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class=section-nav-list-title>Yaml</span><ul class=section-nav-list><li><a href=/documentation/sdks/yaml/>Yaml overview</a></li><li><a href=/documentation/sdks/yaml-udf/>Yaml User Defined Functions</a></li><li><a href=/documentation/sdks/yaml-combine/>Yaml Aggregation</a></li><li><a href=/documentation/sdks/yaml-errors/>Error handling</a></li><li><a href=/documentation/sdks/yaml-inline-python/>Inlining Python</a></li><li><a href=https://beam.apache.org/releases/yamldoc/current/ target=_blank>YAML API reference <img src=/images/external-link-icon.png width=14 height=14 alt="External link."></a></ul></li><li><span class=section-nav-list-title>SQL</span><ul class=section-nav-list><li><a href=/documentation/dsls/sql/overview/>Overview</a></li><li><a href=/documentation/dsls/sql/walkthrough/>Walkthrough</a></li><li><a href=/documentation/dsls/sql/shell/>Shell</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Apache Calcite dialect</span><ul 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href=/documentation/dsls/sql/zetasql/lexical/>Lexical structure</a></li><li><a href=/documentation/dsls/sql/zetasql/data-types/>Data types</a></li><li><a href=/documentation/dsls/sql/zetasql/operators/>Operators</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Scalar functions</span><ul class=section-nav-list><li><a href=/documentation/dsls/sql/zetasql/string-functions/>String functions</a></li><li><a href=/documentation/dsls/sql/zetasql/math-functions/>Mathematical functions</a></li><li><a href=/documentation/dsls/sql/zetasql/conditional-expressions/>Conditional expressions</a></li></ul></li><li><a href=/documentation/dsls/sql/zetasql/aggregate-functions/>Aggregate functions</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Beam SQL extensions</span><ul class=section-nav-list><li><a href=/documentation/dsls/sql/extensions/create-external-table/>CREATE EXTERNAL TABLE</a></li><li><a href=/documentation/dsls/sql/extensions/windowing-and-triggering/>Windowing & triggering</a></li><li><a href=/documentation/dsls/sql/extensions/joins/>Joins</a></li><li><a href=/documentation/dsls/sql/extensions/user-defined-functions/>User-defined functions</a></li><li><a href=/documentation/dsls/sql/extensions/set/>SET pipeline options</a></li></ul></li></ul></li><li><span class=section-nav-list-title>DataFrames</span><ul class=section-nav-list><li><a href=/documentation/dsls/dataframes/overview/>Overview</a></li><li><a href=/documentation/dsls/dataframes/differences-from-pandas/>Differences from pandas</a></li><li><a href=https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/dataframe target=_blank>Example pipelines <img src=/images/external-link-icon.png width=14 height=14 alt="External link."></a></li><li><a href=https://beam.apache.org/releases/pydoc/2.55.1/apache_beam.dataframe.html target=_blank>DataFrame API reference <img src=/images/external-link-icon.png width=14 height=14 alt="External link."></a></li></ul></li></ul></nav></div><nav class="page-nav clearfix" data-offset-top=90 data-offset-bottom=500><nav id=TableOfContents><ul><li><a href=#join-library>Join-library</a></li><li><a href=#sorter>Sorter</a><ul><li><a href=#caveats>Caveats</a></li><li><a href=#options>Options</a></li><li><a href=#example-usage-of-sortvalues>Example usage of <code>SortValues</code></a></li></ul></li></ul></nav></nav><div class="body__contained body__section-nav"><h1 id=apache-beam-java-sdk-extensions>Apache Beam Java SDK Extensions</h1><h2 id=join-library>Join-library</h2><p>Join-library provides inner join, outer left join, and outer right join functions. The aim
is to simplify the most common cases of join to a simple function call.</p><p>The functions are generic and support joins of any Beam-supported types.
Input to the join functions are <code>PCollections</code> of <code>Key</code> / <code>Value</code>s. Both
the left and right <code>PCollection</code>s need the same type for the key. All the join
functions return a <code>Key</code> / <code>Value</code> where <code>Key</code> is the join key and value is
a <code>Key</code> / <code>Value</code> where the key is the left value and right is the value.</p><p>For outer joins, the user must provide a value that represents <code>null</code> because <code>null</code>
cannot be serialized.</p><p>Example usage:</p><pre tabindex=0><code>PCollection&lt;KV&lt;String, String&gt;&gt; leftPcollection = ...
PCollection&lt;KV&lt;String, Long&gt;&gt; rightPcollection = ...
PCollection&lt;KV&lt;String, KV&lt;String, Long&gt;&gt;&gt; joinedPcollection =
Join.innerJoin(leftPcollection, rightPcollection);
</code></pre><h2 id=sorter>Sorter</h2><p>This module provides the <code>SortValues</code> transform, which takes a <code>PCollection&lt;KV&lt;K, Iterable&lt;KV&lt;K2, V>>>></code> and produces a <code>PCollection&lt;KV&lt;K, Iterable&lt;KV&lt;K2, V>>>></code> where, for each primary key <code>K</code> the paired <code>Iterable&lt;KV&lt;K2, V>></code> has been sorted by the byte encoding of secondary key (<code>K2</code>). It is an efficient and scalable sorter for iterables, even if they are large (do not fit in memory).</p><h3 id=caveats>Caveats</h3><ul><li>This transform performs value-only sorting; the iterable accompanying each key is sorted, but <em>there is no relationship between different keys</em>, as Beam does not support any defined relationship between different elements in a <code>PCollection</code>.</li></ul><ul><li>Each <code>Iterable&lt;KV&lt;K2, V>></code> is sorted on a single worker using local memory and disk. This means that <code>SortValues</code> may be a performance and/or scalability bottleneck when used in different pipelines. For example, users are discouraged from using <code>SortValues</code> on a <code>PCollection</code> of a single element to globally sort a large <code>PCollection</code>. A (rough) estimate of the number of bytes of disk space utilized if sorting spills to disk is <code>numRecords * (numSecondaryKeyBytesPerRecord + numValueBytesPerRecord + 16) * 3</code>.</li></ul><h3 id=options>Options</h3><ul><li>The user can customize the temporary location used if sorting requires spilling to disk and the maximum amount of memory to use by creating a custom instance of <code>BufferedExternalSorter.Options</code> to pass into <code>SortValues.create</code>.</li></ul><h3 id=example-usage-of-sortvalues>Example usage of <code>SortValues</code></h3><pre tabindex=0><code>PCollection&lt;KV&lt;String, KV&lt;String, Integer&gt;&gt;&gt; input = ...
// Group by primary key, bringing &lt;SecondaryKey, Value&gt; pairs for the same key together.
PCollection&lt;KV&lt;String, Iterable&lt;KV&lt;String, Integer&gt;&gt;&gt;&gt; grouped =
input.apply(GroupByKey.&lt;String, KV&lt;String, Integer&gt;&gt;create());
// For every primary key, sort the iterable of &lt;SecondaryKey, Value&gt; pairs by secondary key.
PCollection&lt;KV&lt;String, Iterable&lt;KV&lt;String, Integer&gt;&gt;&gt;&gt; groupedAndSorted =
grouped.apply(
SortValues.&lt;String, String, Integer&gt;create(BufferedExternalSorter.options()));
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