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Storm Kafka

Provides core storm and Trident spout implementations for consuming data from Apache Kafka 0.8.x.

##Spouts We support both trident and core storm spouts. For both spout implementation we use a BrokerHost interface that tracks kafka broker host to partition mapping and kafkaConfig that controls some kafka related parameters.

###BrokerHosts In order to initialize your kafka spout/emitter you need to construct and instance of the marker interface BrokerHosts. Currently we support following two implementations:

####ZkHosts ZkHosts is what you should use if you want to dynamically track kafka broker to partition mapping. This class uses Kafka‘s zookeeper’s entries to track brokerHost -> partition mapping. You can instantiate an object by calling

    public ZkHosts(String brokerZkStr, String brokerZkPath) 
    public ZkHosts(String brokerZkStr)

Where brokerZkStr is just ip:port e.g. localhost:9092. brokerZkPath is the root directory under which all the topics and partition information is stored. by Default this is /brokers which is what default kafka implementation uses.

By default the broker-partition mapping is refreshed every 60 seconds from zookeeper. If you want to change it you should set host.refreshFreqSecs to your chosen value.

####StaticHosts This is an alternative implementation where broker -> partition information is static. In order to construct an instance of this class you need to first construct an instance of GlobalPartitionInformation.

    Broker brokerForPartition0 = new Broker("localhost");//localhost:9092
    Broker brokerForPartition1 = new Broker("localhost", 9092);//localhost:9092 but we specified the port explicitly
    Broker brokerForPartition2 = new Broker("localhost:9092");//localhost:9092 specified as one string.
    GlobalPartitionInformation partitionInfo = new GlobalPartitionInformation();
    partitionInfo.addPartition(0, brokerForPartition0);//mapping form partition 0 to brokerForPartition0
    partitionInfo.addPartition(1, brokerForPartition1);//mapping form partition 1 to brokerForPartition1
    partitionInfo.addPartition(2, brokerForPartition2);//mapping form partition 2 to brokerForPartition2
    StaticHosts hosts = new StaticHosts(partitionInfo);

###KafkaConfig The second thing needed for constructing a kafkaSpout is an instance of KafkaConfig.

    public KafkaConfig(BrokerHosts hosts, String topic)
    public KafkaConfig(BrokerHosts hosts, String topic, String clientId)

The BorkerHosts can be any implementation of BrokerHosts interface as described above. the Topic is name of kafka topic. The optional ClientId is used as a part of the zookeeper path where the spout's current consumption offset is stored.

There are 2 extensions of KafkaConfig currently in use.

Spoutconfig is an extension of KafkaConfig that supports 2 additional fields, zkroot and id. The Zkroot will be used as root to store your consumer's offset. The id should uniquely identify your spout.

public SpoutConfig(BrokerHosts hosts, String topic, String zkRoot, String id);

Core KafkaSpout only accepts an instance of SpoutConfig.

TridentKafkaConfig is another extension of KafkaConfig.

public SpoutConfig(BrokerHosts hosts, String topic, String id);

TridentKafkaEmitter only accepts TridentKafkaConfig.

The KafkaConfig class also has bunch of public variables that controls your application's behavior. Here are defaults:

    public int fetchSizeBytes = 1024 * 1024;
    public int socketTimeoutMs = 10000;
    public int fetchMaxWait = 10000;
    public int bufferSizeBytes = 1024 * 1024;
    public MultiScheme scheme = new RawMultiScheme();
    public boolean forceFromStart = false;
    public long startOffsetTime = kafka.api.OffsetRequest.EarliestTime();
    public long maxOffsetBehind = Long.MAX_VALUE;
    public boolean useStartOffsetTimeIfOffsetOutOfRange = true;
    public int metricsTimeBucketSizeInSecs = 60;

Most of them are self explanatory except MultiScheme. ###MultiScheme MultiScheme is an interface that dictates how the byte[] consumed from kafka gets transformed into a storm tuple. It also controls the naming of your output field.

  public Iterable<List<Object>> deserialize(byte[] ser);
  public Fields getOutputFields();

The default RawMultiScheme just takes the byte[] and returns a tuple with byte[] as is. The name of the outputField is “bytes”. There are alternative implementation like SchemeAsMultiScheme and KeyValueSchemeAsMultiScheme which can convert the byte[] to String.


