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new d4204e8 MINOR: fix broken links in streams doc (#5025)
d4204e8 is described below
commit d4204e8b1456c46d4a343e219855cbe1ea20aa4a
Author: Guozhang Wang <[email protected]>
AuthorDate: Wed May 16 13:23:20 2018 -0700
MINOR: fix broken links in streams doc (#5025)
Reviewers: Guozhang Wang <[email protected]>
---
docs/streams/developer-guide/config-streams.html | 4 ++--
docs/streams/developer-guide/dsl-api.html | 8 ++++----
docs/streams/developer-guide/interactive-queries.html | 2 +-
docs/streams/developer-guide/running-app.html | 6 +++---
docs/streams/developer-guide/write-streams.html | 2 +-
5 files changed, 11 insertions(+), 11 deletions(-)
diff --git a/docs/streams/developer-guide/config-streams.html
b/docs/streams/developer-guide/config-streams.html
index e3cae22..a03b53d 100644
--- a/docs/streams/developer-guide/config-streams.html
+++ b/docs/streams/developer-guide/config-streams.html
@@ -373,13 +373,13 @@
Standby replicas are used to minimize the latency of task
failover. A task that was previously running on a failed instance is
preferred to restart on an instance that has standby replicas so
that the local state store restoration process from its
changelog can be minimized. Details about how Kafka Streams
makes use of the standby replicas to minimize the cost of
- resuming tasks on failover can be found in the <a
class="reference internal"
href="../architecture.html#streams-architecture-state"><span class="std
std-ref">State</span></a> section.</div></blockquote>
+ resuming tasks on failover can be found in the <a
class="reference internal"
href="../architecture.html#streams_architecture_state"><span class="std
std-ref">State</span></a> section.</div></blockquote>
</div>
<div class="section" id="num-stream-threads">
<h4><a class="toc-backref" href="#id11">num.stream.threads</a><a
class="headerlink" href="#num-stream-threads" title="Permalink to this
headline"></a></h4>
<blockquote>
<div>This specifies the number of stream threads in an instance of
the Kafka Streams application. The stream processing code runs in these thread.
- For more information about Kafka Streams threading model, see <a
class="reference internal"
href="../architecture.html#streams-architecture-threads"><span class="std
std-ref">Threading Model</span></a>.</div></blockquote>
+ For more information about Kafka Streams threading model, see <a
class="reference internal"
href="../architecture.html#streams_architecture_threads"><span class="std
std-ref">Threading Model</span></a>.</div></blockquote>
</div>
<div class="section" id="partition-grouper">
<span id="streams-developer-guide-partition-grouper"></span><h4><a
class="toc-backref" href="#id12">partition.grouper</a><a class="headerlink"
href="#partition-grouper" title="Permalink to this headline"></a></h4>
diff --git a/docs/streams/developer-guide/dsl-api.html
b/docs/streams/developer-guide/dsl-api.html
index d1e527e..3895522 100644
--- a/docs/streams/developer-guide/dsl-api.html
+++ b/docs/streams/developer-guide/dsl-api.html
@@ -165,7 +165,7 @@
be populated with data from only <strong>a
subset</strong> of the partitions of the input topic. Collectively, across
all application instances, all input topic
partitions are read and processed.</p>
<p>You must provide a name for the table (more
precisely, for the internal
- <a class="reference internal"
href="../architecture.html#streams-architecture-state"><span class="std
std-ref">state store</span></a> that backs the table). This is required for
+ <a class="reference internal"
href="../architecture.html#streams_architecture_state"><span class="std
std-ref">state store</span></a> that backs the table). This is required for
supporting <a class="reference internal"
href="interactive-queries.html#streams-developer-guide-interactive-queries"><span
class="std std-ref">interactive queries</span></a> against the table. When a
name is not provided the table will not queryable
and an internal name will be provided for the state store.</p>
<p>If you do not specify SerDes explicitly, the
default SerDes from the
@@ -189,7 +189,7 @@
<p>In the case of a GlobalKTable, the local
GlobalKTable instance of every application instance will
be populated with data from <strong>all</strong>
the partitions of the input topic.</p>
<p>You must provide a name for the table (more
precisely, for the internal
- <a class="reference internal"
href="../architecture.html#streams-architecture-state"><span class="std
std-ref">state store</span></a> that backs the table). This is required for
+ <a class="reference internal"
href="../architecture.html#streams_architecture_state"><span class="std
std-ref">state store</span></a> that backs the table). This is required for
supporting <a class="reference internal"
href="interactive-queries.html#streams-developer-guide-interactive-queries"><span
