You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@beam.apache.org by gi...@apache.org on 2020/09/15 18:42:09 UTC
[beam] branch asf-site updated: Publishing website 2020/09/15
18:41:41 at commit 8f829dd
This is an automated email from the ASF dual-hosted git repository.
git-site-role pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/beam.git
The following commit(s) were added to refs/heads/asf-site by this push:
new f519409 Publishing website 2020/09/15 18:41:41 at commit 8f829dd
f519409 is described below
commit f5194095c4ef3a61f180985b7a2c2e5ce04efcff
Author: jenkins <bu...@apache.org>
AuthorDate: Tue Sep 15 18:41:42 2020 +0000
Publishing website 2020/09/15 18:41:41 at commit 8f829dd
---
website/generated-content/documentation/index.xml | 295 ++++++++++++++++++---
.../python/aggregation/combineperkey/index.html | 2 +-
.../python/aggregation/groupby/index.html | 91 +++++++
.../python/aggregation/groupbykey/index.html | 2 +-
website/generated-content/sitemap.xml | 2 +-
5 files changed, 352 insertions(+), 40 deletions(-)
diff --git a/website/generated-content/documentation/index.xml b/website/generated-content/documentation/index.xml
index 6d1c133..6224d9b 100644
--- a/website/generated-content/documentation/index.xml
+++ b/website/generated-content/documentation/index.xml
@@ -8085,6 +8085,7 @@ It allows to do additional calculations before extracting a result.</p>
<li><a href="/documentation/transforms/python/aggregation/top">Top</a></li>
<li><a href="/documentation/transforms/python/aggregation/sample">Sample</a></li>
</ul>
+<p>See also <a href="/documentation/transforms/python/aggregation/groupby">GroupBy</a> which allows you to combine more than one field at once.</p>
<table align="left" style="margin-right:1em">
<td>
<a
@@ -11744,7 +11745,260 @@ outputs the results to a BigQuery table.</p>
reads weather station data from a BigQuery table, manipulates BigQuery rows in
memory, and writes the results to a BigQuery table.</p>
</li>
-</ul></description></item><item><title>Documentation: GroupByKey</title><link>/documentation/transforms/java/aggregation/groupbykey/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>/documentation/transforms/java/aggregation/groupbykey/</guid><description>
+</ul></description></item><item><title>Documentation: GroupBy</title><link>/documentation/transforms/python/aggregation/groupby/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>/documentation/transforms/python/aggregation/groupby/</guid><description>
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+<h1 id="groupby">GroupBy</h1>
+<script type="text/javascript">
+localStorage.setItem("language", "language-py")
+</script>
+<table align="left" style="margin-right:1em">
+<td>
+<a
+class="button"
+target="_blank"
+href="https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.core.html#apache_beam.transforms.core.GroupBy"
+><img
+src="https://beam.apache.org/images/logos/sdks/python.png"
+width="32px"
+height="32px"
+alt="Pydoc"
+/>
+Pydoc</a
+>
+</td>
+</table>
+<p><br><br><br></p>
+<p>Takes a collection of elements and produces a collection grouped,
+by properties of those elements.</p>
+<p>Unlike <code>GroupByKey</code>, the key is dynamically created from the elements themselves.</p>
+<h2 id="grouping-examples">Grouping Examples</h2>
+<p>In the following example, we create a pipeline with a <code>PCollection</code> of fruits.</p>
+<p>We use <code>GroupBy</code> to group all fruits by the first letter of their name.</p>
+<div class=language-py>
+<div class="highlight"><pre class="chroma"><code class="language-py" data-lang="py"><span class="k">with</span> <span class="n">beam</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">()</span> <span class="k">as</span> <span class="n">p</span><span class="p">:</span>
+<span class="n">grouped</span> <span class="o">=</span> <span class="p">(</span>
+<span class="n">p</span>
+<span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">Create</span><span class="p">([</span><span class="s1">&#39;strawberry&#39;</span><span class="p">,</span> <span class="s1">&#39;raspberry&#39;</span><span class="p">,</span> <span class="s1">&#39;blueberry&#39;</span><span class="p">,</span> <span class="s1">&#39;blackberry&#39;</span><span [...]
