kafka/docs/streams/quickstart.html

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  <h1>Play with a Streams Application</h1>

<p>
  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><a href="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>
<pre class="brush: java;">
// Serializers/deserializers (serde) for String and Long types
final Serde&lt;String&gt; stringSerde = Serdes.String();
final Serde&lt;Long&gt; 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&lt;String, String&gt; textLines = builder.stream(stringSerde, stringSerde, "streams-file-input");

KTable&lt;String, Long&gt; 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");
</pre>

<p>
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 <b>infinite, unbounded stream</b> 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.
</p>
<p>
  As the first step, we will start Kafka (unless you already have it started) and then we will
  prepare input data to a Kafka topic, which will subsequently be processed by a Kafka Streams application.

  <h4><a id="quickstart_streams_download" href="#quickstart_streams_download">Step 1: Download the code</a></h4>

<a href="https://www.apache.org/dyn/closer.cgi?path=/kafka/{{fullDotVersion}}/kafka_2.11-{{fullDotVersion}}.tgz" title="Kafka downloads">Download</a> the {{fullDotVersion}} release and un-tar it.

<pre class="brush: bash;">
&gt; tar -xzf kafka_2.11-{{fullDotVersion}}.tgz
&gt; cd kafka_2.11-{{fullDotVersion}}
</pre>
</p>
<h4><a id="quickstart_streams_startserver" href="#quickstart_streams_startserver">Step 2: Start the Kafka server</a></h4>

<p>
Kafka uses <a href="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.
</p>

<pre class="brush: bash;">
&gt; 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)
...
</pre>

<p>Now start the Kafka server:</p>
<pre class="brush: bash;">
&gt; 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)
...
</pre>


<h4><a id="quickstart_streams_prepare" href="#quickstart_streams_prepare">Step 3: Prepare data</a></h4>

<!--
<pre>
&gt; <b>./bin/kafka-topics --create \</b>
            <b>--zookeeper localhost:2181 \</b>
            <b>--replication-factor 1 \</b>
            <b>--partitions 1 \</b>
            <b>--topic streams-file-input</b>

</pre>

-->

<pre class="brush: bash;">
&gt; echo -e "all streams lead to kafka\nhello kafka streams\njoin kafka summit" > file-input.txt
</pre>
Or on Windows:
<pre class="brush: bash;">
&gt; echo all streams lead to kafka> file-input.txt
&gt; echo hello kafka streams>> file-input.txt
&gt; echo|set /p=join kafka summit>> file-input.txt
</pre>

<p>
Next, we send this input data to the input topic named <b>streams-file-input</b> 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):
</p>

<pre class="brush: bash;">
&gt; bin/kafka-topics.sh --create \
    --zookeeper localhost:2181 \
    --replication-factor 1 \
    --partitions 1 \
    --topic streams-file-input
</pre>


<pre class="brush: bash;">
&gt; bin/kafka-console-producer.sh --broker-list localhost:9092 --topic streams-file-input < file-input.txt
</pre>

<h4><a id="quickstart_streams_process" href="#quickstart_streams_process">Step 4: Process data</a></h4>

<pre class="brush: bash;">
&gt; bin/kafka-run-class.sh org.apache.kafka.streams.examples.wordcount.WordCountDemo
</pre>

<p>
The demo application will read from the input topic <b>streams-file-input</b>, perform the computations of the WordCount algorithm on each of the read messages,
and continuously write its current results to the output topic <b>streams-wordcount-output</b>.
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.
</p>
<p>
We can now inspect the output of the WordCount demo application by reading from its output topic:
</p>

<pre class="brush: bash;">
&gt; 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
</pre>

<p>
with the following output data being printed to the console:
</p>

<pre class="brush: bash;">
all     1
lead    1
to      1
hello   1
streams 2
join    1
kafka   3
summit  1
</pre>

<p>
Here, the first column is the Kafka message key in <code>java.lang.String</code> format, and the second column is the message value in <code>java.lang.Long</code> 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.
</p>

<p>
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&lt;String, Long&gt;</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>.
</p>

<img src="/{{version}}/images/streams-table-updates-02.png" style="float: right; width: 25%;">
<img src="/{{version}}/images/streams-table-updates-01.png" style="float: right; width: 25%;">

<p>
First the text line "all streams lead to kafka" is being processed.
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>.
</p>
<p>
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.
</p>
<p>
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>

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

<p>You can stop the console consumer via <b>Ctrl-C</b>.</p>

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