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Working with Kafka

Introduction

The Streaming Integrator can consume from a Kafka topic as well as to publish to a Kafka topic in a streaming manner.

This tutorial takes you through consuming from a Kafka topic, processing the messages, and finally, publishing output to a Kafka topic.

Before you begin

Prepare the server to consume from or to publish to Kafka, follow the steps below:

1. Download the Kafka broker from the Apache site and extract it. From here onwards, this directory is referred to as <KAFKA_HOME>.

2. Create a directory named source in a preferred location in your machine and copy the following JARs to it from the <KAFKA_HOME>/libs directory.

   • kafka_2.12-2.3.0.jar

   • kafka-clients-2.3.0.jar

   • metrics-core-2.2.0.jar

   • scala-library-2.12.8.jar

   • zkclient-0.11.jar

   • zookeeper-3.4.14.jar

3. Create another directory named destination in a preferred location in your machine.

4. To convert the Kafka JARS you copied to the source directory, issue the following command:

sh <SI_HOME>/bin/jartobundle.sh <{source}_Directory_Path> <{destination}_Directory_Path>

1. Copy all the JARs from the destination directory to the <SI_HOME>/lib directory.

Consuming data from Kafka

Step 1: Start Kafka

1. Navigate to the <KAFKA_HOME> directory and start a zookeeper node by issuing the following command.

   sh bin/zookeeper-server-start.sh config/zookeeper.properties

2. Navigate to the <KAFKA_HOME> directory and start Kafka server node by issuing the following command.

   sh bin/kafka-server-start.sh config/server.properties

Step 2: Start the Streaming Integrator

Navigate to the <SI_HOME>/bin directory and issue the following command. sh server.sh

The following log appears on the SI console when the server is started successfully.

INFO {org.wso2.carbon.kernel.internal.CarbonStartupHandler} - ESB Streaming Integrator started in 4.240 sec

Step 3: Consume from a Kafka topic

Let's create a basic Siddhi application to consume messages from a Kafka topic.

1. Open a text file and copy-paste following Siddhi application to it.

   @App:name("HelloKafka")
   
   @App:description('Consume events from a Kafka Topic and log the messages on the console.')
   
   @source(type='kafka',
           topic.list='productions',
           threading.option='single.thread',
           group.id="group1",
           bootstrap.servers='localhost:9092',
           @map(type='json'))
   define stream SweetProductionStream (name string, amount double);
   
   @sink(type='log')
   define stream OutputStream (name string, amount double);
   
   -- Query to transform the name to upper case.
   from SweetProductionStream
   select str:upper(name) as name, amount
   insert into OutputStream;

2. Save this file as HelloKafka.siddhi in the <SI_HOME>/wso2/server/deployment/siddhi-files directory.

   The following log appears on the SI console.

   INFO {org.wso2.carbon.streaming.integrator.core.internal.StreamProcessorService} - Siddhi App HelloKafka deployed successfully

Info

You just created a Siddhi application that listens to a Kafka topic named productions and logs any incoming messages. When logging, the name attribute of the message is converted to upper case. However, you have still not created this Kafka topic or published any messages to it. To do this, proceed to the next substep.

1. Generate some Kafka messages that the Streaming Integrator can receive by following the procedure below:

   1. Create a topic named productions in the Kafka server. To do this, navigate to <KAFKA_HOME> and run following command:

      bin/kafka-topics.sh --create --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1 --topic productions

   2. Run the Kafka command line client to push a few messages to the Kafka server.

      bin/kafka-console-producer.sh --broker-list localhost:9092 --topic productions

   3. You are prompted to type messages in the console. Type the following in the command prompt:

      {"event":{ "name":"Almond cookie", "amount":100.0}}

   This pushes a message to the Kafka Server. Then, the Siddhi application you deployed in the Streaming Integrator consumes this message. As a result, the Streaming Integrator log displays the following:

   ```
   INFO {io.siddhi.core.stream.output.sink.LogSink} - HelloKafka : OutputStream : Event{timestamp=1562069868006, data=[ALMOND COOKIE, 100.0], isExpired=false}
   ```

   You may notice that the output message has an uppercase name: ALMOND COOKIE. This is because of the simple message transformation done in the Siddhi application.

