clickhouse-best-practices

ClickHouse Best Practices

Comprehensive guidance for ClickHouse covering schema design, query optimization, and data ingestion. Contains 28 rules across 3 main categories (schema, query, insert), prioritized by impact.

Official docs: ClickHouse Best Practices

IMPORTANT: How to Apply This Skill

Before answering ClickHouse questions, follow this priority order:

1. Check for applicable rules in the rules/ directory
2. If rules exist: Apply them and cite them in your response using "Per rule-name..."
3. If no rule exists: Use the LLM's ClickHouse knowledge or search documentation
4. If uncertain: Use web search for current best practices
5. Always cite your source: rule name, "general ClickHouse guidance", or URL

Why rules take priority: ClickHouse has specific behaviors (columnar storage, sparse indexes, merge tree mechanics) where general database intuition can be misleading. The rules encode validated, ClickHouse-specific guidance.

Langfuse-Specific Rules

• Use packages/shared/src/server/queries/clickhouse-sql/event-query-builder.ts for queries against the events table. Do not hand-roll events SQL unless you first confirm the query builder cannot express the query.
• Never use FINAL on the events table; it is designed so FINAL is not required and the keyword hurts performance.
• ClickHouse query attribution is stored in system.query_log.log_comment as JSON from packages/shared/src/server/clickhouse/queryTags.ts. Parse it with JSONExtractString(log_comment, 'surface'), JSONExtractString(log_comment, 'route'), and JSONExtractString(log_comment, 'projectId'). Known surface values are trpc, publicapi, worker, mcp, and unknown; ClickhouseWriter inserts use projectId = "MULTI_PROJECT".
• Query attribution is propagated through OpenTelemetry baggage. Entry points call contextWithLangfuseProps(...) from packages/shared/src/server/headerPropagation.ts, setting ClickHouse surface, optional route, and optional projectId. The ClickHouse repository layer then reads baggage via normalizeClickHouseQueryTags(...) and writes it to log_comment. Prefer setting attribution at entry points rather than passing tags through every repository call.
• Any migration in packages/shared/clickhouse/migrations/clustered/** with more than one ALTER on the same table must end every metadata ALTER (ADD/DROP/MODIFY COLUMN, ADD/DROP INDEX) with SETTINGS alter_sync = 2, and every mutation-creating ALTER (MATERIALIZE …, UPDATE, DELETE) with SETTINGS mutations_sync = 2. The matching unclustered/ file runs against plain MergeTree and does not need (and should not duplicate) these settings.
• Never use CREATE OR REPLACE VIEW (nor CREATE OR REPLACE TABLE / EXCHANGE TABLES) in ClickHouse migrations. The atomic replace requires renameat2 filesystem support, which NFS-backed self-hosted deployments (e.g. ClickHouse data on AWS EFS) lack — the migration fails and the deployment aborts on startup (GitHub issue #14906). Redefine a plain view as two statements in the same migration file: DROP VIEW IF EXISTS <name> [ON CLUSTER default]; then CREATE VIEW <name> [ON CLUSTER default] AS …. The migration runner passes x-multi-statement=true and golang-migrate splits files on ; without parsing SQL, so keep semicolons out of comments and string literals. Keep every statement idempotent (IF EXISTS/IF NOT EXISTS) so a dirty, half-applied migration can be re-run after migrate force. Readers hitting the view inside the drop→create window fail transiently — acceptable for the analytics_* export views, so keep plain views off product hot paths.
• Never drop-and-recreate a materialized view whose source table receives live inserts: every row inserted between DROP and CREATE is silently and permanently missing from the target table. Change an MV's SELECT with ALTER TABLE <mv> [ON CLUSTER default] MODIFY QUERY <select>, which swaps the transformation without interrupting ingestion. When the change adds columns, ALTER the target table(s) first (ADD COLUMN IF NOT EXISTS …), then MODIFY QUERY; in clustered/ files those target-table ALTERs must carry SETTINGS alter_sync = 2 so no host applies the new MV query before its target replica has the new columns. MODIFY QUERY is only viable for TO-table MVs (all Langfuse MVs use TO).

Review Procedures

For Schema Reviews (CREATE TABLE, ALTER TABLE)

Read these rule files in order:

1. rules/schema-pk-plan-before-creation.md - ORDER BY is immutable
2. rules/schema-pk-cardinality-order.md - Column ordering in keys
3. rules/schema-pk-prioritize-filters.md - Filter column inclusion
4. rules/schema-types-native-types.md - Proper type selection
5. rules/schema-types-minimize-bitwidth.md - Numeric type sizing
6. rules/schema-types-lowcardinality.md - LowCardinality usage
7. rules/schema-types-avoid-nullable.md - Nullable vs DEFAULT
8. rules/schema-partition-low-cardinality.md - Partition count limits
9. rules/schema-partition-lifecycle.md - Partitioning purpose

Check for:

• PRIMARY KEY / ORDER BY column order (low-to-high cardinality)
• Data types match actual data ranges
• LowCardinality applied to appropriate string columns
• Partition key cardinality bounded (100-1,000 values)
• ReplacingMergeTree has version column if used
• Clustered migration files with multiple ALTERs on the same table use SETTINGS alter_sync = 2 (metadata) and SETTINGS mutations_sync = 2 (MATERIALIZE …, UPDATE, DELETE); unclustered mirror has none
• No CREATE OR REPLACE VIEW/TABLE or EXCHANGE TABLES in migrations (breaks NFS/EFS self-hosting); plain views are redefined via DROP VIEW IF EXISTS + CREATE VIEW in the same file
• Materialized views are never dropped and recreated while their source table takes inserts; SELECT changes go through ALTER TABLE <mv> MODIFY QUERY after the target-table ALTERs

