ClickHouse

ClickHouse is our main analytics backend.

Instead of data being inserted directly into ClickHouse, it pulls data from Kafka. This makes our ingestion pipeline more resilient towards outages.

Further reading: For more details on inserting data, how we use Kafka tables, and materialized views, check out our ClickHouse manual entry on ingestion.

Events

To make PostHog scalable, we use a sharded ClickHouse setup.

flowchart LR
    classDef table fill:#f6e486,stroke:#ffc45d;
    kafka_events["kafka_events table<br/>(Kafka table engine)"]:::table
    eventsmv["events_mv table<br/>(Materialized view)"]:::table
    writable_events["writable_events table</br/>(Distributed table engine)"]:::table
    events["events table</br/>(Distributed table engine)"]:::table
    sharded_events["sharded_events table<br/>(ReplicatedReplacingMergeTree table engine)"]:::table

    kafka_events -.clickhouse_events_json topic.- Kafka
    eventsmv --reads from--> kafka_events
    eventsmv --pushes data to--> writable_events
    writable_events -.pushes data to.-> sharded_events

    events -.reads from.-> sharded_events

This includes the following tables:

kafka_events table

The kafka_events table uses the Kafka table engine

Tables using this engine set up Kafka consumers that consume data on read queries to the table, advancing the offset for the consumer group in Kafka.

events_mv materialized view

The events_mv table is a materialized view.

In this case, it acts as a data pipe which periodically pulls data from kafka_events and pushes it into the target (events) table.

writable_events table

The writable_events table uses the distributed table engine.

The schema looks something like as follows:

CREATE TABLE posthog.writable_events (
    `uuid` UUID,
    `event` String,
    `properties` String,
    `timestamp` DateTime64(6, 'UTC'),
    `team_id` Int64,
    `distinct_id` String,
    `elements_hash` String,
    `created_at` DateTime64(6, 'UTC'),
    `_timestamp` DateTime,
    `_offset` UInt64,
    `elements_chain` String
) ENGINE = Distributed('posthog', 'posthog', 'sharded_events', sipHash64(distinct_id))

This table:

• Gets pushed rows from events_mv table.

• For every row, it calculates a hash based on the distinct_id column.

• Based on the hash, sends the row to the right shard on the posthog cluster into the posthog.sharded_events table.

• Does not contain materialized columns as they would hinder INSERT queries.

sharded_events table

The sharded_events table uses the ReplicatedReplacingMergeTree.

This table:

• Stores the event data.
• Is sharded and replicated.
• Is queried indirectly via the events table.

events table

Similar to writable_events, the events table uses the distributed table engine.

This table is being queried from app and for every query, figures out what shard(s) to query, and aggregates the results from shards.

Note: Even though the ReplacingMergeTree engine is used, we should avoid writing duplicate data into the table, as deduplication is not a guarantee.

Persons

The source of truth for person info and person to distinct_id mappings is in PostgreSQL, but to speed up queries we replicate it to ClickHouse. Both tables use the ReplacingMergeTree and collapse by the version column, which is incremented every time a person is updated.

Note: Querying both tables requires handling duplicated rows. Check out PersonQuery code for an example of how it's done.

In sharded setups, person and person_distinct_id tables are not sharded and instead replicated onto each node to avoid JOINs over the network.