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new d1dc26053f3 MINOR: Streams developer guide improvement (#21000)
d1dc26053f3 is described below
commit d1dc26053f307e3b8cabd8e905869a1edb803cf9
Author: Evan Zhou <[email protected]>
AuthorDate: Mon Dec 1 18:52:47 2025 -0600
MINOR: Streams developer guide improvement (#21000)
Add a section about how to prevent crashes and failures to the Kafka
Streams developer guide.
Reviewers: Matthias J. Sax <[email protected]>
---
docs/streams/developer-guide/running-app.html | 18 ++++++++++++++++++
1 file changed, 18 insertions(+)
diff --git a/docs/streams/developer-guide/running-app.html
b/docs/streams/developer-guide/running-app.html
index a6c603f2a3c..16f563b2222 100644
--- a/docs/streams/developer-guide/running-app.html
+++ b/docs/streams/developer-guide/running-app.html
@@ -46,6 +46,7 @@
<li><a class="reference internal"
href="#determining-how-many-application-instances-to-run" id="id8">Determining
how many application instances to run</a></li>
</ul>
</li>
+ <li><a class="reference internal"
href="#handling-crashes-and-failures" id="id9">Handling crashes and
failures</a></li>
</ul>
</div>
<div class="section" id="starting-a-kafka-streams-application">
@@ -134,6 +135,23 @@ $ java -cp path-to-app-fatjar.jar
com.example.MyStreamsApp</code></pre>
<li>Data should be equally distributed across topic
partitions. For example, if two topic partitions each have 1 million messages,
this is better than a single partition with 2 million messages and none in the
other.</li>
<li>Processing workload should be equally distributed
across topic partitions. For example, if the time to process messages varies
widely, then it is better to spread the processing-intensive messages across
partitions rather than storing these messages within the same partition.</li>
</ul>
+ </div>
+ <div class="section" id="handling-crashes-and-failures">
+ <span
id="streams-developer-guide-execution-scaling"></span><h2><a
class="toc-backref" href="#id9">Handling crashes and failures</a><a
class="headerlink" href="#handling-crashes-and-failures" title="Permalink to
this headline"></a></h2>
+ <p>There are a few things you can do to reduce the likelihood
of crashes and failures of your Kafka Streams application.
+ <ul class="simple">
+ <li>Kafka Streams has a few configurations that can
help with resilience in the face of broker failures. They can be found in the
<a class="reference internal"
href="config-streams.html#recommended-configuration-parameters-for-resiliency">configuration
guide.</a></li>
+ <li>Ensure that your application is able to handle
errors and failures. This includes things like
+ configuring the correct exception handlers to
handle errors such as authorization and deserialization errors,
+ and using strategies such as dead letter queues to
handle "poison pill" records. </li>
+ </ul>
+ </p>
+ <p>If your Kafka Streams application does crash or fail, it
will first enter the <code class="docutils literal"><span
class="pre">PENDING_ERROR</span></code> state
+ to gracefully close all of its existing resources, and
then transition into the <code class="docutils literal"><span
class="pre">ERROR</span></code> state.
+ It is important to note that the <code class="docutils
literal"><span class="pre">PENDING_ERROR</span></code> state is not
recoverable, and only a
+ restart will get the application back to the RUNNING
state, thus monitoring for this state in addition to the <code class="docutils
literal"><span class="pre">ERROR</span></code>
+ state is important to ensure that your application is able
to recover.
+ </p>
</div>
</div>
</div>
