Github user miguno commented on a diff in the pull request:
https://github.com/apache/kafka-site/pull/66#discussion_r126674877
--- Diff: 0110/streams/index.html ---
@@ -18,56 +18,192 @@
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- <h1>Streams</h1>
-
- <ol class="toc">
- <li>
- <a href="/{{version}}/documentation/streams/quickstart">Play
with a Streams Application</a>
- </li>
- <li>
- <a
href="/{{version}}/documentation/streams/core-concepts">Core Concepts</a>
- </li>
- <li>
- <a
href="/{{version}}/documentation/streams/architecture">Architecture</a>
- </li>
- <li>
- <a
href="/{{version}}/documentation/streams/developer-guide">Developer Guide</a>
- <ul>
- <li><a
href="/{{version}}/documentation/streams/developer-guide#streams_processor">Low-level
Processor API</a></li>
- <li><a
href="/{{version}}/documentation/streams/developer-guide#streams_dsl">High-level
Streams DSL</a></li>
- <li><a
href="/{{version}}/documentation/streams/developer-guide#streams_interactive_querie">Interactive
Queries</a></li>
- <li><a
href="/{{version}}/documentation/streams/developer-guide#streams_execute">Application
Configuration and Execution</a></li>
- </ul>
- </li>
- <li>
- <a
href="/{{version}}/documentation/streams/upgrade-guide">Upgrade Guide and API
Changes</a>
- </li>
- </ol>
-
- <h2>Overview</h2>
-
- <p>
- Kafka Streams is a client library for processing and analyzing
data stored in Kafka.
- It builds upon important stream processing concepts such as
properly distinguishing between event time and processing time, windowing
support, and simple yet efficient management of application state.
- </p>
- <p>
- Kafka Streams has a <b>low barrier to entry</b>: You can quickly
write and run a small-scale proof-of-concept on a single machine; and you only
need to run additional instances of your application on multiple machines to
scale up to high-volume production workloads.
- Kafka Streams transparently handles the load balancing of multiple
instances of the same application by leveraging Kafka's parallelism model.
- </p>
- <p>
- Some highlights of Kafka Streams:
- </p>
-
- <ul>
- <li>Designed as a <b>simple and lightweight client library</b>,
which can be easily embedded in any Java application and integrated with any
existing packaging, deployment and operational tools that users have for their
streaming applications.</li>
- <li>Has <b>no external dependencies on systems other than Apache
Kafka itself</b> as the internal messaging layer; notably, it uses Kafka's
partitioning model to horizontally scale processing while maintaining strong
ordering guarantees.</li>
- <li>Supports <b>fault-tolerant local state</b>, which enables very
fast and efficient stateful operations like windowed joins and
aggregations.</li>
- <li>Supports <b>exactly-once</b> processing semantics to guarantee
that each record will be processed once and only once even when there is a
failure on either Streams clients or Kafka brokers in the middle of
processing.</li>
- <li>Employs <b>one-record-at-a-time processing</b> to achieve
millisecond processing latency, and supports <b>event-time based windowing
operations</b> with late arrival of records.</li>
- <li>Offers necessary stream processing primitives, along with a
<b>high-level Streams DSL</b> and a <b>low-level Processor API</b>.</li>
+ <h1>Kafka Streams API</h1>
+ <h3 style="max-width: 75rem;">The easiest way to write
mission-critical real-time applications and microservices with all the benefits
of Kafka's server-side cluster technology.</h3>
+
+ <div class="hero">
+ <div class="hero__diagram">
+ <img src="/{{version}}/images/streams-welcome.png" />
+ </div>
+ <div class="hero__cta">
+ <a style="display: none;"
href="/{{version}}/documentation/streams/tutorial" class="btn">Write your first
app</a>
+ <a href="/{{version}}/documentation/streams/quickstart"
class="btn">Play with demo app</a>
+ </div>
+ </div>
+
+ <ul class="feature-list">
+ <li>Write standard Java applications</li>
+ <li>Exactly-once processing semantics</li>
+ <li>No seperate processing cluster required</li>
+ <li>Develop on Mac, Linux, Windows</li>
+ <li>Elastic, highly scalable, fault-tolerant</li>
+ <li>Deploy to containers, VMs, bare metal, cloud</li>
+ <li>Equally viable for small, medium, & large use cases</li>
+ <li>Fully integrated with Kafka security</li>
</ul>
+ <div class="cards">
+ <a class="card"
href="/{{version}}/documentation/streams/developer-guide">
+ <img class="card__icon"
src="/{{version}}/images/icons/documentation.png" />
+ <img class="card__icon card__icon--hover"
src="/{{version}}/images/icons/documentation--white.png" />
+ <span class="card__label">Developer manual</span>
+ </a>
+ <a style="display: none;" class="card"
href="/{{version}}/documentation/streams/tutorial">
+ <img class="card__icon"
src="/{{version}}/images/icons/tutorials.png" />
