mirror of https://github.com/apache/kafka.git
MINOR: Fix some streams web doc nits (#4411)
Reviewers: Derrick Or <derrickor@gmail.com>, Guozhang Wang <wangguoz@gmail.com>
This commit is contained in:
Joel Hamill
Guozhang Wang
parent
b5da5f8bec
commit
6ea53d22e2
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@ -418,7 +418,7 @@
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<strong>must do</strong> if the key and/or value types of the resulting <code class="docutils literal"><span class="pre">KGroupedStream</span></code> do not match the configured default
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SerDes.</p>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p><b>Note</b></p>
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<p class="last"><strong>Grouping vs. Windowing:</strong>
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A related operation is <a class="reference internal" href="#streams-developer-guide-dsl-windowing"><span class="std std-ref">windowing</span></a>, which lets you control how to
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“sub-group” the grouped records <em>of the same key</em> into so-called <em>windows</em> for stateful operations such as
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@ -464,7 +464,7 @@
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<strong>do</strong> if the key and/or value types of the resulting <code class="docutils literal"><span class="pre">KGroupedStream</span></code> or <code class="docutils literal"><span class="pre">KGroupedTable</span></code> do not match the
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configured default SerDes.</p>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p><b>Note</b></p>
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<p class="last"><strong>Grouping vs. Windowing:</strong>
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A related operation is <a class="reference internal" href="#streams-developer-guide-dsl-windowing"><span class="std std-ref">windowing</span></a>, which lets you control how to
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“sub-group” the grouped records <em>of the same key</em> into so-called <em>windows</em> for stateful operations such as
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@ -1204,7 +1204,7 @@
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</pre></div>
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</div>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p><b>Note</b></p>
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<p class="last"><strong>Impact of record caches</strong>:
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For illustration purposes, the column “KTable <code class="docutils literal"><span class="pre">aggregated</span></code>” below shows the table’s state changes over time in a
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very granular way. In practice, you would observe state changes in such a granular way only when
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@ -1334,7 +1334,7 @@
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</pre></div>
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</div>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p><b>Note</b></p>
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<p class="last"><strong>Impact of record caches</strong>:
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For illustration purposes, the column “KTable <code class="docutils literal"><span class="pre">aggregated</span></code>” below shows the table’s state changes over time in a
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very granular way. In practice, you would observe state changes in such a granular way only when
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@ -1531,7 +1531,6 @@
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<td>Not Supported</td>
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<td>Not Supported</td>
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<td>Not Supported</td>
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<td>N/A</td>
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</tr>
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</tbody>
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</table>
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@ -1569,7 +1568,7 @@
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each <code class="docutils literal"><span class="pre">KafkaStreams</span></code> instance, i.e. each instance has a full copy of the changelog stream. Further, a
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<code class="docutils literal"><span class="pre">KeyValueMapper</span></code> allows for non-key based joins from the <code class="docutils literal"><span class="pre">KStream</span></code> to the <code class="docutils literal"><span class="pre">GlobalKTable</span></code>.</p>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p><b>Note</b></p>
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<p class="last"><strong>Kafka Streams partly verifies the co-partitioning requirement:</strong>
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During the partition assignment step, i.e. at runtime, Kafka Streams verifies whether the number of partitions for
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both sides of a join are the same. If they are not, a <code class="docutils literal"><span class="pre">TopologyBuilderException</span></code> (runtime exception) is being
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@ -2691,7 +2690,7 @@
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<a class="reference internal" href="#streams-developer-guide-dsl-aggregating"><span class="std std-ref">aggregations</span></a> or <a class="reference internal" href="#streams-developer-guide-dsl-joins"><span class="std std-ref">joins</span></a> into
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so-called windows. Windows are tracked per record key.</p>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p><b>Note</b></p>
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<p class="last">A related operation is <a class="reference internal" href="#streams-developer-guide-dsl-transformations-stateless"><span class="std std-ref">grouping</span></a>, which groups all
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records that have the same key to ensure that data is properly partitioned (“keyed”) for subsequent operations.
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Once grouped, windowing allows you to further sub-group the records of a key.</p>
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@ -2776,7 +2775,7 @@ become t=300,000).</span></p>
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hopping window with a size 5 minutes and an advance interval of 1 minute. Since hopping windows can overlap – and in
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general they do – a data record may belong to more than one such windows.</p>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p><b>Note</b></p>
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<p class="last"><strong>Hopping windows vs. sliding windows:</strong>
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Hopping windows are sometimes called “sliding windows” in other stream processing tools. Kafka Streams follows the
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terminology in academic literature, where the semantics of sliding windows are different to those of hopping windows.</p>
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@ -3146,7 +3145,7 @@ t=5 (blue), which lead to a merge of sessions and an extension of a session, res
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</tbody>
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</table>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p><b>Note</b></p>
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<p class="last"><strong>When you want to write to systems other than Kafka:</strong>
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Besides writing the data back to Kafka, you can also apply a
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<a class="reference internal" href="#streams-developer-guide-dsl-process"><span class="std std-ref">custom processor</span></a> as a stream sink at the end of the processing to, for
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@ -120,7 +120,7 @@
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</ul>
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<p>You can also <a class="reference internal" href="#streams-developer-guide-interactive-queries-custom-stores"><span class="std std-ref">implement your own QueryableStoreType</span></a> as described in section <a class="reference internal" href="#streams-developer-guide-interactive-queries-custom-stores"><span class="std std-ref">Querying local custom state stores</span></a>.</p>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p><b>Note</b></p>
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<p class="last">Kafka Streams materializes one state store per stream partition. This means your application will potentially manage
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many underlying state stores. The API enables you to query all of the underlying stores without having to know which
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partition the data is in.</p>
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@ -227,7 +227,7 @@
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<div class="section" id="querying-local-custom-state-stores">
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<span id="streams-developer-guide-interactive-queries-custom-stores"></span><h3><a class="toc-backref" href="#id6">Querying local custom state stores</a><a class="headerlink" href="#querying-local-custom-state-stores" title="Permalink to this headline"></a></h3>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p><b>Note</b></p>
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<p class="last">Only the <a class="reference internal" href="processor-api.html#streams-developer-guide-processor-api"><span class="std std-ref">Processor API</span></a> supports custom state stores.</p>
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</div>
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<p>Before querying the custom state stores you must implement these interfaces:</p>
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@ -148,7 +148,7 @@
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</pre></div>
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</div>
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<div class="admonition note">
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<p class="first admonition-title">Note</p>
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<p><b>Note</b></p>
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<p class="last"><strong>Stateful processing with state stores:</strong>
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The <code class="docutils literal"><span class="pre">WordCountProcessor</span></code> defined above can access the currently received record in its <code class="docutils literal"><span class="pre">process()</span></code> method, and it can
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leverage <a class="reference internal" href="#streams-developer-guide-state-store"><span class="std std-ref">state stores</span></a> to maintain processing states to, for example, remember recently
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