Step 4: Consume Kafka messages with an offset

Previously, you consumed messages from the productions topic without specifying an offset. In other words, the Kafka offset was zero. In this section, instead of consuming with a zero offset, you specify an offset value and consume messages from that offset onwards.

For this purpose, you can configure the topic.offsets.map parameter. Let's modify our previous Siddhi application to specify an offset value. Specify an offset value 2 so that the Siddhi application consumes messages with index 2 and above.

1. Open the <SI_HOME>/wso2/server/deployment/siddhi-files/HelloKafka.siddhi file and add the following new configuration parameter.

   topic.offsets.map='productions=2'

   The complete Siddhi application is as follows.

   @App:name("HelloKafka")
   
   @App:description('Consume events from a Kafka Topic and log the messages on the console.')
   
   @source(type='kafka',
           topic.list='productions',
           threading.option='single.thread',
           group.id="group1",
           bootstrap.servers='localhost:9092',
           topic.offsets.map='productions=2',
           @map(type='json'))
   define stream SweetProductionStream (name string, amount double);
   
   @sink(type='log')
   define stream OutputStream (name string, amount double);
   
   from SweetProductionStream
   select str:upper(name) as name, amount
   insert into OutputStream;

2. Save the file.

3. Push the following message to the Kafka server.

   {"event":{ "name":"Baked alaska", "amount":20.0}}

   Note that this is the second message that you pushed (hence bearing index 1), and therefore it is not consumed by the Streaming Integrator.

4. Let's push another message (bearing index 2) to the Kafka server.

   {"event":{ "name":"Cup cake", "amount":300.0}}

The following log appears in the Streaming Integrator Studio console.

```
INFO {io.siddhi.core.stream.output.sink.LogSink} - HelloKafka : OutputStream : Event{timestamp=1562676477785, data=[CUP CAKE, 300.0], isExpired=false}
```

As you configured your Siddhi application to consume messages with offset 2, all messages bearing index 2 or above are consumed.

Step 5: Add more consumers to the consumer group

In our HelloKafka Siddhi application, note the group.id parameter. This parameter defines the Kafka consumer group.

Let's add another Siddhi application HelloKafka_2, to add another Kafka consumer to the same consumer group.

1. Open a text file and copy-paste following Siddhi application to it.

   @App:name("HelloKafka_2")
   
   @App:description('Consume events from a Kafka Topic and log the messages on the console.')
   
   @source(type='kafka',
           topic.list='productions',
           threading.option='single.thread',
           group.id="group1",
           bootstrap.servers='localhost:9092',
           @map(type='json'))        
   define stream SweetProductionStream (name string, amount double);
   
   @sink(type='log')
   define stream OutputStream (name string, amount double);
   
   from SweetProductionStream
   select str:upper(name) as name, amount   
   insert into OutputStream;

2. Save this file as HelloKafka_2.siddhi in the <SI_HOME>/wso2/server/deployment/siddhi-files directory. When the Siddhi application is successfully deployed, the following INFO log appears in the Streaming Integrator console.

   INFO {org.wso2.carbon.stream.processor.core.internal.StreamProcessorService} - Siddhi App HelloKafka_2 deployed successfully

3. Navigate to the <KAFKA_HOME> directory and run following command.

   bin/kafka-topics.sh --alter --bootstrap-server localhost:9092 --partitions 2 --topic productions

This adds another partition to the productions Kafka topic.

1. Push following messages to the Kafka server using the Kafka Console Producer.

   {"event":{ "name":"Doughnut", "amount":500.0}} 

   {"event":{ "name":"Danish pastry", "amount":200.0}} 

   {"event":{ "name":"Eclair", "amount":400.0}} 

   {"event":{ "name":"Eclair toffee", "amount":100.0}} 

Observe the following logs on the SI console.