+ <img class="card__icon card__icon--hover"
src="/{{version}}/images/icons/tutorials--white.png" />
+ <span class="card__label">Tutorials</span>
+ </a>
+ <a class="card"
href="/{{version}}/documentation/streams/core-concepts">
+ <img class="card__icon"
src="/{{version}}/images/icons/architecture.png" />
+ <img class="card__icon card__icon--hover"
src="/{{version}}/images/icons/architecture--white.png" />
+ <span class="card__label">Concepts</span>
+ </a>
+ </div>
+
+ <h3>Hello Kafka Streams</h3>
+ <p>The code example below implements a WordCount application that is
elastic, highly scalable, fault-tolerant, stateful, and ready to run in
production at large scale</p>
+
+ <div class="code-example">
+ <div class="btn-group">
+ <a class="selected b-java-8" data-section="java-8">Java 8+</a>
+ <a class="b-java-7" data-section="java-7">Java 7</a>
+ <a class="b-scala" data-section="scala">Scala</a>
+ </div>
+
+ <div class="code-example__snippet b-java-8 selected">
+ <pre class="brush: java;">
+ import org.apache.kafka.common.serialization.Serdes;
+ import org.apache.kafka.streams.KafkaStreams;
+ import org.apache.kafka.streams.StreamsConfig;
+ import org.apache.kafka.streams.kstream.KStream;
+ import org.apache.kafka.streams.kstream.KStreamBuilder;
+ import org.apache.kafka.streams.kstream.KTable;
+
+ import java.util.Arrays;
+ import java.util.Properties;
+
+ public class WordCountApplication {
+
+ public static void main(final String[] args) throws
Exception {
+ Properties config = new Properties();
+ config.put(StreamsConfig.APPLICATION_ID_CONFIG,
"wordcount-application");
+ config.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG,
"kafka-broker1:9092");
+
config.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG,
Serdes.String().getClass());
+
config.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG,
Serdes.String().getClass());
+
+ KStreamBuilder builder = new KStreamBuilder();
+ KStream<String, String> textLines =
builder.stream("TextLinesTopic");
+ KTable<String, Long> wordCounts = textLines
+ .flatMapValues(textLine ->
Arrays.asList(textLine.toLowerCase().split("\\W+")))
+ .groupBy((key, word) -> word)
+ .count("Counts");
+ wordCounts.to(Serdes.String(), Serdes.Long(),
"WordsWithCountsTopic");
+
+ KafkaStreams streams = new KafkaStreams(builder,
config);
+ streams.start();
+ }
+
+ }
+ </pre>
+ </div>
+
+ <div class="code-example__snippet b-java-7">
+ <pre class="brush: java;">
+ import org.apache.kafka.common.serialization.Serdes;
+ import org.apache.kafka.streams.KafkaStreams;
+ import org.apache.kafka.streams.StreamsConfig;
+ import org.apache.kafka.streams.kstream.KStream;
+ import org.apache.kafka.streams.kstream.KStreamBuilder;
+ import org.apache.kafka.streams.kstream.KTable;
+ import org.apache.kafka.streams.kstream.KeyValueMapper;
+ import org.apache.kafka.streams.kstream.ValueMapper;
+
+ import java.util.Arrays;
+ import java.util.Properties;
+
+ public class WordCountApplication {
+
+ public static void main(final String[] args) throws
Exception {
+ Properties config = new Properties();
+ config.put(StreamsConfig.APPLICATION_ID_CONFIG,
"wordcount-application");
+ config.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG,
"kafka-broker1:9092");
+
config.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG,
Serdes.String().getClass());
+
config.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG,
Serdes.String().getClass());
+
+ KStreamBuilder builder = new KStreamBuilder();
+ KStream<String, String> textLines =
builder.stream("TextLinesTopic");
+ KTable<String, Long> wordCounts = textLines
+ .flatMapValues(new ValueMapper<String,
Iterable<String>>() {
+ @Override
+ public Iterable<String> apply(String
textLine) {
+ return
Arrays.asList(textLine.toLowerCase().split("\\W+"));
+ }
+ })
+ .groupBy(new KeyValueMapper<String, String,
String>() {
+ @Override
+ public String apply(String key, String word) {
+ return word;
+ }
+ })
+ .count("Counts");
+ wordCounts.to(Serdes.String(), Serdes.Long(),
"WordsWithCountsTopic");
+
+ KafkaStreams streams = new KafkaStreams(builder,
config);
+ streams.start();
+ }
+
+ }
+ </pre>
+ </div>
+
+ <div class="code-example__snippet b-scala">
+ <pre class="brush: scala;">
+ import java.lang.Long
+ import java.util.Properties
+ import java.util.concurrent.TimeUnit
+
+ import org.apache.kafka.common.serialization._
+ import org.apache.kafka.streams._
+ import org.apache.kafka.streams.kstream.{KStream,
KStreamBuilder, KTable}
+
+ import
scala.collection.JavaConverters.asJavaIterableConverter
+
+ object WordCountApplication {
+
+ def main(args: Array[String]) {
+ val config: Properties = {
--- End diff --
The indentation of this Scala code example isn't quite right.
See https://gist.github.com/miguno/e3c791f67068efa0cd741d8929a9662a how it
should look like.
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