Storm Apache Kafka integration using the kafka-client jar

This includes the new Apache Kafka consumer API.

Compatibility

Apache Kafka versions 0.10.1.0 onwards. Please be aware that KAFKA-7044 can cause crashes in the spout, so you should upgrade Kafka if you are using an affected version (1.1.0, 1.1.1 or 2.0.0).

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 implementations for the 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 a Kafka key and a Kafka message. If you just want one field as key and one field as value, then you can use the provided FieldNameBasedTupleToKafkaMapper.java 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 an 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 DefaultTopicSelector.java and set the name of the topic in the constructor. FieldNameTopicSelector and FieldIndexTopicSelector can be used to select the topic should to publish a tuple to. A user just needs to specify the field name or field index for the topic name in the tuple itself. When the topic is name not found , the Field*TopicSelector will write messages into default topic . Please make sure the default topic has been created .

Specifying Kafka producer properties

You can provide all the producer properties in your Storm topology by calling KafkaBolt.withProducerProperties() and TridentKafkaStateFactory.withProducerProperties(). Please see http://kafka.apache.org/documentation.html#newproducerconfigs Section “Important configuration properties for the producer” for more details. These are also defined in org.apache.kafka.clients.producer.ProducerConfig

Using wildcard kafka topic match

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

Config config = new Config();
config.put("kafka.topic.wildcard.match",true);

After this you can specify a wildcard topic for matching e.g. clickstream.*.log. This will match all streams matching clickstream.my.log, 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")
);
spout.setCycle(true);
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()
        .withProducerProperties(props)
        .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")
);
spout.setCycle(true);

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()
        .withProducerProperties(props)
        .withKafkaTopicSelector(new DefaultTopicSelector("test"))
        .withTridentTupleToKafkaMapper(new FieldNameBasedTupleToKafkaMapper("word", "count"));
stream.partitionPersist(stateFactory, fields, new TridentKafkaStateUpdater(), new Fields());

Config conf = new Config();
StormSubmitter.submitTopology("kafkaTridentTest", conf, topology.build());

Reading From kafka (Spouts)

Configuration

The spout implementations are configured by use of the KafkaSpoutConfig class. This class uses a Builder pattern and can be started either by calling one of the Builders constructors or by calling the static method builder in the KafkaSpoutConfig class.

The Constructor or static method to create the builder require a few key values (that can be changed later on) but are the minimum config needed to start a spout.

bootstrapServers is the same as the Kafka Consumer Property “bootstrap.servers”. topics The topics the spout will consume can either be a Collection of specific topic names (1 or more) or a regular expression Pattern, which specifies that any topics that match that regular expression will be consumed.

If you are using the Builder Constructors instead of one of the builder methods, you will also need to specify a key deserializer and a value deserializer. This is to help guarantee type safety through the use of Java generics. The deserializers can be specified via the consumer properties set with setProp. See the KafkaConsumer configuration documentation for details.

There are a few key configs to pay attention to.

setFirstPollOffsetStrategy allows you to set where to start consuming data from. This is used both in case of failure recovery and starting the spout for the first time. The allowed values are listed in the FirstPollOffsetStrategy javadocs.

setProcessingGuarantee lets you configure what processing guarantees the spout will provide. This affects how soon consumed offsets can be committed, and the frequency of commits. See the ProcessingGuarantee javadoc for details.

setRecordTranslator allows you to modify how the spout converts a Kafka Consumer Record into a Tuple, and which stream that tuple will be published into. By default the “topic”, “partition”, “offset”, “key”, and “value” will be emitted to the “default” stream. If you want to output entries to different streams based on the topic, storm provides ByTopicRecordTranslator. See below for more examples on how to use these.

setProp and setProps can be used to set KafkaConsumer properties. The list of these properties can be found in the KafkaConsumer configuration documentation on the Kafka website. Note that KafkaConsumer autocommit is unsupported. The KafkaSpoutConfig constructor will throw an exception if the “enable.auto.commit” property is set, and the consumer used by the spout will always have that property set to false. You can configure similar behavior to autocommit through the setProcessingGuarantee method on the KafkaSpoutConfig builder.

Usage Examples

The API is written with java 8 lambda expressions in mind. It works with java7 and below though.

Create a Simple Insecure Spout

The following will consume all events published to “topic” and send them to MyBolt with the fields “topic”, “partition”, “offset”, “key”, “value”.

final TopologyBuilder tp = new TopologyBuilder();
tp.setSpout("kafka_spout", new KafkaSpout<>(KafkaSpoutConfig.builder("127.0.0.1:" + port, "topic").build()), 1);
tp.setBolt("bolt", new myBolt()).shuffleGrouping("kafka_spout");
...