INFO {io.siddhi.core.stream.output.sink.LogSink} - HelloKafka_2 : OutputStream : Event{timestamp=1562759480019, data=[DOUGHNUT, 500.0], isExpired=false}
INFO {io.siddhi.core.stream.output.sink.LogSink} - HelloKafka : OutputStream : Event{timestamp=1562759494710, data=[DANISH PASTRY, 200.0], isExpired=false}
INFO {io.siddhi.core.stream.output.sink.LogSink} - HelloKafka_2 : OutputStream : Event{timestamp=1562759506252, data=[ECLAIR, 400.0], isExpired=false}
INFO {io.siddhi.core.stream.output.sink.LogSink} - HelloKafka : OutputStream : Event{timestamp=1562759508757, data=[ECLAIR TOFFEE, 100.0], isExpired=false}

You can see that the events are being received by the two consumers in a Round Robin manner. Events received by the first consumer are logged by Siddhi application HelloKafka, whilst events received by the second consumer are logged by the HelloKafka_2 Siddhi application.

Step 6: Assign consumers to partitions

In the previous step, you had two partitions for the Kafka topic and two consumers. Instead of assigning the consumers to the partitions, you allowed Kafka do the assignments. Optionally, you can assign consumers to partitions.

This option is useful if you have multiple consumers with different performance speeds, and you need to balance the load among the consumers.

Let's alter your topic to have three partitions. After that, you can assign two partitions to consumer-1, and the remaining partition to consumer-2.

1. Navigate to the <KAFKA_HOME> directory and issue following command.

   bin/kafka-topics.sh --alter --bootstrap-server localhost:9092 --partitions 3 --topic productions

This adds another partition to the productions Kafka topic. As a result, there are three partitions in total.

1. To assign partitions to the consumers, add the partition.no.list parameter as shown below.

   @App:name("HelloKafka")
   
   @App:description('Consume events from a Kafka Topic and log the messages on the console.')
   
   -- consumer-1
   @source(type='kafka',
           topic.list='productions',
           threading.option='single.thread',
           group.id="group1",
           bootstrap.servers='localhost:9092',
           partition.no.list='0,1',
           @map(type='json'))
   define stream SweetProductionStream1 (name string, amount double);
   
   -- consumer-2
   @source(type='kafka',
           topic.list='productions',
           threading.option='single.thread',
           group.id="group1",
           bootstrap.servers='localhost:9092',
           partition.no.list='2',
           @map(type='json'))
   define stream SweetProductionStream2 (name string, amount double);
   
   @sink(type='log')
   define stream OutputStream (name string, amount double, id string);
   
   from SweetProductionStream1
   select str:upper(name) as name, amount, 'consumer-1' as id
   insert into OutputStream;
   
   from SweetProductionStream2
   select str:upper(name) as name, amount, 'consumer-2' as id
   insert into OutputStream;

   Note that consumer-1 is assigned partitions 0 and 1, while consumer-2 is assigned partition 2.

2. Publish some messages as follows, and see how the load is distributed among the consumers with the new partition assignments.

   {"event":{ "name":"Fortune cookie", "amount":100.0}} 

   {"event":{ "name":"Frozen yogurt", "amount":350.0}} 

   {"event":{ "name":"Gingerbread", "amount":450.0}} 

   {"event":{ "name":"Hot-fudge sundae", "amount":150.0}} 

   {"event":{ "name":"Hot-chocolate pudding", "amount":200.0}} 

   {"event":{ "name":"Ice cream cake", "amount":250.0}} 

3. Observe the following Streaming Integrator logs. The following is displayed.

   INFO {io.siddhi.core.stream.output.sink.LogSink} - HelloKafka : OutputStream : Event{timestamp=1562851086792, data=[FORTUNE COOKIE, 100.0, consumer-1], isExpired=false}
   INFO {io.siddhi.core.stream.output.sink.LogSink} - HelloKafka : OutputStream : Event{timestamp=1562851092100, data=[FROZEN YOGURT, 350.0, consumer-1], isExpired=false}
   INFO {io.siddhi.core.stream.output.sink.LogSink} - HelloKafka : OutputStream : Event{timestamp=1562851094459, data=[GINGERBREAD, 450.0, consumer-2], isExpired=false}
   INFO {io.siddhi.core.stream.output.sink.LogSink} - HelloKafka : OutputStream : Event{timestamp=1562851096434, data=[HOT-FUDGE SUNDAE, 150.0, consumer-1], isExpired=false}
   INFO {io.siddhi.core.stream.output.sink.LogSink} - HelloKafka : OutputStream : Event{timestamp=1562851098328, data=[HOT-CHOCOLATE PUDDING, 200.0, consumer-1], isExpired=false}
   INFO {io.siddhi.core.stream.output.sink.LogSink} - HelloKafka : OutputStream : Event{timestamp=1562851100309, data=[ICE CREAM CAKE, 250.0, consumer-2], isExpired=false}

