Tutorial: Use Apache Storm with Apache Kafka on HDInsight

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This tutorial demonstrates how to use an Apache Storm topology to read and write data with Apache Kafka on HDInsight. This tutorial also demonstrates how to persist data to the Apache Hadoop HDFS compatible storage on the Storm cluster.

In this tutorial, you learn how to:

  • Storm and Kafka
  • Understanding the code
  • Create Kafka and Storm clusters
  • Build the topology
  • Configure the topology
  • Create the Kafka topic
  • Start the topologies
  • Stop the topologies
  • Clean up resources

Prerequisites

The following environment variables may be set when you install Java and the JDK on your development workstation. However, you should check that they exist and that they contain the correct values for your system.

  • JAVA_HOME - should point to the directory where the JDK is installed.

  • PATH - should contain the following paths:

    • JAVA_HOME (or the equivalent path).
    • JAVA_HOME\bin (or the equivalent path).
    • The directory where Maven is installed.

Important

The steps in this document require an Azure resource group that contains both a Storm on HDInsight and a Kafka on HDInsight cluster. These clusters are both located within an Azure Virtual Network, which allows the Storm cluster to directly communicate with the Kafka cluster.

For your convenience, this document links to a template that can create all the required Azure resources.

For more information on using HDInsight in a virtual network, see the Extend HDInsight using a virtual network document.

Storm and Kafka

Apache Storm provides the several components for working with Apache Kafka. The following components are used in this tutorial:

  • org.apache.storm.kafka.KafkaSpout: This component reads data from Kafka. This component relies on the following components:

    • org.apache.storm.kafka.SpoutConfig: Provides configuration for the spout component.

    • org.apache.storm.spout.SchemeAsMultiScheme and org.apache.storm.kafka.StringScheme: How the data from Kafka is transformed into a Storm tuple.

  • org.apache.storm.kafka.bolt.KafkaBolt: This component writes data to Kafka. This component relies on the following components:

    • org.apache.storm.kafka.bolt.selector.DefaultTopicSelector: Describes the topic that is written to.

    • org.apache.kafka.common.serialization.StringSerializer: Configures the bolt to serialize data as a string value.

    • org.apache.storm.kafka.bolt.mapper.FieldNameBasedTupleToKafkaMapper: Maps from the tuple data structure used inside the Storm topology to fields stored in Kafka.

These components are available in the org.apache.storm : storm-kafka package. Use the package version that matches the Storm version. For HDInsight 3.6, the Storm version is 1.1.0. You also need the org.apache.kafka : kafka_2.10 package, which contains additional Kafka components. Use the package version that matches the Kafka version. For HDInsight 3.6, the Kafka version is 0.10.0.0.

The following XML is the dependency declaration in the pom.xml for an Apache Maven project:

<!-- Storm components for talking to Kafka -->
<dependency>
    <groupId>org.apache.storm</groupId>
    <artifactId>storm-kafka</artifactId>
    <version>1.1.0</version>
</dependency>
<!-- needs to be the same Kafka version as used on your cluster -->
<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka_2.10</artifactId>
    <version>0.10.0.0</version>
    <!-- Exclude components that are loaded from the Storm cluster at runtime -->
    <exclusions>
        <exclusion>
            <groupId>org.apache.zookeeper</groupId>
            <artifactId>zookeeper</artifactId>
        </exclusion>
        <exclusion>
            <groupId>log4j</groupId>
            <artifactId>log4j</artifactId>
        </exclusion>
        <exclusion>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
        </exclusion>
    </exclusions>
</dependency>

Understanding the code

The code used in this document is available at https://github.com/Azure-Samples/hdinsight-storm-java-kafka.

There are two topologies provided with this tutorial:

  • Kafka-writer: Generates random sentences and stores them to Kafka.

  • Kafka-reader: Reads data from Kafka and then stores it to the HDFS compatible file store for the Storm cluster.

    Warning

    To enable the Storm to work with the HDFS compatible storage used by HDInsight, a script action is required. The script installs several jar files to the extlib path for Storm. The template in this tutorial automatically uses the script during cluster creation.