For Query Reviews (SELECT, JOIN, aggregations)

Read these rule files:

1. rules/query-join-choose-algorithm.md - Algorithm selection
2. rules/query-join-filter-before.md - Pre-join filtering
3. rules/query-join-use-any.md - ANY vs regular JOIN
4. rules/query-index-skipping-indices.md - Secondary index usage
5. rules/schema-pk-filter-on-orderby.md - Filter alignment with ORDER BY

Check for:

• Filters use ORDER BY prefix columns
• JOINs filter tables before joining (not after)
• Correct JOIN algorithm for table sizes
• Skipping indices for non-ORDER BY filter columns

For Insert Strategy Reviews (data ingestion, updates, deletes)

Read these rule files:

1. rules/insert-batch-size.md - Batch sizing requirements
2. rules/insert-mutation-avoid-update.md - UPDATE alternatives
3. rules/insert-mutation-avoid-delete.md - DELETE alternatives
4. rules/insert-async-small-batches.md - Async insert usage
5. rules/insert-optimize-avoid-final.md - OPTIMIZE TABLE risks

Check for:

• Batch size 10K-100K rows per INSERT
• No ALTER TABLE UPDATE for frequent changes
• ReplacingMergeTree or CollapsingMergeTree for update patterns
• Async inserts enabled for high-frequency small batches

Output Format

Structure your response as follows:

## Rules Checked
- `rule-name-1` - Compliant / Violation found
- `rule-name-2` - Compliant / Violation found
...

## Findings

### Violations
- **`rule-name`**: Description of the issue
  - Current: [what the code does]
  - Required: [what it should do]
  - Fix: [specific correction]

### Compliant
- `rule-name`: Brief note on why it's correct

## Recommendations
[Prioritized list of changes, citing rules]

Rule Categories by Priority

Quick Reference

Schema Design - Primary Key (CRITICAL)

• schema-pk-plan-before-creation - Plan ORDER BY before table creation (immutable)
• schema-pk-cardinality-order - Order columns low-to-high cardinality
• schema-pk-prioritize-filters - Include frequently filtered columns
• schema-pk-filter-on-orderby - Query filters must use ORDER BY prefix

Schema Design - Data Types (CRITICAL)

• schema-types-native-types - Use native types, not String for everything
• schema-types-minimize-bitwidth - Use smallest numeric type that fits
• schema-types-lowcardinality - LowCardinality for <10K unique strings
• schema-types-enum - Enum for finite value sets with validation
• schema-types-avoid-nullable - Avoid Nullable; use DEFAULT instead

Schema Design - Partitioning (HIGH)

• schema-partition-low-cardinality - Keep partition count 100-1,000
• schema-partition-lifecycle - Use partitioning for data lifecycle, not queries
• schema-partition-query-tradeoffs - Understand partition pruning trade-offs
• schema-partition-start-without - Consider starting without partitioning

Schema Design - JSON (MEDIUM)

• schema-json-when-to-use - JSON for dynamic schemas; typed columns for known

Query Optimization - JOINs (CRITICAL)

• query-join-choose-algorithm - Select algorithm based on table sizes
• query-join-use-any - ANY JOIN when only one match needed
• query-join-filter-before - Filter tables before joining
• query-join-consider-alternatives - Dictionaries/denormalization vs JOIN
• query-join-null-handling - join_use_nulls=0 for default values

Query Optimization - Indices (HIGH)

• query-index-skipping-indices - Skipping indices for non-ORDER BY filters

Query Optimization - Materialized Views (HIGH)

• query-mv-incremental - Incremental MVs for real-time aggregations
• query-mv-refreshable - Refreshable MVs for complex joins

Insert Strategy - Batching (CRITICAL)

• insert-batch-size - Batch 10K-100K rows per INSERT

Insert Strategy - Async (HIGH)

• insert-async-small-batches - Async inserts for high-frequency small batches
• insert-format-native - Native format for best performance

Insert Strategy - Mutations (CRITICAL)

• insert-mutation-avoid-update - ReplacingMergeTree instead of ALTER UPDATE
• insert-mutation-avoid-delete - Lightweight DELETE or DROP PARTITION

Insert Strategy - Optimization (HIGH)

• insert-optimize-avoid-final - Let background merges work

When to Apply

This skill activates when you encounter:

• CREATE TABLE statements
• ALTER TABLE modifications
• ORDER BY or PRIMARY KEY discussions
• Data type selection questions
• Slow query troubleshooting
• JOIN optimization requests
• Data ingestion pipeline design
• Update/delete strategy questions
• ReplacingMergeTree or other specialized engine usage
• Partitioning strategy decisions

Rule File Structure

Each rule file in rules/ contains:

• YAML frontmatter: title, impact level, tags
• Brief explanation: Why this rule matters
• Incorrect example: Anti-pattern with explanation
• Correct example: Best practice with explanation
• Additional context: Trade-offs, when to apply, references