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package zio.kafka.consumer
import org.apache.kafka.clients.consumer.{
Consumer => JConsumer,
ConsumerRecord,
OffsetAndMetadata,
OffsetAndTimestamp
}
import org.apache.kafka.common._
import zio._
import zio.kafka.consumer.diagnostics.DiagnosticEvent.Finalization
import zio.kafka.consumer.diagnostics.Diagnostics
import zio.kafka.consumer.internal.{ ConsumerAccess, RunloopAccess }
import zio.kafka.serde.{ Deserializer, Serde }
import zio.kafka.utils.SslHelper
import zio.stream._
import scala.jdk.CollectionConverters._
import scala.util.control.NoStackTrace
trait Consumer {
/**
* Returns the topic-partitions that this consumer is currently assigned.
*
* This is subject to consumer rebalancing, unless using a manual subscription.
*/
def assignment: Task[Set[TopicPartition]]
def beginningOffsets(
partitions: Set[TopicPartition],
timeout: Duration = Duration.Infinity
): Task[Map[TopicPartition, Long]]
def endOffsets(
partitions: Set[TopicPartition],
timeout: Duration = Duration.Infinity
): Task[Map[TopicPartition, Long]]
/**
* Retrieve the last committed offset for the given topic-partitions
*/
def committed(
partitions: Set[TopicPartition],
timeout: Duration = Duration.Infinity
): Task[Map[TopicPartition, Option[OffsetAndMetadata]]]
def listTopics(timeout: Duration = Duration.Infinity): Task[Map[String, List[PartitionInfo]]]
/**
* Create a stream that emits chunks whenever new partitions are assigned to this consumer.
*
* The top-level stream will emit chunks whenever the consumer rebalances, unless a manual subscription was made. When
* rebalancing occurs, new topic-partition streams may be emitted and existing streams may be completed.
*
* All streams can be completed by calling [[stopConsumption]].
*
* Multiple subscriptions on one Consumer are supported, as long as the subscriptions are of the same type (topics,
* patterns, manual). Each subscription will only receive messages from the topic-partitions that match the
* subscription. When subscriptions overlap, kafka records will be divided over the overlapping subscriptions
* non-deterministically.
*
* On completion of the stream, the consumer is unsubscribed. In case of multiple subscriptions, the total consumer
* subscription is changed to exclude this subscription.
*/
def partitionedAssignmentStream[R, K, V](
subscription: Subscription,
keyDeserializer: Deserializer[R, K],
valueDeserializer: Deserializer[R, V]
): Stream[Throwable, Chunk[(TopicPartition, ZStream[R, Throwable, CommittableRecord[K, V]])]]
/**
* Create a stream with messages on the subscribed topic-partitions by topic-partition
*
* The top-level stream will emit new topic-partition streams for each topic-partition that is assigned to this
* consumer. This is subject to consumer rebalancing, unless a manual subscription was made. When rebalancing occurs,
* new topic-partition streams may be emitted and existing streams may be completed.
*
* All streams can be completed by calling [[stopConsumption]].
*
* Multiple subscriptions on one Consumer are supported, as long as the subscriptions are of the same type (topics,
* patterns, manual). Each subscription will only receive messages from the topic-partitions that match the
* subscription. When subscriptions overlap, kafka records will be divided over the overlapping subscriptions
* non-deterministically.
*
* On completion of the stream, the consumer is unsubscribed. In case of multiple subscriptions, the total consumer
* subscription is changed to exclude this subscription.
*/
def partitionedStream[R, K, V](
subscription: Subscription,
keyDeserializer: Deserializer[R, K],
valueDeserializer: Deserializer[R, V]
): Stream[Throwable, (TopicPartition, ZStream[R, Throwable, CommittableRecord[K, V]])]
/**
* Create a stream with all messages on the subscribed topic-partitions
*
* The stream will emit messages from all topic-partitions interleaved. Per-partition record order is guaranteed, but
* the topic-partition interleaving is non-deterministic.
*
* Up to `bufferSize` chunks may be buffered in memory by this operator.
*
* The stream can be completed by calling [[stopConsumption]].
*
* Multiple subscriptions on one Consumer are supported, as long as the subscriptions are of the same type (topics,
* patterns, manual). Each subscription will only receive messages from the topic-partitions that match the
* subscription. When subscriptions overlap, kafka records will be divided over the overlapping subscriptions
* non-deterministically.
