
img		img
output		output
proxy		proxy
translator		translator
types		types
.dockerignore		.dockerignore
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
Dockerfile		Dockerfile
LICENSE.md		LICENSE.md
Makefile		Makefile
NOTICE		NOTICE
README.md		README.md
docker-compose.yaml		docker-compose.yaml
errors.go		errors.go
go.mod		go.mod
go.sum		go.sum
go.work		go.work
go.work.sum		go.work.sum

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PromClick

What it does

Benchmark: PromClick vs Prometheus

UI

Three Binaries, One Image

promclick-proxy (query server)

promclick-writer (remote_write receiver)

promclick-downsampler (DDL + backfill)

Docker Compose (all-in-one)

Architecture

Three binaries, one purpose

How PromQL becomes SQL

Step 1: Parse & Transpile

Step 2: Fetch from ClickHouse

Step 3: Evaluate in Go

Step 4: Series-first evaluation

Step 5: Streaming JSON response

Downsampling

How it works

Query routing

Gauge functions on tiers

Counter functions on tiers

Data Schema

Label Cache

Performance Stack

Benchmark: eval micro-benchmarks

Supported PromQL

Functions (70)

Binary operators

Not yet supported

Quick Start

Kubernetes? Sure.

Project Stats

Why not just use [X]?

TODO

Attribution

License

Contact

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