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guozhangwang merged 3 commits into from
Apr 15, 2018
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KAFKA-6790 #4874

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22bfac1
fix broken links
ro7m Apr 15, 2018
b577238
fix broken links
ro7m Apr 15, 2018
7c0837b
fix broken links
ro7m Apr 15, 2018
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2 changes: 1 addition & 1 deletion docs/streams/core-concepts.html
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Expand Up @@ -63,7 +63,7 @@ <h3><a id="streams_topology" href="#streams_topology">Stream Processing Topology
<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>stream processor</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 <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>
</ul>

There are two special processors in the topology:
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4 changes: 2 additions & 2 deletions docs/streams/developer-guide/memory-mgmt.html
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Expand Up @@ -55,9 +55,9 @@
<p>For such <code class="docutils literal"><span class="pre">KTable</span></code> instances, the record cache is used for:</p>
<ul class="simple">
<li>Internal caching and compacting of output records before they are written by the underlying stateful
<a class="reference internal" href="../concepts.html#streams-concepts-processor"><span class="std std-ref">processor node</span></a> to its internal state stores.</li>
<a class="reference internal" href="../core-concepts#streams_processor_node"><span class="std std-ref">processor node</span></a> to its internal state stores.</li>
<li>Internal caching and compacting of output records before they are forwarded from the underlying stateful
<a class="reference internal" href="../concepts.html#streams-concepts-processor"><span class="std std-ref">processor node</span></a> to any of its downstream processor nodes.</li>
<a class="reference internal" href="../core-concepts#streams_processor_node"><span class="std std-ref">processor node</span></a> to any of its downstream processor nodes.</li>
</ul>
<p>Use the following example to understand the behaviors with and without record caching. In this example, the input is a
<code class="docutils literal"><span class="pre">KStream&lt;String,</span> <span class="pre">Integer&gt;</span></code> with the records <code class="docutils literal"><span class="pre">&lt;K,V&gt;:</span> <span class="pre">&lt;A,</span> <span class="pre">1&gt;,</span> <span class="pre">&lt;D,</span> <span class="pre">5&gt;,</span> <span class="pre">&lt;A,</span> <span class="pre">20&gt;,</span> <span class="pre">&lt;A,</span> <span class="pre">300&gt;</span></code>. The focus in this example is
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