clickhouse-architecture-advisor
GitHub用于设计ClickHouse架构、选择摄入或建模模式,并将最佳实践转化为特定工作负载的系统设计。通过识别工作负载形状、引用官方文档并标注建议来源(官方/推导/经验),提供结构化决策框架与验证方法。
Trigger Scenarios
Install
npx skills add MapleTechLabs/maple --skill clickhouse-architecture-advisor -g -y
SKILL.md
Frontmatter
{
"name": "clickhouse-architecture-advisor",
"license": "Apache-2.0",
"metadata": {
"author": "ClickHouse Inc",
"version": "0.1.0"
},
"description": "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."
}
ClickHouse Architecture Advisor
This skill adds workload-aware architecture decisioning on top of clickhouse-best-practices.
Official docs remain the source of truth. This skill must always prefer official ClickHouse documentation when available.
Required behavior
Before producing recommendations:
- Identify the workload shape
- observability
- security / SIEM
- product analytics
- IoT / telemetry
- market data / financial services
- mixed OLAP with point-lookups
- Read the relevant decision rule files in
rules/ - Use
mappings/doc_links.yamlto attach official documentation - Classify every recommendation as:
officialderivedfield
- Never present field guidance as official guidance
- If a recommendation is uncertain, say so explicitly
Provenance rules
official
Use this when the recommendation is directly backed by official docs.
derived
Use this when the recommendation is not stated verbatim in docs but follows logically from documented ClickHouse behavior.
field
Use this only for experience-based guidance that may be situational.
When using field, include:
- a disclaimer that the advice is heuristic
- a relevant official doc if one partially applies
- the reason the advice depends on workload context
Read these rule files by scenario
Real-time ingestion design
rules/decision-ingestion-strategy.mdrules/decision-real-time-preaggregation.md- Relevant best-practices insert rules
Time-series and retention design
rules/decision-partitioning-timeseries.md- Relevant best-practices schema partition rules
Enrichment and dimension lookups
rules/decision-join-enrichment.md- Relevant best-practices query join rules
Mutable state / late-arriving events
rules/decision-late-arriving-upserts.md- Relevant best-practices mutation avoidance rules
Output format
Structure responses like this:
## Workload Summary
- workload:
- latency target:
- data shape:
- primary query patterns:
- operational constraints:
## Key Decisions
- ...
- ...
## Recommendations
### <Recommendation title>
**What**
...
**Why**
...
**How**
...
**Category**
official | derived | field
**Confidence**
high | medium | heuristic
**Source**
- doc link(s)
**Validation**
- concrete SQL, metric, or smoke test
Architecture-specific guidance
Prefer decision frameworks over generic advice. Good responses should:
- explain tradeoffs
- identify the likely operating bottleneck
- separate immediate actions from structural redesign
- provide target architecture patterns, not just isolated settings
Full reference
See AGENTS.md for the compiled version and examples/ for sample outputs.
Version History
- 01a5dc6 Current 2026-07-05 18:15