You can observe a pattern where the load is distributed among consumer-1 and consumer-2 in the 2:1 ratio. This is because you assigned two partitions to consumer-1 and assigned only one partition to consumer-2.

Step 7: Publish to a Kafka topic

Let's create a new Siddhi application to consume from the productions topic, filter the incoming messages based on a condition, and then publish those filtered messages to another Kafka topic.

1. Create a new topic named bulk-orders in the Kafka server.

2. To publish the filtered messages to the bulk-orders Kafka topic you created, issue the following command.

   bin/kafka-topics.sh --create --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1 --topic bulk-orders

3. Next, let's create the Siddhi application. Open a text file, and copy-paste following Siddhi application into it.

       @App:name("PublishToKafka")
   
       @App:description('Consume events from a Kafka Topic, do basic filtering and publish filtered messages to a Kafka topic.')
   
       @source(type='kafka',
               topic.list='productions',
               threading.option='single.thread',
               group.id="group2",
               bootstrap.servers='localhost:9092',
               @map(type='json'))
       define stream SweetProductionStream (name string, amount double);
   
       @sink(type='kafka',
             topic='bulk-orders',
             bootstrap.servers='localhost:9092',
             partition.no='0',
             @map(type='json'))
       define stream BulkOrdersStream (name string, amount double);
   
       from SweetProductionStream[amount > 50]
       select *
       insert into BulkOrdersStream;

4. Save this file as PublishToKafka.siddhi in the <SI_HOME>/wso2/server/deployment/siddhi-files directory. When the Siddhi application is successfully deployed, the following INFO log appears in the Streaming Integrator console.

   INFO {org.wso2.carbon.streaming.integrator.core.internal.StreamProcessorService} - Siddhi App PublishToKafka deployed successfully

   Info

   The PublishToKafka Siddhi application consumes all the messages from the productions topic and populates the SweetProductionStream stream. All the sweet production runs where the amount is greater than 100 are inserted into the BulkOrdersStream stream. These events are pushed to the bulk-orders Kafka topic.

5. To observe the messages in the bulk-orders topic, run a Kafka Console Consumer. Then navigate to the <KAFKA_HOME> directory and issue the following command.

   bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic bulk-orders --from-beginning

   You can see the following message in the Kafka Consumer log. These indicate the production runs of which the amount is greater than 50.

   {"event":{ "name":"Almond cookie", "amount":100.0}}

Step 8: Preserve the state of the application through a system failure

Let's try out a scenario in which you deploy a Siddhi application to count the total number of productions.

Info

In this scenario, the SI server is required to remember the current count through system failures so that when the system is restored, the count is not reset to zero.

To achieve this, you can use the state persistence capability in the Streaming Integrator.

1. Enable state persistence feature in SI server as follows. Open the <SI_HOME>/conf/server/deployment.yaml file on a text editor and locate the state.persistence section.

     # Periodic Persistence Configuration
   state.persistence:
     enabled: true
     intervalInMin: 1
     revisionsToKeep: 2
     persistenceStore: org.wso2.carbon.streaming.integrator.core.persistence.FileSystemPersistenceStore
     config:
       location: siddhi-app-persistence

   Set enabled parameter to true and save the file.

2. Enable state persistence debug logs as follows. Open the <SI_HOME>/conf/server/log4j2.xml file on a text editor and locate following line in it.

    <Logger name="com.zaxxer.hikari" level="error"/>

   Add following <Logger> element below that.

