ClickHouse/docs/en/table_engines/kafka.md

Kafka

The engine works with Apache Kafka.

Kafka lets you:

• Publish or subscribe to data flows.
• Organize fault-tolerant storage.
• Process streams as they become available.

Kafka(broker_list, topic_list, group_name, format[, schema])

Parameters:

• broker_list – A comma-separated list of brokers (localhost:9092).
• topic_list – A list of Kafka topics (my_topic).
• group_name – A group of Kafka consumers (group1). Reading margins are tracked for each group separately. If you don't want messages to be duplicated in the cluster, use the same group name everywhere.
• --format – Message format. Uses the same notation as the SQL FORMAT function, such as JSONEachRow.
• schema – An optional parameter that must be used if the format requires a schema definition. For example, Cap'n Proto requires the path to the schema file and the name of the root schema.capnp:Message object.

Example:

CREATE TABLE queue (
    timestamp UInt64,
    level String,
    message String
  ) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow');

  SELECT * FROM queue LIMIT 5;

The delivered messages are tracked automatically, so each message in a group is only counted once. If you want to get the data twice, then create a copy of the table with another group name.

Groups are flexible and synced on the cluster. For instance, if you have 10 topics and 5 copies of a table in a cluster, then each copy gets 2 topics. If the number of copies changes, the topics are redistributed across the copies automatically. For more information, see http://kafka.apache.org/intro.

SELECT is not particularly useful for reading messages (except for debugging), because each message can be read only once. It is more practical to create real-time threads using materialized views. For this purpose, the following was done:

1. Use the engine to create a Kafka consumer and consider it a data stream.
2. Create a table with the desired structure.
3. Create a materialized view that converts data from the engine and puts it into a previously created table.

When the MATERIALIZED VIEW joins the engine, it starts collecting data in the background. This allows you to continually receive messages from Kafka and convert them to the required format using SELECT

Example:

CREATE TABLE queue (
    timestamp UInt64,
    level String,
    message String
  ) ENGINE = Kafka('localhost:9092', 'topic', 'group1', 'JSONEachRow');

  CREATE TABLE daily (
    day Date,
    level String,
    total UInt64
  ) ENGINE = SummingMergeTree(day, (day, level), 8192);
  
  CREATE MATERIALIZED VIEW consumer TO daily
    AS SELECT toDate(toDateTime(timestamp)) AS day, level, count() as total
    FROM queue GROUP BY day, level;

SELECT level, sum(total) FROM daily GROUP BY level;

To improve performance, received messages are grouped into blocks the size of max_block_size. If the block wasn't formed within stream_flush_interval_ms milliseconds, the data will be flushed to the table regardless of the completeness of the block.

To stop receiving topic data or to change the conversion logic, detach the materialized view:

DETACH TABLE consumer;
ATTACH MATERIALIZED VIEW consumer;

If you want to change the target table by using ALTERmaterialized view, we recommend disabling the material view to avoid discrepancies between the target table and the data from the view.