KAFKA-SITE DOCS 8227 - Add missing links Core Concepts duality of streams tables

10/streams/core-concepts.html

@@ -57,13 +57,14 @@ <h1>Core Concepts</h1>
     <p>
         We first summarize the key concepts of Kafka Streams.
     </p>
+    <a id="streams_processor_node" href="#streams_processor_node"></a>
 
     <h3><a id="streams_topology" href="#streams_topology">Stream Processing Topology</a></h3>
 
     <ul>
         <li>A <b>stream</b> is the most important abstraction provided by Kafka Streams: it represents an unbounded, continuously updating data set. A stream is an ordered, replayable, and fault-tolerant sequence of immutable data records, where a <b>data record</b> is defined as a key-value pair.</li>
         <li>A <b>stream processing application</b> is any program that makes use of the Kafka Streams library. It defines its computational logic through one or more <b>processor topologies</b>, where a processor topology is a graph of stream processors (nodes) that are connected by streams (edges).</li>
-        <li>A <b><a href="#streams_processor_node">stream processor</a></b> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors. </li>
+        <li>A <a id="defining-a-stream-processor" href="/{{version}}/documentation/streams/developer-guide/processor-api#defining-a-stream-processor"><b>stream processor</b></a> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors. </li>
     </ul>
 
     There are two special processors in the topology:

10/streams/developer-guide/processor-api.html

@@ -66,7 +66,7 @@ <h2><a class="toc-backref" href="#id1">Overview</a><a class="headerlink" href="#
         </div>
         <div class="section" id="defining-a-stream-processor">
             <span id="streams-developer-guide-stream-processor"></span><h2><a class="toc-backref" href="#id2">Defining a Stream Processor</a><a class="headerlink" href="#defining-a-stream-processor" title="Permalink to this headline"></a></h2>
-            <p>A <a class="reference internal" href="../concepts.html#streams-concepts"><span class="std std-ref">stream processor</span></a> is a node in the processor topology that represents a single processing step.
+            <p>A <a class="reference internal" href="../core-concepts.html#streams_processor_node"><span class="std std-ref">stream processor</span></a> is a node in the processor topology that represents a single processing step.
                 With the Processor API, you can define arbitrary stream processors that processes one received record at a time, and connect
                 these processors with their associated state stores to compose the processor topology.</p>
             <p>You can define a customized stream processor by implementing the <code class="docutils literal"><span class="pre">Processor</span></code> interface, which provides the <code class="docutils literal"><span class="pre">process()</span></code> API method.

11/streams/core-concepts.html

@@ -57,13 +57,14 @@ <h1>Core Concepts</h1>
     <p>
         We first summarize the key concepts of Kafka Streams.
     </p>
+    <a id="streams_processor_node" href="#streams_processor_node"></a>
 
     <h3><a id="streams_topology" href="#streams_topology">Stream Processing Topology</a></h3>
-
+    <a id="streams_processor_node" href="#streams_processor_node"></a>
     <ul>
         <li>A <b>stream</b> is the most important abstraction provided by Kafka Streams: it represents an unbounded, continuously updating data set. A stream is an ordered, replayable, and fault-tolerant sequence of immutable data records, where a <b>data record</b> is defined as a key-value pair.</li>
         <li>A <b>stream processing application</b> is any program that makes use of the Kafka Streams library. It defines its computational logic through one or more <b>processor topologies</b>, where a processor topology is a graph of stream processors (nodes) that are connected by streams (edges).</li>
-        <li>A <b><a id="#streams_processor_node" href="#streams_processor_node">stream processor</a></b> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors. </li>
+        <li>A <a id="defining-a-stream-processor" href="/{{version}}/documentation/streams/developer-guide/processor-api#defining-a-stream-processor"><b>stream processor</b></a> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors. </li>
     </ul>
 
     There are two special processors in the topology:

11/streams/developer-guide/processor-api.html

@@ -66,7 +66,7 @@ <h2><a class="toc-backref" href="#id1">Overview</a><a class="headerlink" href="#
         </div>
         <div class="section" id="defining-a-stream-processor">
             <span id="streams-developer-guide-stream-processor"></span><h2><a class="toc-backref" href="#id2">Defining a Stream Processor</a><a class="headerlink" href="#defining-a-stream-processor" title="Permalink to this headline"></a></h2>
-            <p>A <a class="reference internal" href="../concepts.html#streams-concepts"><span class="std std-ref">stream processor</span></a> is a node in the processor topology that represents a single processing step.
+            <p>A <a class="reference internal" href="../core-concepts.html#streams_processor_node"><span class="std std-ref">stream processor</span></a> is a node in the processor topology that represents a single processing step.
                 With the Processor API, you can define arbitrary stream processors that processes one received record at a time, and connect
                 these processors with their associated state stores to compose the processor topology.</p>
             <p>You can define a customized stream processor by implementing the <code class="docutils literal"><span class="pre">Processor</span></code> interface, which provides the <code class="docutils literal"><span class="pre">process()</span></code> API method.

