clickhouse/agent-skills · Official

clickhouse-managed-postgres-rca

MUST USE when investigating performance issues on a ClickHouse-managed Postgres instance.

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Installation

$ npx skills add clickhouse/agent-skills --skill clickhouse-managed-postgres-rca

Summary

  • MUST USE when investigating performance issues on a ClickHouse-managed Postgres instance.
  • Provides an evidence-based RCA workflow that scrapes the Prometheus endpoint for system signal, pulls per-digest evidence from the Slow Query Patterns API, and recommends (does not apply) a fix.

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Stars 530
License LICENSE
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Skill metadata

Parsed from SKILL.md frontmatter.

Version0.1.0
LicenseApache-2.0
More metadata
author
ClickHouse Inc
version
0.1.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 4,914 B
  • docs AGENTS.md 32,677 B
  • docs README.md 1,368 B
  • docs SUMMARY.md 320 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 6,000 installs

SKILL.md

ClickHouse Managed Postgres RCA

When to use

Trigger whenever a user reports slowness, high CPU, low throughput, cache thrash, or any unexplained pain on a ClickHouse-managed Postgres instance.

What you have access to

Two APIs on https://api.clickhouse.cloud (HTTP Basic auth using a ClickHouse Cloud API key/secret pair):

  • Prometheus metrics — operation postgresInstancePrometheusGet

under the Prometheus tag. Returns Prometheus exposition format. System and workload metrics for one Postgres service.

  • Slow Query Patterns — operation slowQueryPatternsGetList

under the Postgres tag. Returns per-digest latency, IO, and call statistics for normalized query patterns. Beta.

Both endpoints require an organizationId and a serviceId as path parameters. The user must supply both, plus the API key/secret pair.

What you do NOT have

  • Query plans / EXPLAIN output.
  • Per-table scan-type counters (seqscan / idxscan).
  • Autovacuum or last-ANALYZE timestamps.

Reason from IO and timing signals, not from a plan tree.

Workflow

Six steps, in order. Do not skip ahead.

Steps 2 and 3 only share auth — no data dependency between them. Run them in parallel (background curls, & + wait) to cut wall time from sequential ~2s to ~1s.

1. Discover the live API shape

These endpoints are Beta — paths, params, and JSON field names can shift. Follow rules/openapi-discovery.md to:

  1. Fetch the OpenAPI spec from https://api.clickhouse.cloud/v1.
  2. Locate the two operations by operationId:

- postgresInstancePrometheusGet (Prometheus tag) - slowQueryPatternsGetList (Postgres tag)

  1. Resolve their path templates, required query parameters,

and (for the slow-query endpoint) the response schema.

  1. Build a session-scoped role map from the schema property

descriptions: { semantic role → actual field name }.

Use the resolved names in every subsequent request and citation. Never hardcode field names from memory.

2. Scrape Prom once for system gauges

Follow rules/prometheus-scrape.md. One scrape, no wait. You're after gauges (current values) that don't need a delta: CacheHitRatio, ActiveConnections, MemoryUsedPercent, FilesystemUsedPercent.

A CacheHitRatio well below ~95% on a workload that should fit in cache is a real signal on its own. Climbing ActiveConnections toward the pool ceiling is a real signal on its own. These don't need rate-of-change.

A second scrape for counter deltas is opt-in, used only when Step 4 triage points at write-congestion (where deadlock and rollback rates matter and the Slow Query Patterns API can't substitute). For the read-path case (the most common RCA shape) the single scrape is enough.

3. Pull top slow query patterns

Request the slow query patterns. Follow rules/slow-query-patterns-fields.md for the fields that matter and how to read them. This is the primary diagnostic — it returns per-pattern accumulated totals (call count, runtime, blocks, rows) over the window you request, which is the "rate-of-change" data you'd otherwise derive from two Prom scrapes — but per query and without waiting.

If no patterns return a meaningful totalDurationUs, the report may be overstated or the issue isn't query-shaped. Stop and tell the user what you looked at.

4. Triage: pick the right heuristic

Follow rules/triage.md. Match the combined Prom + slow-query signal to one of the heuristic shapes. Each shape points to a specific heuristic file:

  • rules/heuristic-full-scan.md — read-path full scan.
  • rules/heuristic-hot-loop.md — N+1 / hot loop from the app.
  • rules/heuristic-write-congestion.md — deadlocks, slow

writes, high rollback rate.

If the signal does not match any shape cleanly, do not invent a hypothesis. Surface the top patterns and ask the user which workload they recognize. New heuristics are welcome as PRs.

5. Reason, then recommend

Use the format in rules/output-template.md. Always include: symptom, evidence, hypothesis (noting any alternative cause you cannot rule out from this surface alone), short-term fix, and long-term follow-ups.

6. Do not apply the fix

Follow rules/recommend-only.md. Never run DDL. Never call pgcancelbackend or pgterminatebackend. Write the recommendation, explain why, and let the human apply it.

Full Compiled Document

For the complete guide with every rule expanded in a single context load: AGENTS.md.