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.

• packages/shared/clickhouse/migrations/canonical/** is the single canonical

template tree rendered for clustered and unclustered installs. Put {CLICKHOUSE_CLUSTER_CLAUSE} at every cluster-aware DDL position. Use {CLICKHOUSE_REPLICATION_PREFIX} only for engines that deliberately differ by mode; some tables intentionally stay non-replicated in both modes.

• Every metadata ALTER (ADD/DROP/MODIFY COLUMN, ADD/DROP INDEX) in a new

canonical migration must include {CLICKHOUSE_CLUSTERED_ONLY: SETTINGS alter_sync = 2}, and every mutation-creating ALTER (MATERIALIZE …, UPDATE, DELETE) must include {CLICKHOUSE_CLUSTERED_ONLY: SETTINGS mutations_sync = 2}. This applies to a file holding a single ALTER too — the race is across migration files, not within one. alter_sync defaults to 1, so the statement returns as soon as the initiating replica has bumped the table's metadata version in Keeper; golang-migrate then opens the next file immediately, and its first ALTER on that table can land on a replica still on the previous version. ClickHouse refuses to queue it and aborts the whole run with code 517 because the replica metadata version is behind the common metadata version. Note that mutations_sync does not substitute for alter_sync: it governs when mutations finish, not metadata propagation. The renderer omits these fragments for unclustered MergeTree migrations. Use {CLICKHOUSE_UNCLUSTERED_ONLY:...} only for a deliberate mode-specific difference. Do not retrofit synchronization settings into already-shipped migrations merely to normalize them; the historical compatibility test intentionally protects their existing output.

• 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. First use DROP VIEW IF EXISTS <name> {CLICKHOUSE_CLUSTER_CLAUSE};, then CREATE VIEW <name> {CLICKHOUSE_CLUSTER_CLAUSE} 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> {CLICKHOUSE_CLUSTER_CLAUSE} 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; those target-table ALTERs must carry the clustered-only alter_sync template fragment 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
• [ ] Every metadata ALTER in a new canonical migration includes {CLICKHOUSE_CLUSTERED_ONLY: SETTINGS alter_sync = 2} — including files with a single ALTER, since the next migration file is what breaks — and every MATERIALIZE … / UPDATE / DELETE includes the corresponding mutations_sync fragment; mutations_sync is not a substitute for alter_sync; do not normalize already-shipped migration output; both rendered modes pass prepareMigrations.test.ts
• [ ] 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 - joinusenulls=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