+<span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">GroupBy</span><span class="p">(</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span></code></pre></div>
+</div>
+<p class="notebook-skip">Output:</p>
+<div class=notebook-skip>
+<pre><code>(&#39;s&#39;, [&#39;strawberry&#39;]),
+(&#39;r&#39;, [&#39;raspberry&#39;]),
+(&#39;b&#39;, [&#39;banana&#39;, &#39;blackberry&#39;, &#39;blueberry&#39;]),</code></pre>
+</div>
+<table align="left" style="margin-right:1em" class=".language-py" >
+<td>
+<a class="button" target="_blank" href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" width="32px" height="32px" alt="View source code" /> View source code</a>
+</td>
+</table>
+<p><br><br><br></p>
+<p>We can group by a composite key consisting of multiple properties if desired.</p>
+<div class=language-py>
+<div class="highlight"><pre class="chroma"><code class="language-py" data-lang="py"><span class="k">with</span> <span class="n">beam</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">()</span> <span class="k">as</span> <span class="n">p</span><span class="p">:</span>
+<span class="n">grouped</span> <span class="o">=</span> <span class="p">(</span>
+<span class="n">p</span>
+<span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">Create</span><span class="p">([</span><span class="s1">&#39;strawberry&#39;</span><span class="p">,</span> <span class="s1">&#39;raspberry&#39;</span><span class="p">,</span> <span class="s1">&#39;blueberry&#39;</span><span class="p">,</span> <span class="s1">&#39;blackberry&#39;</span><span [...]
+<span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">GroupBy</span><span class="p">(</span><span class="n">letter</span><span class="o">=</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">is_berry</span><span c [...]
+</div>
+<p>The resulting key is a named tuple with the two requested attributes, and the
+values are grouped accordingly.</p>
+<p class="notebook-skip">Output:</p>
+<div class=notebook-skip>
+<pre><code>(NamedTuple(letter=&#39;s&#39;, is_berry=True), [&#39;strawberry&#39;]),
+(NamedTuple(letter=&#39;r&#39;, is_berry=True), [&#39;raspberry&#39;]),
+(NamedTuple(letter=&#39;b&#39;, is_berry=True), [&#39;blackberry&#39;, &#39;blueberry&#39;]),
+(NamedTuple(letter=&#39;b&#39;, is_berry=False), [&#39;banana&#39;]),</code></pre>
+</div>
+<table align="left" style="margin-right:1em" class=".language-py" >
+<td>
+<a class="button" target="_blank" href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" width="32px" height="32px" alt="View source code" /> View source code</a>
+</td>
+</table>
+<p><br><br><br></p>
+<p>In the case that the property one wishes to group by is an attribute, a string
+may be passed to <code>GroupBy</code> in the place of a callable expression. For example,
+suppose I have the following data</p>
+<div class=language-py>
+<div class="highlight"><pre class="chroma"><code class="language-py" data-lang="py"><span class="n">GROCERY_LIST</span> <span class="o">=</span> <span class="p">[</span>
+<span class="n">beam</span><span class="o">.</span><span class="n">Row</span><span class="p">(</span><span class="n">recipe</span><span class="o">=</span><span class="s1">&#39;pie&#39;</span><span class="p">,</span> <span class="n">fruit</span><span class="o">=</span><span class="s1">&#39;strawberry&#39;</span><span class="p">,</span> <span class="n">quantity</span><span class="o">=</span> [...]
+<span class="n">beam</span><span class="o">.</span><span class="n">Row</span><span class="p">(</span><span class="n">recipe</span><span class="o">=</span><span class="s1">&#39;pie&#39;</span><span class="p">,</span> <span class="n">fruit</span><span class="o">=</span><span class="s1">&#39;raspberry&#39;</span><span class="p">,</span> <span class="n">quantity</span><span class="o">=</span>& [...]
+<span class="n">beam</span><span class="o">.</span><span class="n">Row</span><span class="p">(</span><span class="n">recipe</span><span class="o">=</span><span class="s1">&#39;pie&#39;</span><span class="p">,</span> <span class="n">fruit</span><span class="o">=</span><span class="s1">&#39;blackberry&#39;</span><span class="p">,</span> <span class="n">quantity</span><span class="o">=</span> [...]
