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Showing content from https://kafka.apache.org/07/documentation.html below:

Apache Kafka

• Quickstart – Get up and running quickly.
• Configuration – All the knobs.
• Performance – Some performance results.
• Operations – Notes on running the system.
• API Docs – Scaladoc for the api.

a recent stable release.

> tar xzf kafka-<VERSION>.tgz
> cd kafka-<VERSION>
> ./sbt update
> ./sbt package

Kafka brokers and consumers use this for co-ordination.

First start the zookeeper server. You can use the convenience script packaged with kafka to get a quick-and-dirty single-node zookeeper instance.

> bin/zookeeper-server-start.sh config/zookeeper.properties
[2010-11-21 23:45:02,335] INFO Reading configuration from: config/zookeeper.properties
...

Now start the Kafka server:

> bin/kafka-server-start.sh config/server.properties
jkreps-mn-2:kafka-trunk jkreps$ bin/kafka-server-start.sh config/server.properties
[2010-11-21 23:51:39,608] INFO starting log cleaner every 60000 ms (kafka.log.LogManager)
[2010-11-21 23:51:39,628] INFO connecting to ZK: localhost:2181 (kafka.server.KafkaZooKeeper)
...

Kafka comes with a command line client that will take input from standard in and send it out as messages to the Kafka cluster. By default each line will be sent as a separate message. The topic

is created automatically when messages are sent to it. Omitting logging you should see something like this:

> bin/kafka-console-producer.sh --zookeeper localhost:2181 --topic test
This is a message
This is another message

Kafka also has a command line consumer that will dump out messages to standard out.

> bin/kafka-console-consumer.sh --zookeeper localhost:2181 --topic test --from-beginning
This is a message
This is another message

If you have each of the above commands running in a different terminal then you should now be able to type messages into the producer terminal and see them appear in the consumer terminal.

Both of these command line tools have additional options. Running the command with no arguments will display usage information documenting them in more detail.

Below is some very simple examples of using Kafka for sending messages, more complete examples can be found in the Kafka source code in the examples/ directory.

Here are examples of using the producer API -

-

1. First, start a local instance of the zookeeper server

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

2. Next, start a kafka broker

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

3. Now, create the producer with all configuration defaults and use zookeeper based broker discovery.

   import java.util.Arrays;
   import java.util.List;
   import java.util.Properties;
   import kafka.javaapi.producer.SyncProducer;
   import kafka.javaapi.message.ByteBufferMessageSet;
   import kafka.message.Message;
   import kafka.producer.SyncProducerConfig;
   
   ...
   
   Properties props = new Properties();
   props.put(“zk.connect”, “127.0.0.1:2181”);
   props.put("serializer.class", "kafka.serializer.StringEncoder");
   ProducerConfig config = new ProducerConfig(props);
   Producer<String, String> producer = new Producer<String, String>(config);
   

4. Send a single message

   // The message is sent to a randomly selected partition registered in ZK
   ProducerData<String, String> data = new ProducerData<String, String>("test-topic", "test-message");
   producer.send(data);
   

5. Send multiple messages to multiple topics in one request

   List<String> messages = new java.util.ArrayList<String>();
   messages.add("test-message1");
   messages.add("test-message2");
   ProducerData<String, String> data1 = new ProducerData<String, String>("test-topic1", messages);
   ProducerData<String, String> data2 = new ProducerData<String, String>("test-topic2", messages);
   List<ProducerData<String, String>> dataForMultipleTopics = new ArrayList<ProducerData<String, String>>();
   dataForMultipleTopics.add(data1);
   dataForMultipleTopics.add(data2);
   producer.send(dataForMultipleTopics);
   

6. Send a message with a partition key. Messages with the same key are sent to the same partition

   ProducerData<String, String> data = new ProducerData<String, String>("test-topic", "test-key", "test-message");
   producer.send(data);
   

7. Use your custom partitioner

   If you are using zookeeper based broker discovery, kafka.producer.Producer<T> routes your data to a particular broker partition based on a kafka.producer.Partitioner<T>, specified through the partitioner.class config parameter. It defaults to kafka.producer.DefaultPartitioner. If you don't supply a partition key, then it sends each request to a random broker partition.

   class MemberIdPartitioner extends Partitioner[MemberIdLocation] {
     def partition(data: MemberIdLocation, numPartitions: Int): Int = {
       (data.location.hashCode % numPartitions)
     }
   }
   // create the producer config to plug in the above partitioner
   Properties props = new Properties();
   props.put(“zk.connect”, “127.0.0.1:2181”);
   props.put("serializer.class", "kafka.serializer.StringEncoder");
   props.put("partitioner.class", "xyz.MemberIdPartitioner");
   ProducerConfig config = new ProducerConfig(props);
   Producer<String, String> producer = new Producer<String, String>(config);
   