*
* On completion of the stream, the consumer is unsubscribed. In case of multiple subscriptions, the total consumer
* subscription is changed to exclude this subscription.
*/
def plainStream[R, K, V](
subscription: Subscription,
keyDeserializer: Deserializer[R, K],
valueDeserializer: Deserializer[R, V],
bufferSize: Int = 4
): ZStream[R, Throwable, CommittableRecord[K, V]]
/**
* Stops consumption of data, drains buffered records, and ends the attached streams while still serving commit
* requests.
*/
def stopConsumption: UIO[Unit]
/**
* See [[Consumer.consumeWith]].
*/
def consumeWith[R: EnvironmentTag, R1: EnvironmentTag, K, V](
subscription: Subscription,
keyDeserializer: Deserializer[R, K],
valueDeserializer: Deserializer[R, V],
commitRetryPolicy: Schedule[Any, Any, Any] = Schedule.exponential(1.second) && Schedule.recurs(3)
)(
f: ConsumerRecord[K, V] => URIO[R1, Unit]
): ZIO[R & R1, Throwable, Unit]
/**
* Look up the offsets for the given partitions by timestamp. The returned offset for each partition is the earliest
* offset whose timestamp is greater than or equal to the given timestamp in the corresponding partition.
*
* The consumer does not have to be assigned the partitions. If no messages exist yet for a partition, it will not
* exist in the returned map.
*/
def offsetsForTimes(
timestamps: Map[TopicPartition, Long],
timeout: Duration = Duration.Infinity
): Task[Map[TopicPartition, OffsetAndTimestamp]]
def partitionsFor(topic: String, timeout: Duration = Duration.Infinity): Task[List[PartitionInfo]]
def position(partition: TopicPartition, timeout: Duration = Duration.Infinity): Task[Long]
def subscription: Task[Set[String]]
/**
* Expose internal consumer metrics
*/
def metrics: Task[Map[MetricName, Metric]]
}
object Consumer {
case object RunloopTimeout extends RuntimeException("Timeout in Runloop") with NoStackTrace
case object CommitTimeout extends RuntimeException("Commit timeout") with NoStackTrace
val offsetBatches: ZSink[Any, Nothing, Offset, Nothing, OffsetBatch] =
ZSink.foldLeft[Offset, OffsetBatch](OffsetBatch.empty)(_ add _)
def live: RLayer[ConsumerSettings & Diagnostics, Consumer] =
ZLayer.scoped {
for {
settings <- ZIO.service[ConsumerSettings]
diagnostics <- ZIO.service[Diagnostics]
consumer <- make(settings, diagnostics)
} yield consumer
}
def make(
settings: ConsumerSettings,
diagnostics: Diagnostics = Diagnostics.NoOp
): ZIO[Scope, Throwable, Consumer] =
for {
_ <- ZIO.addFinalizer(diagnostics.emit(Finalization.ConsumerFinalized))
_ <- SslHelper.validateEndpoint(settings.driverSettings)
consumerAccess <- ConsumerAccess.make(settings)
runloopAccess <- RunloopAccess.make(settings, consumerAccess, diagnostics)
} yield new ConsumerLive(consumerAccess, runloopAccess)
/**
* Create a zio-kafka Consumer from an org.apache.kafka KafkaConsumer
*
* You are responsible for creating and closing the KafkaConsumer
*/
def fromJavaConsumer(
javaConsumer: JConsumer[Array[Byte], Array[Byte]],
settings: ConsumerSettings,
diagnostics: Diagnostics = Diagnostics.NoOp
): ZIO[Scope, Throwable, Consumer] =
for {
_ <- ZIO.addFinalizer(diagnostics.emit(Finalization.ConsumerFinalized))
consumerAccess <- ConsumerAccess.make(javaConsumer)
runloopAccess <- RunloopAccess.make(settings, consumerAccess, diagnostics)
} yield new ConsumerLive(consumerAccess, runloopAccess)
/**
* Accessor method
*/
def assignment: RIO[Consumer, Set[TopicPartition]] =
ZIO.serviceWithZIO(_.assignment)
/**
* Accessor method
*/
def beginningOffsets(
partitions: Set[TopicPartition],
timeout: Duration = Duration.Infinity
): RIO[Consumer, Map[TopicPartition, Long]] =
ZIO.serviceWithZIO(_.beginningOffsets(partitions, timeout))
/**
* Accessor method
*/
def committed(
partitions: Set[TopicPartition],
timeout: Duration = Duration.Infinity
): RIO[Consumer, Map[TopicPartition, Option[OffsetAndMetadata]]] =
