About Kafka & Installation of Kafka

Introduction

Apache Kafka is an open-source scalable and high-throughput messaging system developed by the Apache Software Foundation written in Scala. Apache Kafka is specially designed to allow a single cluster to serve as the central data backbone for a large environment. It has a much higher throughput compared to other message brokers systems like ActiveMQ and RabbitMQ. It is capable of handling large volumes of real-time data efficiently. You can deploy Kafka on single Apache server or in a distributed clustered environment.

Features

The general features of Kafka are as follows :

1. Persist message on disk that provide constant time performance.

2. High throughput with disk structures that supporting hundreds of thousands of messages per second.

3. Distributed system scales easily with no downtime.

4. Supports multi-subscribers and automatically balances the consumers during failure.

This tutorial shows how to install and configure Apache Kafka on a Ubuntu 16.04 server.

Requirements

•    A Ubuntu 16.04 server.

•    Non-root user account with sudo privilege set up on your server.

Getting Started

Let's start making sure that your Ubuntu 16.04 server is fully up to date.

You can update your server by running the following command:

sudo apt-get update -y

sudo apt-get upgrade -y

Installing Java

Before installing Kafka, you will need to install Java on your system. You can install Oracle JDK 8 using the Webupd8 team PPA repository.

To add the repository, run the following command:

sudo add-apt-repository -y ppa:webupd8team/java

You should see the following output:

gpg: keyring `/tmp/tmpkjrm4mnm/secring.gpg' created

gpg: keyring `/tmp/tmpkjrm4mnm/pubring.gpg' created

gpg: requesting key EEA14886 from hkp server keyserver.ubuntu.com

gpg: /tmp/tmpkjrm4mnm/trustdb.gpg: trustdb created

gpg: key EEA14886: public key "Launchpad VLC" imported

gpg: no ultimately trusted keys found

gpg: Total number processed: 1

gpg:               imported: 1  (RSA: 1)

OK

Next, update the metadata of the new repository by running the following command:

sudo apt-get update

Once you have finished, run the following command to install JDK 8:

sudo apt-get install oracle-java8-installer -y

You can also verify that JDK 8 is installed properly by running the following command:

sudo java -version

You should see the output something like this:

java version "1.8.0_66"

Java(TM) SE Runtime Environment (build 1.8.0_66-b17)

Java HotSpot(TM) 64-Bit Server VM (build 25.66-b17, mixed mode)

Install ZooKeeper

Before installing Apache Kafka, you will need to have zookeeper available and running. ZooKeeper is an open source service for maintaining configuration information, providing distributed synchronization, naming and providing group services.

By default ZooKeeper package is available in Ubuntu's default repository, you can install it by running the following command:

sudo apt-get install zookeeperd

Once installation is finished, it will be started as a daemon automatically. By default ZooKeeper will run on port 2181.

You can test it by running the following command:

netstat -ant | grep :2181

If everything's fine, you should see the following Output:

tcp6       0      0 :::2181                 :::*                    LISTEN

Install and Start Kafka Server

Now that Java and ZooKeeper are installed, it is time to download and extract Kafka from Apache website. You can use wget to download Kafka:

wget http://mirror.fibergrid.in/apache/kafka/0.10.0.1/kafka_2.10-0.10.0.1.tgz

Next, create a directory for Kafka installation:

sudo mkdir /opt/Kafka

cd /opt/Kafka

Extract the downloaded archive using tar command in /opt/Kafka:

sudo tar -xvf kafka_2.10-0.10.0.1.tgz -C /opt/Kafka/

The next step is to start Kafka server, you can start it by running kafka-server-start.sh script located at /opt/Kafka/kafka_2.10-0.10.0.1/bin/ directory.

sudo  /opt/Kafka/kafka_2.10-0.10.0.1/bin/kafka-server-start.sh /opt/Kafka/kafka_2.10-0.10.0.1/config/server.properties

You should see the following output, if the server has started successfully:

[2016-08-22 21:43:48,279] WARN No meta.properties file under dir /tmp/kafka-logs/meta.properties (kafka.server.BrokerMetadataCheckpoint)

[2016-08-22 21:43:48,516] INFO Kafka version : 0.10.0.1 (org.apache.kafka.common.utils.AppInfoParser)

[2016-08-22 21:43:48,525] INFO Kafka commitId : a7a17cdec9eaa6c5 (org.apache.kafka.common.utils.AppInfoParser)

[2016-08-22 21:43:48,527] INFO [Kafka Server 0], started (kafka.server.KafkaServer)

[2016-08-22 21:43:48,555] INFO New leader is 0 (kafka.server.ZookeeperLeaderElector$LeaderChangeListener)

You can use nohup with script to start the Kafka server as a background process:

sudo nohup /opt/Kafka/kafka_2.10-0.10.0.1/bin/kafka-server-start.sh /opt/Kafka/kafka_2.10-0.10.0.1/config/server.properties /tmp/kafka.log 2>&1 &

You now have a Kafka server running and listening on port 9092.