8. Use custom Encoder

   The producer takes in a required config parameter serializer.class that specifies an Encoder<T> to convert T to a Kafka Message. Default is the no-op kafka.serializer.DefaultEncoder. Here is an example of a custom Encoder -

   class TrackingDataSerializer extends Encoder<TrackingData> {
     // Say you want to use your own custom Avro encoding
     CustomAvroEncoder avroEncoder = new CustomAvroEncoder();
     def toMessage(event: TrackingData):Message = {
   	new Message(avroEncoder.getBytes(event));
     }
   }
   

   If you want to use the above Encoder, pass it in to the "serializer.class" config parameter

   Properties props = new Properties();
   props.put("serializer.class", "xyz.TrackingDataSerializer");
   

9. Using static list of brokers, instead of zookeeper based broker discovery

   Some applications would rather not depend on zookeeper. In that case, the config parameter broker.list can be used to specify the list of all brokers in the Kafka cluster.- the list of all brokers in your Kafka cluster in the following format - broker_id1:host1:port1, broker_id2:host2:port2...

   // you can stop the zookeeper instance as it is no longer required
   ./bin/zookeeper-server-stop.sh
   // create the producer config object 
   Properties props = new Properties();
   props.put(“broker.list”, “0:localhost:9092”);
   props.put("serializer.class", "kafka.serializer.StringEncoder");
   ProducerConfig config = new ProducerConfig(props);
   // send a message using default partitioner 
   Producer<String, String> producer = new Producer<String, String>(config);
   List<String> messages = new java.util.ArrayList<String>();
   messages.add("test-message");
   ProducerData<String, String> data = new ProducerData<String, String>("test-topic", messages);
   producer.send(data);
   

10. Use the asynchronous producer along with GZIP compression. This buffers writes in memory until either batch.size or queue.time is reached. After that, data is sent to the Kafka brokers

    Properties props = new Properties();
    props.put("zk.connect"‚ "127.0.0.1:2181");
    props.put("serializer.class", "kafka.serializer.StringEncoder");
    props.put("producer.type", "async");
    props.put("compression.codec", "1");
    ProducerConfig config = new ProducerConfig(props);
    Producer<String, String> producer = new Producer<String, String>(config);
    ProducerData<String, String> data = new ProducerData<String, String>("test-topic", "test-message");
    producer.send(data);
    

11. Finally, the producer should be closed, through

    producer.close();

Data can also be produced to a Kafka server in the form of a log4j appender. In this way, minimal code needs to be written in order to send some data across to the Kafka server. Here is an example of how to use the Kafka Log4j appender - Start by defining the Kafka appender in your log4j.properties file.

// define the kafka log4j appender config parameters
log4j.appender.KAFKA=kafka.producer.KafkaLog4jAppender
// REQUIRED: set the hostname of the kafka server
log4j.appender.KAFKA.Host=localhost
// REQUIRED: set the port on which the Kafka server is listening for connections
log4j.appender.KAFKA.Port=9092
// REQUIRED: the topic under which the logger messages are to be posted
log4j.appender.KAFKA.Topic=test
// the serializer to be used to turn an object into a Kafka message. Defaults to kafka.producer.DefaultStringEncoder
log4j.appender.KAFKA.Serializer=kafka.test.AppenderStringSerializer
// do not set the above KAFKA appender as the root appender
log4j.rootLogger=INFO
// set the logger for your package to be the KAFKA appender
log4j.logger.your.test.package=INFO, KAFKA

Data can be sent using a log4j appender as follows -

Logger logger = Logger.getLogger([your.test.class])
logger.info("message from log4j appender");

If your log4j appender fails to send messages, please verify that the correct log4j properties file is being used. You can add

to your VM parameters to verify this.

The consumer code is slightly more complex as it enables multithreaded consumption:

// specify some consumer properties
Properties props = new Properties();
props.put("zk.connect", "localhost:2181");
props.put("zk.connectiontimeout.ms", "1000000");
props.put("groupid", "test_group");

// Create the connection to the cluster
ConsumerConfig consumerConfig = new ConsumerConfig(props);
ConsumerConnector consumerConnector = Consumer.createJavaConsumerConnector(consumerConfig);

// create 4 partitions of the stream for topic “test”, to allow 4 threads to consume
Map<String, List<KafkaStream<Message>>> topicMessageStreams =
    consumerConnector.createMessageStreams(ImmutableMap.of("test", 4));
List<KafkaStream<Message>> streams = topicMessageStreams.get("test");

// create list of 4 threads to consume from each of the partitions
ExecutorService executor = Executors.newFixedThreadPool(4);

// consume the messages in the threads
for(final KafkaStream<Message> stream: streams) {
  executor.submit(new Runnable() {
    public void run() {
      for(MessageAndMetadata msgAndMetadata: stream) {
        // process message (msgAndMetadata.message())
      }
    }
  });
}

Providing a horizontally scalable solution for aggregating and loading data into Hadoop was one of our basic use cases. To support this use case, we provide a Hadoop-based consumer which spawns off many map tasks to pull data from the Kafka cluster in parallel. This provides extremely fast pull-based Hadoop data load capabilities (we were able to fully saturate the network with only a handful of Kafka servers).

