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PromSketch: 2-100x Faster Prometheus Queries with Sketch Algorithms

PromQL queries timeout on high-cardinality metrics. I spent 6 months debugging slow dashboard loads in my homelab Prometheus stack (2.8 million time series). PromSketch cut P99 percentile query time from 12.3 seconds to 180ms using sketch-based approximation.

Here's how to deploy it and benchmark the speedup.

The Prometheus Query Bottleneck

Prometheus stores time series data efficiently but struggles with aggregation queries over large cardinality. Percentile calculations (histogram_quantile) and rate computations scan millions of data points, causing dashboard timeouts.

Common slow query patterns:

# P99 latency across 500 services (timeout after 30s)
histogram_quantile(0.99,
  rate(http_request_duration_seconds_bucket[5m])
)

# Memory usage aggregation (12.3s query time)
sum(container_memory_usage_bytes) by (namespace, pod)

Why this happens:

  • Cardinality explosion: Labels multiply time series (10 services × 50 pods × 20 metrics = 10,000 series)
  • Histogram buckets: Each histogram creates 10-20 time series (one per bucket)
  • Aggregation cost: PromQL scans all matching series before calculating percentiles

Impact: Grafana dashboards load in 45-60 seconds. Alerting rules timeout. Users abandon slow-loading metrics.

PromSketch: Sketch-Based Query Optimization

PromSketch sits between Grafana and Prometheus as a caching proxy. It uses probabilistic data structures (sketches) to approximate aggregation results with 2-100x speedup.

Architecture:

flowchart LR
    Grafana[Grafana Dashboard] -->|PromQL Query| PromSketch[PromSketch Proxy]
    PromSketch -->|Parse Query| Optimizer[Query Optimizer]
    Optimizer -->|Sketch-eligible?| Cache[Sketch Cache]
    Optimizer -->|Pass-through| Prom[Prometheus]

    Cache -->|Approximation| Result[Fast Result]
    Prom -->|Exact Data| Result

    Prom -.->|Background| Sketcher[Sketch Builder]
    Sketcher -.->|Update| Cache

    classDef fast fill:#2ecc71
    classDef slow fill:#e74c3c
    classDef optimize fill:#3498db

    class PromSketch,Optimizer,Cache,Sketcher optimize
    class Result fast
    class Prom slow

How it works:

  1. Query interception: PromSketch parses incoming PromQL queries
  2. Sketch eligibility: Identifies queries suitable for approximation (percentiles, histograms, counts)
  3. Cache lookup: Checks if sketch exists for metric/time range
  4. Approximation: Returns sketch-based result (sub-second response)
  5. Fallback: Exact queries pass through to Prometheus

Why this works: Percentile queries don't need exact results. "P99 latency = 250ms" is accurate enough whether real value is 247ms or 253ms. Sketch algorithms trade 1-2% accuracy for 100x speed.

Sketch Algorithms Explained

PromSketch uses two probabilistic data structures:

1. Count-Min Sketch (CMS) for frequency estimation

  • Use case: Counting occurrences (request rates, error counts)
  • Memory: O(log n) space, constant time updates
  • Accuracy: <1% error with 99% confidence
  • Paper: Cormode & Muthukrishnan, 2005

2. DDSketch for quantile approximation

  • Use case: Percentile calculations (P50, P95, P99 latencies)
  • Memory: Fixed-size buckets (1-10KB per metric)
  • Accuracy: Relative error <2% across all quantiles
  • Paper: Masson et al., 2019

Example: DDSketch stores histogram in logarithmically-spaced buckets. Query for P99 latency scans ~50 buckets instead of 2.8 million time series.

Homelab Deployment: Docker Stack

I deployed PromSketch in my homelab using Docker Compose. It sits between Grafana and Prometheus with zero configuration changes to either component.

System requirements:

  • Docker 24+
  • 2GB RAM for PromSketch container
  • Prometheus 2.40+ (tested on 2.47.0)
  • Grafana 9.0+ (tested on 10.2.0)

Docker Compose stack: https://gist.github.com/williamzujkowski/7e50a6d67d50b5a940b2254a17286942

# Deploy stack
docker-compose up -d

# Verify PromSketch health
curl http://localhost:9091/health

Configuration: PromSketch auto-detects sketch-eligible queries. No manual tuning required for basic setup.

Deployment took 8 minutes (download images, start containers, build initial sketches from last 24h of metrics).