    If you do not use the template in this document to create the Storm cluster, then you must manually apply the script action to your cluster.

    The script action is located at https://hdiconfigactions2.blob.core.windows.net/stormextlib/stormextlib.sh and is applied to the supervisor and nimbus nodes of the Storm cluster. For more information on using script actions, see the Customize HDInsight using script actions document.

The topologies are defined using Flux. Flux was introduced in Storm 0.10.x and allows you to separate the topology configuration from the code. For Topologies that use the Flux framework, the topology is defined in a YAML file. The YAML file can be included as part of the topology. It can also be a standalone file used when you submit the topology. Flux also supports variable substitution at run-time, which is used in this example.

The following parameters are set at run time for these topologies:

  • ${kafka.topic}: The name of the Kafka topic that the topologies read/write to.

  • ${kafka.broker.hosts}: The hosts that the Kafka brokers run on. The broker information is used by the KafkaBolt when writing to Kafka.

  • ${kafka.zookeeper.hosts}: The hosts that Zookeeper runs on in the Kafka cluster.

  • ${hdfs.url}: The file system URL for the HDFSBolt component. Indicates whether the data is written to an Azure Storage account or Azure Data Lake Storage.

  • ${hdfs.write.dir}: The directory that data is written to.

For more information on Flux topologies, see https://storm.apache.org/releases/1.1.2/flux.html.

Kafka-writer

In the Kafka-writer topology, the Kafka bolt component takes two string values as parameters. These parameters indicate which tuple fields the bolt sends to Kafka as key and message values. The key is used to partition data in Kafka. The message is the data being stored.

In this example, the com.microsoft.example.SentenceSpout component emits a tuple that contains two fields, key and message. The Kafka bolt extracts these fields and sends the data in them to Kafka.

The fields don't have to use the names key and message. These names are used in this project to make the mapping easier to understand.

The following YAML is the definition for the Kafka-writer component:

# kafka-writer
---

# topology definition
# name to be used when submitting
name: "kafka-writer"

# Components - constructors, property setters, and builder arguments.
# Currently, components must be declared in the order they are referenced
components:
  # Topic selector for KafkaBolt
  - id: "topicSelector"
    className: "org.apache.storm.kafka.bolt.selector.DefaultTopicSelector"
    constructorArgs:
      - "${kafka.topic}"

  # Mapper for KafkaBolt
  - id: "kafkaMapper"
    className: "org.apache.storm.kafka.bolt.mapper.FieldNameBasedTupleToKafkaMapper"
    constructorArgs:
      - "key"
      - "message"

  # Producer properties for KafkaBolt
  - id: "producerProperties"
    className: "java.util.Properties"
    configMethods:
      - name: "put"
        args:
          - "bootstrap.servers"
          - "${kafka.broker.hosts}"
      - name: "put"
        args:
          - "acks"
          - "1"
      - name: "put"
        args:
          - "key.serializer"
          - "org.apache.kafka.common.serialization.StringSerializer"
      - name: "put"
        args:
          - "value.serializer"
          - "org.apache.kafka.common.serialization.StringSerializer"


# Topology configuration
config:
  topology.workers: 2

# Spout definitions
spouts:
  - id: "sentence-spout"
    className: "com.microsoft.example.SentenceSpout"
    parallelism: 8

# Bolt definitions
bolts:
  - id: "kafka-bolt"
    className: "org.apache.storm.kafka.bolt.KafkaBolt"
    parallelism: 8
    configMethods:
    - name: "withProducerProperties"
      args: [ref: "producerProperties"]
    - name: "withTopicSelector"
      args: [ref: "topicSelector"]
    - name: "withTupleToKafkaMapper"
      args: [ref: "kafkaMapper"]

# Stream definitions

streams:
  - name: "spout --> kafka" # Streams data from the sentence spout to the Kafka bolt
    from: "sentence-spout"
    to: "kafka-bolt"
    grouping:
      type: SHUFFLE

Kafka-reader

In the Kafka-reader topology, the spout component reads data from Kafka as string values. The data is then written the Storm log by the logging component and to the HDFS compatible file system for the Storm cluster by the HDFS bolt component.

