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From guozh...@apache.org
Subject [kafka-site] branch asf-site updated: MINOR: fix more streams ref links
Date Mon, 16 Apr 2018 15:40:58 GMT
This is an automated email from the ASF dual-hosted git repository.

guozhang pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/kafka-site.git


The following commit(s) were added to refs/heads/asf-site by this push:
     new 4290e35  MINOR: fix more streams ref links
4290e35 is described below

commit 4290e350b01695bc29092bd394e7e092647939b2
Author: Guozhang Wang <wangguoz@gmail.com>
AuthorDate: Mon Apr 16 08:40:45 2018 -0700

    MINOR: fix more streams ref links
---
 10/streams/core-concepts.html | 8 ++++----
 11/streams/core-concepts.html | 8 ++++----
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/10/streams/core-concepts.html b/10/streams/core-concepts.html
index f2f32ad..1e48805 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><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 <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>
     </ul>
 
     There are two special processors in the topology:
@@ -78,14 +78,14 @@
     <img class="centered" src="/{{version}}/images/streams-architecture-topology.jpg"
style="width:400px">
 
     <p>
-        Kafka Streams offers two ways to define the stream processing topology: the <a
href="/{{version}}/documentation/streams/developer-guide#streams_dsl"><b>Kafka Streams
DSL</b></a> provides
-        the most common data transformation operations such as <code>map</code>,
<code>filter</code>, <code>join</code> and <code>aggregations</code>
out of the box; the lower-level <a href="/{{version}}/documentation/streams/developer-guide#streams_processor"><b>Processor
API</b></a> allows
+        Kafka Streams offers two ways to define the stream processing topology: the <a
href="/{{version}}/documentation/streams/developer-guide/dsl-api.html"><b>Kafka Streams
DSL</b></a> provides
+        the most common data transformation operations such as <code>map</code>,
<code>filter</code>, <code>join</code> and <code>aggregations</code>
out of the box; the lower-level <a href="/{{version}}/documentation/streams/developer-guide/processor-api.html"><b>Processor
API</b></a> allows
         developers define and connect custom processors as well as to interact with <a
href="#streams_state">state stores</a>.
     </p>
 
     <p>
         A processor topology is merely a logical abstraction for your stream processing code.
-        At runtime, the logical topology is instantiated and replicated inside the application
for parallel processing (see <a href="#streams_architecture_tasks"><b>Stream Partitions
and Tasks</b></a> for details).
+        At runtime, the logical topology is instantiated and replicated inside the application
for parallel processing (see <a href="/{{version}}/documentation/streams/architecture#streams_architecture_tasks"><b>Stream
Partitions and Tasks</b></a> for details).
     </p>
 
     <h3><a id="streams_time" href="#streams_time">Time</a></h3>
diff --git a/11/streams/core-concepts.html b/11/streams/core-concepts.html
index 0b0f43b..b5c676c 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><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 <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>
     </ul>
 
     There are two special processors in the topology:
@@ -78,14 +78,14 @@
     <img class="centered" src="/{{version}}/images/streams-architecture-topology.jpg"
style="width:400px">
 
     <p>
-        Kafka Streams offers two ways to define the stream processing topology: the <a
href="/{{version}}/documentation/streams/developer-guide#streams_dsl"><b>Kafka Streams
DSL</b></a> provides
-        the most common data transformation operations such as <code>map</code>,
<code>filter</code>, <code>join</code> and <code>aggregations</code>
out of the box; the lower-level <a href="/{{version}}/documentation/streams/developer-guide#streams_processor"><b>Processor
API</b></a> allows
+        Kafka Streams offers two ways to define the stream processing topology: the <a
href="/{{version}}/documentation/streams/developer-guide/dsl-api.html"><b>Kafka Streams
DSL</b></a> provides
+        the most common data transformation operations such as <code>map</code>,
<code>filter</code>, <code>join</code> and <code>aggregations</code>
out of the box; the lower-level <a href="/{{version}}/documentation/streams/developer-guide/processor-api.html"><b>Processor
API</b></a> allows
         developers define and connect custom processors as well as to interact with <a
href="#streams_state">state stores</a>.
     </p>
 
     <p>
         A processor topology is merely a logical abstraction for your stream processing code.
-        At runtime, the logical topology is instantiated and replicated inside the application
for parallel processing (see <a href="#streams_architecture_tasks"><b>Stream Partitions
and Tasks</b></a> for details).
+        At runtime, the logical topology is instantiated and replicated inside the application
for parallel processing (see <a href="/{{version}}/documentation/streams/architecture#streams_architecture_tasks"><b>Stream
Partitions and Tasks</b></a> for details).
     </p>
 
     <h3><a id="streams_time" href="#streams_time">Time</a></h3>

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