KAFKA INPUT OPERATOR

Introduction

Apache Kafka is a pull-based and distributed publish subscribe messaging system, topics are partitioned and replicated across nodes.

The Kafka input operator consumes data from the partitions of a Kafka topic for processing in Apex. The operator has the ability to automatically scale with the Kafka partitioning for high throughput. It is fault-tolerant (consumer offset checkpointing) and guarantees idempotency to allow exactly-once results in the downstream pipeline.

For more information about the operator design see this presentation and for processing guarantees this blog.

There are two separate implementations of the input operator, one built against Kafka 0.8 client and a newer version for the Kafka 0.9 consumer API that also works with MapR Streams. These reside in different packages and are described separately below.

Kafka Input Operator for Kafka 0.8.x

Package: com.datatorrent.contrib.kafka

Maven artifact: malhar-contrib

AbstractKafkaInputOperator

This is the abstract implementation that serves as base class for consuming messages from Kafka messaging system. This class doesn’t have any ports.

Configuration Parameters

Abstract Methods

void emitTuple(Message message): Abstract method that emits tuples extracted from Kafka message.

KafkaConsumer

This is an abstract implementation of Kafka consumer. It sends the fetch requests to the leading brokers of Kafka partitions. For each request, it receives the set of messages and stores them into the buffer which is ArrayBlockingQueue. SimpleKafkaConsumer which extends KafkaConsumer and serves the functionality of Simple Consumer API and HighLevelKafkaConsumer which extends KafkaConsumer and serves the functionality of High Level Consumer API.

Pre-requisites

This operator uses the Kafka 0.8.2.1 client consumer API and will work with 0.8.x and 0.7.x versions of Kafka broker.

Configuration Parameters

Abstract Methods

void commitOffset(): Commit the offsets at checkpoint.
Map <KafkaPartition, Long> getCurrentOffsets(): Return the current offset status.
resetPartitionsAndOffset(Set <KafkaPartition> partitionIds, Map <KafkaPartition, Long> startOffset): Reset the partitions with parittionIds and offsets with startOffset.

Configuration Parameters for SimpleKafkaConsumer

OffsetManager

This is an interface for offset management and is useful when consuming data from specified offsets. Updates the offsets for all the Kafka partitions periodically. Below is the code snippet:

public interface OffsetManager
{
  public Map<KafkaPartition, Long> loadInitialOffsets();
  public void updateOffsets(Map<KafkaPartition, Long> offsetsOfPartitions);
}

Abstract Methods

Map <KafkaPartition, Long> loadInitialOffsets(): Specifies the initial offset for consuming messages; called at the activation stage.

updateOffsets(Map<KafkaPartition, Long> offsetsOfPartitions): This method is called at every repartitionCheckInterval to update offsets.

Partitioning

The logical instance of the KafkaInputOperator acts as the Partitioner as well as a StatsListener. This is because the AbstractKafkaInputOperator implements both the com.datatorrent.api.Partitioner and com.datatorrent.api.StatsListener interfaces and provides an implementation of definePartitions(...) and processStats(...) which makes it auto-scalable.

Response processStats(BatchedOperatorStats stats)

The application master invokes this method on the logical instance with the stats (tuplesProcessedPS, bytesPS, etc.) of each partition. Re-partitioning happens based on whether any new Kafka partitions added for the topic or bytesPS and msgPS cross their respective upper bounds.

DefinePartitions

Based on the repartitionRequired field of the Response object which is returned by processStats(...) method, the application master invokes definePartitions(...) on the logical instance which is also the partitioner instance. Dynamic partition can be disabled by setting the parameter repartitionInterval value to a negative value.

AbstractSinglePortKafkaInputOperator

This class extends AbstractKafkaInputOperator to emit messages through single output port.

Ports

outputPort <T>: Tuples extracted from Kafka messages are emitted through this port.

Abstract Methods

T getTuple(Message msg): Converts the Kafka message to tuple.

Concrete Classes

KafkaSinglePortStringInputOperator: extends AbstractSinglePortKafkaInputOperator, extracts string from Kafka message.
KafkaSinglePortByteArrayInputOperator: extends AbstractSinglePortKafkaInputOperator, extracts byte array from Kafka message.

Application Example

This section builds an Apex application using Kafka input operator. Below is the code snippet:

@ApplicationAnnotation(name = "KafkaApp")
public class ExampleKafkaApplication implements StreamingApplication
{
  @Override
  public void populateDAG(DAG dag, Configuration entries)
  {
    KafkaSinglePortByteArrayInputOperator input =  dag.addOperator("MessageReader", new KafkaSinglePortByteArrayInputOperator());
    ConsoleOutputOperator output = dag.addOperator("Output", new ConsoleOutputOperator());
    dag.addStream("MessageData", input.outputPort, output.input);
  }
}

Below is the configuration for “test” Kafka topic name and “localhost:2181” is the zookeeper forum:

<property>
  <name>dt.operator.MessageReader.prop.topic</name>
  <value>test</value>
</property>

<property>
  <name>dt.operator.KafkaInputOperator.prop.zookeeper</nam>
  <value>localhost:2181</value>
</property>

Kafka Input Operator for Kafka 0.9.x

Package: org.apache.apex.malhar.kafka

Maven Artifact: malhar-kafka

This version uses the new 0.9 version of consumer API and works with Kafka broker version 0.9 and later. The operator is fault-tolerant, scalable and supports input from multiple clusters and multiple topics in a single operator instance.

