org.apache.beam.sdk.io.kafka

Class KafkaIO.Read<K,V>

java.lang.Object

<any>

org.apache.beam.sdk.io.kafka.KafkaIO.Read<K,V>

Enclosing class:

KafkaIO

public abstract static class KafkaIO.Read<K,V>
extends <any>

A PTransform to read from Kafka topics. See KafkaIO for more information on usage and configuration.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	KafkaIO.Read.External Exposes KafkaIO.TypedWithoutMetadata as an external transform for cross-language usage.

Field Summary

Fields

Modifier and Type	Field and Description
static org.apache.beam.sdk.runners.PTransformOverride	KAFKA_READ_OVERRIDE A PTransformOverride for runners to swap ReadFromKafkaViaSDF to legacy Kafka read if runners doesn't have a good support on executing unbounded Splittable DoFn.

Constructor Summary

Constructors

Constructor and Description
Read()

Method Summary

Modifier and Type	Method and Description
KafkaIO.Read<K,V>	commitOffsetsInFinalize() Finalized offsets are committed to Kafka.
PCollection<KafkaRecord<K,V>>	expand(PBegin input)
void	populateDisplayData(DisplayData.Builder builder)
KafkaIO.Read<K,V>	updateConsumerProperties(java.util.Map<java.lang.String,java.lang.Object> configUpdates) Deprecated. as of version 2.13. Use withConsumerConfigUpdates(Map) instead
KafkaIO.Read<K,V>	withBootstrapServers(java.lang.String bootstrapServers) Sets the bootstrap servers for the Kafka consumer.
KafkaIO.Read<K,V>	withCheckStopReadingFn(SerializableFunction<TopicPartition,java.lang.Boolean> checkStopReadingFn) A custom SerializableFunction that determines whether the ReadFromKafkaDoFn should stop reading from the given TopicPartition.
KafkaIO.Read<K,V>	withConsumerConfigUpdates(java.util.Map<java.lang.String,java.lang.Object> configUpdates) Update configuration for the backend main consumer.
KafkaIO.Read<K,V>	withConsumerFactoryFn(SerializableFunction<java.util.Map<java.lang.String,java.lang.Object>,Consumer<byte[],byte[]>> consumerFactoryFn) A factory to create Kafka Consumer from consumer configuration.
KafkaIO.Read<K,V>	withCreateTime(Duration maxDelay) Sets the timestamps policy based on KafkaTimestampType.CREATE_TIME timestamp of the records.
KafkaIO.Read<K,V>	withDynamicRead(Duration duration) Configure the KafkaIO to use WatchKafkaTopicPartitionDoFn to detect and emit any new available TopicPartition for ReadFromKafkaDoFn to consume during pipeline execution time.
KafkaIO.Read<K,V>	withKeyDeserializer(java.lang.Class<? extends Deserializer<K>> keyDeserializer) Sets a Kafka Deserializer to interpret key bytes read from Kafka.
KafkaIO.Read<K,V>	withKeyDeserializer(org.apache.beam.sdk.io.kafka.DeserializerProvider<K> deserializerProvider)
KafkaIO.Read<K,V>	withKeyDeserializerAndCoder(java.lang.Class<? extends Deserializer<K>> keyDeserializer, Coder<K> keyCoder) Sets a Kafka Deserializer for interpreting key bytes read from Kafka along with a Coder for helping the Beam runner materialize key objects at runtime if necessary.