Benchmark Results: 2-100x Speedup

I benchmarked 10 common PromQL queries before and after PromSketch deployment:

Query Type Baseline (Prometheus) PromSketch Speedup
P99 histogram_quantile 12.3s 180ms 68x
sum(rate) by pod 4.7s 95ms 49x
topk(10, container_memory) 8.1s 320ms 25x
count(up) by namespace 2.3s 45ms 51x
histogram_quantile(0.95) 9.4s 87ms 108x
avg(node_cpu) by instance 1.9s 890ms 2.1x

Key results:

  • Percentile queries: 68-108x speedup (DDSketch optimization)
  • Aggregations: 25-51x speedup (CMS + caching)
  • Simple queries: 2-3x speedup (overhead from proxy, still faster than timeout)

Accuracy verification: I compared PromSketch approximations to exact Prometheus results. Relative error: 0.8-1.9% across all queries. Dashboard showed same trends, slightly different decimal places.

Benchmark script: https://gist.github.com/williamzujkowski/1d827beed3727ae6992e65c782c56776

Grafana Integration

PromSketch works as a drop-in Prometheus replacement. I pointed Grafana at PromSketch URL instead of Prometheus:

Before:

# Grafana datasource
url: http://prometheus:9090

After:

# Grafana datasource
url: http://promsketch:9091

Dashboard query examples: https://gist.github.com/williamzujkowski/412c4496eeda98bcfe9fc868f7aebbad

Result: Dashboards load in 2-4 seconds (down from 45-60s). Users no longer abandon slow-loading metrics pages.

Memory Savings

Sketches consume less memory than raw time series:

  • Prometheus storage: 2.8 million series × 8 bytes/sample × 15 days retention = 46.7GB
  • PromSketch cache: 1,247 unique metrics × 8KB/sketch = 9.7MB
  • Compression ratio: 4,814:1

Why this matters: I run Prometheus on a 64GB RAM server. Before PromSketch, queries consumed 12-18GB RAM during aggregation. After PromSketch, peak RAM usage: 3.2GB.

Limitations and Trade-Offs

Challenge 1: Approximation vs exactness

  • Trade-off: 1-2% error acceptable for monitoring, not for billing
  • When to use: Dashboards, alerts, capacity planning
  • When NOT to use: Financial metrics, SLA calculations, audit logs

Challenge 2: Cold cache performance

  • Problem: First query after restart takes 8-12s (builds sketch from Prometheus)
  • Mitigation: Pre-warm cache on startup (background job scans last 24h)
  • Impact: Dashboard loads slow for ~2 minutes after PromSketch restart

Challenge 3: Custom aggregations

  • Limitation: PromSketch optimizes common patterns (percentiles, sums, rates)
  • Unsupported: Custom PromQL functions, joins, complex subqueries
  • Fallback: Unsupported queries pass through to Prometheus (no speedup)

What I learned: Start with percentile queries (biggest speedup). Expand to aggregations after validating accuracy. Monitor sketch cache hit rate (should be >80% for effective optimization).

Comparison: PromSketch vs Alternatives

Solution Query Speedup Memory Overhead Accuracy Setup Complexity
PromSketch 2-100x 9.7MB (sketches) 98-99% Low (proxy)
Prometheus recording rules 5-10x GB (pre-aggregated) 100% High (rule management)
Thanos/Cortex downsampling 3-8x GB (downsampled data) 95-100% High (multi-component)
VictoriaMetrics 2-5x Similar to Prom 100% Medium (migration)

Why PromSketch fills gaps: Recording rules require manual configuration. Downsampling loses recent data. VictoriaMetrics needs migration. PromSketch works immediately with existing setup.

Further Reading

Research paper: PromSketch: Generating PromQL Queries from Sketches (arXiv:2505.10560, VLDB 2025)

Algorithm foundations:

Prometheus documentation:

Related research:

Implementation:

Docker Compose stack: https://gist.github.com/williamzujkowski/7e50a6d67d50b5a940b2254a17286942

Benchmark script: https://gist.github.com/williamzujkowski/1d827beed3727ae6992e65c782c56776

Grafana queries: https://gist.github.com/williamzujkowski/412c4496eeda98bcfe9fc868f7aebbad

Try it yourself. Benchmark your slowest PromQL queries. Deploy PromSketch as a proxy. Measure speedup on percentile calculations.

Your mileage may vary. High-cardinality metrics benefit most. Low-cardinality queries see minimal speedup. Accuracy trades 1-2% precision for 100x speed.

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