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/clickhouse-architecture-advisor

@5e162d6 official
by clickhouseclickhouse/agent-skills543 stars
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MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system designs. Complements clickhouse-best-practices with decision frameworks and explicit provenance labels.

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Use this Skill: https://skilld.dev/gh/clickhouse/agent-skills/clickhouse-architecture-advisor

This session only. Nothing lands on disk.

rulesdecision-real-time-preaggregation.md

≈501 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Choose raw-only vs incremental materialized views vs refreshable materialized views

Principle

Real-time workloads should not be forced into either “everything raw” or “precompute everything.” The correct choice depends on freshness, query repetition, and transformation complexity.

Decision framework

Condition Recommendation Category
Queries are ad hoc and freshness matters most Query raw tables derived
Repeated aggregation pattern over append-only data Incremental MV official
Complex joins or scheduled batch recomputation Refreshable MV official
Very hot dashboard or alerting path Incremental rollup table plus raw table fallback derived

Guidance

Recommendation: incremental MVs for repeated real-time aggregation

Why Incremental MVs are the documented best fit for continuously maintained rollups over insert streams.

Official sources

Recommendation: refreshable MVs for heavier joins or scheduled transforms

Why Refreshable MVs better fit complex transformations that do not need per-row trigger semantics.

Official sources

Recommendation: dual-path design for hot dashboards

Why A raw table preserves flexibility while a rollup path protects latency-sensitive workloads.

Category derived

Official context

Validation

  • Identify repeated dashboard queries
  • Compare raw scan cost against incremental aggregation maintenance
  • Confirm whether the source is append-only enough for incremental MV semantics

Source: SKILL.md on GitHub

Third-party checks

No alerts17d4 checks · Risk SAFE
  • Gen Agent Trust Hub17d

    The skill is a safe architectural advisor for ClickHouse workloads. It provides structured decision frameworks for ingestion, partitioning, and schema design based on official documentation. No malicious patterns, data exfiltration, or dangerous execution triggers were detected.

  • Socket17d

    No alerts

  • Snyk17d

    Risk: LOW · No issues

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Provenance

Signed by skilld at 5e162d6. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 3 days ago.

Activeupdated 6 months ago

Capability

metadata
{
  "author": "ClickHouse Inc",
  "version": "0.1.0"
}

Topics

  • clickhouse
  • architecture
  • olap
  • ingestion
  • time-series
  • schema-design
  • partitioning
  • joins
  • telemetry

README badge

README badge for clickhouse/agent-skills/clickhouse-architecture-advisor

What it does

Guides ClickHouse architecture decisions for specific workloads—observability, analytics, IoT, financial services—by mapping workload shape to ingestion, partitioning, and join strategies with official documentation links. Classifies recommendations by provenance (official, derived, field) to separate documented behavior from heuristic field guidance.

Generated from the current SKILL.md.

Frequently asked

Does this skill replace the clickhouse-best-practices skill?
No. This skill complements clickhouse-best-practices by adding workload-aware decision frameworks and provenance labels. Official documentation remains the source of truth for both.
What workload types does this skill cover?
Observability, security/SIEM, product analytics, IoT/telemetry, market data/financial services, and mixed OLAP with point-lookups. Each has scenario-specific rule files for ingestion, time-series retention, enrichment, and late-arriving events.
How does this skill distinguish between official, derived, and field guidance?
Official recommendations are directly from ClickHouse docs. Derived recommendations follow logically from documented behavior. Field recommendations are experience-based and include a disclaimer that they are heuristic and workload-dependent.
What should I do if a recommendation is uncertain?
The skill explicitly states when a recommendation is uncertain rather than presenting it as confident guidance.

Generated from the current SKILL.md. These answers refresh after source changes.