class="std std-ref">interactive queries</span></a> against the table. When a
name is not provided the table will not queryable
and an internal name will be provided for the state store.</p>
<div class="highlight-java"><div
class="highlight"><pre><span></span><span class="kn">import</span> <span
class="nn">org.apache.kafka.common.serialization.Serdes</span><span
class="o">;</span>
@@ -690,12 +690,12 @@
</div>
<div class="section" id="stateful-transformations">
<span
id="streams-developer-guide-dsl-transformations-stateful"></span><h3><a
class="toc-backref" href="#id11">Stateful transformations</a><a
class="headerlink" href="#stateful-transformations" title="Permalink to this
headline"></a></h3>
- <p
id="streams-developer-guide-dsl-transformations-stateful-overview">Stateful
transformations depend on state for processing inputs and producing outputs and
require a <a class="reference internal"
href="../architecture.html#streams-architecture-state"><span class="std
std-ref">state store</span></a> associated with the stream processor. For
example, in aggregating operations, a windowing state store is used to collect
the latest aggregation results per
+ <p
id="streams-developer-guide-dsl-transformations-stateful-overview">Stateful
transformations depend on state for processing inputs and producing outputs and
require a <a class="reference internal"
href="../architecture.html#streams_architecture_state"><span class="std
std-ref">state store</span></a> associated with the stream processor. For
example, in aggregating operations, a windowing state store is used to collect
the latest aggregation results per
window. In join operations, a windowing state store is
used to collect all of the records received so far within the
defined window boundary.</p>
<p>Note, that state stores are fault-tolerant.
In case of failure, Kafka Streams guarantees to fully
restore all state stores prior to resuming the processing.
- See <a class="reference internal"
href="../architecture.html#streams-architecture-fault-tolerance"><span
class="std std-ref">Fault Tolerance</span></a> for further information.</p>
+ See <a class="reference internal"
href="../architecture.html#streams_architecture_recovery"><span class="std
std-ref">Fault Tolerance</span></a> for further information.</p>
<p>Available stateful transformations in the DSL include:</p>
<ul class="simple">
<li><a class="reference internal"
href="#streams-developer-guide-dsl-aggregating"><span class="std
std-ref">Aggregating</span></a></li>
diff --git a/docs/streams/developer-guide/interactive-queries.html
b/docs/streams/developer-guide/interactive-queries.html
index 4675d8a..9b64ddb 100644
--- a/docs/streams/developer-guide/interactive-queries.html
+++ b/docs/streams/developer-guide/interactive-queries.html
@@ -52,7 +52,7 @@
<li><a class="reference internal" href="#demo-applications"
id="id11">Demo applications</a></li>
</ul>
</div>
- <p>The full state of your application is typically <a class="reference
internal" href="../architecture.html#streams-architecture-state"><span
class="std std-ref">split across many distributed instances of your
application</span></a>, and across many state stores that are managed locally
by these application instances.</p>
+ <p>The full state of your application is typically <a class="reference
internal" href="../architecture.html#streams_architecture_state"><span
class="std std-ref">split across many distributed instances of your
application</span></a>, and across many state stores that are managed locally
by these application instances.</p>
<div class="figure align-center">
<img class="centered"
src="/{{version}}/images/streams-interactive-queries-03.png">
</div>
diff --git a/docs/streams/developer-guide/running-app.html
b/docs/streams/developer-guide/running-app.html
index bd1a77e..f83210d 100644
--- a/docs/streams/developer-guide/running-app.html
+++ b/docs/streams/developer-guide/running-app.html
@@ -77,7 +77,7 @@ $ java -cp path-to-app-fatjar.jar com.example.MyStreamsApp
<a class="reference internal"
href="../code-examples.html#streams-code-examples"><span class="std
std-ref">Streams code examples</span></a>.</p>
<p>When you start your application you are launching a Kafka
Streams instance of your application. You can run multiple
instances of your application. A common scenario is that
there are multiple instances of your application running in
- parallel. For more information, see <a class="reference
internal"
href="../architecture.html#streams-architecture-parallelism-model"><span
class="std std-ref">Parallelism Model</span></a>.</p>
+ parallel. For more information, see <a class="reference
internal" href="../architecture.html#streams_architecture_tasks"><span
class="std std-ref">Parallelism Model</span></a>.</p>
<p>When the application instance starts running, the defined
processor topology will be initialized as one or more stream tasks.
