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From guozh...@apache.org
Subject [09/15] kafka-site git commit: MINOR: Improve Streams Dev Guide
Date Wed, 20 Dec 2017 21:23:25 GMT
http://git-wip-us.apache.org/repos/asf/kafka-site/blob/12dbff55/10/index.html
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+<!--
+   Licensed to the Apache Software Foundation (ASF) under one or more
+   contributor license agreements.  See the NOTICE file distributed with
+   this work for additional information regarding copyright ownership.
+   The ASF licenses this file to You under the Apache License, Version 2.0
+   (the "License"); you may not use this file except in compliance with
+   the License.  You may obtain a copy of the License at
+      http://www.apache.org/licenses/LICENSE-2.0
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+   -->
+<script>
+  <!--#include virtual="../js/templateData.js" -->
+</script>
+<script id="streams-template" type="text/x-handlebars-template">
+  <h1>Kafka Streams</h1>
+       <div class="sub-nav-sticky">
+          <div class="sticky-top">
+             <div style="height:35px">
+                <a  class="active-menu-item" href="/{{version}}/documentation/streams/">Introduction</a>
+                <a href="/{{version}}/documentation/streams/developer-guide/">Developer Guide</a>
+                <a href="/{{version}}/documentation/streams/core-concepts">Concepts</a>
+                <a href="/{{version}}/documentation/streams/quickstart">Run Demo App</a>
+                <a href="/{{version}}/documentation/streams/tutorial">Tutorial: Write App</a>
+             </div>
+           </div>
+       </div>
+       <h3 class="streams_intro">The easiest way to write mission-critical real-time applications and microservices</h3>
+       <p class="streams__description">Kafka Streams is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It 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.</p>
+       <div class="video__series__grid">
+          <div class="yt__video__block">
+            <div class="yt__video__inner__block">
+                <iframe  class="yt_series video_1 active" style="display:block" src="https://www.youtube.com/embed/Z3JKCLG3VP4?rel=0&showinfo=0&end=602" frameborder="0" allowfullscreen></iframe>
+                <iframe  class="yt_series video_2" src="https://www.youtube.com/embed/LxxeXI1mPKo?rel=0&showinfo=0&end=622" frameborder="0" allowfullscreen></iframe>
+                <iframe  class="yt_series video_3" src="https://www.youtube.com/embed/7JYEEx7SBuE?rel=0&showinfo=0end=557" frameborder="0" allowfullscreen></iframe>
+                <iframe  class="yt_series video_4" src="https://www.youtube.com/embed/3kJgYIkAeHs?rel=0&showinfo=0&end=564" frameborder="0" allowfullscreen></iframe>
+             </div>
+            </div>
+            <div class="video__block">
+                <h3>TOUR OF THE STREAMS API</h3>
+                <div class="video__list">
+                   <p class="video__item video_list_1 active" onclick="$('.video__item').removeClass('active'); $(this).addClass('active');$('.yt_series').hide();$('.video_1').show();">
+                       <span class="number">1</span><span class="video__text">Intro to Streams</span>
+                   </p>
+                   <p class="video__item video_list_2" onclick="$('.video__item').removeClass('active'); $(this).addClass('active');$('.yt_series').hide();$('.video_2').show();">
+                       <span class="number">2</span><span class="video__text">Creating a Streams Application</span>
+                   </p>
+                   <p class="video__item video_list_3" onclick="$('.video__item').removeClass('active'); $(this).addClass('active');$('.yt_series').hide();$('.video_3').show();">
+                       <span class="number">3</span><span class="video__text">Transforming Data Pt. 1</span>
+                   </p>
+                   <p class="video__item video_list_4" onclick="$('.video__item').removeClass('active'); $(this).addClass('active');$('.yt_series').hide();$('.video_4').show();">
+                      <span class="number">4</span><span class="video__text">Transforming Data Pt. 11</span>
+                   </p>
+                </div>
+            </div>
+       </div>
+       <hr class="separator"> 
+       <div class="use-item-section">
+           <div class="use__list__sec">
+               <h3>Why you'll love using Kafka Streams!</h3>
+               <ul class="use-feature-list">
+                  <li>Elastic, highly scalable, fault-tolerant</li>
+                  <li>Deploy to containers, VMs, bare metal, cloud</li>
+                  <li>Equally viable for small, medium, &amp; large use cases</li>
+                  <li>Fully integrated with Kafka security</li>
+                  <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>
+                  
+               </ul>
+           </div>
+           <div class="first__app__cta">
+               <a href="/{{version}}/documentation/streams/tutorial" class="first__app__btn">Write your first app</a>
+           </div>
+       </div>
+       <hr class="separator"> 
+        <h3 class="stream__text">Streams API use cases</h3>
+         <div class="customers__grid">
+           <div class="customer__grid">
+             <div class="customer__item streams_logo_grid streams__ny__grid">