KafkaIO.Read<K,V>	withLogAppendTime() Sets TimestampPolicy to TimestampPolicyFactory.LogAppendTimePolicy.
KafkaIO.Read<K,V>	withMaxNumRecords(long maxNumRecords) Similar to Read.Unbounded.withMaxNumRecords(long).
KafkaIO.Read<K,V>	withMaxReadTime(Duration maxReadTime) Similar to Read.Unbounded.withMaxReadTime(Duration).
KafkaIO.Read<K,V>	withOffsetConsumerConfigOverrides(java.util.Map<java.lang.String,java.lang.Object> offsetConsumerConfig) Set additional configuration for the backend offset consumer.
PTransform<PBegin,PCollection<KV<K,V>>>	withoutMetadata() Returns a PTransform for PCollection of KV, dropping Kafka metatdata.
KafkaIO.Read<K,V>	withProcessingTime() Sets TimestampPolicy to TimestampPolicyFactory.ProcessingTimePolicy.
KafkaIO.Read<K,V>	withReadCommitted() Sets "isolation_level" to "read_committed" in Kafka consumer configuration.
KafkaIO.Read<K,V>	withStartReadTime(Instant startReadTime) Use timestamp to set up start offset.
KafkaIO.Read<K,V>	withTimestampFn(SerializableFunction<KV<K,V>,Instant> timestampFn) Deprecated. as of version 2.4. Use withTimestampPolicyFactory(TimestampPolicyFactory) instead.
KafkaIO.Read<K,V>	withTimestampFn2(SerializableFunction<KafkaRecord<K,V>,Instant> timestampFn) Deprecated. as of version 2.4. Use withTimestampPolicyFactory(TimestampPolicyFactory) instead.
KafkaIO.Read<K,V>	withTimestampPolicyFactory(TimestampPolicyFactory<K,V> timestampPolicyFactory) Provide custom TimestampPolicyFactory to set event times and watermark for each partition.
KafkaIO.Read<K,V>	withTopic(java.lang.String topic) Sets the topic to read from.
KafkaIO.Read<K,V>	withTopicPartitions(java.util.List<TopicPartition> topicPartitions) Sets a list of partitions to read from.
KafkaIO.Read<K,V>	withTopics(java.util.List<java.lang.String> topics) Sets a list of topics to read from.
KafkaIO.Read<K,V>	withValueDeserializer(java.lang.Class<? extends Deserializer<V>> valueDeserializer) Sets a Kafka Deserializer to interpret value bytes read from Kafka.
KafkaIO.Read<K,V>	withValueDeserializer(org.apache.beam.sdk.io.kafka.DeserializerProvider<V> deserializerProvider)
KafkaIO.Read<K,V>	withValueDeserializerAndCoder(java.lang.Class<? extends Deserializer<V>> valueDeserializer, Coder<V> valueCoder) Sets a Kafka Deserializer for interpreting value bytes read from Kafka along with a Coder for helping the Beam runner materialize value objects at runtime if necessary.
KafkaIO.Read<K,V>	withWatermarkFn(SerializableFunction<KV<K,V>,Instant> watermarkFn) Deprecated. as of version 2.4. Use withTimestampPolicyFactory(TimestampPolicyFactory) instead.
KafkaIO.Read<K,V>	withWatermarkFn2(SerializableFunction<KafkaRecord<K,V>,Instant> watermarkFn) Deprecated. as of version 2.4. Use withTimestampPolicyFactory(TimestampPolicyFactory) instead.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

