Version: 2.5.1

# Streams

Memgraph can connect to existing Kafka, Redpanda, and Pulsar sources to ingest the data, which you can then query with the power of MAGE algorithms or your own custom procedures.

To use streams, a user must:

1. Create a transformation module
2. Load the transformation module into Memgraph
3. Create the stream with a CREATE <streaming platform> STREAM query and optionally set its offset with CALL mg.kafka_set_stream_offset(stream_name, offset)
4. Start the stream with a START STREAM query

You can write Python transformation modules, create and start streams using the Stream section in the Memgraph Lab, check out how.

tip

Check out the example-streaming-app on GitHub to see a sample Memgraph-Kafka application.

## Create a stream​

The syntax for creating a stream depends on the type of the source because each specific type supports a different set of configuration options.

There is no strict order for specifying the configuration options.

### Kafka and Redpanda​

CREATE KAFKA STREAM <stream name>  TOPICS <topic1> [, <topic2>, ...]  TRANSFORM <transform procedure>  [CONSUMER_GROUP <consumer group>]  [BATCH_INTERVAL <batch interval duration>]  [BATCH_SIZE <batch size>]  [BOOTSTRAP_SERVERS <bootstrap servers>]  [CONFIGS { <key1>: <value1> [, <key2>: <value2>, ...]}]  [CREDENTIALS { <key1>: <value1> [, <key2>: <value2>, ...]}];
OptionDescriptionTypeExampleDefault
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 procedure namefunctionmy_transformation.my_procedure/
consumer groupName of the consumer group in Memgraphplain textmy_groupmg_consumer
batch interval durationMaximum waiting time in milliseconds for consuming messages before calling the transform procedureint9999100
batch sizeMaximum number of messages to wait for before calling the transform procedureint991000
bootstrap serversComma-separated list of bootstrap serversstring"localhost:9092"/
configsString key-value pairs of configuration options for the Kafka consumermap with string key-value pairs{"sasl.username": "michael.scott"}/
credentialsString key-value pairs of configuration options for the Kafka consumer, but their value aren't shown in the Kafka specific stream informationmap with string key-value pairs{"sasl.password": "password"}/
danger

The credentials are stored on the disk without any encryption, which means everybody who has access to the data directory of Memgraph can get the credentials.

To check the list of possible configuration options and their values, please check the documentation of librdkafka library, which is used in Memgraph. At the time of writing this documentation Memgraph uses version 1.7.0 of librdkafka.

### Pulsar​

CREATE PULSAR STREAM <stream name>  TOPICS <topic1> [, <topic2>, ...]  TRANSFORM <transform procedure>  [BATCH_INTERVAL <batch interval duration>]  [BATCH_SIZE <batch size>]  [SERVICE_URL <service url>];
OptionDescriptionTypeExampleDefault
stream nameName of the stream in Memgraphplain textmy_stream/
topicName of the topic in Pulsarplain textmy_topic/
transform procedureName of the transformation file followed by a procedure namefunctionmy_transformation.my_procedure/
batch interval durationMaximum waiting time in milliseconds for consuming messages before calling the transform procedureint9999100
batch sizeMaximum number of messages to wait for before calling the transform procedureint991000
service urlURL to the running Pulsar clusterstring"pulsar://127.0.0.1:6650"/

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

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

After each message is processed, the stream will acknowledge them. If the stream is stopped, the next time it starts, it will continue processing the message from the last acknowledged message.

The user who executes the CREATE query is the owner of the stream.

Memgraph Community doesn't support authentication and authorization, so the owner is always Null, and the privileges are not checked.

In Memgraph Enterprise, owner privileges are checked upon executing the queries returned from the transformation procedures. If the owner doesn't have the required privileges, the execution of the queries will fail. Find more information about how the owner affects the stream in the reference guide.

## Start a stream​

The following query will start a specific stream with name <stream name> to consume <count> number of batches for a maximum duration of <milliseconds> milliseconds.

