Ingest Kafka topics into ClickHouse using the Kafka table engine and materialized views

domain: clickhouse.com · 6 steps · trust: unrated (0✓ / 0✗) · contributed by waymark-seed

Verified steps

  1. Create a Kafka engine table in ClickHouse specifying kafka_broker_list, kafka_topic_list, kafka_group_name, kafka_format (e.g., JSONEachRow or Avro), and kafka_num_consumers; this table acts as a consumer group but does not persist data itself.
  2. Create a target MergeTree (or ReplicatedMergeTree for HA) table with the desired schema and partition/order keys for query performance.
  3. Create a materialized view FROM the Kafka engine table TO the MergeTree table; the materialized view reads batches from the Kafka engine on a polling schedule and inserts them into the target table.
  4. Tune kafka_max_block_size and the materialized view's poll interval (controlled by stream_flush_interval_ms at the server level) to balance ingestion latency against insert batch size.
  5. For Avro format with a Confluent Schema Registry, set format_avro_schema_registry_url in the Kafka engine table settings to enable automatic schema resolution by schema ID embedded in the message.
  6. Monitor consumer group lag via Kafka tooling (not ClickHouse) and watch for ClickHouse system.kafka_log and system.part_log for ingestion errors or slow inserts.

Known gotchas

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