ClickHouse Migrations

Read posthog/clickhouse/migrations/AGENTS.md for comprehensive patterns, cluster setup, examples, and ingestion layer details.

Quick reference

Migration structure

operations = [
    run_sql_with_exceptions(
        SQL_FUNCTION(),
        node_roles=[...],
        sharded=False,  # True for sharded tables
        is_alter_on_replicated_table=False  # True for ALTER on replicated tables
    ),
]

Node roles (choose based on table type)

• [NodeRole.DATA]: Sharded tables (data nodes only)
• [NodeRole.DATA, NodeRole.COORDINATOR]: Non-sharded data tables, distributed read tables, replicated tables, views, dictionaries
• [NodeRole.INGESTION_SMALL]: Writable tables, Kafka tables, materialized views on ingestion layer

Table engines quick reference

MergeTree engines:

• AggregatingMergeTree(table, replication_scheme=ReplicationScheme.SHARDED) for sharded tables
• ReplacingMergeTree(table, replication_scheme=ReplicationScheme.REPLICATED) for non-sharded
• Other variants: CollapsingMergeTree, ReplacingMergeTreeDeleted

Distributed engine:

• Sharded: Distributed(data_table="sharded_events", sharding_key="sipHash64(person_id)")
• Non-sharded: Distributed(data_table="my_table", cluster=settings.CLICKHOUSE_SINGLE_SHARD_CLUSTER)

Critical rules

• NEVER use ON CLUSTER clause in SQL statements
• Always use IF EXISTS / IF NOT EXISTS clauses
• When dropping and recreating replicated table in same migration, use DROP TABLE IF EXISTS ... SYNC
• If a function generating SQL has on_cluster param, always set on_cluster=False
• Use sharded=True when altering sharded tables
• Use is_alter_on_replicated_table=True when altering non-sharded replicated tables
• Never write CODEC(ZSTD(1)) on a column — the server already compresses every column with ZSTD, so it buys nothing. Declare a CODEC only where it beats that default, and check the ORDER BY first: Delta/DoubleDelta need the column near-sorted in storage order (a leading sort-key prefix), and lose on a column the key only buckets or omits. T64/Gorilla are ordering-independent. Put it on the storage table only — a CODEC on a Distributed or Kafka table is inert metadata that drifts from the sharded table it fronts.
• Never write a DROP COLUMN migration yourself — DROP COLUMN can get stuck in ClickHouse and block releases. Column removal is a two-step process: (1) the ClickHouse team drops the column directly on the cluster, then (2) you write a migration with the matching DROP COLUMN so the codebase schema stays in sync. Never initiate the drop from a migration without the ClickHouse team having done step 1 first.
• Never drop or recreate kafka_events_json_ws or events_json_ws_mv — these tables are a no-go zone. The MV definition differs significantly between US prod, EU prod, and dev (dozens of environment-specific mat_* columns) and those differences are not reflected in the repo. Dropping and recreating from repo SQL would destroy the environment-specific schema and break event ingestion. Any change must go through the ClickHouse team.

PR scope

A PR that contains a ClickHouse migration must be migration-only. Do not mix migration files with feature code, API changes, model changes, or frontend changes in the same PR. Migration-related files are:

• The migration file itself (posthog/clickhouse/migrations/0NNN_*.py)
• SQL definition files the migration depends on (e.g. posthog/clickhouse/sql/*.py, table engine helpers)
• Tests that directly exercise the migration or the SQL definitions it touches

If you need both a schema change and application code that uses the new schema, ship the migration first in its own PR and merge it before the application-code PR.

Local setup parity

No table should exist only in the cloud. Every table created via migration must also exist in a local dev environment.

Some migrations are cloud-guarded and skipped in local/hobby dev:

operations = (
    []
    if settings.CLOUD_DEPLOYMENT not in ("US", "EU", "DEV")
    else [...]
)

If you create a new table inside such a guard, you must also add its SQL function to posthog/clickhouse/schema.py in the appropriate tuple so the table gets created locally:

Table type	Tuple in schema.py
MergeTree / base table	CREATE_MERGETREE_TABLE_QUERIES
Distributed / writable	CREATE_DISTRIBUTED_TABLE_QUERIES
Kafka consumer	CREATE_KAFKA_TABLE_QUERIES
Materialized view	CREATE_MV_TABLE_QUERIES
Non-materialized view	CREATE_VIEW_QUERIES
Dictionary	CREATE_DICTIONARY_QUERIES

The only exception is tables whose definition intentionally differs per environment and is not tracked in the repo (e.g. the no-go zone events_json_ws_mv table).

Dictionary credentials: when a dictionary uses a SOURCE(CLICKHOUSE(...)), resolve the source user/password via get_clickhouse_creds(ClickHouseUser.DICT_READER) and interpolate them into the USER/PASSWORD clause — do not hardcode default/CLICKHOUSE_USER or omit credentials. This keeps dictionary auth on the dedicated low-privilege dict_reader user, decoupled from default; it falls back to default creds when the env vars are unset. See posthog/models/exchange_rate/sql.py for the pattern.

Testing

Delete entry from infi_clickhouse_orm_migrations table to re-run a migration.