   <Logger name="org.wso2.carbon.streaming.integrator.core.persistence" level="debug"/>

   Save the file.

3. Restart the Streaming Integrator server for above change to be effective.

4. Let's create a new topic named sandwich_productions in the Kafka server. To do this, navigate to <KAFKA_HOME> and run following command:

   bin/kafka-topics.sh --create --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1 --topic sandwich_productions

5. Open a text file and copy-paste following Siddhi application to it.

       @App:name("CountProductions")
   
       @App:description('Siddhi application to count the total number of orders.')
   
       @source(type='kafka',
               topic.list='sandwich_productions',
               threading.option='single.thread',
               group.id="group3",
               bootstrap.servers='localhost:9092',
               partition.no.list='0',
               @map(type='json'))
       define stream SandwichProductionStream (name string, amount double);
   
       @sink(type='log')
       define stream OutputStream (totalProductions double);
   
       from SandwichProductionStream
       select sum(amount) as totalProductions
       insert into OutputStream;

6. Save this file as CountProductions.siddhi in the <SI_HOME>/wso2/server/deployment/siddhi-files directory. When the Siddhi application is successfully deployed, the following INFO log appears in the Streaming Integrator console.

   INFO {org.wso2.carbon.stream.processor.core.internal.StreamProcessorService} - Siddhi App CountProductions deployed successfully

7. Run the Kafka command line client to push a few messages to the Kafka server. Navigate to <KAFKA_HOME> and run following command:

   bin/kafka-console-producer.sh --broker-list localhost:9092 --topic sandwich_productions

8. You are prompted to type the messages in the console. Type following in the command prompt:

   {"event":{ "name":"Bagel", "amount":100.0}}

   {"event":{ "name":"Buterbrod", "amount":100.0}} 

   The following logs appear on the SI console.

   INFO {io.siddhi.core.stream.output.sink.LogSink} - CountProductions : OutputStream : Event{timestamp=1563903034768, data=[100.0], isExpired=false}
   INFO {io.siddhi.core.stream.output.sink.LogSink} - CountProductions : OutputStream : Event{timestamp=1563903034768, data=[200.0], isExpired=false}

   These logs print the sandwich production count. Note that the current count of sandwich productions is being printed as 200 in the second log. This is because the production count up to now is 200 sandwiches: 100 bagels and 100 buterbrods.

9. Wait for following log to appear on the SI console

   DEBUG {org.wso2.carbon.streaming.integrator.core.persistence.FileSystemPersistenceStore} - Periodic persistence of CountProductions persisted successfully

   This log indicates that the current state of the Siddhi application is successfully persisted. Siddhi application state is persisted every minute. Therefore, you can notice this log appearing every minute.

   Next, let's push two sandwich production messages to the Kafka server and shutdown the SI server before state persistence happens (i.e., before the above log appears).

   Tip

   It is better to start pushing messages immediately after the state persistence log appears, so that you have plenty of time to push messages and shutdown the server, until next log appears.

10. Push following messages to the Kafka server using the Kafka Console Producer:

    {"event":{ "name":"Croissant", "amount":100.0}}

    {"event":{ "name":"Croutons", "amount":100.0}} 

11. Shutdown SI server. Here you are deliberately creating a scenario where the server crashes before the SI server could persist the latest production count.

    Info

    Here the SI server crashes before the state is persisted. Therefore the SI server cannot persist the latest count (which should include the last two productions 100 Croissants and 100 Croutons). The good news is, the Kafka source replays the last two messages, thereby allowing the Streaming Integrator to successfully recover from the server crash.

12. Restart the SI server and wait for about one minute to observe the following logs.

    INFO {io.siddhi.core.stream.output.sink.LogSink} - CountProductions : OutputStream : Event{timestamp=1563904912073, data=[300.0], isExpired=false}
    INFO {io.siddhi.core.stream.output.sink.LogSink} - CountProductions : OutputStream : Event{timestamp=1563904912076, data=[400.0], isExpired=false}

Note that the Kafka source has replayed the last two messages. As a result, the sandwich productions count is correctly restored.

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