Version: 1.2.4

Storm Kafka Integration (0.8.x)


Storm-kafka has been deprecated and will be removed in a future Storm release. Please upgrade to storm-kafka-client. If you need to migrate the committed offsets to the new spout, consider using the storm-kafka-migration tool.

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


We support both Trident and core Storm spouts. For both spout implementations, we use a BrokerHost interface that tracks Kafka broker host to partition mapping and kafkaConfig that controls some Kafka related parameters.


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


ZkHosts is what you should use if you want to dynamically track Kafka broker to partition mapping. This class uses Kafka's ZooKeeper 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:2181). brokerZkPath is the root directory under which all the topics and partition information is stored. By default this is /brokers which is what the 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.


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 from partition 0 to brokerForPartition0
partitionInfo.addPartition(1, brokerForPartition1);//mapping from partition 1 to brokerForPartition1
partitionInfo.addPartition(2, brokerForPartition2);//mapping from partition 2 to brokerForPartition2
StaticHosts hosts = new StaticHosts(partitionInfo);


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 BrokerHosts 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 additional fields with ZooKeeper connection info and for controlling behavior specific to KafkaSpout. The clientId will be used to identify requests which are made using the Kafka Protocol. 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 clientId, String zkRoot, String id);
public SpoutConfig(BrokerHosts hosts, String topic, String zkRoot, String id);
public SpoutConfig(BrokerHosts hosts, String topic, String id);

In addition to these parameters, SpoutConfig contains the following fields that control how KafkaSpout behaves:

// setting for how often to save the current Kafka offset to ZooKeeper
public long stateUpdateIntervalMs = 2000;

// Retry strategy for failed messages
public String failedMsgRetryManagerClass = ExponentialBackoffMsgRetryManager.class.getName();

// Exponential back-off retry settings.  These are used by ExponentialBackoffMsgRetryManager for retrying messages after a bolt
// calls These come into effect only if ExponentialBackoffMsgRetryManager is being used.
// Initial delay between successive retries
public long retryInitialDelayMs = 0;
public double retryDelayMultiplier = 1.0;

// Maximum delay between successive retries    
public long retryDelayMaxMs = 60 * 1000;
// Failed message will be retried infinitely if retryLimit is less than zero. 
public int retryLimit = -1;     

Core KafkaSpout only accepts an instance of SpoutConfig.

TridentKafkaConfig is another extension of KafkaConfig. 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 ignoreZkOffsets = 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 is an interface that dictates how the ByteBuffer consumed from Kafka gets transformed into a storm tuple. It also controls the naming of your output field.

public Iterable<List<Object>> deserialize(ByteBuffer ser);
public Fields getOutputFields();

The default RawMultiScheme just takes the ByteBuffer and returns a tuple with the ByteBuffer converted to a byte[]. The name of the outputField is "bytes". There are alternative implementations like SchemeAsMultiScheme and KeyValueSchemeAsMultiScheme which can convert the ByteBuffer to String.

There is also an extension of SchemeAsMultiScheme, MessageMetadataSchemeAsMultiScheme, which has an additional deserialize method that accepts the message ByteBuffer in addition to the Partition and offset associated with the message.

public Iterable<List<Object>> deserializeMessageWithMetadata(ByteBuffer message, Partition partition, long offset)

This is useful for auditing/replaying messages from arbitrary points on a Kafka topic, saving the partition and offset of each message of a discrete stream instead of persisting the entire message.

Failed message retry

FailedMsgRetryManager is an interface which defines the retry strategy for a failed message. Default implementation is ExponentialBackoffMsgRetryManager which retries with exponential delays between consecutive retries. To use a custom implementation, set SpoutConfig.failedMsgRetryManagerClass to the full classname of implementation. Here is the interface

    // Spout initialization can go here. This can be called multiple times during lifecycle of a worker. 
    void prepare(SpoutConfig spoutConfig, Map stormConf);

    // Message corresponding to offset has failed. This method is called only if retryFurther returns true for offset.
    void failed(Long offset);

    // Message corresponding to offset has been acked.  
    void acked(Long offset);

    // Message corresponding to the offset, has been re-emitted and under transit.
    void retryStarted(Long offset);

     * The offset of message, which is to be re-emitted. Spout will fetch messages starting from this offset
     * and resend them, except completed messages.
    Long nextFailedMessageToRetry();

     * @return True if the message corresponding to the offset should be emitted NOW. False otherwise.
    boolean shouldReEmitMsg(Long offset);

     * Spout will clean up the state for this offset if false is returned. If retryFurther is set to true,
     * spout will called failed(offset) in next call and acked(offset) otherwise 
    boolean retryFurther(Long offset);

     * Clear any offsets before kafkaOffset. These offsets are no longer available in kafka.
    Set<Long> clearOffsetsBefore(Long kafkaOffset);

Version incompatibility

In Storm versions prior to 1.0, the MultiScheme methods accepted a byte[] instead of ByteBuffer. The MultScheme and the related Scheme apis were changed in version 1.0 to accept a ByteBuffer instead of a byte[].

