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From bbej...@apache.org
Subject [kafka] branch 2.1 updated: KAFKA-8227 DOCS Fixed missing links duality of streams tables (#6625)
Date Wed, 24 Apr 2019 22:02:24 GMT
This is an automated email from the ASF dual-hosted git repository.

bbejeck pushed a commit to branch 2.1
in repository https://gitbox.apache.org/repos/asf/kafka.git


The following commit(s) were added to refs/heads/2.1 by this push:
     new 0fd930b  KAFKA-8227 DOCS Fixed missing links duality of streams tables (#6625)
0fd930b is described below

commit 0fd930bf826114d589e741e55a5753e2a2a87f58
Author: Victoria Bialas <londoncalling@users.noreply.github.com>
AuthorDate: Wed Apr 24 14:54:29 2019 -0700

    KAFKA-8227 DOCS Fixed missing links duality of streams tables (#6625)
    
    Fixed missing links duality of streams tables
    
    Reviewers: Jim Galasyn <jim.galasyn@confluent.io> Bill Bejeck <bbejeck@gmail.com>
---
 docs/streams/core-concepts.html | 39 +++++++++++++++++++--------------------
 1 file changed, 19 insertions(+), 20 deletions(-)

diff --git a/docs/streams/core-concepts.html b/docs/streams/core-concepts.html
index 1e1aeb7..474cac9 100644
--- a/docs/streams/core-concepts.html
+++ b/docs/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 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:
@@ -159,25 +159,24 @@
     </p>
 
     <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>
-        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.
-        Essentially, this duality means that a stream can be viewed as a table, and a table
can be viewed as a stream.
-    </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 
+      <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 <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>
 
     <h3><a id="streams_state" href="#streams_state">States</a></h3>
 


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