+<span class="n">beam</span><span class="o">.</span><span class="n">Row</span><span class="p">(</span><span class="n">recipe</span><span class="o">=</span><span class="s1">&#39;pie&#39;</span><span class="p">,</span> <span class="n">fruit</span><span class="o">=</span><span class="s1">&#39;blueberry&#39;</span><span class="p">,</span> <span class="n">quantity</span><span class="o">=</span>& [...]
+<span class="n">beam</span><span class="o">.</span><span class="n">Row</span><span class="p">(</span><span class="n">recipe</span><span class="o">=</span><span class="s1">&#39;muffin&#39;</span><span class="p">,</span> <span class="n">fruit</span><span class="o">=</span><span class="s1">&#39;blueberry&#39;</span><span class="p">,</span> <span class="n">quantity</span><span class="o">=</spa [...]
+<span class="n">beam</span><span class="o">.</span><span class="n">Row</span><span class="p">(</span><span class="n">recipe</span><span class="o">=</span><span class="s1">&#39;muffin&#39;</span><span class="p">,</span> <span class="n">fruit</span><span class="o">=</span><span class="s1">&#39;banana&#39;</span><span class="p">,</span> <span class="n">quantity</span><span class="o">=</span>& [...]
+<span class="p">]</span></code></pre></div>
+</div>
+<p>We can then do</p>
+<div class=language-py>
+<div class="highlight"><pre class="chroma"><code class="language-py" data-lang="py"><span class="k">with</span> <span class="n">beam</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">()</span> <span class="k">as</span> <span class="n">p</span><span class="p">:</span>
+<span class="n">grouped</span> <span class="o">=</span> <span class="n">p</span> <span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">Create</span><span class="p">(</span><span class="n">GROCERY_LIST</span><span class="p">)</span> <span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">GroupBy</span><span class="p">(</span>&l [...]
+</div>
+<p class="notebook-skip">Output:</p>
+<div class=notebook-skip>
+<pre><code>(&#39;pie&#39;,
+[
+beam.Row(recipe=&#39;pie&#39;, fruit=&#39;strawberry&#39;, quantity=3, unit_price=1.50),
+beam.Row(recipe=&#39;pie&#39;, fruit=&#39;raspberry&#39;, quantity=1, unit_price=3.50),
+beam.Row(recipe=&#39;pie&#39;, fruit=&#39;blackberry&#39;, quantity=1, unit_price=4.00),
+beam.Row(recipe=&#39;pie&#39;, fruit=&#39;blueberry&#39;, quantity=1, unit_price=2.00),
+]),
+(&#39;muffin&#39;,
+[
+beam.Row(recipe=&#39;muffin&#39;, fruit=&#39;blueberry&#39;, quantity=2, unit_price=2.00),
+beam.Row(recipe=&#39;muffin&#39;, fruit=&#39;banana&#39;, quantity=3, unit_price=1.00),
+]),</code></pre>
+</div>
+<table align="left" style="margin-right:1em" class=".language-py" >
+<td>
+<a class="button" target="_blank" href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" width="32px" height="32px" alt="View source code" /> View source code</a>
+</td>
+</table>
+<p><br><br><br></p>
+<p>It is possible to mix and match attributes and expressions, for example</p>
+<div class=language-py>
+<div class="highlight"><pre class="chroma"><code class="language-py" data-lang="py"><span class="k">with</span> <span class="n">beam</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">()</span> <span class="k">as</span> <span class="n">p</span><span class="p">:</span>
+<span class="n">grouped</span> <span class="o">=</span> <span class="p">(</span>
+<span class="n">p</span> <span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">Create</span><span class="p">(</span><span class="n">GROCERY_LIST</span><span class="p">)</span>
+<span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">GroupBy</span><span class="p">(</span><span class="s1">&#39;recipe&#39;</span><span class="p">,</span> <span class="n">is_berry</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">&#39;berry&#39;</span> <span class="ow">in</sp [...]