Testing Kafka Server

Now, it is time to verify the Kafka server is operating correctly.

To test Kafka, create a sample topic with name "testing" in Apache Kafka using the following command:

sudo /opt/Kafka/kafka_2.10-0.10.0.1/bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1  --partitions 1 --topic testing

You should see the following output:

Created topic "testing".

Now, ask Zookeeper to list available topics on Apache Kafka by running the following command:

sudo /opt/Kafka/kafka_2.10-0.10.0.1/bin/kafka-topics.sh --list --zookeeper localhost:2181

You should see the following output:

testing

Now, publish a sample messages to Apache Kafka topic called testing by using the following producer command:

sudo /opt/Kafka/kafka_2.10-0.10.0.1/bin/kafka-console-producer.sh --broker-list localhost:9092 --topic testing

After running above command, enter some messages like "Hi how are you?" press enter, then enter another message like "Where are you?"

Now, use consumer command to check for messages on Apache Kafka Topic called testing by running the following command:

sudo /opt/Kafka/kafka_2.10-0.10.0.1/bin/kafka-console-consumer.sh --zookeeper localhost:2181 --topic testing --from-beginning

You should see the following output:

Hi how are you?

Where are you?

With this above testing you have successfully verified that you have a valid Apache Kafka setup with Apache Zookeeper.

Summary

At this point, we have installed, configured, and tested Kafka on a Ubuntu 16.04 server. You can adapt the setup to make use of it in your production environment. To learn more about Kafka check out the Kafka documentation.

Quickstart

This tutorial assumes you are starting fresh and have no existing Kafka or ZooKeeper data. Since Kafka console scripts are different for Unix-based and Windows platforms, on Windows platforms use bin\windows\ instead of bin/, and change the script extension to .bat.

Step 1: Download the code

Download the 0.10.2.0 release and un-tar it.

> tar -xzf kafka_2.11-0.10.2.0.tgz

> cd kafka_2.11-0.10.2.0

Step 2: Start the server

Kafka uses ZooKeeper so you need to first start a ZooKeeper server if you don't already have one. 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

[2016-04-22 15:01:37,495] INFO Reading configuration from: config/zookeeper.properties (org.apache.zookeeper.server.quorum.QuorumPeerConfig)

...

Now start the Kafka server:

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

[2016-04-22 15:01:47,028] INFO Verifying properties (kafka.utils.VerifiableProperties)

[2016-04-22 15:01:47,051] INFO Property socket.send.buffer.bytes is overridden to 1048576 (kafka.utils.VerifiableProperties)

...

Step 3: Create a topic

Let's create a topic named "test" with a single partition and only one replica:

> bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test

We can now see that topic if we run the list topic command:

> bin/kafka-topics.sh --list --zookeeper localhost:2181

test

Alternatively, instead of manually creating topics you can also configure your brokers to auto-create topics when a non-existent topic is published to.

Step 4: Send some messages

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

Run the producer and then type a few messages into the console to send to the server.

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

This is a message

This is another message

Step 5: Start a consumer

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

> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --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.

All of the command line tools have additional options; running the command with no arguments will display usage information documenting them in more detail.

Step 6: Setting up a multi-broker cluster

So far we have been running against a single broker, but that's no fun. For Kafka, a single broker is just a cluster of size one, so nothing much changes other than starting a few more broker instances. But just to get feel for it, let's expand our cluster to three nodes (still all on our local machine).

First we make a config file for each of the brokers (on Windows use the copy command instead):

> cp config/server.properties config/server-1.properties

> cp config/server.properties config/server-2.properties

Now edit these new files and set the following properties:

config/server-1.properties:

    broker.id=1

    listeners=PLAINTEXT://:9093

    log.dir=/tmp/kafka-logs-1

config/server-2.properties:

    broker.id=2

    listeners=PLAINTEXT://:9094

    log.dir=/tmp/kafka-logs-2

The broker.id property is the unique and permanent name of each node in the cluster. We have to override the port and log directory only because we are running these all on the same machine and we want to keep the brokers from all trying to register on the same port or overwrite each other's data.