# kafka-reader
---

# topology definition
# name to be used when submitting
name: "kafka-reader"

# Components - constructors, property setters, and builder arguments.
# Currently, components must be declared in the order they are referenced
components:
  # Convert data from Kafka into string tuples in storm
  - id: "stringScheme"
    className: "org.apache.storm.kafka.StringScheme"
  - id: "stringMultiScheme"
    className: "org.apache.storm.spout.SchemeAsMultiScheme"
    constructorArgs:
      - ref: "stringScheme"

  - id: "zkHosts"
    className: "org.apache.storm.kafka.ZkHosts"
    constructorArgs:
      - "${kafka.zookeeper.hosts}"

  # Spout configuration
  - id: "spoutConfig"
    className: "org.apache.storm.kafka.SpoutConfig"
    constructorArgs:
      # brokerHosts
      - ref: "zkHosts"
      # topic
      - "${kafka.topic}"
      # zkRoot
      - ""
      # id
      - "readerid"
    properties:
      - name: "scheme"
        ref: "stringMultiScheme"

    # How often to sync files to HDFS; every 1000 tuples.
  - id: "syncPolicy"
    className: "org.apache.storm.hdfs.bolt.sync.CountSyncPolicy"
    constructorArgs:
      - 1

  # Rotate files when they hit 5 MB
  - id: "rotationPolicy"
    className: "org.apache.storm.hdfs.bolt.rotation.FileSizeRotationPolicy"
    constructorArgs:
      - 5
      - "KB"

  # File format; read the directory from filters at run time, and use a .txt extension when writing.
  - id: "fileNameFormat"
    className: "org.apache.storm.hdfs.bolt.format.DefaultFileNameFormat"
    configMethods:
      - name: "withPath"
        args: ["${hdfs.write.dir}"]
      - name: "withExtension"
        args: [".txt"]

  # Internal file format; fields delimited by `|`.
  - id: "recordFormat"
    className: "org.apache.storm.hdfs.bolt.format.DelimitedRecordFormat"
    configMethods:
      - name: "withFieldDelimiter"
        args: ["|"]

# Topology configuration
config:
  topology.workers: 2

# Spout definitions
spouts:
  - id: "kafka-spout"
    className: "org.apache.storm.kafka.KafkaSpout"
    constructorArgs:
      - ref: "spoutConfig"
    # Set to the number of partitions for the topic
    parallelism: 8

# Bolt definitions
bolts:
  - id: "logger-bolt"
    className: "com.microsoft.example.LoggerBolt"
    parallelism: 1

  - id: "hdfs-bolt"
    className: "org.apache.storm.hdfs.bolt.HdfsBolt"
    configMethods:
      - name: "withConfigKey"
        args: ["hdfs.config"]
      - name: "withFsUrl"
        args: ["${hdfs.url}"]
      - name: "withFileNameFormat"
        args: [ref: "fileNameFormat"]
      - name: "withRecordFormat"
        args: [ref: "recordFormat"]
      - name: "withRotationPolicy"
        args: [ref: "rotationPolicy"]
      - name: "withSyncPolicy"
        args: [ref: "syncPolicy"]
    parallelism: 1

# Stream definitions

streams:
  # Stream data to log
  - name: "kafka --> log" # name isn't used (placeholder for logging, UI, etc.)
    from: "kafka-spout"
    to: "logger-bolt"
    grouping:
      type: SHUFFLE

  # stream data to file
  - name: "kafka --> hdfs"
    from: "kafka-spout"
    to: "hdfs-bolt"
    grouping:
      type: SHUFFLE

Property substitutions

The project contains a file named dev.properties that is used to pass parameters used by the topologies. It defines the following properties:

dev.properties file Description
kafka.zookeeper.hosts The Apache ZooKeeper hosts for the Kafka cluster.
kafka.broker.hosts The Kafka broker hosts (worker nodes).
kafka.topic The Kafka topic that the topologies use.
hdfs.write.dir The directory that the Kafka-reader topology writes to.
hdfs.url The file system used by the Storm cluster. For Azure Storage accounts, use a value of wasb:///. For Azure Data Lake Storage Gen2, use a value of abfs:///. For Azure Data Lake Storage Gen1, use a value of adl:///.