Usage information on the hadoop consumer can be found here.

Kafka has a lower-level consumer api for reading message chunks directly from servers. Under most circumstances this should not be needed. But just in case, it's usage is as follows:

import kafka.api.FetchRequest;
import kafka.javaapi.consumer.SimpleConsumer;
import kafka.javaapi.message.ByteBufferMessageSet;
import kafka.message.Message;
import kafka.message.MessageSet;
import kafka.utils.Utils;

...

// create a consumer to connect to the kafka server running on localhost, port 9092, socket timeout of 10 secs, socket receive buffer of ~1MB
SimpleConsumer consumer = new SimpleConsumer("127.0.0.1", 9092, 10000, 1024000);

long offset = 0;
while (true) {
  // create a fetch request for topic “test”, partition 0, current offset, and fetch size of 1MB
  FetchRequest fetchRequest = new FetchRequest("test", 0, offset, 1000000);

  // get the message set from the consumer and print them out
  ByteBufferMessageSet messages = consumer.fetch(fetchRequest);
  for(MessageAndOffset msg : messages) {
    System.out.println("consumed: " + Utils.toString(msg.message.payload(), "UTF-8"));
    // advance the offset after consuming each message
    offset = msg.offset;
  }
}

More details about server configuration can be found in the scala class kafka.server.KafkaConfig.

More details about consumer configuration can be found in the scala class kafka.consumer.ConsumerConfig.

• smallest: automatically reset the offset to the smallest offset available on the broker.
• largest : automatically reset the offset to the largest offset available on the broker.
• anything else: throw an exception to the consumer.

More details about producer configuration can be found in the scala class kafka.producer.ProducerConfig.

• async for asynchronous batching send through kafka.producer.AyncProducer
• sync for synchronous send through kafka.producer.SyncProducer

The following tests give some basic information on Kafka throughput as the number of topics, consumers and producers and overall data size varies. Since Kafka nodes are independent, these tests are run with a single producer, consumer, and broker machine. Results can be extrapolated for a larger cluster.

We run producer and consumer tests separately to isolate their performance. For the consumer these tests test cold performance, that is consuming a large uncached backlog of messages. Simultaneous production and consumption tends to help performance since the cache is hot.

We took below setting for some of the parameters:

• message size = 200 bytes
• batch size = 200 messages
• fetch size = 1MB
• flush interval = 600 messages

In our performance tests, we run experiments to answer below questions.

We can push about 50MB/sec to the system. However, this number changes with the batch size. The below graphs show the relation between these two quantities.

According to our experiments, we can consume about 100M/sec from a broker and the total does not seem to change much as we increase the number of consumer threads.

We are able to max out production with only a few threads.

Based on our experiments, the number of topic has a minimal effect on the total data produced. The below graph is an experiment where we used 40 producers and varied the number of topics

The performance related code is under perf folder. To run the simulator :

 ../run-simulator.sh -kafkaServer=localhost -numTopic=10  -reportFile=report-html/data -time=15 -numConsumer=20 -numProducer=40 -xaxis=numTopic

It will run a simulator with 40 producer and 20 consumer threads producing/consuming from a local kafkaserver.  The simulator is going to run 15 minutes and the results are going to be saved under report-html/data

and they will be plotted from there. Basically it will write MB of data consumed/produced, number of messages consumed/produced given a number of topic and report.html will plot the charts.

Other parameters include numParts, fetchSize, messageSize.

In order to test how the number of topic affects the performance the below script can be used (it is under utl-bin)

#!/bin/bash
for i in 1 10 20 30 40 50;
do
  ../kafka-server.sh server.properties 2>&1 >kafka.out&
sleep 60
 ../run-simulator.sh -kafkaServer=localhost -numTopic=$i  -reportFile=report-html/data -time=15 -numConsumer=20 -numProducer=40 -xaxis=numTopic
 ../stop-server.sh
 rm -rf /tmp/kafka-logs
 sleep 300
done

The charts similar to above graphs can be plotted with report.html automatically.

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