We already have Zookeeper and our single node started, so we just need to start the two new nodes:

> bin/kafka-server-start.sh config/server-1.properties &

...

> bin/kafka-server-start.sh config/server-2.properties &

...

Now create a new topic with a replication factor of three:

> bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 3 --partitions 1 --topic my-replicated-topic

Okay but now that we have a cluster how can we know which broker is doing what? To see that run the "describe topics" command:

> bin/kafka-topics.sh --describe --zookeeper localhost:2181 --topic my-replicated-topic

Topic:my-replicated-topic       PartitionCount:1        ReplicationFactor:3     Configs:

        Topic: my-replicated-topic      Partition: 0    Leader: 1       Replicas: 1,2,0 Isr: 1,2,0

Here is an explanation of output. The first line gives a summary of all the partitions, each additional line gives information about one partition. Since we have only one partition for this topic there is only one line.

•    "leader" is the node responsible for all reads and writes for the given partition. Each node will be the leader for a randomly selected portion of the partitions.

•    "replicas" is the list of nodes that replicate the log for this partition regardless of whether they are the leader or even if they are currently alive.

•    "isr" is the set of "in-sync" replicas. This is the subset of the replicas list that is currently alive and caught-up to the leader.

Note that in my example node 1 is the leader for the only partition of the topic.

We can run the same command on the original topic we created to see where it is:

> bin/kafka-topics.sh --describe --zookeeper localhost:2181 --topic test

Topic:test      PartitionCount:1        ReplicationFactor:1     Configs:

        Topic: test     Partition: 0    Leader: 0       Replicas: 0     Isr: 0

So there is no surprise there—the original topic has no replicas and is on server 0, the only server in our cluster when we created it.

Let's publish a few messages to our new topic:

> bin/kafka-console-producer.sh --broker-list localhost:9092 --topic my-replicated-topic

...

my test message 1

my test message 2

^C

Now let's consume these messages:

> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --from-beginning --topic my-replicated-topic

...

my test message 1

my test message 2

^C

Now let's test out fault-tolerance. Broker 1 was acting as the leader so let's kill it:

> ps aux | grep server-1.properties

7564 ttys002    0:15.91 /System/Library/Frameworks/JavaVM.framework/Versions/1.8/Home/bin/java...

> kill -9 7564

On Windows use:

> wmic process get processid,caption,commandline | find "java.exe" | find "server-1.properties"

java.exe    java  -Xmx1G -Xms1G -server -XX:+UseG1GC ... build\libs\kafka_2.10-0.10.2.0.jar"  kafka.Kafka config\server-1.properties    644

> taskkill /pid 644 /f

Leadership has switched to one of the slaves and node 1 is no longer in the in-sync replica set:

> bin/kafka-topics.sh --describe --zookeeper localhost:2181 --topic my-replicated-topic

Topic:my-replicated-topic       PartitionCount:1        ReplicationFactor:3     Configs:

        Topic: my-replicated-topic      Partition: 0    Leader: 2       Replicas: 1,2,0 Isr: 2,0

But the messages are still available for consumption even though the leader that took the writes originally is down:

> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --from-beginning --topic my-replicated-topic

...

my test message 1

my test message 2

^C

Step 7: Use Kafka Connect to import/export data

Writing data from the console and writing it back to the console is a convenient place to start, but you'll probably want to use data from other sources or export data from Kafka to other systems. For many systems, instead of writing custom integration code you can use Kafka Connect to import or export data.

Kafka Connect is a tool included with Kafka that imports and exports data to Kafka. It is an extensible tool that runs connectors, which implement the custom logic for interacting with an external system. In this quickstart we'll see how to run Kafka Connect with simple connectors that import data from a file to a Kafka topic and export data from a Kafka topic to a file.

First, we'll start by creating some seed data to test with:

> echo -e "foo\nbar" > test.txt

Next, we'll start two connectors running in standalone mode, which means they run in a single, local, dedicated process. We provide three configuration files as parameters. The first is always the configuration for the Kafka Connect process, containing common configuration such as the Kafka brokers to connect to and the serialization format for data. The remaining configuration files each specify a connector to create. These files include a unique connector name, the connector class to instantiate, and any other configuration required by the connector.

> bin/connect-standalone.sh config/connect-standalone.properties config/connect-file-source.properties config/connect-file-sink.properties

These sample configuration files, included with Kafka, use the default local cluster configuration you started earlier and create two connectors: the first is a source connector that reads lines from an input file and produces each to a Kafka topic and the second is a sink connector that reads messages from a Kafka topic and produces each as a line in an output file.