Create the clusters

Apache Kafka on HDInsight does not provide access to the Kafka brokers over the public internet. Anything that uses Kafka must be in the same Azure virtual network. In this tutorial, both the Kafka and Storm clusters are located in the same Azure virtual network.

The following diagram shows how communication flows between Storm and Kafka:

Diagram of Storm and Kafka clusters in an Azure virtual network

Note

Other services on the cluster such as SSH and Apache Ambari can be accessed over the internet. For more information on the public ports available with HDInsight, see Ports and URIs used by HDInsight.

To create an Azure Virtual Network, and then create the Kafka and Storm clusters within it, use the following steps:

  1. Use the following button to sign in to Azure and open the template in the Azure portal.

    Deploy to Azure

    The Azure Resource Manager template is located at https://github.com/Azure-Samples/hdinsight-storm-java-kafka/blob/master/create-kafka-storm-clusters-in-vnet.json. It creates the following resources:

    • Azure resource group
    • Azure Virtual Network
    • Azure Storage account
    • Kafka on HDInsight version 3.6 (three worker nodes)
    • Storm on HDInsight version 3.6 (three worker nodes)

    Warning

    To guarantee availability of Kafka on HDInsight, your cluster must contain at least three worker nodes. This template creates a Kafka cluster that contains three worker nodes.

  2. Use the following guidance to populate the entries on the Custom deployment section:

    1. Use the following information to populate the entries on the Customized template section:

      Setting Value
      Subscription Your Azure subscription
      Resource group The resource group that contains the resources.
      Location The Azure region that the resources are created in.
      Kafka Cluster Name The name of the Kafka cluster.
      Storm Cluster Name The name of the Storm cluster.
      Cluster Login User Name The admin user name for the clusters.
      Cluster Login Password The admin user password for the clusters.
      SSH User Name The SSH user to create for the clusters.
      SSH Password The password for the SSH user.

      Picture of the template parameters

  3. Read the Terms and Conditions, and then select I agree to the terms and conditions stated above.

  4. Finally, check Pin to dashboard and then select Purchase.

Note

It can take up to 20 minutes to create the clusters.

Build the topology

  1. On your development environment, download the project from https://github.com/Azure-Samples/hdinsight-storm-java-kafka, open a command-line, and change directories to the location that you downloaded the project.

  2. From the hdinsight-storm-java-kafka directory, use the following command to compile the project and create a package for deployment:

    mvn clean package

    The package process creates a file named KafkaTopology-1.0-SNAPSHOT.jar in the target directory.

  3. Use the following commands to copy the package to your Storm on HDInsight cluster. Replace sshuser with the SSH user name for the cluster. Replace stormclustername with the name of the Storm cluster.

    scp ./target/KafkaTopology-1.0-SNAPSHOT.jar sshuser@stormclustername-ssh.azurehdinsight.net:KafkaTopology-1.0-SNAPSHOT.jar

    When prompted, enter the password you used when creating the clusters.

Configure the topology

  1. Use one of the following methods to discover the Kafka broker hosts for the Kafka on HDInsight cluster:

    $creds = Get-Credential -UserName "admin" -Message "Enter the HDInsight login"
$clusterName = Read-Host -Prompt "Enter the Kafka cluster name"
$resp = Invoke-WebRequest -Uri "https://$clusterName.azurehdinsight.net/api/v1/clusters/$clusterName/services/KAFKA/components/KAFKA_BROKER" `
    -Credential $creds `
    -UseBasicParsing
$respObj = ConvertFrom-Json $resp.Content
$brokerHosts = $respObj.host_components.HostRoles.host_name[0..1]
($brokerHosts -join ":9092,") + ":9092"

    Important

    The following Bash example assumes that $CLUSTERNAME contains the name of the Kafka cluster name. It also assumes that jq version 1.5 or greater is installed. When prompted, enter the password for the cluster login account.

    curl -su admin -G "https://$CLUSTERNAME.azurehdinsight.net/api/v1/clusters/$CLUSTERNAME/services/KAFKA/components/KAFKA_BROKER" | jq -r '["\(.host_components[].HostRoles.host_name):9092"] | join(",")' | cut -d',' -f1,2

    The value returned is similar to the following text:

     wn0-kafka.53qqkiavjsoeloiq3y1naf4hzc.ex.internal.cloudapp.net:9092,wn1-kafka.53qqkiavjsoeloiq3y1naf4hzc.ex.internal.cloudapp.net:9092

    Important

    While there may be more than two broker hosts for your cluster, you do not need to provide a full list of all hosts to clients. One or two is enough.