####Core Spout

BrokerHosts hosts = new ZkHosts(zkConnString);
SpoutConfig spoutConfig = new SpoutConfig(hosts, topicName, "/" + topicName, UUID.randomUUID().toString());
spoutConf.scheme = new SchemeAsMultiScheme(new StringScheme());
KafkaSpout kafkaSpout = new KafkaSpout(spoutConfig);

####Trident Spout

TridentTopology topology = new TridentTopology();
BrokerHosts zk = new ZkHosts("localhost");
TridentKafkaConfig spoutConf = new TridentKafkaConfig(zk, "test-topic");
spoutConf.scheme = new SchemeAsMultiScheme(new StringScheme());
OpaqueTridentKafkaSpout spout = new OpaqueTridentKafkaSpout(spoutConf);

Using storm-kafka with different versions of Scala

Storm-kafka's Kafka dependency is defined as provided scope in maven, meaning it will not be pulled in as a transitive dependency. This allows you to use a version of Kafka built against a specific Scala version.

When building a project with storm-kafka, you must explicitly add the Kafka dependency. For example, to use Kafka built against Scala 2.10, you would use the following dependency in your pom.xml:


Note that the ZooKeeper and log4j dependencies are excluded to prevent version conflicts with Storm's dependencies.

##Writing to Kafka as part of your topology You can create an instance of storm.kafka.bolt.KafkaBolt and attach it as a component to your topology or if you are using trident you can use storm.kafka.trident.TridentState, storm.kafka.trident.TridentStateFactory and storm.kafka.trident.TridentKafkaUpdater.

You need to provide implementation of following 2 interfaces

###TupleToKafkaMapper and TridentTupleToKafkaMapper These interfaces have 2 methods defined:

    K getKeyFromTuple(Tuple/TridentTuple tuple);
    V getMessageFromTuple(Tuple/TridentTuple tuple);

as the name suggests these methods are called to map a tuple to kafka key and kafka message. If you just want one field as key and one field as value then you can use the provided implementation. In the KafkaBolt, the implementation always looks for a field with field name “key” and “message” if you use the default constructor to construct FieldNameBasedTupleToKafkaMapper for backward compatibility reasons. Alternatively you could also specify a different key and message field by using the non default constructor. In the TridentKafkaState you must specify what is the field name for key and message as there is no default constructor. These should be specified while constructing and instance of FieldNameBasedTupleToKafkaMapper.

###KafkaTopicSelector and trident KafkaTopicSelector This interface has only one method

public interface KafkaTopicSelector {
    String getTopics(Tuple/TridentTuple tuple);

The implementation of this interface should return topic to which the tuple's key/message mapping needs to be published You can return a null and the message will be ignored. If you have one static topic name then you can use and set the name of the topic in the constructor.

Specifying kafka producer properties

You can provide all the produce properties , see section “Important configuration properties for the producer”, in your storm topology config by setting the properties map with key

###Putting it all together

For the bolt :

        TopologyBuilder builder = new TopologyBuilder();
        Fields fields = new Fields("key", "message");
        FixedBatchSpout spout = new FixedBatchSpout(fields, 4,
                    new Values("storm", "1"),
                    new Values("trident", "1"),
                    new Values("needs", "1"),
                    new Values("javadoc", "1")
        builder.setSpout("spout", spout, 5);
        KafkaBolt bolt = new KafkaBolt()
                .withKafkaTopicSelector(new DefaultTopicSelector("test"))
                .withTridentTupleToKafkaMapper(new FieldNameBasedTupleToKafkaMapper());
        builder.setBolt("forwardToKafka", bolt, 8).shuffleGrouping("spout");
        Config conf = new Config();
        //set producer properties.
        Properties props = new Properties();
        props.put("", "localhost:9092");
        props.put("request.required.acks", "1");
        props.put("serializer.class", "kafka.serializer.StringEncoder");
        conf.put(TridentKafkaState.KAFKA_BROKER_PROPERTIES, props);
        StormSubmitter.submitTopology("kafkaboltTest", conf, builder.createTopology());

For Trident:

        Fields fields = new Fields("word", "count");
        FixedBatchSpout spout = new FixedBatchSpout(fields, 4,
                new Values("storm", "1"),
                new Values("trident", "1"),
                new Values("needs", "1"),
                new Values("javadoc", "1")

        TridentTopology topology = new TridentTopology();
        Stream stream = topology.newStream("spout1", spout);

        TridentKafkaStateFactory stateFactory = new TridentKafkaStateFactory()
                .withKafkaTopicSelector(new DefaultTopicSelector("test"))
                .withTridentTupleToKafkaMapper(new FieldNameBasedTupleToKafkaMapper("word", "count"));
        stream.partitionPersist(stateFactory, fields, new TridentKafkaUpdater(), new Fields());

        Config conf = new Config();
        //set producer properties.
        Properties props = new Properties();
        props.put("", "localhost:9092");
        props.put("request.required.acks", "1");
        props.put("serializer.class", "kafka.serializer.StringEncoder");
        conf.put(TridentKafkaState.KAFKA_BROKER_PROPERTIES, props);
        StormSubmitter.submitTopology("kafkaTridentTest", conf,;

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