+               <a href="https://open.nytimes.com/publishing-with-apache-kafka-at-the-new-york-times-7f0e3b7d2077" target="_blank" class="grid__logo__link">
+                 <span class="grid__item__logo" style="background-image: url('/images/powered-by/NYT.jpg');"></span>
+               </a>
+               <p class="grid__item__customer__description extra__space">
+                 <a href="https://open.nytimes.com/publishing-with-apache-kafka-at-the-new-york-times-7f0e3b7d2077" target="_blank">The New York Times uses Apache Kafka </a>and the Kafka Streams API to store and distribute, in real-time, published content to the various applications and systems that make it available to the readers.
+               </p>
+             </div>
+           </div>
+           <div class="customer__grid">
+             <div class="customer__item  streams_logo_grid streams__zalando__grid">
+               <a href="https://www.confluent.io/blog/ranking-websites-real-time-apache-kafkas-streams-api/" target="_blank" class="grid__logo__link">
+                 <span class="grid__item__logo" style="background-image: url('/images/powered-by/zalando.jpg');"></span>
+               </a>
+               <p class="grid__item__customer__description extra__space">As the leading online fashion retailer in Europe, Zalando uses Kafka as an ESB (Enterprise Service Bus), which helps us in transitioning from a monolithic to a micro services architecture. Using Kafka for processing
+                 <a href="https://www.confluent.io/blog/ranking-websites-real-time-apache-kafkas-streams-api/" target='blank'> event streams</a> enables our technical team to do near-real time business intelligence.
+               </p>
+           </div>
+           </div>  
+           <div class="customer__grid">
+             <div class="customer__item  streams_logo_grid streams__line__grid">
+               <a href="https://engineering.linecorp.com/en/blog/detail/80" target="_blank" class="grid__logo__link">
+                 <span class="grid__item__logo" style="background-image: url('/images/powered-by/line.svg');width:9rem"></span>
+               </a>
+                 <p class="grid__item__customer__description extra__space"><a href="https://engineering.linecorp.com/en/blog/detail/80" target="_blank">LINE uses Apache Kafka</a> as a central datahub for our services to communicate to one another. Hundreds of billions of messages are produced daily and are used to execute various business logic, threat detection, search indexing and data analysis. LINE leverages Kafka Streams to reliably transform and filter topics enabling sub topics consumers can efficiently consume, meanwhile retaining easy maintainability thanks to its sophisticated yet minimal code base.</p>
+             </div>
+           </div>
+           <div class="customer__grid">
+             <div class="customer__item streams_logo_grid streams__ny__grid">
+               <a href="https://medium.com/@Pinterest_Engineering/using-kafka-streams-api-for-predictive-budgeting-9f58d206c996" target="_blank" class="grid__logo__link">
+                 <span class="grid__item__logo" style="background-image: url('/images/powered-by/pinterest.png');"></span>
+               </a>
+               <p class="grid__item__customer__description">
+                   <a href="https://medium.com/@Pinterest_Engineering/using-kafka-streams-api-for-predictive-budgeting-9f58d206c996" target="_blank">Pinterest uses Apache Kafka and the Kafka Streams API</a> at large scale to power the real-time, predictive budgeting system of their advertising infrastructure. With Kafka Streams, spend predictions are more accurate than ever.
+               </p>
+             </div>
+           </div> 
+           <div class="customer__grid">
+             <div class="customer__item  streams_logo_grid streams__rabobank__grid">
+               <a href="https://www.confluent.io/blog/real-time-financial-alerts-rabobank-apache-kafkas-streams-api/" target="_blank" class="grid__logo__link">
+                 <span class="grid__item__logo" style="background-image: url('/images/powered-by/rabobank.jpg');"></span>
+               </a>
+                 <p class="grid__item__customer__description">Rabobank is one of the 3 largest banks in the Netherlands. Its digital nervous system, the Business Event Bus, is powered by Apache Kafka. It is used by an increasing amount of financial processes and services, one of which is Rabo Alerts. This service alerts customers in real-time upon financial events and is <a href="https://www.confluent.io/blog/real-time-financial-alerts-rabobank-apache-kafkas-streams-api/" target="_blank">built using Kafka Streams.</a></p>
+             </div>
+           </div>        
+           <div class="customer__grid">
+             <div class="customer__item streams_logo_grid streams__ny__grid">
+               <a href="https://speakerdeck.com/xenji/kafka-and-debezium-at-trivago-code-dot-talks-2017-edition" target="_blank" class="grid__logo__link">
+                 <span class="grid__item__logo" style="background-image: url('/images/powered-by/trivago.png');"></span>
+               </a>
+               <p class="grid__item__customer__description">