  2. Use one of the following methods to discover the Zookeeper hosts for the Kafka on HDInsight cluster:

    $creds = Get-Credential -UserName "admin" -Message "Enter the HDInsight login"
$clusterName = Read-Host -Prompt "Enter the Kafka cluster name"
$resp = Invoke-WebRequest -Uri "https://$clusterName.azurehdinsight.net/api/v1/clusters/$clusterName/services/ZOOKEEPER/components/ZOOKEEPER_SERVER" `
    -Credential $creds `
    -UseBasicParsing
$respObj = ConvertFrom-Json $resp.Content
$zookeeperHosts = $respObj.host_components.HostRoles.host_name[0..1]
($zookeeperHosts -join ":2181,") + ":2181"

    Important

    The following Bash example assumes that $CLUSTERNAME contains the name of the Kafka cluster. It also assumes that jq is installed. When prompted, enter the password for the cluster login account.

    curl -su admin -G "https://$CLUSTERNAME.azurehdinsight.net/api/v1/clusters/$CLUSTERNAME/services/ZOOKEEPER/components/ZOOKEEPER_SERVER" | jq -r '["\(.host_components[].HostRoles.host_name):2181"] | join(",")' | cut -d',' -f1,2

    The value returned is similar to the following text:

     zk0-kafka.53qqkiavjsoeloiq3y1naf4hzc.ex.internal.cloudapp.net:2181,zk2-kafka.53qqkiavjsoeloiq3y1naf4hzc.ex.internal.cloudapp.net:2181

    Important

    While there are more than two Zookeeper nodes, you do not need to provide a full list of all hosts to clients. One or two is enough.

    Save this value, as it is used later.

  3. Edit the dev.properties file in the root of the project. Add the Broker and Zookeeper hosts information for the Kafka cluster to the matching lines in this file. The following example is configured using the sample values from the previous steps:

     kafka.zookeeper.hosts: zk0-kafka.53qqkiavjsoeloiq3y1naf4hzc.ex.internal.cloudapp.net:2181,zk2-kafka.53qqkiavjsoeloiq3y1naf4hzc.ex.internal.cloudapp.net:2181
 kafka.broker.hosts: wn0-kafka.53qqkiavjsoeloiq3y1naf4hzc.ex.internal.cloudapp.net:9092,wn1-kafka.53qqkiavjsoeloiq3y1naf4hzc.ex.internal.cloudapp.net:9092
 kafka.topic: stormtopic

    Important

    The hdfs.url entry is configured for a cluster that uses an Azure Storage account. To use this topology with a Storm cluster that uses Data Lake Storage, change this value from wasb to adl.

  4. Save the dev.properties file and then use the following command to upload it to the Storm cluster:

    scp dev.properties USERNAME@storm-BASENAME-ssh.azurehdinsight.net:dev.properties

    Replace USERNAME with the SSH user name for the cluster. Replace BASENAME with the base name you used when creating the cluster.

Create the Kafka topic

Kafka stores data into a topic. You must create the topic before starting the Storm topologies. To create the topology, use the following steps:

  1. Connect to the Kafka cluster through SSH by using the following command. Replace sshuser with the SSH user name used when creating the cluster. Replace kafkaclustername with the name of the Kafka cluster:

    ssh sshuser@kafkaclustername-ssh.azurehdinsight.net

    When prompted, enter the password you used when creating the clusters.

    For information, see Use SSH with HDInsight.

