This tutorial assumes you are starting fresh and have no existing Kafka or ZooKeeper data. However, if you have already started Kafka and
Zookeeper, feel free to skip the first two steps.
</p>
<p>
Kafka Streams is a client library for building mission-critical real-time applications and microservices,
where the input and/or output data is stored in Kafka clusters. Kafka Streams combines the simplicity of
writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's
server-side cluster technology to make these applications highly scalable, elastic, fault-tolerant, distributed,
and much more.
</p>
<p>
This quickstart example will demonstrate how to run a streaming application coded in this library. Here is the gist
of the <code><ahref="https://github.com/apache/kafka/blob/{{dotVersion}}/streams/examples/src/main/java/org/apache/kafka/streams/examples/wordcount/WordCountDemo.java">WordCountDemo</a></code> example code (converted to use Java 8 lambda expressions for easy reading).
</p>
<preclass="brush: java;">
// Serializers/deserializers (serde) for String and Long types
final Serde<String> stringSerde = Serdes.String();
final Serde<Long> longSerde = Serdes.Long();
<ahref="https://www.apache.org/dyn/closer.cgi?path=/kafka/{{fullDotVersion}}/kafka_{{scalaVersion}}-{{fullDotVersion}}.tgz"title="Kafka downloads">Download</a> the {{fullDotVersion}} release and un-tar it.
Note that there are multiple downloadable Scala versions and we choose to use the recommended version ({{scalaVersion}}) here:
<h4><aid="quickstart_streams_startserver"href="#quickstart_streams_startserver">Step 2: Start the Kafka server</a></h4>
<p>
Kafka uses <ahref="https://zookeeper.apache.org/">ZooKeeper</a> 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.
The demo application will read from the input topic <b>streams-plaintext-input</b>, perform the computations of the WordCount algorithm on each of the read messages,
Now let's write some message with the console producer into the input topic <b>streams-plaintext-input</b> by entering a single line of text and then hit <RETURN>.
This will send a new message to the input topic, where the message key is null and the message value is the string encoded text line that you just entered
(in practice, input data for applications will typically be streaming continuously into Kafka, rather than being manually entered as we do in this quickstart):
This message will be processed by the Wordcount application and the following output data will be written to the <b>streams-wordcount-output</b> topic and printed by the console consumer:
Here, the first column is the Kafka message key in <code>java.lang.String</code> format and represents a word that is being counted, and the second column is the message value in <code>java.lang.Long</code>format, representing the word's latest count.
Here the last printed lines <b>kafka 2</b> and <b>streams 2</b> indicate updates to the keys <b>kafka</b> and <b>streams</b> whose counts have been incremented from <b>1</b> to <b>2</b>.
Whenever you write further input messages to the input topic, you will observe new messages being added to the <b>streams-wordcount-output</b> topic,
representing the most recent word counts as computed by the WordCount application.
Let's enter one final input text line "join kafka summit" and hit <RETURN> in the console producer to the input topic <b>streams-wordcount-input</b> before we wrap up this quickstart:
As one can see, outputs of the Wordcount application is actually a continuous stream of updates, where each output 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 <code>KTable<String, Long></code> that is counting word occurrences for <code>count</code>.
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 <b>streams-wordcount-output</b>.
The <code>KTable</code> 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 <code>KStream</code>.
When the second text line "hello kafka streams" is processed, we observe, for the first time, that existing entries in the <code>KTable</code> 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.
</p>
<p>
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.
<p>You can now stop the console consumer, the console producer, the Wordcount application, the Kafka broker and the Zookeeper server in order via <b>Ctrl-C</b>.</p>
