optimize-clickhouse-sql

name	optimize-clickhouse-sql
description	Optimize slow queries, analyze SQL performance, and collect evidence for expensive workloads.
metadata	{"author":"System","show-in-sql-editor-quick-action":true}

SQL Optimization Skill

Workflow is evidence-driven: collect evidence with tools, then recommend based on evidence only.

Pre-flight Check

1. HAS SQL: Conversation contains a SQL query -> Go to WORKFLOW step 2 (Collect Evidence).
2. HAS QUERY_ID: Conversation contains query_id -> Go to WORKFLOW step 2 (Call collect_sql_optimization_evidence immediately).
3. DISCOVERY REQUEST: User asks to optimize the slowest/heaviest queries but does not provide SQL/query_id -> Go to WORKFLOW step 1 (Discovery).
4. NEITHER: Call ask_user_question with exactly one question:
   • header: Please provide one of the following for optimization
   • options:
     • { "id": "sql", "label": "Provide SQL", "input": "text" }
     • { "id": "query_id", "label": "Provide query_id", "input": "text" }
     • { "id": "resource", "label": "Find the query that consumes the most", "input": "select", "choices": ["duration", "cpu", "memory", "disk"] } After the tool returns:
   • If optionId is sql, treat value as the SQL text and continue with evidence collection.
   • If optionId is query_id, treat value as the query_id and continue with evidence collection.
   • If optionId is resource, treat value as the ranking metric and run discovery for the top 1 query in the last 1 day before continuing.

Discovery

• Prefer search_query_log for discovery from system.query_log (slowest, most expensive, user-scoped, database-scoped, text-scoped, etc.).
• If search_query_log cannot express the request, then load the clickhouse-system-queries skill, immediately call skill_resource for references/system-query-log.md, and follow that reference strictly.
• Do NOT write ad-hoc SQL against system.query_log from this skill when search_query_log can satisfy the request.
• Extract query_id from the discovery results for the next step (evidence collection).

Time Filtering

• time_window: Relative minutes from now (e.g., 60 = last hour).
• time_range: Absolute range { from: "ISO date", to: "ISO date" }.
• When calling collect_sql_optimization_evidence after discovery, you MUST pass the same time_window or time_range used in discovery.

Mode Selection

• Default collect_sql_optimization_evidence to light mode for the first pass.
• Prefer omitting the mode argument entirely unless full detail is required.
• Use mode: "full" only when the user explicitly asks for detailed/raw evidence or the light pass is insufficient.
• Do not choose full just because the request says "optimize", "analyze", or "investigate".

Workflow

1. Discovery (if needed): Prefer search_query_log to find candidates. If the request exceeds the tool's schema, then load clickhouse-system-queries, load references/system-query-log.md via skill_resource, and use that reference. Extract query_id from the results.
2. Collect Evidence: Call collect_sql_optimization_evidence with query_id (preferred) or sql (and same time params if coming from discovery).
3. Analyze: Review evidence for optimization opportunities.
4. Recommendations: Rank by Impact/Risk/Effort. Prefer low-risk query rewrites first.
5. Validate: Use validate_sql for any proposed SQL changes. Add inline comments (-- comment) to highlight key changes.

Table Schema Evidence

• Use table_schema fields: columns, engine, partition_key, primary_key, sorting_key, secondary_indexes.
• When optimization_target is present, treat it as the real local-table schema behind a Distributed table and base key/index recommendations on it.
• Suggest secondary indexes only when evidence shows frequent WHERE filters on selective columns and the index type fits the predicate.
  • Use minmax for range predicates on sorted columns.
  • Use set for low-cardinality equality filters.
  • Use bloom_filter for high-cardinality equality filters (e.g., trace_id, user_id).
  • Use tokenbf_v1 for frequent token-based text search.

Rules

• Do NOT recommend based on assumptions. If evidence is missing, collect it with tools.
• If tools return NO meaningful evidence, output only a brief 3-5 sentence message explaining what's missing.
• Always validate proposed SQL with validate_sql before recommending.
• If discovery results include both query text and query_id, prefer query_id to avoid truncation issues.
• If the SQL appears incomplete (truncated/ellipsized/ends mid-clause), use query_id instead of sql.
• When both query_id and SQL are available, prefer query_id to reduce tokens and avoid truncation issues.