KAFKA_READ_OVERRIDE

@Internal
public static final org.apache.beam.sdk.runners.PTransformOverride KAFKA_READ_OVERRIDE

A PTransformOverride for runners to swap ReadFromKafkaViaSDF to legacy Kafka read if runners doesn't have a good support on executing unbounded Splittable DoFn.

Constructor Detail

Read

public Read()

Method Detail

withBootstrapServers

public KafkaIO.Read<K,V> withBootstrapServers(java.lang.String bootstrapServers)

Sets the bootstrap servers for the Kafka consumer.

withTopic

public KafkaIO.Read<K,V> withTopic(java.lang.String topic)

Sets the topic to read from.

See KafkaUnboundedSource.split(int, PipelineOptions) for description of how the partitions are distributed among the splits.

withTopics

public KafkaIO.Read<K,V> withTopics(java.util.List<java.lang.String> topics)

Sets a list of topics to read from. All the partitions from each of the topics are read.

See KafkaUnboundedSource.split(int, PipelineOptions) for description of how the partitions are distributed among the splits.

withTopicPartitions

public KafkaIO.Read<K,V> withTopicPartitions(java.util.List<TopicPartition> topicPartitions)

Sets a list of partitions to read from. This allows reading only a subset of partitions for one or more topics when (if ever) needed.

See KafkaUnboundedSource.split(int, PipelineOptions) for description of how the partitions are distributed among the splits.

withKeyDeserializer

public KafkaIO.Read<K,V> withKeyDeserializer(java.lang.Class<? extends Deserializer<K>> keyDeserializer)

Sets a Kafka Deserializer to interpret key bytes read from Kafka.

In addition, Beam also needs a Coder to serialize and deserialize key objects at runtime. KafkaIO tries to infer a coder for the key based on the Deserializer class, however in case that fails, you can use withKeyDeserializerAndCoder(Class, Coder) to provide the key coder explicitly.

withKeyDeserializerAndCoder

public KafkaIO.Read<K,V> withKeyDeserializerAndCoder(java.lang.Class<? extends Deserializer<K>> keyDeserializer,
                                                     Coder<K> keyCoder)

Sets a Kafka Deserializer for interpreting key bytes read from Kafka along with a Coder for helping the Beam runner materialize key objects at runtime if necessary.

Use this method only if your pipeline doesn't work with plain withKeyDeserializer(Class).

withKeyDeserializer

public KafkaIO.Read<K,V> withKeyDeserializer(org.apache.beam.sdk.io.kafka.DeserializerProvider<K> deserializerProvider)

withValueDeserializer

public KafkaIO.Read<K,V> withValueDeserializer(java.lang.Class<? extends Deserializer<V>> valueDeserializer)

Sets a Kafka Deserializer to interpret value bytes read from Kafka.

In addition, Beam also needs a Coder to serialize and deserialize value objects at runtime. KafkaIO tries to infer a coder for the value based on the Deserializer class, however in case that fails, you can use withValueDeserializerAndCoder(Class, Coder) to provide the value coder explicitly.

withValueDeserializerAndCoder

public KafkaIO.Read<K,V> withValueDeserializerAndCoder(java.lang.Class<? extends Deserializer<V>> valueDeserializer,
                                                       Coder<V> valueCoder)

Sets a Kafka Deserializer for interpreting value bytes read from Kafka along with a Coder for helping the Beam runner materialize value objects at runtime if necessary.

Use this method only if your pipeline doesn't work with plain withValueDeserializer(Class).

withValueDeserializer

public KafkaIO.Read<K,V> withValueDeserializer(org.apache.beam.sdk.io.kafka.DeserializerProvider<V> deserializerProvider)

withConsumerFactoryFn

public KafkaIO.Read<K,V> withConsumerFactoryFn(SerializableFunction<java.util.Map<java.lang.String,java.lang.Object>,Consumer<byte[],byte[]>> consumerFactoryFn)

A factory to create Kafka Consumer from consumer configuration. This is useful for supporting another version of Kafka consumer. Default is KafkaConsumer.

updateConsumerProperties

@Deprecated
public KafkaIO.Read<K,V> updateConsumerProperties(java.util.Map<java.lang.String,java.lang.Object> configUpdates)

Deprecated. as of version 2.13. Use withConsumerConfigUpdates(Map) instead

Update consumer configuration with new properties.

withMaxNumRecords

public KafkaIO.Read<K,V> withMaxNumRecords(long maxNumRecords)

Similar to Read.Unbounded.withMaxNumRecords(long). Mainly used for tests and demo applications.

withStartReadTime

public KafkaIO.Read<K,V> withStartReadTime(Instant startReadTime)

Use timestamp to set up start offset. It is only supported by Kafka Client 0.10.1.0 onwards and the message format version after 0.10.0.

Note that this take priority over start offset configuration ConsumerConfig.AUTO_OFFSET_RESET_CONFIG and any auto committed offsets.