ZIO.serviceWithZIO(_.committed(partitions, timeout))
/**
* Accessor method
*/
def endOffsets(
partitions: Set[TopicPartition],
timeout: Duration = Duration.Infinity
): RIO[Consumer, Map[TopicPartition, Long]] =
ZIO.serviceWithZIO(_.endOffsets(partitions, timeout))
/**
* Accessor method
*/
def listTopics(
timeout: Duration = Duration.Infinity
): RIO[Consumer, Map[String, List[PartitionInfo]]] =
ZIO.serviceWithZIO(_.listTopics(timeout))
/**
* Accessor method
*/
def partitionedAssignmentStream[R, K, V](
subscription: Subscription,
keyDeserializer: Deserializer[R, K],
valueDeserializer: Deserializer[R, V]
): ZStream[Consumer, Throwable, Chunk[(TopicPartition, ZStream[R, Throwable, CommittableRecord[K, V]])]] =
ZStream.serviceWithStream[Consumer](_.partitionedAssignmentStream(subscription, keyDeserializer, valueDeserializer))
/**
* Accessor method
*/
def partitionedStream[R, K, V](
subscription: Subscription,
keyDeserializer: Deserializer[R, K],
valueDeserializer: Deserializer[R, V]
): ZStream[
Consumer,
Throwable,
(TopicPartition, ZStream[R, Throwable, CommittableRecord[K, V]])
] =
ZStream.serviceWithStream[Consumer](_.partitionedStream(subscription, keyDeserializer, valueDeserializer))
/**
* Accessor method
*/
def plainStream[R, K, V](
subscription: Subscription,
keyDeserializer: Deserializer[R, K],
valueDeserializer: Deserializer[R, V],
bufferSize: Int = 4
): ZStream[R & Consumer, Throwable, CommittableRecord[K, V]] =
ZStream.serviceWithStream[Consumer](
_.plainStream(subscription, keyDeserializer, valueDeserializer, bufferSize)
)
/**
* Accessor method
*/
def stopConsumption: RIO[Consumer, Unit] =
ZIO.serviceWithZIO(_.stopConsumption)
/**
* Execute an effect for each record and commit the offset after processing
*
* This method is the easiest way of processing messages on a Kafka topic.
*
* Messages on a single partition are processed sequentially, while the processing of multiple partitions happens in
* parallel.
*
* Offsets are committed after execution of the effect. They are batched when a commit action is in progress to avoid
* backpressuring the stream. When commits fail due to a
* org.apache.kafka.clients.consumer.RetriableCommitFailedException they are retried according to commitRetryPolicy
*
* The effect should absorb any failures. Failures should be handled by retries or ignoring the error, which will
* result in the Kafka message being skipped.
*
* Messages are processed with 'at least once' consistency: it is not guaranteed that every message that is processed
* by the effect has a corresponding offset commit before stream termination.
*
* Usage example:
*
* {{{
* val settings: ConsumerSettings = ???
* val subscription = Subscription.Topics(Set("my-kafka-topic"))
*
* val consumerIO = Consumer.consumeWith(settings, subscription, Serdes.string, Serdes.string) { record =>
* // Process the received record here
* putStrLn(s"Received record: \${record.key()}: \${record.value()}")
* }
* }}}
*
* @param settings
* Settings for creating a [[Consumer]]
* @param subscription
* Topic subscription parameters
* @param keyDeserializer
* Deserializer for the key of the messages
* @param valueDeserializer
* Deserializer for the value of the messages
* @param commitRetryPolicy
* Retry commits that failed due to a RetriableCommitFailedException according to this schedule
* @param f
* Function that returns the effect to execute for each message. It is passed the
* [[org.apache.kafka.clients.consumer.ConsumerRecord]].
* @tparam R
* Environment for the consuming effect
* @tparam R1
* Environment for the deserializers
* @tparam K
* Type of keys (an implicit `Deserializer` should be in scope)
* @tparam V
* Type of values (an implicit `Deserializer` should be in scope)
* @return
* Effect that completes with a unit value only when interrupted. May fail when the [[Consumer]] fails.
