Create materialized views in ClickHouse®#

Learn how to create and use materialized views to persist data from the Kafka® table engine.

About materialized views#

One way of integrating your ClickHouse® service with Kafka® is using the Kafka® table engine, which enables, for example, inserting data into ClickHouse® from Kafka.

In such a scenario, ClickHouse can read from a Kafka® topic directly. This is, however, one-time retrieval so the data cannot be re-read. When a new block of data is inserted in a table (whether the table is backed by a MergeTree or Kafka® engine), the table expression defined by the materialized view is triggered with the block of data. With Kafka®, the block of data lives in a buffer in memory and the trigger flushes the buffer. For this reason, the data consumed from the topic by the Kafka® engine cannot be read twice.

Persisting the data from the Kafka® table engine read requires capturing and inserting it into a different table. Here’s where materialized views come in. A materialized view triggers a read on the table engine. The destination of the data (for example, a Merge Tree family table) is defined by the TO clause. This process is illustrated in the following diagram:

flowchart LR a[Kafka topic] b{{Kafka table engine}} c[Materialized view] d{{Merge Tree<br /> family table}} a --> b --> c --> d

Create a materialized view#

To store the Kafka® messages by creating a materialized view on top of the Kafka® table, you need to run the following query:

CREATE MATERIALIZED VIEW default.my_view TO destination_name
ENGINE = ReplicatedMergeTree
ORDER BY x AS SELECT x,y FROM service_kaf.table_name


All ClickHouse® nodes share the same consumer group: Each message is consumed and stored by a single node. However, since materialized views use the ReplicatedMergeTree engine, the stored messages are exchanged and replicated across all the nodes.

See also

For more information on how to integrate Aiven for ClickHouse® with Apache Kafka®, see Connect Apache Kafka® to Aiven for ClickHouse®.