This results in hard failures in either of the following two cases : 1. If one of more partitions do not contain any messages with timestamp larger than or equal to desired timestamp. 2. If the message format version in a partition is before 0.10.0, i.e. the messages do not have timestamps.

withMaxReadTime

public KafkaIO.Read<K,V> withMaxReadTime(Duration maxReadTime)

Similar to Read.Unbounded.withMaxReadTime(Duration). Mainly used for tests and demo applications.

withLogAppendTime

public KafkaIO.Read<K,V> withLogAppendTime()

Sets TimestampPolicy to TimestampPolicyFactory.LogAppendTimePolicy. The policy assigns Kafka's log append time (server side ingestion time) to each record. The watermark for each Kafka partition is the timestamp of the last record read. If a partition is idle, the watermark advances to couple of seconds behind wall time. Every record consumed from Kafka is expected to have its timestamp type set to 'LOG_APPEND_TIME'.

In Kafka, log append time needs to be enabled for each topic, and all the subsequent records wil have their timestamp set to log append time. If a record does not have its timestamp type set to 'LOG_APPEND_TIME' for any reason, it's timestamp is set to previous record timestamp or latest watermark, whichever is larger.

The watermark for the entire source is the oldest of each partition's watermark. If one of the readers falls behind possibly due to uneven distribution of records among Kafka partitions, it ends up holding the watermark for the entire source.

withProcessingTime

public KafkaIO.Read<K,V> withProcessingTime()

Sets TimestampPolicy to TimestampPolicyFactory.ProcessingTimePolicy. This is the default timestamp policy. It assigns processing time to each record. Specifically, this is the timestamp when the record becomes 'current' in the reader. The watermark aways advances to current time. If server side time (log append time) is enabled in Kafka, withLogAppendTime() is recommended over this.

withCreateTime

public KafkaIO.Read<K,V> withCreateTime(Duration maxDelay)

Sets the timestamps policy based on KafkaTimestampType.CREATE_TIME timestamp of the records. It is an error if a record's timestamp type is not KafkaTimestampType.CREATE_TIME. The timestamps within a partition are expected to be roughly monotonically increasing with a cap on out of order delays (e.g. 'max delay' of 1 minute). The watermark at any time is '(Min(now(), Max(event timestamp so far)) - max delay)'. However, watermark is never set in future and capped to 'now - max delay'. In addition, watermark advanced to 'now - max delay' when a partition is idle.

Parameters:

maxDelay - For any record in the Kafka partition, the timestamp of any subsequent record is expected to be after current record timestamp - maxDelay.

withTimestampPolicyFactory

public KafkaIO.Read<K,V> withTimestampPolicyFactory(TimestampPolicyFactory<K,V> timestampPolicyFactory)

Provide custom TimestampPolicyFactory to set event times and watermark for each partition. TimestampPolicyFactory.createTimestampPolicy(TopicPartition, Optional) is invoked for each partition when the reader starts.

See Also:

withLogAppendTime(), withCreateTime(Duration), withProcessingTime()

withTimestampFn2

@Deprecated
public KafkaIO.Read<K,V> withTimestampFn2(SerializableFunction<KafkaRecord<K,V>,Instant> timestampFn)

Deprecated. as of version 2.4. Use withTimestampPolicyFactory(TimestampPolicyFactory) instead.

A function to assign a timestamp to a record. Default is processing timestamp.

withWatermarkFn2

@Deprecated
public KafkaIO.Read<K,V> withWatermarkFn2(SerializableFunction<KafkaRecord<K,V>,Instant> watermarkFn)

Deprecated. as of version 2.4. Use withTimestampPolicyFactory(TimestampPolicyFactory) instead.

A function to calculate watermark after a record. Default is last record timestamp.

See Also:

withTimestampFn(SerializableFunction)

withTimestampFn

@Deprecated
public KafkaIO.Read<K,V> withTimestampFn(SerializableFunction<KV<K,V>,Instant> timestampFn)

Deprecated. as of version 2.4. Use withTimestampPolicyFactory(TimestampPolicyFactory) instead.