+                   Trivago is a global hotel search platform. We are focused on reshaping the way travelers search for and compare hotels, while enabling hotel advertisers to grow their businesses by providing access to a broad audience of travelers via our websites and apps. As of 2017, we offer access to approximately 1.8 million hotels and other accommodations in over 190 countries. We use Kafka, Kafka Connect, and Kafka Streams to <a href="https://speakerdeck.com/xenji/kafka-and-debezium-at-trivago-code-dot-talks-2017-edition" target="_blank">enable our developers</a> to access data freely in the company. Kafka Streams powers parts of our analytics pipeline and delivers endless options to explore and operate on the data sources we have at hand.
+               </p>
+             </div>
+           </div>  
+         
+       </div>
+       <h3 style="margin-top: 5.3rem;">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.StreamsBuilder;
+                   import org.apache.kafka.streams.StreamsConfig;
+                   import org.apache.kafka.streams.Topology;
+                   import org.apache.kafka.streams.kstream.Materialized;
+                   import org.apache.kafka.streams.kstream.Produced;
+                   import org.apache.kafka.streams.state.KeyValueStore;
+       
+                   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());
+       
+                           StreamsBuilder builder = new StreamsBuilder();
+                           KStream&lt;String, String&gt; textLines = builder.stream("TextLinesTopic");
+                           KTable&lt;String, Long&gt; wordCounts = textLines
+                               .flatMapValues(textLine -> Arrays.asList(textLine.toLowerCase().split("\\W+")))
+                               .groupBy((key, word) -> word)
+                               .count(Materialized.&lt;String, Long, KeyValueStore&lt;Bytes, byte[]&gt;&gt;as("counts-store"));
+                           wordCounts.toStream().to("WordsWithCountsTopic", Produced.with(Serdes.String(), Serdes.Long()));
+       
+                           KafkaStreams streams = new KafkaStreams(builder.build(), 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.StreamsBuilder;
+                   import org.apache.kafka.streams.StreamsConfig;
+                   import org.apache.kafka.streams.Topology;
+                   import org.apache.kafka.streams.kstream.KeyValueMapper;
+                   import org.apache.kafka.streams.kstream.Materialized;
+                   import org.apache.kafka.streams.kstream.Produced;
+                   import org.apache.kafka.streams.kstream.ValueMapper;
+                   import org.apache.kafka.streams.state.KeyValueStore;
+
+                   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());
+       
+                           StreamsBuilder builder = new StreamsBuilder();
+                           KStream&lt;String, String&gt; textLines = builder.stream("TextLinesTopic");
+                           KTable&lt;String, Long&gt; wordCounts = textLines
+                               .flatMapValues(new ValueMapper&lt;String, Iterable&lt;String&gt;&gt;() {
+                                   @Override
+                                   public Iterable&lt;String&gt; apply(String textLine) {
+                                       return Arrays.asList(textLine.toLowerCase().split("\\W+"));
+                                   }
+                               })
+                               .groupBy(new KeyValueMapper&lt;String, String, String&gt;() {
+                                   @Override
+                                   public String apply(String key, String word) {
+                                       return word;
+                                   }
+                               })
+                               .count(Materialized.&lt;String, Long, KeyValueStore&lt;Bytes, byte[]&gt;&gt;as("counts-store"));
+
+
+                           wordCounts.toStream().to("WordsWithCountsTopic", Produced.with(Serdes.String(), Serdes.Long()));
+       
+                           KafkaStreams streams = new KafkaStreams(builder.build(), 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.{KeyValueMapper, Materialized, Produced, ValueMapper}
+                   import org.apache.kafka.streams.state.KeyValueStore;
+       
+                   import scala.collection.JavaConverters.asJavaIterableConverter
+       
+                   object WordCountApplication {
+       
+                       def main(args: Array[String]) {
+                           val config: Properties = {
+                               val p = new Properties()
+                               p.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-application")
+                               p.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092")
+                               p.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass)
+                               p.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass)
+                               p
+                           }
+       
+                           val builder: StreamsBuilder = new StreamsBuilder()
+                           val textLines: KStream[String, String] = builder.stream("TextLinesTopic")
+                           val wordCounts: KTable[String, Long] = textLines
+                               .flatMapValues(textLine => textLine.toLowerCase.split("\\W+").toIterable.asJava)
+                               .groupBy((_, word) => word)
+                               .count(Materialized.as("counts-store").asInstanceOf[Materialized[String, Long, KeyValueStore[Bytes, Array[Byte]]]])
+                           wordCounts.toStream().to("WordsWithCountsTopic", Produced.with(Serdes.String(), Serdes.Long()))
+       
+                           val streams: KafkaStreams = new KafkaStreams(builder.build(), config)
+                           streams.start()
+       
+                           Runtime.getRuntime.addShutdownHook(new Thread(() => {
+                               streams.close(10, TimeUnit.SECONDS)
+                           }))
+                       }
+       
+                   }
+               </pre>
+           </div>
+       </div>
+       
+       <div class="pagination">
+           <a href="#" class="pagination__btn pagination__btn__prev pagination__btn--disabled">Previous</a>
+           <a href="/{{version}}/documentation/streams/quickstart" class="pagination__btn pagination__btn__next">Next</a>
+       </div>
+     
+</script>
+<!--#include virtual="../../includes/_header.htm" -->
+<!--#include virtual="../../includes/_top.htm" -->
+<div class="content documentation documentation--current">
+  <!--#include virtual="../../includes/_nav.htm" -->
+  <div class="right">
+    <!--#include virtual="../../includes/_docs_banner.htm" -->
+    <ul class="breadcrumbs">
+      <li><a href="/documentation">Documentation</a>
+      <li><a href="/documentation/streams">Kafka Streams</a></li>
+      </li>
+    </ul>
+    <div class="p-streams"></div>
+  </div>
+</div>
+<!--#include virtual="../../includes/_footer.htm" -->
+<script>
+  $(function() {
+         
+         // Show selected style on nav item
+         $('.b-nav__streams').addClass('selected');
+    
+         $('.video_list_1').click(function(){    
+             $('.video_2').attr('src', $('.video_2').attr('src'));
+             $('.video_3').attr('src', $('.video_3').attr('src'));
+             $('.video_4').attr('src', $('.video_4').attr('src'));
+
+           });
+
+         $('.video_list_2').click(function(){    
+               $('.video_1').attr('src', $('.video_1').attr('src'));
+               $('.video_3').attr('src', $('.video_3').attr('src'));
+               $('.video_4').attr('src', $('.video_4').attr('src'));
+
+           });
+
+         $('.video_list_3').click(function(){    
+              $('.video_1').attr('src', $('.video_1').attr('src'));
+              $('.video_2').attr('src', $('.video_2').attr('src'));
+              $('.video_4').attr('src', $('.video_4').attr('src'));
+           });
+
+         $('.video_list_4').click(function(){    
+              $('.video_1').attr('src', $('.video_1').attr('src'));
+              $('.video_2').attr('src', $('.video_2').attr('src'));
+              $('.video_3').attr('src', $('.video_3').attr('src'));
+           });
+           
+
+          //sticky secondary nav
+          var $navbar = $(".sub-nav-sticky"),
+               y_pos = $navbar.offset().top,
+               height = $navbar.height();
+       
+           $(window).scroll(function() {
+               var scrollTop = $(window).scrollTop();
+           
+               if (scrollTop > y_pos - height) {
+                   $navbar.addClass("navbar-fixed")
+               } else if (scrollTop <= y_pos) {
+                   $navbar.removeClass("navbar-fixed")
+               }
+           });
+       
+         // Display docs subnav items
+         $('.b-nav__docs').parent().toggleClass('nav__item__with__subs--expanded');
+         // Show selected code example
+         $('.btn-group a').click(function(){
+             var targetClass = '.b-' + $(this).data().section;
+             $('.code-example__snippet, .btn-group a').removeClass('selected');
+             $(targetClass).addClass('selected');
+         });
+       });
+</script>
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/kafka-site/blob/12dbff55/10/quickstart.html
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diff --git a/10/quickstart.html b/10/quickstart.html
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--- a/10/quickstart.html
+++ b/10/quickstart.html
@@ -14,25 +14,88 @@
  See the License for the specific language governing permissions and
  limitations under the License.
 -->
+<script><!--#include virtual="../js/templateData.js" --></script>
+
+<script id="content-template" type="text/x-handlebars-template">
+
+  <h1>Run Streams Demo Application</h1>
+  <div class="sub-nav-sticky">
+      <div class="sticky-top">
+        <div style="height:35px">
+          <a href="/{{version}}/documentation/streams/">Introduction</a>
+          <a href="/{{version}}/documentation/streams/developer-guide/">Developer Guide</a>
+          <a href="/{{version}}/documentation/streams/core-concepts">Concepts</a>
+          <a class="active-menu-item" href="/{{version}}/documentation/streams/quickstart">Run Demo App</a>
+          <a href="/{{version}}/documentation/streams/tutorial">Tutorial: Write App</a>
+        </div>
+      </div>
+  </div> 
+<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-plaintext-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("streams-plaintext-input",
+    Consumed.with(stringSerde, stringSerde);
+
+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()
 
-<script><!--#include virtual="js/templateData.js" --></script>
+// Store the running counts as a changelog stream to the output topic.
+wordCounts.toStream().to("streams-wordcount-output", Produced.with(Serdes.String(), Serdes.Long()));
+</pre>
 
-<script id="quickstart-template" type="text/x-handlebars-template">
 <p>
-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 <code>bin\windows\</code> instead of <code>bin/</code>, and change the script extension to <code>.bat</code>.
+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.
 </p>
 
-<h4><a id="quickstart_download" href="#quickstart_download">Step 1: Download the code</a></h4>
+<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.
+<a href="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:
 
 <pre class="brush: bash;">
-&gt; tar -xzf kafka_2.11-{{fullDotVersion}}.tgz
-&gt; cd kafka_2.11-{{fullDotVersion}}
+&gt; tar -xzf kafka_{{scalaVersion}}-{{fullDotVersion}}.tgz
+&gt; cd kafka_{{scalaVersion}}-{{fullDotVersion}}
 </pre>
 
-<h4><a id="quickstart_startserver" href="#quickstart_startserver">Step 2: Start the server</a></h4>
+<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.
@@ -52,249 +115,273 @@ Kafka uses <a href="https://zookeeper.apache.org/">ZooKeeper</a> so you need to
 ...
 </pre>
 
-<h4><a id="quickstart_createtopic" href="#quickstart_createtopic">Step 3: Create a topic</a></h4>
 
-<p>Let's create a topic named "test" with a single partition and only one replica:</p>
+<h4><a id="quickstart_streams_prepare" href="#quickstart_streams_prepare">Step 3: Prepare input topic and start Kafka producer</a></h4>
+
+<!--
+
 <pre class="brush: bash;">
-&gt; bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic test
+&gt; echo -e "all streams lead to kafka\nhello kafka streams\njoin kafka summit" > file-input.txt
 </pre>
-
-<p>We can now see that topic if we run the list topic command:</p>
+Or on Windows:
 <pre class="brush: bash;">
-&gt; bin/kafka-topics.sh --list --zookeeper localhost:2181
-test
+&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>Alternatively, instead of manually creating topics you can also configure your brokers to auto-create topics when a non-existent topic is published to.</p>
 
-<h4><a id="quickstart_send" href="#quickstart_send">Step 4: Send some messages</a></h4>
+-->
 
-<p>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.</p>
-<p>
-Run the producer and then type a few messages into the console to send to the server.</p>
+Next, we create the input topic named <b>streams-plaintext-input</b> and the output topic named <b>streams-wordcount-output</b>:
 
 <pre class="brush: bash;">
-&gt; bin/kafka-console-producer.sh --broker-list localhost:9092 --topic test
-This is a message
-This is another message
+&gt; bin/kafka-topics.sh --create \
+    --zookeeper localhost:2181 \
+    --replication-factor 1 \
+    --partitions 1 \
+    --topic streams-plaintext-input
+Created topic "streams-plaintext-input".
 </pre>
 
-<h4><a id="quickstart_consume" href="#quickstart_consume">Step 5: Start a consumer</a></h4>
-
-<p>Kafka also has a command line consumer that will dump out messages to standard output.</p>
+Note: we create the output topic with compaction enabled because the output stream is a changelog stream
+(cf. <a href="#anchor-changelog-output">explanation of application output</a> below).
 
 <pre class="brush: bash;">
-&gt; bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic test --from-beginning
-This is a message
-This is another message
+&gt; bin/kafka-topics.sh --create \
+    --zookeeper localhost:2181 \
+    --replication-factor 1 \
+    --partitions 1 \
+    --topic streams-wordcount-output \
+    --config cleanup.policy=compact
+Created topic "streams-wordcount-output".
 </pre>
-<p>
-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.
-</p>
-<p>
-All of the command line tools have additional options; running the command with no arguments will display usage information documenting them in more detail.
-</p>
 
-<h4><a id="quickstart_multibroker" href="#quickstart_multibroker">Step 6: Setting up a multi-broker cluster</a></h4>
+The created topic can be described with the same <b>kafka-topics</b> tool:
 
-<p>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).</p>
-<p>
-First we make a config file for each of the brokers (on Windows use the <code>copy</code> command instead):
-</p>
 <pre class="brush: bash;">
-&gt; cp config/server.properties config/server-1.properties
-&gt; cp config/server.properties config/server-2.properties
-</pre>
+&gt; bin/kafka-topics.sh --zookeeper localhost:2181 --describe
 