Wildcard Topics

Wildcard topics will consume from all topics that exist in the specified brokers list and match the pattern. So in the following example “topic”, “topic_foo” and “topic_bar” will all match the pattern “topic.*”, but “not_my_topic” would not match.

final TopologyBuilder tp = new TopologyBuilder();
tp.setSpout("kafka_spout", new KafkaSpout<>(KafkaSpoutConfig.builder("127.0.0.1:" + port, Pattern.compile("topic.*")).build()), 1);
tp.setBolt("bolt", new myBolt()).shuffleGrouping("kafka_spout");
...

Multiple Streams

This uses java 8 lambda expressions.

final TopologyBuilder tp = new TopologyBuilder();

//By default all topics not covered by another rule, but consumed by the spout will be emitted to “STREAM_1” as “topic”, “key”, and “value” ByTopicRecordTranslator<String, String> byTopic = new ByTopicRecordTranslator<>( (r) -> new Values(r.topic(), r.key(), r.value()), new Fields(“topic”, “key”, “value”), “STREAM_1”); //For topic_2 all events will be emitted to “STREAM_2” as just “key” and “value” byTopic.forTopic(“topic_2”, (r) -> new Values(r.key(), r.value()), new Fields(“key”, “value”), “STREAM_2”);

tp.setSpout(“kafka_spout”, new KafkaSpout<>(KafkaSpoutConfig.builder(“127.0.0.1:” + port, “topic_1”, “topic_2”, “topic_3”).build()), 1); tp.setBolt(“bolt”, new myBolt()).shuffleGrouping(“kafka_spout”, “STREAM_1”); tp.setBolt(“another”, new myOtherBolt()).shuffleGrouping(“kafka_spout”, “STREAM_2”); …