*/
def consumeWith[R: EnvironmentTag, R1: EnvironmentTag, K, V](
settings: ConsumerSettings,
subscription: Subscription,
keyDeserializer: Deserializer[R, K],
valueDeserializer: Deserializer[R, V],
commitRetryPolicy: Schedule[Any, Any, Any] = Schedule.exponential(1.second) && Schedule.recurs(3)
)(f: ConsumerRecord[K, V] => URIO[R1, Unit]): RIO[R & R1, Unit] =
ZIO.scoped[R & R1] {
Consumer
.make(settings)
.flatMap(_.consumeWith[R, R1, K, V](subscription, keyDeserializer, valueDeserializer, commitRetryPolicy)(f))
}
/**
* Accessor method
*/
def offsetsForTimes(
timestamps: Map[TopicPartition, Long],
timeout: Duration = Duration.Infinity
): RIO[Consumer, Map[TopicPartition, OffsetAndTimestamp]] =
ZIO.serviceWithZIO(_.offsetsForTimes(timestamps, timeout))
/**
* Accessor method
*/
def partitionsFor(
topic: String,
timeout: Duration = Duration.Infinity
): RIO[Consumer, List[PartitionInfo]] =
ZIO.serviceWithZIO(_.partitionsFor(topic, timeout))
/**
* Accessor method
*/
def position(
partition: TopicPartition,
timeout: Duration = Duration.Infinity
): RIO[Consumer, Long] =
ZIO.serviceWithZIO(_.position(partition, timeout))
/**
* Accessor method
*/
def subscription: RIO[Consumer, Set[String]] =
ZIO.serviceWithZIO(_.subscription)
/**
* Accessor method
*/
def metrics: RIO[Consumer, Map[MetricName, Metric]] =
ZIO.serviceWithZIO(_.metrics)
sealed trait OffsetRetrieval
object OffsetRetrieval {
final case class Auto(reset: AutoOffsetStrategy = AutoOffsetStrategy.Latest) extends OffsetRetrieval
final case class Manual(getOffsets: Set[TopicPartition] => Task[Map[TopicPartition, Long]]) extends OffsetRetrieval
}
sealed trait AutoOffsetStrategy { self =>
final def toConfig: String = self match {
case AutoOffsetStrategy.Earliest => "earliest"
case AutoOffsetStrategy.Latest => "latest"
case AutoOffsetStrategy.None => "none"
}
}
object AutoOffsetStrategy {
case object Earliest extends AutoOffsetStrategy
case object Latest extends AutoOffsetStrategy
case object None extends AutoOffsetStrategy
}
}
private[consumer] final class ConsumerLive private[consumer] (
consumer: ConsumerAccess,
runloopAccess: RunloopAccess
) extends Consumer {
import Consumer._
override def assignment: Task[Set[TopicPartition]] =
consumer.withConsumer(_.assignment().asScala.toSet)
override def beginningOffsets(
partitions: Set[TopicPartition],
timeout: Duration = Duration.Infinity
): Task[Map[TopicPartition, Long]] =
consumer.withConsumer(
_.beginningOffsets(partitions.asJava, timeout.asJava).asScala.map { case (tp, l) =>
tp -> l.longValue()
}.toMap
)
override def endOffsets(
partitions: Set[TopicPartition],
timeout: Duration = Duration.Infinity
): Task[Map[TopicPartition, Long]] =
consumer.withConsumer { eo =>
val offs = eo.endOffsets(partitions.asJava, timeout.asJava)
offs.asScala.map { case (k, v) => k -> v.longValue() }.toMap
}
override def committed(
partitions: Set[TopicPartition],
timeout: Duration = Duration.Infinity
): Task[Map[TopicPartition, Option[OffsetAndMetadata]]] =
consumer.withConsumer(
_.committed(partitions.asJava, timeout.asJava).asScala.map { case (k, v) => k -> Option(v) }.toMap
)
override def listTopics(timeout: Duration = Duration.Infinity): Task[Map[String, List[PartitionInfo]]] =
consumer.withConsumer(_.listTopics(timeout.asJava).asScala.map { case (k, v) => k -> v.asScala.toList }.toMap)
override def partitionedAssignmentStream[R, K, V](
subscription: Subscription,