-<p>
-Now edit these new files and set the following properties:
-</p>
-<pre class="brush: text;">
+Topic:streams-plaintext-input	PartitionCount:1	ReplicationFactor:1	Configs:
+    Topic: streams-plaintext-input	Partition: 0	Leader: 0	Replicas: 0	Isr: 0
+Topic:streams-wordcount-output	PartitionCount:1	ReplicationFactor:1	Configs:
+	Topic: streams-wordcount-output	Partition: 0	Leader: 0	Replicas: 0	Isr: 0
+</pre>
 
-config/server-1.properties:
-    broker.id=1
-    listeners=PLAINTEXT://:9093
-    log.dir=/tmp/kafka-logs-1
+<h4><a id="quickstart_streams_start" href="#quickstart_streams_start">Step 4: Start the Wordcount Application</a></h4>
 
-config/server-2.properties:
-    broker.id=2
-    listeners=PLAINTEXT://:9094
-    log.dir=/tmp/kafka-logs-2
-</pre>
-<p>The <code>broker.id</code> 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.</p>
-<p>
-We already have Zookeeper and our single node started, so we just need to start the two new nodes:
-</p>
-<pre class="brush: bash;">
-&gt; bin/kafka-server-start.sh config/server-1.properties &amp;
-...
-&gt; bin/kafka-server-start.sh config/server-2.properties &amp;
-...
-</pre>
+The following command starts the WordCount demo application:
 
-<p>Now create a new topic with a replication factor of three:</p>
 <pre class="brush: bash;">
-&gt; bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 3 --partitions 1 --topic my-replicated-topic
+&gt; bin/kafka-run-class.sh org.apache.kafka.streams.examples.wordcount.WordCountDemo
 </pre>
 
-<p>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:</p>
-<pre class="brush: bash;">
-&gt; 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
-</pre>
-<p>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.</p>
-<ul>
-  <li>"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.
-  <li>"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.
-  <li>"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.
-</ul>
-<p>Note that in my example node 1 is the leader for the only partition of the topic.</p>
 <p>
-We can run the same command on the original topic we created to see where it is:
+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,
+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.
 </p>
-<pre class="brush: bash;">
-&gt; 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
-</pre>
-<p>So there is no surprise there&mdash;the original topic has no replicas and is on server 0, the only server in our cluster when we created it.</p>
-<p>
-Let's publish a few messages to our new topic:
-</p>
-<pre class="brush: bash;">
-&gt; bin/kafka-console-producer.sh --broker-list localhost:9092 --topic my-replicated-topic
-...
-my test message 1
-my test message 2
-^C
-</pre>
-<p>Now let's consume these messages:</p>
-<pre class="brush: bash;">
-&gt; bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --from-beginning --topic my-replicated-topic
-...
-my test message 1
-my test message 2
-^C
-</pre>
 
-<p>Now let's test out fault-tolerance. Broker 1 was acting as the leader so let's kill it:</p>
-<pre class="brush: bash;">
-&gt; ps aux | grep server-1.properties
-7564 ttys002    0:15.91 /System/Library/Frameworks/JavaVM.framework/Versions/1.8/Home/bin/java...
-&gt; kill -9 7564
-</pre>
+Now we can start the console producer in a separate terminal to write some input data to this topic:
 
-On Windows use:
 <pre class="brush: bash;">
-&gt; wmic process where "caption = 'java.exe' and commandline like '%server-1.properties%'" get processid
-ProcessId
-6016
-&gt; taskkill /pid 6016 /f
+&gt; bin/kafka-console-producer.sh --broker-list localhost:9092 --topic streams-plaintext-input
 </pre>
 
-<p>Leadership has switched to one of the slaves and node 1 is no longer in the in-sync replica set:</p>
+and inspect the output of the WordCount demo application by reading from its output topic with the console consumer in a separate terminal:
 
 <pre class="brush: bash;">
-&gt; 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
-</pre>
-<p>But the messages are still available for consumption even though the leader that took the writes originally is down:</p>
-<pre class="brush: bash;">
-&gt; bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --from-beginning --topic my-replicated-topic
-...
-my test message 1
-my test message 2
-^C
+&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>
 
 
-<h4><a id="quickstart_kafkaconnect" href="#quickstart_kafkaconnect">Step 7: Use Kafka Connect to import/export data</a></h4>
-
-<p>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.</p>
-
-<p>Kafka Connect is a tool included with Kafka that imports and exports data to Kafka. It is an extensible tool that runs
-<i>connectors</i>, 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.</p>
+<h4><a id="quickstart_streams_process" href="#quickstart_streams_process">Step 5: Process some data</a></h4>
 
-<p>First, we'll start by creating some seed data to test with:</p>
+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 &lt;RETURN&gt;.
+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):
 
 <pre class="brush: bash;">
-&gt; echo -e "foo\nbar" > test.txt
-</pre>
-Or on Windows:
-<pre class="brush: bash;">
-&gt; echo foo> test.txt
-&gt; echo bar>> test.txt
+&gt; bin/kafka-console-producer.sh --broker-list localhost:9092 --topic streams-plaintext-input
+all streams lead to kafka
 </pre>
 