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

The stream will automatically stop after consuming the given number of batches or reaching the timeout. If <count> number of batches are not processed within the specified TIMEOUT, probably because not enough messages was received, an exception is thrown. TIMEOUT is measured in milliseconds, and its default value is 30000. It can only be used in combination with the BATCH_LIMIT option.

If BATCH_LIMIT (and TIMEOUT) is not provided, the <stream name> stream will run for an infinite number of batches without a timeout limit.

START STREAM <stream name>;

The following query will start all streams for an infinite number of batches and without a timeout limit.

START ALL STREAMS;

When a stream is started, it resumes ingesting data from the last committed offset. If no offset is committed for the consumer group, the largest offset will be used. Therefore, only the new messages will be consumed.

## Stop a stream​

The following queries stop a specific stream or all streams.

STOP STREAM <stream name>;
STOP ALL STREAMS;

## Delete a stream​

The following query drops a stream with the name <stream name>.

DROP STREAM <stream name>;

## Show streams​

To show streams, use the following query:

SHOW STREAMS;

It shows a list of existing streams with the following information:

• stream name
• stream type
• batch interval
• batch size
• transformation procedure name
• the owner of the streams
• whether the stream is running or not

## Check stream​

To perform a dry-run on the stream and get the results of the transformation, use the following query:

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

The CHECK STREAM clause will do a dry-run on the <stream name> stream with <count> number of batches and return the result of the transformation, that is, the queries and parameters that would be executed in a normal run. If <count> number of batches are not processed within the specified TIMEOUT, probably because not enough messages were received, an exception is thrown.

The default value of <count> is 1. TIMEOUT is measured in milliseconds, and its default value is 30000.

## Get stream information​

CALL mg.kafka_stream_info("stream_name") YIELD *;

This procedure will return information about the bootstrap server, set configuration, consumer group, credentials, and topics.

CALL mg.pulsar_stream_info("stream_name") YIELD *;

The procedure will return the service URL and topics.

## Kafka producer delivery semantics​

In stream processing, it is important to consider 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 processing. If the committing fails, 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. If the processing fails, the same messages won't be processed again.

Missing a message can result in missing an edge that would connect two independent components of a graph. Therefore, the general opinion in Memgraph is that missing some information is a bigger problem in graphs databases than having duplicated information, so Memgraph uses at least once semantics, i.e., the queries returned by the transformations are first executed and committed to the database for every batch of messages, and only then is the message offset committed to the Kafka cluster.

However, even though Memgraph cannot guarantee exactly once semantics, it tries 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.

## Configuring stream transactions​

A stream can fail for various reasons. One important type of failure is when a transaction (in which the returned queries of the transformation are executed) fails to commit because of another conflicting transaction. This is a side effect of isolation levels and can be remedied by the following Memgraph flag:

--stream-transaction-conflict-retries=TIMES_TO_RETRY

By default, Memgraph will always try to execute a transaction once. However, for streams, if Memgraph fails because of transaction conflicts, it will retry to execute the transaction again for up to TIMES_TO_RETRY times (default value is 30).

Moreover, the interval of retries is also important and can be configured with the following Memgraph flag:

--stream-transaction-retry-interval=INTERVAL_TIME

The INTERVAL_TIME is measured in milliseconds and the default value is 500ms.

## Setting a stream offset​

When using a Kafka stream, you can manually set the offset of the next consumed message with a call to the query procedure mg.kafka_set_stream_offset:

CALL mg.kafka_set_stream_offset(stream_name, offset)
OptionDescriptionTypeExampleDefault
stream_nameName of the stream to set the offset forstring"my_stream"/
offsetOffset numberint0/
• An offset of -1 denotes the start of the stream, i.e., the beginning offset available for the given topic/partition.
• An offset of -2 denotes the end of the stream, i.e., for each topic/partition, its logical end such that only the next produced message will be consumed.

Stream can consume messages from multiple topics with multiple partitions. Therefore, when setting the offsets to an arbitrary number be aware that setting the offset of a stream internally sets all of the associated offsets of that stream (topics/partitions) to that value.