#### Trident

```java
final TridentTopology tridentTopology = new TridentTopology();
final Stream spoutStream = tridentTopology.newStream("kafkaSpout",
    new KafkaTridentSpoutOpaque<>(KafkaSpoutConfig.builder("127.0.0.1:" + port, Pattern.compile("topic.*")).build()))
      .parallelismHint(1)
...

Trident does not support multiple streams and will ignore any streams set for output. If however the Fields are not identical for each output topic it will throw an exception and not continue.

Custom RecordTranslators (ADVANCED)

In most cases the built in SimpleRecordTranslator and ByTopicRecordTranslator should cover your use case. If you do run into a situation where you need a custom one then this documentation will describe how to do this properly, and some of the less than obvious classes involved.

The point of apply is to take a ConsumerRecord and turn it into a List<Object> that can be emitted. What is not obvious is how to tell the spout to emit it to a specific stream. To do this you will need to return an instance of org.apache.storm.kafka.spout.KafkaTuple. This provides a method routedTo that will say which specific stream the tuple should go to.

For Example:

return new KafkaTuple(1, 2, 3, 4).routedTo("bar");

Will cause the tuple to be emitted on the “bar” stream.

Be careful when writing custom record translators because just like in a storm spout it needs to be self consistent. The streams method should return a full set of streams that this translator will ever try to emit on. Additionally getFieldsFor should return a valid Fields object for each of those streams. If you are doing this for Trident a value must be in the List returned by apply for every field in the Fields object for that stream, otherwise trident can throw exceptions.

Manual Partition Assigment (ADVANCED)

By default the KafkaSpout instances will be assigned partitions using a round robin strategy. If you need to customize partition assignment, you must implement the ManualPartitioner interface. The implementation can be passed to the ManualPartitionSubscription constructor, and the Subscription can then be set in the KafkaSpoutConfig via the KafkaSpoutConfig.Builder constructor. Please take care when supplying a custom implementation, since an incorrect ManualPartitioner implementation could leave some partitions unread, or concurrently read by multiple spout instances. See the RoundRobinManualPartitioner for an example of how to implement this functionality.

Use the Maven Shade Plugin to Build the Uber Jar

Add the following to REPO_HOME/storm/external/storm-kafka-client/pom.xml

<plugin>
    <groupId>org.apache.maven.plugins</groupId>
    <artifactId>maven-shade-plugin</artifactId>
    <version>2.4.1</version>
    <executions>
        <execution>
            <phase>package</phase>
            <goals>
                <goal>shade</goal>
            </goals>
            <configuration>
                <transformers>
                    <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                        <mainClass>org.apache.storm.kafka.spout.test.KafkaSpoutTopologyMain</mainClass>
                    </transformer>
                </transformers>
            </configuration>
        </execution>
    </executions>
</plugin>

create the uber jar by running the command:

mvn package -f REPO_HOME/storm/external/storm-kafka-client/pom.xml

This will create the uber jar file with the name and location matching the following pattern:

REPO_HOME/storm/external/storm-kafka-client/target/storm-kafka-client-1.0.x.jar

Run Storm Topology

Copy the file REPO_HOME/storm/external/storm-kafka-client/target/storm-kafka-client-*.jar to STORM_HOME/extlib

Using the Kafka command line tools create three topics [test, test1, test2] and use the Kafka console producer to populate the topics with some data

Execute the command STORM_HOME/bin/storm jar REPO_HOME/storm/external/storm/target/storm-kafka-client-*.jar org.apache.storm.kafka.spout.test.KafkaSpoutTopologyMain

With the debug level logs enabled it is possible to see the messages of each topic being redirected to the appropriate Bolt as defined by the streams defined and choice of shuffle grouping.

Using storm-kafka-client with different versions of kafka

Storm-kafka-client’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-client, you must explicitly add the Kafka clients dependency. For example, to use Kafka-clients 0.10.0.0, you would use the following dependency in your pom.xml:

        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>0.10.0.0</version>
        </dependency>

You can also override the kafka clients version while building from maven, with parameter storm.kafka.client.version e.g. mvn clean install -Dstorm.kafka.client.version=0.10.0.0

When selecting a kafka client version, you should ensure -

1. kafka api is compatible. storm-kafka-client module only supports 0.10 or newer kafka client API. For older versions, you can use storm-kafka module (https://github.com/apache/storm/tree/master/external/storm-kafka).
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.

Kafka Spout Performance Tuning

The Kafka spout provides two internal parameters to control its performance. The parameters can be set using the setOffsetCommitPeriodMs and setMaxUncommittedOffsets methods.

• “offset.commit.period.ms” controls how often the spout commits to Kafka
• “max.uncommitted.offsets” controls how many offsets can be pending commit before another poll can take place
  

The [Kafka consumer config] (http://kafka.apache.org/documentation.html#consumerconfigs) parameters may also have an impact on the performance of the spout. The following Kafka parameters are likely the most influential in the spout performance:

• “fetch.min.bytes”
• “fetch.max.wait.ms”
• Kafka Consumer instance poll timeout, which is specified for each Kafka spout using the setPollTimeoutMs method.
  

Depending on the structure of your Kafka cluster, distribution of the data, and availability of data to poll, these parameters will have to be configured appropriately. Please refer to the Kafka documentation on Kafka parameter tuning.

Default values

Currently the Kafka spout has has the following default values, which have been shown to give good performance in the test environment as described in this [blog post] (https://hortonworks.com/blog/microbenchmarking-storm-1-0-performance/)

• poll.timeout.ms = 200
• offset.commit.period.ms = 30000 (30s)
• max.uncommitted.offsets = 10000000
  

Tuple Tracking

By default the spout only tracks emitted tuples when the processing guarantee is AT_LEAST_ONCE. It may be necessary to track emitted tuples with other processing guarantees to benefit from Storm features such as showing complete latency in the UI, or enabling backpressure with Config.TOPOLOGY_MAX_SPOUT_PENDING.

KafkaSpoutConfig<String, String> kafkaConf = KafkaSpoutConfig
  .builder(String bootstrapServers, String ... topics)
  .setProcessingGuarantee(ProcessingGuarantee.AT_MOST_ONCE)
  .setTupleTrackingEnforced(true)

Note: This setting has no effect with AT_LEAST_ONCE processing guarantee, where tuple tracking is required and therefore always enabled.

Mapping from storm-kafka to storm-kafka-client spout properties

This may not be an exhaustive list because the storm-kafka configs were taken from Storm 0.9.6 SpoutConfig and KafkaConfig. storm-kafka-client spout configurations were taken from Storm 1.0.6 KafkaSpoutConfig and Kafka 0.10.1.0 ConsumerConfig.

If you are using this table to upgrade your topology to use storm-kafka-client instead of storm-kafka, then you will also need to migrate the consumer offsets from ZooKeeper to Kafka broker. Use storm-kafka-migration tool to migrate the Kafka consumer offsets.

Helper table for setting FirstPollOffsetStrategy

Pick and set FirstPollOffsetStrategy based on startOffsetTime & forceFromStart settings: