Sample programs for the Kafka 0.9 API
Java

README.md

Sample Programs for Kafka 0.9 API

This project provides a simple but realistic example of a Kafka producer and consumer. These programs are written in a style and a scale that will allow you to adapt them to get something close to a production style. There is a surprising dearth of examples for the new Kafka API that arrived with 0.9.0, which is a pity since the new API is so much better than the previous API.

This README takes you through the steps for downloading and installing a single node version of Kafka. We don't focus on the requirements for a production Kafka cluster because we want to focus on the code itself and various aspects of starting and restarting.

Pre-requisites

To start, you need to get Kafka up and running and create some topics.

Step 1: Download Kafka

Download the 0.9.0.0 release and un-tar it.

$ tar -xzf kafka_2.11-0.9.0.0.tgz
$ cd kafka_2.11-0.9.0.0

Step 2: Start the server

Start a ZooKeeper server. Kafka has a single node Zookeeper configuration built-in.

$ bin/zookeeper-server-start.sh config/zookeeper.properties &
[2013-04-22 15:01:37,495] INFO Reading configuration from: config/zookeeper.properties (org.apache.zookeeper.server.quorum.QuorumPeerConfig)
...

Note that this will start Zookeeper in the background. To stop Zookeeper, you will need to bring it back to the foreground and use control-C or you will need to find the process and kill it.

Now start Kafka itself:

$ bin/kafka-server-start.sh config/server.properties &
[2013-04-22 15:01:47,028] INFO Verifying properties (kafka.utils.VerifiableProperties)
[2013-04-22 15:01:47,051] INFO Property socket.send.buffer.bytes is overridden to 1048576 (kafka.utils.VerifiableProperties)
...

As with Zookeeper, this runs the Kafka broker in the background. To stop Kafka, you will need to bring it back to the foreground or find the process and kill it explicitly using kill.

Step 3: Create the topics for the example programs

We need two topics for the example program

$ bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic fast-messages
$ bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic summary-markers

These can be listed

$ bin/kafka-topics.sh --list --zookeeper localhost:2181
fast-messages
summary-markers

Note that you will see log messages from the Kafka process when you run Kafka commands. You can switch to a different window if these are distracting.

The broker can be configured to auto-create new topics as they are mentioned, but that is often considered a bit dangerous because mis-spelling a topic name doesn't cause a failure.

Now for the real fun

At this point, you should have a working Kafka broker running on your machine. The next steps are to compile the example programs and play around with the way that they work.

Step 4: Compile and package up the example programs

Go back to the directory where you have the example programs and compile and build the example programs.

$ cd ..
$ mvn package
...

For convenience, the example programs project is set up so that the maven package target produces a single executable, target/kafka-example, that includes all of the example programs and dependencies.

Step 5: Run the example producer

The producer will send a large number of messages to fast-messages along with occasional messages to summary-markers. Since there isn't any consumer running yet, nobody will receive the messages.

$ target/kafka-example producer
Sent msg number 0
Sent msg number 1000
...
Sent msg number 998000
Sent msg number 999000

Step 6: Start the example consumer

Running the consumer will not actually cause any messages to be processed. The reason is that the first time that the consumer is run, this will be the first time that the Kafka broker has ever seen the consumer group that the consumer is using. That means that the consumer group will be created and the default behavior is to position newly created consumer groups at the end of all existing data.

$ target/kafka-example consumer
SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.

After running the consumer once, however, if we run the producer again and then run the consumer again, we will see the consumer pick up and start processing messages shortly after it starts.

$ target/kafka-example consumer
SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
Got 31003 records after 0 timeouts
1 messages received in period, latency(min, max, avg, 99%) = 20352, 20479, 20416.0, 20479 (ms)
1 messages received overall, latency(min, max, avg, 99%) = 20352, 20479, 20416.0, 20479 (ms)
1000 messages received in period, latency(min, max, avg, 99%) = 19840, 20095, 19968.3, 20095 (ms)
1001 messages received overall, latency(min, max, avg, 99%) = 19840, 20479, 19968.7, 20095 (ms)
...
1000 messages received in period, latency(min, max, avg, 99%) = 12032, 12159, 12119.4, 12159 (ms)
998001 messages received overall, latency(min, max, avg, 99%) = 12032, 20479, 15073.9, 19583 (ms)
1000 messages received in period, latency(min, max, avg, 99%) = 12032, 12095, 12064.0, 12095 (ms)
999001 messages received overall, latency(min, max, avg, 99%) = 12032, 20479, 15070.9, 19583 (ms)

Note that there is a significant latency listed in the summaries for the messsage batches. This is because the consumer wasn't running when the message were sent to Kafka and thus it is only getting them much later, long after they were sent.

The consumer should, however, gnaw its way through the backlog pretty quickly, however and the per batch latency should be shorter by the end of the run than at the beginning. If the producer is still running by the time the consumer catches up, the latencies will probably drop into the single digit millisecond range.

Step 7: Send more messages

In a separate window, run the producer again without stopping the consumer. Note how the messages are displayed almost instantly by the consumer and the latencies measured by the consumer are now quite small, especially compared to the first time the consumer was run.

This isn't a real production-scale benchmark, but it does show that two processes can send and receive messages at a pretty high rate and with pretty low latency.

Fun Step: Mess with the consumer

While the producer is producing messages (you may want to put it in a loop so it keeps going) and the consumer is eating them, try killing and restarting the consumer. The new consumer will wait for about 10 seconds before Kafka assigns it to the partitions that the killed consumer was handling. Once the consumer gets cooking, however, the latency on the records it is processing should drop quickly to the steady state rate of a few milliseconds. You can have similar effect by using control-Z to pause the consumer for a few seconds but if you do that, the consumer should restart processing immediately as soon as you let it continue. The way that this works is if you pause for a short time, the consumer still has the topic partition assigned to it by the Kafka broker, so it can start right back up. On the other hand, if you pause for more than about 10 seconds, the broker will decide the consumer has died and that there is nobody available to handle the partitions in the topic for that consumer group. As soon as the consumer program comes back, however, it will reclaim ownership and continue reading data.

Remaining Mysteries

If you change the consumer properties, particular the buffer sizes near the end of properties file, you may notice that the consumer can easily get into a state where it has about 5 seconds of timeouts during which no data comes from Kafka and then a full bufferful arrives. Once in this mode, the consumer tends to not recover to normal processing. It isn't clear what is going on, but setting the buffer sizes large enough can avoid the problem.

Cleaning Up

When you are done playing, stop Kafka and Zookeeper and delete the data directories they were using from /tmp

$ fg
bin/kafka-server-start.sh config/server.properties
^C[2016-02-06 18:06:56,683] INFO [Kafka Server 0], shutting down (kafka.server.KafkaServer)
...
[2016-02-06 18:06:58,977] INFO EventThread shut down (org.apache.zookeeper.ClientCnxn)
[2016-02-06 18:06:58,978] INFO Closed socket connection for client /fe80:0:0:0:0:0:0:1%1:65170 which had sessionid 0x152b958c3300000 (org.apache.zookeeper.server.NIOServerCnxn)
[2016-02-06 18:06:58,979] INFO [Kafka Server 0], shut down completed (kafka.server.KafkaServer)
$ fg
bin/zookeeper-server-start.sh config/zookeeper.properties
^C
$ rm -rf /tmp/zookeeper/version-2/log.1  ; rm -rf /tmp/kafka-logs/
$

Credits

Note that this example was derived in part from the documentation provided by the Apache Kafka project. We have added short, realistic sample programs that illustrate how real programs are written using Kafka.