20/streams/core-concepts.html

@@ -57,13 +57,14 @@ <h1>Core Concepts</h1>
     <p>
         We first summarize the key concepts of Kafka Streams.
     </p>
+    <a id="streams_processor_node" href="#streams_processor_node"></a>
 
     <h3><a id="streams_topology" href="#streams_topology">Stream Processing Topology</a></h3>
 
     <ul>
         <li>A <b>stream</b> is the most important abstraction provided by Kafka Streams: it represents an unbounded, continuously updating data set. A stream is an ordered, replayable, and fault-tolerant sequence of immutable data records, where a <b>data record</b> is defined as a key-value pair.</li>
         <li>A <b>stream processing application</b> is any program that makes use of the Kafka Streams library. It defines its computational logic through one or more <b>processor topologies</b>, where a processor topology is a graph of stream processors (nodes) that are connected by streams (edges).</li>
-        <li>A <b><a id="streams_processor_node" href="#streams_processor_node">stream processor</a></b> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors. </li>
+        <li>A <a id="defining-a-stream-processor" href="/{{version}}/documentation/streams/developer-guide/processor-api#defining-a-stream-processor"><b>stream processor</b></a> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors. </li>
     </ul>
 
     There are two special processors in the topology:
@@ -160,21 +161,26 @@ <h3><a id="streams_concepts_duality" href="#streams-concepts-duality">Duality of
 
     <p>
         Any stream processing technology must therefore provide <strong>first-class support for streams and tables</strong>.
-        Kafka's Streams API provides such functionality through its core abstractions for
-        <code class="interpreted-text" data-role="ref">streams &lt;streams_concepts_kstream&gt;</code> and
-        <code class="interpreted-text" data-role="ref">tables &lt;streams_concepts_ktable&gt;</code>, which we will talk about in a minute.
-        Now, an interesting observation is that there is actually a <strong>close relationship between streams and tables</strong>,
-        the so-called stream-table duality.
-        And Kafka exploits this duality in many ways: for example, to make your applications
+        Kafka's Streams API provides such functionality through its core abstractions for 
+        <a id="streams_concepts_kstream" href="/{{version}}/documentation/streams/developer-guide/dsl-api#streams_concepts_kstream">streams</a>
+        <code class="interpreted-text" data-role="ref">&lt;streams_concepts_kstreams&gt;</code>
+        and <a id="streams_concepts_ktable" href="/{{version}}/documentation/streams/developer-guide/dsl-api#streams_concepts_ktable">tables</a>
+        <code class="interpreted-text" data-role="ref">&lt;streams_concepts_kstreams&gt;</code>,
+        which we will talk about in a minute. Now, an interesting observation is that there is actually a <strong>close relationship between streams and tables</strong>,
+        the so-called stream-table duality. And Kafka exploits this duality in many ways: for example, to make your applications
+        <a id="streams-developer-guide-execution-scaling" href="/{{version}}/documentation/streams/developer-guide/running-app#elastic-scaling-of-your-application">elastic</a>
         <code class="interpreted-text" data-role="ref">elastic &lt;streams_developer-guide_execution-scaling&gt;</code>,
-        to support <code class="interpreted-text" data-role="ref">fault-tolerant stateful processing &lt;streams_developer-guide_state-store_fault-tolerance&gt;</code>,
-        or to run <code class="interpreted-text" data-role="ref">interactive queries &lt;streams_concepts_interactive-queries&gt;</code>
+        to support <a id="streams_architecture_recovery" href="/{{version}}/documentation/streams/architecture#streams_architecture_recovery">fault-tolerant stateful processing</a>
+        <code class="interpreted-text" data-role="ref">&lt;streams_developer-guide_state-store_fault-tolerance&gt;</code>,
+        or to run <a id="streams-developer-guide-interactive-queries" href="/{{version}}/documentation/streams/developer-guide/interactive-queries#interactive-queries">interactive queries</a>
+        <code class="interpreted-text" data-role="ref">&lt;streams_concepts_interactive-queries&gt;</code>
         against your application's latest processing results. And, beyond its internal usage, the Kafka Streams API
         also allows developers to exploit this duality in their own applications.
     </p>
 
     <p>
-        Before we discuss concepts such as <code class="interpreted-text" data-role="ref">aggregations &lt;streams_concepts_aggregations&gt;</code>
+        Before we discuss concepts such as <a id="streams-developer-guide-dsl-aggregating" href="/{{version}}/documentation/streams/developer-guide/dsl-api#aggregating">aggregations</a>
+        <code class="interpreted-text" data-role="ref">&lt;streams_concepts_aggregations&gt;</code>,
         in Kafka Streams we must first introduce <strong>tables</strong> in more detail, and talk about the aforementioned stream-table duality.
         Essentially, this duality means that a stream can be viewed as a table, and a table can be viewed as a stream.
     </p>