If the processor topology defines any state stores, these
are also constructed during the initialization period. For
more information, see the <a class="reference internal"
href="#streams-developer-guide-execution-scaling-state-restoration"><span
class="std std-ref">State restoration during workload rebalance</span></a>
section).</p>
@@ -87,7 +87,7 @@ $ java -cp path-to-app-fatjar.jar com.example.MyStreamsApp
<p>Kafka Streams makes your stream processing applications
elastic and scalable. You can add and remove processing capacity
dynamically during application runtime without any downtime
or data loss. This makes your applications
resilient in the face of failures and for allows you to
perform maintenance as needed (e.g. rolling upgrades).</p>
- <p>For more information about this elasticity, see the <a
class="reference internal"
href="../architecture.html#streams-architecture-parallelism-model"><span
class="std std-ref">Parallelism Model</span></a> section. Kafka Streams
+ <p>For more information about this elasticity, see the <a
class="reference internal"
href="../architecture.html#streams_architecture_tasks"><span class="std
std-ref">Parallelism Model</span></a> section. Kafka Streams
leverages the Kafka group management functionality, which is
built right into the <a class="reference external"
href="https://cwiki.apache.org/confluence/display/KAFKA/A+Guide+To+The+Kafka+Protocol";>Kafka
wire protocol</a>. It is the foundation that enables the
elasticity of Kafka Streams applications: members of a group
coordinate and collaborate jointly on the consumption and
processing of data in Kafka. Additionally, Kafka Streams
provides stateful processing and allows for fault-tolerant
@@ -128,7 +128,7 @@ $ java -cp path-to-app-fatjar.jar com.example.MyStreamsApp
<li>If no local state store exists, the changelog is
replayed from the earliest to the current offset. This reconstructs the local
state store to the most recent snapshot.</li>
<li>If a local state store exists, the changelog is
replayed from the previously checkpointed offset. The changes are applied and
the state is restored to the most recent snapshot. This method takes less time
because it is applying a smaller portion of the changelog.</li>
</ul>
- <p>For more information, see <a class="reference internal"
href="config-streams.html#streams-developer-guide-standby-replicas"><span
class="std std-ref">Standby Replicas</span></a>.</p>
+ <p>For more information, see <a class="reference internal"
href="config-streams.html#num-standby-replicas"><span class="std
std-ref">Standby Replicas</span></a>.</p>
</div>
<div class="section"
id="determining-how-many-application-instances-to-run">
<h3><a class="toc-backref" href="#id8">Determining how many
application instances to run</a><a class="headerlink"
href="#determining-how-many-application-instances-to-run" title="Permalink to
this headline"></a></h3>
diff --git a/docs/streams/developer-guide/write-streams.html
b/docs/streams/developer-guide/write-streams.html
index 23ca764..0007b3e 100644
--- a/docs/streams/developer-guide/write-streams.html
+++ b/docs/streams/developer-guide/write-streams.html
@@ -155,7 +155,7 @@
</div>
<p>If there are other instances of this stream processing application
running elsewhere (e.g., on another machine), Kafka
Streams transparently re-assigns tasks from the existing instances to
the new instance that you just started.
- For more information, see <a class="reference internal"
href="../architecture.html#streams-architecture-tasks"><span class="std
std-ref">Stream Partitions and Tasks</span></a> and <a class="reference
internal" href="../architecture.html#streams-architecture-threads"><span
class="std std-ref">Threading Model</span></a>.</p>
+ For more information, see <a class="reference internal"
href="../architecture.html#streams_architecture_tasks"><span class="std
std-ref">Stream Partitions and Tasks</span></a> and <a class="reference
internal" href="../architecture.html#streams-architecture-threads"><span
class="std std-ref">Threading Model</span></a>.</p>
<p>To catch any unexpected exceptions, you can set an <code
class="docutils literal"><span
class="pre">java.lang.Thread.UncaughtExceptionHandler</span></code> before you
start the
application. This handler is called whenever a stream thread is
terminated by an unexpected exception:</p>
<div class="highlight-java"><div
class="highlight"><pre><span></span><span class="c1">// Java 8+, using lambda
expressions</span>
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