abstract class KafkaProducer[F[_], K, V] extends AnyRef

KafkaProducer represents a producer of Kafka records, with the ability to produce ProducerRecords using produce.

Self Type
KafkaProducer[F, K, V]
Source
KafkaProducer.scala
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Instance Constructors

  1. new KafkaProducer()

Abstract Value Members

  1. abstract def initTransactions: F[Unit]

    Enables transactions for the producer.

    Enables transactions for the producer.

    If using KafkaProducer.transactional or KafkaProducer.transactionalStream, then this will automatically be done when the producer is created. If not, then this function has to be called manually before using any of the transaction methods:

    or an IllegalStateException exception will be raised.

  2. abstract def metrics: F[Map[MetricName, Metric]]

    Returns producer metrics.

    Returns producer metrics.

    See also

    org.apache.kafka.clients.producer.KafkaProducer#metrics

  3. abstract def partitionsFor(topic: String): F[List[PartitionInfo]]

    Returns partition metadata for the given topic.

    Returns partition metadata for the given topic.

    See also

    org.apache.kafka.clients.producer.KafkaProducer#partitionsFor

  4. abstract def produce(records: ProducerRecords[K, V]): F[F[ProducerResult[K, V]]]

    Produces the specified ProducerRecords in two steps: the first effect puts the records in the buffer of the producer, and the second effect waits for the records to send.

    Produces the specified ProducerRecords in two steps: the first effect puts the records in the buffer of the producer, and the second effect waits for the records to send.

    It's possible to flatten the result from this function to have an effect which both sends the records and waits for them to finish sending.

    Waiting for individual records to send can substantially limit performance. In some cases, this is necessary, and so we might want to consider the following alternatives.

    • Wait for the produced records in batches, improving the rate at which records are produced, but loosing the guarantee where produce >> otherAction means otherAction executes after the record has been sent.
    • Run several produce.flatten >> otherAction concurrently, improving the rate at which records are produced, and still have otherAction execute after records have been sent, but losing the order of produced records.
  5. abstract def produceAndCommitTransactionally(records: TransactionalProducerRecords[F, K, V]): F[ProducerResult[K, V]]

    Produces the ProducerRecords in the specified TransactionalProducerRecords in four steps: first a transaction is initialized, then the records are placed in the buffer of the producer, then the offsets of the records are sent to the transaction, and lastly the transaction is committed.

    Produces the ProducerRecords in the specified TransactionalProducerRecords in four steps: first a transaction is initialized, then the records are placed in the buffer of the producer, then the offsets of the records are sent to the transaction, and lastly the transaction is committed. If errors or cancellation occurs, the transaction is aborted. The returned effect succeeds if the whole transaction completes successfully.

  6. abstract def produceTransactionally(records: ProducerRecords[K, V]): F[ProducerResult[K, V]]

    Produces the ProducerRecords in the specified ProducerRecords in three steps: first a transaction is initialized, then the records are placed in the buffer of the producer, and lastly the transaction is committed.

    Produces the ProducerRecords in the specified ProducerRecords in three steps: first a transaction is initialized, then the records are placed in the buffer of the producer, and lastly the transaction is committed. If errors or cancellation occurs, the transaction is aborted. The returned effect succeeds if the whole transaction completes successfully.

  7. abstract def sendOffsetsToTransaction(offsets: Map[TopicPartition, OffsetAndMetadata], groupMetadata: ConsumerGroupMetadata): F[Unit]

    Sends the specified offsets and KafkaConsumer.groupMetadata to be committed as part of a transaction.

  8. abstract def transaction: Resource[F, Unit]

    Return a resource which handles the transaction lifecycle.

    Return a resource which handles the transaction lifecycle.

    The returned resource begins a transaction on use and:

    • commits the transaction if the use finishes successfully, or
    • aborts the transaction if an error occurs or the process is canceled.

    Note initTransactions must have been called before using this method, either manually or automatically through the use of KafkaProducer.transactional or KafkaProducer.transactionalStream.

    Also note only one transaction can be open at any time. If a second transaction is started within the lifecycle of a first transaction, the second transaction will deadlock.

    producer.transaction.surround {
      producer.transaction.surround { // deadlocks waiting for the outer transaction
        IO.unit
      }
    }
  9. abstract def withSerializers[K2, V2](keySerializer: KeySerializer[F, K2], valueSerializer: ValueSerializer[F, V2]): KafkaProducer[F, K2, V2]

    Returns a new KafkaProducer using the same underlying producer but with different key and value serializers.

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  11. final def imapK[G[_]](fk: ~>[F, G], gk: ~>[G, F])(implicit arg0: MonadCancelThrow[G], F: MonadCancelThrow[F]): KafkaProducer[G, K, V]

    Creates a new KafkaProducer in which the effect type has been changed using the specified FunctionKs.

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