Producer client

AIOKafkaProducer is a client that publishes records to the Kafka cluster. Most simple usage would be:

producer = aiokafka.AIOKafkaProducer(bootstrap_servers="localhost:9092")
await producer.start()
    await producer.send_and_wait("my_topic", b"Super message")
    await producer.stop()

Under the hood, AIOKafkaProducer does quite some work on message delivery including batching, retries, etc. All of it can be configured, so let’s go through some components for a better understanding of the configuration options.

Message buffering

While the user would expect the example above to send "Super message" directly to the broker, it’s actually not sent right away, but rather added to a buffer space. A background task will then get batches of messages and send them to appropriate nodes in the cluster. This batching scheme allows more throughput and more efficient compression. To see it more clearly lets avoid the send_and_wait() shortcut:

# Will add the message to 1st partition's batch. If this method times out,
# we can say for sure that message will never be sent.

fut = await producer.send("my_topic", b"Super message", partition=1)

# Message will either be delivered or an unrecoverable error will occur.
# Cancelling this future will not cancel the send.
msg = await fut

Batches themselves are created per partition with a maximum size of max_batch_size. Messages in a batch are strictly in append order and only 1 batch per partition is sent at a time (aiokafka does not support max.inflight.requests.per.connection option present in Java client). This makes a strict guarantee on message order in a partition.

By default, a new batch is sent immediately after the previous one (even if it’s not full). If you want to reduce the number of requests you can set linger_ms to something other than 0. This will add an additional delay before sending next batch if it’s not yet full.

aiokafka does not (yet!) support some options, supported by Java’s client:

  • buffer.memory to limit how much buffer space is used by Producer to schedule requests in all partitions.

  • to limit the amount of time send() coroutine will wait for buffer append when the memory limit is reached. For now use:

    await asyncio.wait_for(producer.send(...), timeout=timeout)

If your use case requires direct batching control, see Direct batch control.

Retries and Message acknowledgement

aiokafka will retry most errors automatically, but only until request_timeout_ms. If a request is expired, the last error will be raised to the application. Retrying messages on application level after an error will potentially lead to duplicates, so it’s up to the user to decide.

For example, if RequestTimedOutError is raised, Producer can’t be sure if the Broker wrote the request or not.

The acks option controls when the produce request is considered acknowledged.

The most durable setting is acks="all". Broker will wait for all available replicas to write the request before replying to Producer. Broker will consult it’s min.insync.replicas setting to know the minimal amount of replicas to write. If there’s not enough in sync replicas either NotEnoughReplicasError or NotEnoughReplicasAfterAppendError will be raised. It’s up to the user what to do in those cases, as the errors are not retriable.

The default is ack=1 setting. It will not wait for replica writes, only for Leader to write the request.

The least safe is ack=0 when there will be no acknowledgement from Broker, meaning client will never retry, as it will never see any errors.

Idempotent produce

As of Kafka 0.11 the Brokers support idempotent producing, that will prevent the Producer from creating duplicates on retries. aiokafka supports this mode by passing the parameter enable_idempotence=True to AIOKafkaProducer:

producer = aiokafka.AIOKafkaProducer(
await producer.start()
    await producer.send_and_wait("my_topic", b"Super message")
    await producer.stop()

This option will change a bit the logic on message delivery:

  • The above mentioned ack="all" will be forced. If any other value is explicitly passed with enable_idempotence=True a ValueError will be raised.

  • I contrast to general mode, will not raise RequestTimedOutError errors and will not expire batch delivery after request_timeout_ms passed.

New in version 0.5.0.

Transactional producer

As of Kafka 0.11 the Brokers support transactional message producer, meaning that messages sent to one or more topics will only be visible on consumers after the transaction is committed. To use the transactional producer and the attendant APIs, you must set the transactional_id configuration property:

producer = aiokafka.AIOKafkaProducer(
await producer.start()
    async with producer.transaction():
        res = await producer.send_and_wait(
            "test-topic", b"Super transactional message")
    await producer.stop()

If the transactional_id is set, idempotence is automatically enabled along with the producer configs which idempotence depends on. Further, topics which are included in transactions should be configured for durability. In particular, the replication.factor should be at least 3, and the min.insync.replicas for these topics should be set to 2. Finally, in order for transactional guarantees to be realized from end-to-end, the consumers must be configured to read only committed messages as well. See Reading Transactional Messages.

The purpose of the transactional_id is to enable transaction recovery across multiple sessions of a single producer instance. It would typically be derived from the shard identifier in a partitioned, stateful, application. As such, it should be unique to each producer instance running within a partitioned application. Using the same transactional_id will cause the previous instance to raise an exception ProducerFenced that is not retriable and will force it to exit.

Besides, the transaction() shortcut producer also supports a set of API’s similar to ones in Java Client. See the AIOKafkaProducer API docs.

Besides being able to commit several topics atomically, as offsets are also stored in a separate system topic it’s possible to commit a consumer offset as part of the same transaction:

async with producer.transaction():
    commit_offsets = {
        TopicPartition("some-topic", 0): 100
    await producer.send_offsets_to_transaction(
        commit_offsets, "some-consumer-group")

See a more full example in Transactional Consume-Process-Produce.

New in version 0.5.0.

Returned RecordMetadata object

After a message is sent, the user receives a RecordMetadata object.


In a very rare case, when Idempotent or Transactional producer is used and there was a long wait between batch initial send and a retry, producer may return offset == -1 and timestamp == -1 as Broker already expired the metadata for this produce sequence and only knows that it’s a duplicate due to a larger sequence present

Direct batch control

Users who need precise control over batch flow may use the lower-level create_batch() and send_batch() interfaces:

# Create the batch without queueing for delivery.
batch = producer.create_batch()

# Populate the batch. The append() method will return metadata for the
# added message or None if batch is full.
for i in range(2):
    metadata = batch.append(value=b"msg %d" % i, key=None, timestamp=None)
    assert metadata is not None

# Optionally close the batch to further submission. If left open, the batch
# may be appended to by producer.send().

# Add the batch to the first partition's submission queue. If this method
# times out, we can say for sure that batch will never be sent.
fut = await producer.send_batch(batch, "my_topic", partition=1)

# Batch will either be delivered or an unrecoverable error will occur.
# Cancelling this future will not cancel the send.
record = await fut

While any number of batches may be created, only a single batch per partition is sent at a time. Additional calls to send_batch() against the same partition will wait for the inflight batch to be delivered before sending.

Upon delivery, the record’s offset will match the batch’s first message.