21/streams/core-concepts.html

@@ -57,13 +57,13 @@ <h1>Core Concepts</h1>
     <p>
         We first summarize the key concepts of Kafka Streams.
     </p>
-
+    <a id="streams_processor_node" href="#streams_processor_node"></a>
     <h3><a id="streams_topology" href="#streams_topology">Stream Processing Topology</a></h3>
 
     <ul>
         <li>A <b>stream</b> is the most important abstraction provided by Kafka Streams: it represents an unbounded, continuously updating data set. A stream is an ordered, replayable, and fault-tolerant sequence of immutable data records, where a <b>data record</b> is defined as a key-value pair.</li>
         <li>A <b>stream processing application</b> is any program that makes use of the Kafka Streams library. It defines its computational logic through one or more <b>processor topologies</b>, where a processor topology is a graph of stream processors (nodes) that are connected by streams (edges).</li>
-        <li>A <b><a id="streams_processor_node" href="#streams_processor_node">stream processor</a></b> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors. </li>
+        <li>A <a id="defining-a-stream-processor" href="/{{version}}/documentation/streams/developer-guide/processor-api#defining-a-stream-processor"><b>stream processor</b></a> is a node in the processor topology; it represents a processing step to transform data in streams by receiving one input record at a time from its upstream processors in the topology, applying its operation to it, and may subsequently produce one or more output records to its downstream processors. </li>
     </ul>
 
     There are two special processors in the topology:
@@ -160,22 +160,21 @@ <h3><a id="streams_concepts_duality" href="#streams-concepts-duality">Duality of
 
     <p>
         Any stream processing technology must therefore provide <strong>first-class support for streams and tables</strong>.
-        Kafka's Streams API provides such functionality through its core abstractions for
-        <code class="interpreted-text" data-role="ref">streams &lt;streams_concepts_kstream&gt;</code> and
-        <code class="interpreted-text" data-role="ref">tables &lt;streams_concepts_ktable&gt;</code>, which we will talk about in a minute.
-        Now, an interesting observation is that there is actually a <strong>close relationship between streams and tables</strong>,
-        the so-called stream-table duality.
-        And Kafka exploits this duality in many ways: for example, to make your applications
-        <code class="interpreted-text" data-role="ref">elastic &lt;streams_developer-guide_execution-scaling&gt;</code>,
-        to support <code class="interpreted-text" data-role="ref">fault-tolerant stateful processing &lt;streams_developer-guide_state-store_fault-tolerance&gt;</code>,
-        or to run <code class="interpreted-text" data-role="ref">interactive queries &lt;streams_concepts_interactive-queries&gt;</code>
+        Kafka's Streams API provides such functionality through its core abstractions for 
+        <a id="streams_concepts_kstream" href="/{{version}}/documentation/streams/developer-guide/dsl-api#streams_concepts_kstream">streams</a>
+        and <a id="streams_concepts_ktable" href="/{{version}}/documentation/streams/developer-guide/dsl-api#streams_concepts_ktable">tables</a>,
+        which we will talk about in a minute. Now, an interesting observation is that there is actually a <strong>close relationship between streams and tables</strong>,
+        the so-called stream-table duality. And Kafka exploits this duality in many ways: for example, to make your applications
+        <a id="streams-developer-guide-execution-scaling" href="/{{version}}/documentation/streams/developer-guide/running-app#elastic-scaling-of-your-application">elastic</a>,
+        to support <a id="streams_architecture_recovery" href="/{{version}}/documentation/streams/architecture#streams_architecture_recovery">fault-tolerant stateful processing</a>,
+        or to run <a id="streams-developer-guide-interactive-queries" href="/{{version}}/documentation/streams/developer-guide/interactive-queries#interactive-queries">interactive queries</a>
         against your application's latest processing results. And, beyond its internal usage, the Kafka Streams API
         also allows developers to exploit this duality in their own applications.
     </p>
 
     <p>
-        Before we discuss concepts such as <code class="interpreted-text" data-role="ref">aggregations &lt;streams_concepts_aggregations&gt;</code>
-        in Kafka Streams we must first introduce <strong>tables</strong> in more detail, and talk about the aforementioned stream-table duality.
+        Before we discuss concepts such as <a id="streams-developer-guide-dsl-aggregating" href="/{{version}}/documentation/streams/developer-guide/dsl-api#aggregating">aggregations</a>
+        in Kafka Streams, we must first introduce <strong>tables</strong> in more detail, and talk about the aforementioned stream-table duality.
         Essentially, this duality means that a stream can be viewed as a table, and a table can be viewed as a stream.
     </p>