-<p>Next, we'll start two connectors running in <i>standalone</i> 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.</p>
+<p>
+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:
+</p>
 
 <pre class="brush: bash;">
-&gt; bin/connect-standalone.sh config/connect-standalone.properties config/connect-file-source.properties config/connect-file-sink.properties
+&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
+
+all	    1
+streams	1
+lead	1
+to	    1
+kafka	1
 </pre>
 
 <p>
-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.
-</p>
-
-<p>
-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 <code>test.txt</code> and
-producing them to the topic <code>connect-test</code>, and the sink connector should start reading messages from the topic <code>connect-test</code>
-and write them to the file <code>test.sink.txt</code>. We can verify the data has been delivered through the entire pipeline
-by examining the contents of the output file:
+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.
 </p>
 
+Now let's continue writing one more message with the console producer into the input topic <b>streams-plaintext-input</b>.
+Enter the text line "hello kafka streams" and hit &lt;RETURN&gt;.
+Your terminal should look as follows:
 
 <pre class="brush: bash;">
-&gt; more test.sink.txt
-foo
-bar
+&gt; bin/kafka-console-producer.sh --broker-list localhost:9092 --topic streams-plaintext-input
+all streams lead to kafka
+hello kafka streams
 </pre>
 
-<p>
-Note that the data is being stored in the Kafka topic <code>connect-test</code>, so we can also run a console consumer to see the
-data in the topic (or use custom consumer code to process it):
-</p>
+In your other terminal in which the console consumer is running, you will observe that the WordCount application wrote new output data:
+
+<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
+
+all	    1
+streams	1
+lead	1
+to	    1
+kafka	1
+hello	1
+kafka	2
+streams	2
+</pre>
 
+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 &lt;RETURN&gt; in the console producer to the input topic <b>streams-wordcount-input</b> before we wrap up this quickstart:
 
 <pre class="brush: bash;">
-&gt; 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"}
-...
+&gt; bin/kafka-console-producer.sh --broker-list localhost:9092 --topic streams-wordcount-input
+all streams lead to kafka
+hello kafka streams
+join kafka summit
 </pre>
 
-<p>The connectors continue to process data, so we can add data to the file and see it move through the pipeline:</p>
+<a name="anchor-changelog-output"></a>
+The <b>streams-wordcount-output</b> topic will subsequently show the corresponding updated word counts (see last three lines):
 
 <pre class="brush: bash;">
-&gt; echo Another line>> test.txt
+&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
+
+all	    1
+streams	1
+lead	1
+to	    1
+kafka	1
+hello	1
+kafka	2
+streams	2
+join	1
+kafka	3
+summit	1
 </pre>
 
-<p>You should see the line appear in the console consumer output and in the sink file.</p>
+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.
 
-<h4><a id="quickstart_kafkastreams" href="#quickstart_kafkastreams">Step 8: Use Kafka Streams to process data</a></h4>
+<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>
-  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. This <a href="/{{version}}/documentation/streams/quickstart">quickstart example</a> will demonstrate how
-  to run a streaming application coded in this library. 
+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>
+
+<h4><a id="quickstart_streams_stop" href="#quickstart_streams_stop">Step 6: Teardown the application</a></h4>
 
+<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>
+
+ <div class="pagination">
+        <a href="/{{version}}/documentation/streams" class="pagination__btn pagination__btn__prev">Previous</a>
+        <a href="/{{version}}/documentation/streams/tutorial" class="pagination__btn pagination__btn__next">Next</a>
+    </div>
 </script>
 
-<div class="p-quickstart"></div>
+<div class="p-quickstart-streams"></div>
+
+<!--#include virtual="../../includes/_header.htm" -->
+<!--#include virtual="../../includes/_top.htm" -->
+<div class="content documentation documentation--current">
+    <!--#include virtual="../../includes/_nav.htm" -->
+    <div class="right">
+        <!--#include virtual="../../includes/_docs_banner.htm" -->
+        <ul class="breadcrumbs">
+            <li><a href="/documentation">Documentation</a></li>
+            <li><a href="/documentation/streams">Kafka Streams</a></li>
+        </ul>
+        <div class="p-content"></div>
+    </div>
+</div>
+<!--#include virtual="../../includes/_footer.htm" -->
+<script>
+$(function() {
+  // Show selected style on nav item
+  $('.b-nav__streams').addClass('selected');
+
+
+     //sticky secondary nav
+    var $navbar = $(".sub-nav-sticky"),
+               y_pos = $navbar.offset().top,
+               height = $navbar.height();
+       
+           $(window).scroll(function() {
+               var scrollTop = $(window).scrollTop();
+           
+               if (scrollTop > y_pos - height) {
+                   $navbar.addClass("navbar-fixed")
+               } else if (scrollTop <= y_pos) {
+                   $navbar.removeClass("navbar-fixed")
+               }
+           });
+
+  // Display docs subnav items
+  $('.b-nav__docs').parent().toggleClass('nav__item__with__subs--expanded');
+});
+</script>