Pre-requisites

This operator requires version 0.9.0 or later of the Kafka Consumer API.

AbstractKafkaInputOperator

Ports

This abstract class doesn't have any ports.

Configuration properties

clusters - String[]
- Mandatory Parameter.
- Specifies the Kafka clusters that you want to consume messages from. To configure multi-cluster support, you need to specify the clusters separated by “;”.
topics - String[]
- Mandatory Parameter.
- Specified the Kafka topics that you want to consume messages from. If you want multi-topic support, then specify the topics separated by “,”.
strategy - PartitionStrategy
- Operator supports two types of partitioning strategies, ONE_TO_ONE and ONE_TO_MANY.
  ONE_TO_ONE: If this is enabled, the AppMaster creates one input operator instance per Kafka topic partition. So the number of Kafka topic partitions equals the number of operator instances. ONE_TO_MANY: The AppMaster creates K = min(initialPartitionCount, N) Kafka input operator instances where N is the number of Kafka topic partitions. If K is less than N, the remaining topic partitions are assigned to the K operator instances in round-robin fashion. If K is less than initialPartitionCount, the AppMaster creates one input operator instance per Kafka topic partition. For example, if initialPartitionCount = 5 and number of Kafka partitions(N) = 2 then AppMaster creates 2 Kafka input operator instances. Default Value = PartitionStrategy.ONE_TO_ONE.
initialPartitionCount - Integer
- When the ONE_TO_MANY partition strategy is enabled, this value indicates the number of Kafka input operator instances. Default Value = 1.
repartitionInterval - Long
- Interval specified in milliseconds. This value specifies the minimum time required between two repartition actions. Default Value = 30 Seconds.
repartitionCheckInterval - Long
- Interval specified in milliseconds. This value specifies the minimum interval between two stat checks. Default Value = 5 Seconds.
maxTuplesPerWindow - Integer
- Controls the maximum number of messages emitted in each streaming window from this operator. Minimum value is 1. Default value = MAX_VALUE
initialOffset - InitialOffset
- Indicates the type of offset i.e, EARLIEST or LATEST or APPLICATION_OR_EARLIEST or APPLICATION_OR_LATEST. LATEST => Consume new messages from latest offset in the topic. EARLIEST => Consume all messages available in the topic. APPLICATION_OR_EARLIEST => Consume messages from committed position from last run. If there is no committed offset, then start consuming from beginning. APPLICATION_OR_LATEST => Consumes messages from committed position from last run. If a committed offset is unavailable, then start consuming from latest position. Default value = InitialOffset.APPLICATION_OR_LATEST
metricsRefreshInterval - Long
- Interval specified in milliseconds. This value specifies the minimum interval between two metric stat updates. Default value = 5 Seconds.
consumerTimeout - Long
- Indicates the time waiting in poll when data is not available. Default value = 5 Seconds.
holdingBufferSize - Long
- Indicates the maximum number of messages kept in memory for emitting. Default value = 1024.
consumerProps - Properties
- Specify the [consumer properties[(http://kafka.apache.org/090/documentation.html#newconsumerconfigs) which are not yet set to the operator.
windowDataManager - WindowDataManager
- If set to a value other than the default, such as FSWindowDataManager, specifies that the operator will process the same set of messages in a window before and after a failure. This is important but it comes with higher cost because at the end of each window the operator needs to persist some state with respect to that window. Default value = WindowDataManager.NoopWindowDataManager.

Abstract Methods

void emitTuple(String cluster, ConsumerRecord<byte[], byte[]> message): Abstract method that emits tuples extracted from Kafka message.

Concrete Classes

KafkaSinglePortInputOperator

This class extends from AbstractKafkaInputOperator and defines the getTuple() method which extracts byte array from Kafka message.

Ports

outputPort <byte[]>: Tuples extracted from Kafka messages are emitted through this port.

Application Example

This section builds an Apex application using Kafka input operator. Below is the code snippet:

@ApplicationAnnotation(name = "KafkaApp")
public class ExampleKafkaApplication implements StreamingApplication
{
  @Override
  public void populateDAG(DAG dag, Configuration entries)
  {
    KafkaSinglePortInputOperator input =  dag.addOperator("MessageReader", new KafkaSinglePortInputOperator());
    ConsoleOutputOperator output = dag.addOperator("Output", new ConsoleOutputOperator());
    dag.addStream("MessageData", input.outputPort, output.input);
  }
}

Below is the configuration for “test” Kafka topic name and “localhost:9092” is the Broker:

<property>
  <name>dt.operator.MessageReader.prop.topics</name>
  <value>test</value>
</property>

<property>
  <name>dt.operator.KafkaInputOperator.prop.clusters</nam>
  <value>localhost:9092</value>
</property>

By adding following lines to properties file, Kafka Input Operator supports multi-topic and multi-cluster:

<property>
  <name>dt.operator.MessageReader.prop.topics</name>
  <value>test1, test2</value>
</property>
 
<property>
  <name>dt.operator.KafkaInputOperator.prop.clusters</nam>
  <value>localhost:9092; localhost:9093; localhost:9094</value>
</property>

For a full example application project, refer to https://github.com/DataTorrent/examples/tree/master/tutorials/kafka