This means that pre 1.0 kafka spouts will not work with Storm versions 1.0 and higher. While running topologies in Storm version 1.0 and higher, it must be ensured that the storm-kafka version is at least 1.0. Pre 1.0 shaded topology jars that bundles storm-kafka classes must be rebuilt with storm-kafka version 1.0 for running in clusters with storm 1.0 and higher.


Core Spout

BrokerHosts hosts = new ZkHosts(zkConnString);
SpoutConfig spoutConfig = new SpoutConfig(hosts, topicName, "/" + topicName, UUID.randomUUID().toString());
spoutConfig.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);

How KafkaSpout stores offsets of a Kafka topic and recovers in case of failures

As shown in the above KafkaConfig properties, you can control from where in the Kafka topic the spout begins to read by setting KafkaConfig.startOffsetTime as follows:

  1. kafka.api.OffsetRequest.EarliestTime(): read from the beginning of the topic (i.e. from the oldest messages onwards)
  2. kafka.api.OffsetRequest.LatestTime(): read from the end of the topic (i.e. any new messsages that are being written to the topic)
  3. A Unix timestamp aka seconds since the epoch (e.g. via System.currentTimeMillis()): see How do I accurately get offsets of messages for a certain timestamp using OffsetRequest? in the Kafka FAQ

As the topology runs the Kafka spout keeps track of the offsets it has read and emitted by storing state information under the ZooKeeper path SpoutConfig.zkRoot+ "/" + In the case of failures it recovers from the last written offset in ZooKeeper.

Important: When re-deploying a topology make sure that the settings for SpoutConfig.zkRoot and were not modified, otherwise the spout will not be able to read its previous consumer state information (i.e. the offsets) from ZooKeeper -- which may lead to unexpected behavior and/or to data loss, depending on your use case.

This means that when a topology has run once the setting KafkaConfig.startOffsetTime will not have an effect for subsequent runs of the topology because now the topology will rely on the consumer state information (offsets) in ZooKeeper to determine from where it should begin (more precisely: resume) reading. If you want to force the spout to ignore any consumer state information stored in ZooKeeper, then you should set the parameter KafkaConfig.ignoreZkOffsets to true. If true, the spout will always begin reading from the offset defined by KafkaConfig.startOffsetTime as described above.

Using storm-kafka with different versions of Kafka

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 dependency-compatible with your Kafka cluster.

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.

You can also override the kafka dependency version while building from maven, with parameter storm.kafka.version and e.g. mvn clean install -Dstorm.kafka.version=

When selecting a kafka dependency version, you should ensure -

  1. kafka api is compatible with storm-kafka. Currently, only 0.9.x and 0.8.x client API is supported by storm-kafka module. If you want to use a higher version, storm-kafka-client module should be used instead.
  2. The kafka client selected by you should be wire compatible with the broker. e.g. 0.9.x client will not work with 0.8.x broker.

Writing to Kafka as part of your topology

You can create an instance of org.apache.storm.kafka.bolt.KafkaBolt and attach it as a component to your topology or if you are using trident you can use org.apache.storm.kafka.trident.TridentState, org.apache.storm.kafka.trident.TridentStateFactory and org.apache.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 the 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. FieldNameTopicSelector and FieldIndexTopicSelector use to support decided which topic should to push message from tuple. User could specify the field name or field index in tuple ,selector will use this value as topic name which to publish message. When the topic name not found , KafkaBolt will write messages into default topic . Please make sure the default topic have created .

Specifying Kafka producer properties

You can provide all the produce properties in your Storm topology by calling KafkaBolt.withProducerProperties() and TridentKafkaStateFactory.withProducerProperties(). Please see Section "Important configuration properties for the producer" for more details.

Using wildcard kafka topic match

You can do a wildcard topic match by adding the following config

Config config = new Config();

After this you can specify a wildcard topic for matching e.g. clickstream.*.log. This will match all streams matching, clickstream.cart.log etc

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);
//set producer properties.
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("acks", "1");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

KafkaBolt bolt = new KafkaBolt()
        .withTopicSelector(new DefaultTopicSelector("test"))
        .withTupleToKafkaMapper(new FieldNameBasedTupleToKafkaMapper());
builder.setBolt("forwardToKafka", bolt, 8).shuffleGrouping("spout");

Config conf = new Config();

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);

//set producer properties.
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("acks", "1");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

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();
StormSubmitter.submitTopology("kafkaTridentTest", conf,;

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