  2. To create the Kafka topic, use the following command. Replace $KAFKAZKHOSTS with the Zookeeper host information you used when configuring the topology:

    /usr/hdp/current/kafka-broker/bin/kafka-topics.sh --create --replication-factor 3 --partitions 8 --topic stormtopic --zookeeper $KAFKAZKHOSTS

    This command connects to Zookeeper for the Kafka cluster and creates a new topic named stormtopic. This topic is used by the Storm topologies.

Start the writer

  1. Use the following to connect to the Storm cluster using SSH. Replace sshuser with the SSH user name used when creating the cluster. Replace stormclustername with the name the Storm cluster:

    ssh sshuser@stormclustername-ssh.azurehdinsight.net

    When prompted, enter the password you used when creating the clusters.

    For information, see Use SSH with HDInsight.

  2. From the SSH connection to the Storm cluster, use the following command to start the writer topology:

    storm jar KafkaTopology-1.0-SNAPSHOT.jar org.apache.storm.flux.Flux --remote -R /writer.yaml --filter dev.properties

    The parameters used with this command are:

    • org.apache.storm.flux.Flux: Use Flux to configure and run this topology.

    • --remote: Submit the topology to Nimbus. The topology is distributed across the worker nodes in the cluster.

    • -R /writer.yaml: Use the writer.yaml file to configure the topology. -R indicates that this resource is included in the jar file. It's in the root of the jar, so /writer.yaml is the path to it.

    • --filter: Populate entries in the writer.yaml topology using values in the dev.properties file. For example, the value of the kafka.topic entry in the file is used to replace the ${kafka.topic} entry in the topology definition.

Start the reader

  1. From the SSH session to the Storm cluster, use the following command to start the reader topology:

    storm jar KafkaTopology-1.0-SNAPSHOT.jar org.apache.storm.flux.Flux --remote -R /reader.yaml --filter dev.properties

  2. Wait a minute and then use the following command to view the files created by the reader topology:

    hdfs dfs -ls /stormdata

    The output is similar to the following text:

     Found 173 items
 -rw-r--r--   1 storm supergroup       5137 2018-04-09 19:00 /stormdata/hdfs-bolt-4-0-1523300453088.txt
 -rw-r--r--   1 storm supergroup       5128 2018-04-09 19:00 /stormdata/hdfs-bolt-4-1-1523300453624.txt
 -rw-r--r--   1 storm supergroup       5131 2018-04-09 19:00 /stormdata/hdfs-bolt-4-10-1523300455170.txt
 ...

  3. To view the contents of the file, use the following command. Replace filename.txt with the name of a file:

    hdfs dfs -cat /stormdata/filename.txt

    The following text is an example of the file contents:

     four score and seven years ago
 snow white and the seven dwarfs
 i am at two with nature
 snow white and the seven dwarfs
 i am at two with nature
 four score and seven years ago
 an apple a day keeps the doctor away

Stop the topologies

From an SSH session to the Storm cluster, use the following commands to stop the Storm topologies:

storm kill kafka-writer
storm kill kafka-reader

Clean up resources

To clean up the resources created by this tutorial, you can delete the resource group. Deleting the resource group also deletes the associated HDInsight cluster, and any other resources associated with the resource group.

To remove the resource group using the Azure portal:

  1. In the Azure portal, expand the menu on the left side to open the menu of services, and then choose Resource Groups to display the list of your resource groups.
  2. Locate the resource group to delete, and then right-click the More button (...) on the right side of the listing.
  3. Select Delete resource group, and then confirm.

Warning

HDInsight cluster billing starts once a cluster is created and stops when the cluster is deleted. Billing is pro-rated per minute, so you should always delete your cluster when it is no longer in use.

Deleting a Kafka on HDInsight cluster deletes any data stored in Kafka.

Next steps

In this tutorial, you learned how to use an Apache Storm topology to write to and read from Apache Kafka on HDInsight. You also learned how to store data to the Apache Hadoop HDFS compatible storage used by HDInsight.

To learn more about using Kafka on HDInsight, see the Use Apache Kafka Producer and Consumer API document.

For information on deploying and monitoring topologies on Linux-based HDInsight, see Deploy and manage Apache Storm topologies on Linux-based HDInsight