A function to assign a timestamp to a record. Default is processing timestamp.

withWatermarkFn

@Deprecated
public KafkaIO.Read<K,V> withWatermarkFn(SerializableFunction<KV<K,V>,Instant> watermarkFn)

Deprecated. as of version 2.4. Use withTimestampPolicyFactory(TimestampPolicyFactory) instead.

A function to calculate watermark after a record. Default is last record timestamp.

See Also:

withTimestampFn(SerializableFunction)

withReadCommitted

public KafkaIO.Read<K,V> withReadCommitted()

Sets "isolation_level" to "read_committed" in Kafka consumer configuration. This is ensures that the consumer does not read uncommitted messages. Kafka version 0.11 introduced transactional writes. Applications requiring end-to-end exactly-once semantics should only read committed messages. See JavaDoc for KafkaConsumer for more description.

commitOffsetsInFinalize

public KafkaIO.Read<K,V> commitOffsetsInFinalize()

Finalized offsets are committed to Kafka. See UnboundedSource.CheckpointMark.finalizeCheckpoint(). It helps with minimizing gaps or duplicate processing of records while restarting a pipeline from scratch. But it does not provide hard processing guarantees. There could be a short delay to commit after UnboundedSource.CheckpointMark.finalizeCheckpoint() is invoked, as reader might be blocked on reading from Kafka. Note that it is independent of 'AUTO_COMMIT' Kafka consumer configuration. Usually either this or AUTO_COMMIT in Kafka consumer is enabled, but not both.

withDynamicRead

public KafkaIO.Read<K,V> withDynamicRead(Duration duration)

Configure the KafkaIO to use WatchKafkaTopicPartitionDoFn to detect and emit any new available TopicPartition for ReadFromKafkaDoFn to consume during pipeline execution time. The KafkaIO will regularly check the availability based on the given duration. If the duration is not specified as null, the default duration is 1 hour.

withOffsetConsumerConfigOverrides

public KafkaIO.Read<K,V> withOffsetConsumerConfigOverrides(java.util.Map<java.lang.String,java.lang.Object> offsetConsumerConfig)

Set additional configuration for the backend offset consumer. It may be required for a secured Kafka cluster, especially when you see similar WARN log message 'exception while fetching latest offset for partition {}. will be retried'.

In KafkaIO.read(), there're two consumers running in the backend actually:
1. the main consumer, which reads data from kafka;
2. the secondary offset consumer, which is used to estimate backlog, by fetching latest offset;

By default, offset consumer inherits the configuration from main consumer, with an auto-generated ConsumerConfig.GROUP_ID_CONFIG. This may not work in a secured Kafka which requires more configurations.

withConsumerConfigUpdates

public KafkaIO.Read<K,V> withConsumerConfigUpdates(java.util.Map<java.lang.String,java.lang.Object> configUpdates)

Update configuration for the backend main consumer. Note that the default consumer properties will not be completely overridden. This method only updates the value which has the same key.

In KafkaIO.read(), there're two consumers running in the backend actually:
1. the main consumer, which reads data from kafka;
2. the secondary offset consumer, which is used to estimate backlog, by fetching latest offset;

By default, main consumer uses the configuration from KafkaIOUtils.DEFAULT_CONSUMER_PROPERTIES.

withCheckStopReadingFn

public KafkaIO.Read<K,V> withCheckStopReadingFn(SerializableFunction<TopicPartition,java.lang.Boolean> checkStopReadingFn)

A custom SerializableFunction that determines whether the ReadFromKafkaDoFn should stop reading from the given TopicPartition.

withoutMetadata

public PTransform<PBegin,PCollection<KV<K,V>>> withoutMetadata()

Returns a PTransform for PCollection of KV, dropping Kafka metatdata.

expand

public PCollection<KafkaRecord<K,V>> expand(PBegin input)

populateDisplayData

public void populateDisplayData(DisplayData.Builder builder)