keyDeserializer: Deserializer[R, K],
valueDeserializer: Deserializer[R, V]
): Stream[Throwable, Chunk[(TopicPartition, ZStream[R, Throwable, CommittableRecord[K, V]])]] = {
val onlyByteArraySerdes: Boolean = (keyDeserializer eq Serde.byteArray) && (valueDeserializer eq Serde.byteArray)
ZStream.unwrapScoped {
for {
stream <- runloopAccess.subscribe(subscription)
} yield stream
.map(_.exit)
.flattenExitOption
.map {
_.collect {
case (tp, partitionStream) if Subscription.subscriptionMatches(subscription, tp) =>
val stream: ZStream[R, Throwable, CommittableRecord[K, V]] =
if (onlyByteArraySerdes)
partitionStream.asInstanceOf[ZStream[R, Throwable, CommittableRecord[K, V]]]
else partitionStream.mapChunksZIO(_.mapZIO(_.deserializeWith(keyDeserializer, valueDeserializer)))
tp -> stream
}
}
}
}
override def partitionedStream[R, K, V](
subscription: Subscription,
keyDeserializer: Deserializer[R, K],
valueDeserializer: Deserializer[R, V]
): ZStream[Any, Throwable, (TopicPartition, ZStream[R, Throwable, CommittableRecord[K, V]])] =
partitionedAssignmentStream(subscription, keyDeserializer, valueDeserializer).flattenChunks
override def plainStream[R, K, V](
subscription: Subscription,
keyDeserializer: Deserializer[R, K],
valueDeserializer: Deserializer[R, V],
bufferSize: Int
): ZStream[R, Throwable, CommittableRecord[K, V]] =
partitionedStream(subscription, keyDeserializer, valueDeserializer).flatMapPar(
n = Int.MaxValue,
bufferSize = bufferSize
)(_._2)
override def stopConsumption: UIO[Unit] =
ZIO.logDebug("stopConsumption called") *>
runloopAccess.stopConsumption
override def consumeWith[R: EnvironmentTag, R1: EnvironmentTag, K, V](
subscription: Subscription,
keyDeserializer: Deserializer[R, K],
valueDeserializer: Deserializer[R, V],
commitRetryPolicy: Schedule[Any, Any, Any] = Schedule.exponential(1.second) && Schedule.recurs(3)
)(
f: ConsumerRecord[K, V] => URIO[R1, Unit]
): ZIO[R & R1, Throwable, Unit] =
for {
r <- ZIO.environment[R & R1]
_ <- partitionedStream(subscription, keyDeserializer, valueDeserializer)
.flatMapPar(Int.MaxValue) { case (_, partitionStream) =>
partitionStream.mapChunksZIO(_.mapZIO((c: CommittableRecord[K, V]) => f(c.record).as(c.offset)))
}
.provideEnvironment(r)
.aggregateAsync(offsetBatches)
.mapZIO(_.commitOrRetry(commitRetryPolicy))
.runDrain
} yield ()
override def offsetsForTimes(
timestamps: Map[TopicPartition, Long],
timeout: Duration = Duration.Infinity
): Task[Map[TopicPartition, OffsetAndTimestamp]] =
consumer.withConsumer(
_.offsetsForTimes(timestamps.map { case (k, v) => k -> Long.box(v) }.asJava, timeout.asJava).asScala.toMap
// If a partition doesn't exist yet, the map will have 'null' as entry.
// It's more idiomatic scala to then simply not have that map entry.
.filter(_._2 != null)
)
override def partitionsFor(
topic: String,
timeout: Duration = Duration.Infinity
): Task[List[PartitionInfo]] =
consumer.withConsumer { c =>
val partitions = c.partitionsFor(topic, timeout.asJava)
if (partitions eq null) List.empty else partitions.asScala.toList
}
override def position(partition: TopicPartition, timeout: Duration = Duration.Infinity): Task[Long] =
consumer.withConsumer(_.position(partition, timeout.asJava))
override def subscription: Task[Set[String]] =
consumer.withConsumer(_.subscription().asScala.toSet)
override def metrics: Task[Map[MetricName, Metric]] =
consumer.withConsumer(_.metrics().asScala.toMap)
}
