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new 0fd930b KAFKA-8227 DOCS Fixed missing links duality of streams tables
(#6625)
0fd930b is described below
commit 0fd930bf826114d589e741e55a5753e2a2a87f58
Author: Victoria Bialas <londoncall...@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.gala...@confluent.io> Bill Bejeck
<bbej...@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
<streams_concepts_kstream></code> and
- <code class="interpreted-text" data-role="ref">tables
<streams_concepts_ktable></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
<streams_developer-guide_execution-scaling></code>,
- to support <code class="interpreted-text"
data-role="ref">fault-tolerant stateful processing
<streams_developer-guide_state-store_fault-tolerance></code>,
- or to run <code class="interpreted-text" data-role="ref">interactive
queries <streams_concepts_interactive-queries></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 <streams_concepts_aggregations></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>