During startup you'll see a number of log messages, including some indicating that the connectors are being instantiated. Once the Kafka Connect process has started, the source connector should start reading lines from test.txt and producing them to the topic connect-test, and the sink connector should start reading messages from the topic connect-test and write them to the file test.sink.txt. We can verify the data has been delivered through the entire pipeline by examining the contents of the output file:

> cat test.sink.txt

foo

bar

Note that the data is being stored in the Kafka topic connect-test, so we can also run a console consumer to see the data in the topic (or use custom consumer code to process it):

> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic connect-test --from-beginning

{"schema":{"type":"string","optional":false},"payload":"foo"}

{"schema":{"type":"string","optional":false},"payload":"bar"}

...

The connectors continue to process data, so we can add data to the file and see it move through the pipeline:

> echo "Another line" >> test.txt

You should see the line appear in the console consumer output and in the sink file.

Step 8: Use Kafka Streams to process data

Kafka Streams is a client library of Kafka for real-time stream processing and analyzing data stored in Kafka brokers. This quickstart example will demonstrate how to run a streaming application coded in this library. Here is the gist of the WordCountDemo example code (converted to use Java 8 lambda expressions for easy reading).

// Serializers/deserializers (serde) for String and Long types

final Serde<String> stringSerde = Serdes.String();

final Serde<Long> longSerde = Serdes.Long();

// Construct a `KStream` from the input topic ""streams-file-input", where message values

// represent lines of text (for the sake of this example, we ignore whatever may be stored

// in the message keys).

KStream<String, String> textLines = builder.stream(stringSerde, stringSerde, "streams-file-input");

KTable<String, Long> wordCounts = textLines

    // Split each text line, by whitespace, into words.

    .flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))

    // Group the text words as message keys

    .groupBy((key, value) -> value)

    // Count the occurrences of each word (message key).

    .count("Counts")

// Store the running counts as a changelog stream to the output topic.

wordCounts.to(stringSerde, longSerde, "streams-wordcount-output");

It implements the WordCount algorithm, which computes a word occurrence histogram from the input text. However, unlike other WordCount examples you might have seen before that operate on bounded data, the WordCount demo application behaves slightly differently because it is designed to operate on an infinite, unbounded stream of data. Similar to the bounded variant, it is a stateful algorithm that tracks and updates the counts of words. However, since it must assume potentially unbounded input data, it will periodically output its current state and results while continuing to process more data because it cannot know when it has processed "all" the input data.

As the first step, we will prepare input data to a Kafka topic, which will subsequently be processed by a Kafka Streams application.

> echo -e "all streams lead to kafka\nhello kafka streams\njoin kafka summit" > file-input.txt

Or on Windows:

> echo all streams lead to kafka> file-input.txt

> echo hello kafka streams>> file-input.txt

> echo|set /p=join kafka summit>> file-input.txt

Next, we send this input data to the input topic named streams-file-input using the console producer, which reads the data from STDIN line-by-line, and publishes each line as a separate Kafka message with null key and value encoded a string to the topic (in practice, stream data will likely be flowing continuously into Kafka where the application will be up and running):

> bin/kafka-topics.sh --create \

            --zookeeper localhost:2181 \

            --replication-factor 1 \

            --partitions 1 \

            --topic streams-file-input

> bin/kafka-console-producer.sh --broker-list localhost:9092 --topic streams-file-input < file-input.txt

We can now run the WordCount demo application to process the input data:

> bin/kafka-run-class.sh org.apache.kafka.streams.examples.wordcount.WordCountDemo

The demo application will read from the input topic streams-file-input, perform the computations of the WordCount algorithm on each of the read messages, and continuously write its current results to the output topic streams-wordcount-output. Hence there won't be any STDOUT output except log entries as the results are written back into in Kafka. The demo will run for a few seconds and then, unlike typical stream processing applications, terminate automatically.

We can now inspect the output of the WordCount demo application by reading from its output topic:

> bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 \

            --topic streams-wordcount-output \

            --from-beginning \

            --formatter kafka.tools.DefaultMessageFormatter \

            --property print.key=true \

            --property print.value=true \

            --property key.deserializer=org.apache.kafka.common.serialization.StringDeserializer \

            --property value.deserializer=org.apache.kafka.common.serialization.LongDeserializer

with the following output data being printed to the console:

all     1

lead    1

to      1

hello   1

streams 2

join    1

kafka   3

summit  1

Here, the first column is the Kafka message key in java.lang.String format, and the second column is the message value in java.lang.Long format. Note that the output is actually a continuous stream of updates, where each data record (i.e. each line in the original output above) is an updated count of a single word, aka record key such as "kafka". For multiple records with the same key, each later record is an update of the previous one.

