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Version: 1.6.1


Memgraph can connect to existing Kafka streams. To use streams, a user must:

  1. Create a transformation module
  2. Configure Memgraph to connect to, e.g. Kafka, by providing the appropriate flag --kafka-bootstrap-servers=localhost:9092
  3. Create the stream with a CREATE STREAM query
  4. Start the stream with a START STREAM query
Check out the example-streaming-app on

GitHub to see a sample Memgraph-Kafka application.

Creating a stream

The general syntax for creating a stream is:

CREATE STREAM <stream name>
TOPICS <topic1> [, <topic2>, ...]
TRANSFORM <transform procedure>
[CONSUMER_GROUP <consumer group>]
[BATCH_INTERVAL <batch interval length>]
[BATCH_SIZE <batch size>];
stream nameName of the stream in Memgraphplain textmy_stream/
topicName of the topic in Kafkaplain textmy_topic/
transform procedureName of the transformation file followed by a function namefunctionmy_transformation.my_transform/
consumer groupName of the consumer group in Memgraphplain textmy_groupmg_consumer
batch interval durationMaximum wait time in milliseconds for consuming messages before calling the transform procedureint9999100
batch sizeMaximum number of messages to wait for before calling the transform procedureint991000

The transformation procedure is called if either the BATCH_INTERVAL or the BATCH_SIZE is reached, and there is at least one received message.

The BATCH_INTERVAL starts when the:

  • the stream is started
  • the processing of the previous batch is completed
  • the previous batch interval ended without receiving any messages

The user who executes the CREATE query is going to be the owner of the stream. Authentication and authorization are not supported in Memgraph Community, thus the owner will always be Null, and the privileges are not checked in Memgraph Community. In Memgraph Enterprise, the privileges of the owner are used when executing the queries returned from a transformation. In other words, the execution of the queries will fail if the owner doesn't have the required privileges. More information about how the owner affects the stream can be found in the reference guide.

Deleting a stream

DROP STREAM <stream name>;

Drops a stream with name <stream name>.

Start a stream

START STREAM <stream name>;

Starts a stream (or all streams) with name <stream name>.

When a stream is started, it should resume from the last committed offset. If there is no committed offset for the consumer group, then the largest offset will be used. Therefore only the new messages will be consumed.

Stop a stream

STOP STREAM <stream name>;

Stops a stream (or all streams) with name <stream name>.



Shows a list of existing streams with the following information:

  • stream name
  • list of topics
  • consumer group id
  • batch interval
  • batch size
  • transformation procedure name
  • the owner of the streams
  • whether the stream is running

Check stream

CHECK STREAM <stream name> [BATCH_LIMIT <count>] [TIMEOUT <milliseconds>] ;

Does a dry-run on stream with name <stream name> with <count> number of batches and returns the result of the transformation: the queries and their parameters that would be executed in a normal run. If <count> is unspecified, its default value is 1. After <count> batches are processed, the transformation result is returned. If <count> number of batches are not processed within the specified timeout, then an exception is thrown. This might be caused by not receiving enough messages. TIMEOUT is measured in milliseconds, and it's defaulted to 30000.

Checking a stream won't commit any offsets.

At least once semantics

In stream processing, it is important to have some guarantees about how failures are handled. When connecting an external application such as Memgraph to a Kafka stream, there are two possible ways to handle failures during message processing:

  1. Every message is processed at least once: the message offsets are committed to the Kafka cluster after the processing is done. This means if the committing fails, then the messages can get processed multiple times.
  2. Every message is processed at most once: the message offsets are committed to the Kafka cluster right after they are received before the processing is started. This means if the processing fails, then the same messages won't be processed again.

Missing a message can result in missing an edge that would connect two independent components of the graph. Therefore, we think that missing some information is a bigger problem for graphs than having some information duplicated, so we implemented our streams using the at least once semantics, i.e. for every batch of messages the queries returned by the transformations are executed and committed to the database before committing the message offset to the Kafka cluster. However, even though we cannot guarantee exactly once semantics, we tried to minimize the possibility of processing messages multiple times. This means committing the message offsets to the Kafka cluster happens right after the transaction is committed to the database.