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The following commit(s) were added to refs/heads/asf-site by this push:
     new 2912d98  MINOR: fix processor node broken link
2912d98 is described below

commit 2912d9832317a421260d4611f3e360e0e4a9a09b
Author: Guozhang Wang <wangg...@gmail.com>
AuthorDate: Sun Apr 15 10:08:28 2018 -0700

    MINOR: fix processor node broken link
---
 10/streams/core-concepts.html               | 2 +-
 10/streams/developer-guide/memory-mgmt.html | 4 ++--
 11/streams/core-concepts.html               | 2 +-
 11/streams/developer-guide/memory-mgmt.html | 4 ++--
 4 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/10/streams/core-concepts.html b/10/streams/core-concepts.html
index d803b3a..f2f32ad 100644
--- a/10/streams/core-concepts.html
+++ b/10/streams/core-concepts.html
@@ -63,7 +63,7 @@
     <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:
diff --git a/10/streams/developer-guide/memory-mgmt.html 
b/10/streams/developer-guide/memory-mgmt.html
index b9ee1f3..e3a1033 100644
--- a/10/streams/developer-guide/memory-mgmt.html
+++ b/10/streams/developer-guide/memory-mgmt.html
@@ -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  [...]
diff --git a/11/streams/core-concepts.html b/11/streams/core-concepts.html
index 2f22be7..0b0f43b 100644
--- a/11/streams/core-concepts.html
+++ b/11/streams/core-concepts.html
@@ -63,7 +63,7 @@
     <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:
diff --git a/11/streams/developer-guide/memory-mgmt.html 
b/11/streams/developer-guide/memory-mgmt.html
index 6c6fd2f..a73a814 100644
--- a/11/streams/developer-guide/memory-mgmt.html
+++ b/11/streams/developer-guide/memory-mgmt.html
@@ -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  [...]

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