The two diagrams below illustrate what is essentially happening behind the scenes. The first column shows the evolution of the current state of the KTable<String, Long> that is counting word occurrences for count. The second column shows the change records that result from state updates to the KTable and that are being sent to the output Kafka topic streams-wordcount-output.

First the text line “all streams lead to kafka” is being processed. The KTable is being built up as each new word results in a new table entry (highlighted with a green background), and a corresponding change record is sent to the downstream KStream.

When the second text line “hello kafka streams” is processed, we observe, for the first time, that existing entries in the KTable are being updated (here: for the words “kafka” and for “streams”). And again, change records are being sent to the output topic.

And so on (we skip the illustration of how the third line is being processed). This explains why the output topic has the contents we showed above, because it contains the full record of changes.

Looking beyond the scope of this concrete example, what Kafka Streams is doing here is to leverage the duality between a table and a changelog stream (here: table = the KTable, changelog stream = the downstream KStream): you can publish every change of the table to a stream, and if you consume the entire changelog stream from beginning to end, you can reconstruct the contents of the table.

Now you can write more input messages to the streams-file-input topic and observe additional messages added to streams-wordcount-output topic, reflecting updated word counts (e.g., using the console producer and the console consumer, as described above).

You can stop the console consumer via Ctrl-C.

Use cases

Here is a description of a few of the popular use cases for Apache Kafka™. For an overview of a number of these areas in action, see this blog post.

Messaging

Kafka works well as a replacement for a more traditional message broker. Message brokers are used for a variety of reasons (to decouple processing from data producers, to buffer unprocessed messages, etc). In comparison to most messaging systems Kafka has better throughput, built-in partitioning, replication, and fault-tolerance which makes it a good solution for large scale message processing applications.

In our experience messaging uses are often comparatively low-throughput, but may require low end-to-end latency and often depend on the strong durability guarantees Kafka provides.

In this domain Kafka is comparable to traditional messaging systems such as ActiveMQ or RabbitMQ.

Website Activity Tracking

The original use case for Kafka was to be able to rebuild a user activity tracking pipeline as a set of real-time publish-subscribe feeds. This means site activity (page views, searches, or other actions users may take) is published to central topics with one topic per activity type. These feeds are available for subscription for a range of use cases including real-time processing, real-time monitoring, and loading into Hadoop or offline data warehousing systems for offline processing and reporting.

Activity tracking is often very high volume as many activity messages are generated for each user page view.

Metrics

Kafka is often used for operational monitoring data. This involves aggregating statistics from distributed applications to produce centralized feeds of operational data.

Log Aggregation

Many people use Kafka as a replacement for a log aggregation solution. Log aggregation typically collects physical log files off servers and puts them in a central place (a file server or HDFS perhaps) for processing. Kafka abstracts away the details of files and gives a cleaner abstraction of log or event data as a stream of messages. This allows for lower-latency processing and easier support for multiple data sources and distributed data consumption. In comparison to log-centric systems like Scribe or Flume, Kafka offers equally good performance, stronger durability guarantees due to replication, and much lower end-to-end latency.

Stream Processing

Many users of Kafka process data in processing pipelines consisting of multiple stages, where raw input data is consumed from Kafka topics and then aggregated, enriched, or otherwise transformed into new topics for further consumption or follow-up processing. For example, a processing pipeline for recommending news articles might crawl article content from RSS feeds and publish it to an "articles" topic; further processing might normalize or deduplicate this content and published the cleansed article content to a new topic; a final processing stage might attempt to recommend this content to users. Such processing pipelines create graphs of real-time data flows based on the individual topics. Starting in 0.10.0.0, a light-weight but powerful stream processing library called Kafka Streams is available in Apache Kafka to perform such data processing as described above. Apart from Kafka Streams, alternative open source stream processing tools include Apache Storm and Apache Samza.

Event Sourcing

Event sourcing is a style of application design where state changes are logged as a time-ordered sequence of records. Kafka's support for very large stored log data makes it an excellent backend for an application built in this style.

Commit Log

Kafka can serve as a kind of external commit-log for a distributed system. The log helps replicate data between nodes and acts as a re-syncing mechanism for failed nodes to restore their data. The log compaction feature in Kafka helps support this usage. In this usage Kafka is similar to Apache BookKeeper project.