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index 0dbb1dc..efc01bd 100644
--- a/10/streams/architecture.html
+++ b/10/streams/architecture.html
@@ -110,7 +110,7 @@
     <p>
         Kafka Streams builds on fault-tolerance capabilities integrated natively within Kafka. Kafka partitions are highly available and replicated; so when stream data is persisted to Kafka it is available
         even if the application fails and needs to re-process it. Tasks in Kafka Streams leverage the fault-tolerance capability
-        offered by the <a href="https://www.confluent.io/blog/tutorial-getting-started-with-the-new-apache-kafka-0.9-consumer-client/">Kafka consumer client</a> to handle failures.
+        offered by the Kafka consumer client to handle failures.
         If a task runs on a machine that fails, Kafka Streams automatically restarts the task in one of the remaining running instances of the application.
     </p>
 
@@ -143,7 +143,7 @@
         <!--#include virtual="../../includes/_docs_banner.htm" -->
         <ul class="breadcrumbs">
             <li><a href="/documentation">Documentation</a></li>
-            <li><a href="/documentation/streams">Kafka Streams API</a></li>
+            <li><a href="/documentation/streams">Kafka Streams</a></li>
         </ul>
         <div class="p-content"></div>
     </div>

http://git-wip-us.apache.org/repos/asf/kafka-site/blob/12dbff55/10/streams/core-concepts.html
----------------------------------------------------------------------
diff --git a/10/streams/core-concepts.html b/10/streams/core-concepts.html
index eea9ed8..1675c1f 100644
--- a/10/streams/core-concepts.html
+++ b/10/streams/core-concepts.html
@@ -20,16 +20,16 @@
 <script id="content-template" type="text/x-handlebars-template">
     <h1>Core Concepts</h1>
     <div class="sub-nav-sticky">
-        <div class="sticky-top">
-            <div style="height:35px">
-                <a href="/{{version}}/documentation/streams/">Introduction</a>
-                <a href="/{{version}}/documentation/streams/developer-guide">Developer Guide</a>
-                <a class="active-menu-item"  href="/{{version}}/documentation/streams/core-concepts">Concepts</a>
-                <a href="/{{version}}/documentation/streams/quickstart">Run Demo App</a>
-                <a href="/{{version}}/documentation/streams/tutorial">Tutorial: Write App</a>
-            </div>
+      <div class="sticky-top">
+        <div style="height:35px">
+          <a href="/{{version}}/documentation/streams/">Introduction</a>
+          <a href="/{{version}}/documentation/streams/developer-guide/">Developer Guide</a>
+          <a class="active-menu-item" href="/{{version}}/documentation/streams/core-concepts">Concepts</a>
+          <a href="/{{version}}/documentation/streams/quickstart">Run Demo App</a>
+          <a href="/{{version}}/documentation/streams/tutorial">Tutorial: Write App</a>
         </div>
-    </div>
+      </div>
+  </div> 
     <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 and real-time querying of application state.
@@ -180,7 +180,7 @@
         <!--#include virtual="../../includes/_docs_banner.htm" -->
         <ul class="breadcrumbs">
             <li><a href="/documentation">Documentation</a></li>
-            <li><a href="/documentation/streams">Kafka Streams API</a></li>
+            <li><a href="/documentation/streams">Kafka Streams</a></li>
         </ul>
         <div class="p-content"></div>
     </div>
@@ -188,11 +188,11 @@
 <!--#include virtual="../../includes/_footer.htm" -->
 <script>
 $(function() {
-          // Show selected style on nav item
-          $('.b-nav__streams').addClass('selected');
+  // Show selected style on nav item
+  $('.b-nav__streams').addClass('selected');
+
 
-   
-          //sticky secondary nav
+     //sticky secondary nav
           var $navbar = $(".sub-nav-sticky"),
                y_pos = $navbar.offset().top,
                height = $navbar.height();
@@ -206,7 +206,8 @@ $(function() {
                    $navbar.removeClass("navbar-fixed")
                }
            });
-           // Display docs subnav items
-           $('.b-nav__docs').parent().toggleClass('nav__item__with__subs--expanded');
+
+  // Display docs subnav items
+  $('.b-nav__docs').parent().toggleClass('nav__item__with__subs--expanded');
 });
 </script>


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