+</div>
+<p class="notebook-skip">Output:</p>
+<div class=notebook-skip>
+<pre><code>(NamedTuple(recipe=&#39;pie&#39;, is_berry=True),
+[
+beam.Row(recipe=&#39;pie&#39;, fruit=&#39;strawberry&#39;, quantity=3, unit_price=1.50),
+beam.Row(recipe=&#39;pie&#39;, fruit=&#39;raspberry&#39;, quantity=1, unit_price=3.50),
+beam.Row(recipe=&#39;pie&#39;, fruit=&#39;blackberry&#39;, quantity=1, unit_price=4.00),
+beam.Row(recipe=&#39;pie&#39;, fruit=&#39;blueberry&#39;, quantity=1, unit_price=2.00),
+]),
+(NamedTuple(recipe=&#39;muffin&#39;, is_berry=True),
+[
+beam.Row(recipe=&#39;muffin&#39;, fruit=&#39;blueberry&#39;, quantity=2, unit_price=2.00),
+]),
+(NamedTuple(recipe=&#39;muffin&#39;, is_berry=False),
+[
+beam.Row(recipe=&#39;muffin&#39;, fruit=&#39;banana&#39;, quantity=3, unit_price=1.00),
+]),</code></pre>
+</div>
+<p>
+<table align="left" style="margin-right:1em" class=".language-py" >
+<td>
+<a class="button" target="_blank" href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" width="32px" height="32px" alt="View source code" /> View source code</a>
+</td>
+</table>
+<p><br><br><br></p>
+.</p>
+<h2 id="aggregation">Aggregation</h2>
+<p>Grouping is often used in conjunction with aggregation, and the
+<code>aggregate_field</code> method of the ``GroupBy<code>transform can be used to accomplish this easily. This method takes three parameters: the field (or expression) which to aggregate, the</code>CombineFn<code>(or associative</code>callable`) with which to aggregate
+by, and finally a field name in which to store the result.
+For example, suppose one wanted to compute the amount of each fruit to buy.
+One could write</p>
+<div class=language-py>
+<div class="highlight"><pre class="chroma"><code class="language-py" data-lang="py"><span class="k">with</span> <span class="n">beam</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">()</span> <span class="k">as</span> <span class="n">p</span><span class="p">:</span>
+<span class="n">grouped</span> <span class="o">=</span> <span class="p">(</span>
+<span class="n">p</span>
+<span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">Create</span><span class="p">(</span><span class="n">GROCERY_LIST</span><span class="p">)</span>
+<span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">GroupBy</span><span class="p">(</span><span class="s1">&#39;fruit&#39;</span><span class="p">)</span>
+<span class="o">.</span><span class="n">aggregate_field</span><span class="p">(</span><span class="s1">&#39;quantity&#39;</span><span class="p">,</span> <span class="nb">sum</span><span class="p">,</span> <span class="s1">&#39;total_quantity&#39;</span><span class="p">))</span></code></pre></div>
+</div>
+<p class="notebook-skip">Output:</p>
+<div class=notebook-skip>
+<pre><code>NamedTuple(fruit=&#39;strawberry&#39;, total_quantity=3),
+NamedTuple(fruit=&#39;raspberry&#39;, total_quantity=1),
+NamedTuple(fruit=&#39;blackberry&#39;, total_quantity=1),
+NamedTuple(fruit=&#39;blueberry&#39;, total_quantity=3),
+NamedTuple(fruit=&#39;banana&#39;, total_quantity=3),</code></pre>
+</div>
+<p>
+<table align="left" style="margin-right:1em" class=".language-py" >
+<td>
+<a class="button" target="_blank" href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" width="32px" height="32px" alt="View source code" /> View source code</a>
+</td>
+</table>
+<p><br><br><br></p>
+.</p>
+<p>Similar to the parameters in GroupBy, one can also aggregate multiple fields
+and by expressions.</p>
+<div class=language-py>
+<div class="highlight"><pre class="chroma"><code class="language-py" data-lang="py"><span class="k">with</span> <span class="n">beam</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">()</span> <span class="k">as</span> <span class="n">p</span><span class="p">:</span>
+<span class="n">grouped</span> <span class="o">=</span> <span class="p">(</span>
+<span class="n">p</span>
+<span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">Create</span><span class="p">(</span><span class="n">GROCERY_LIST</span><span class="p">)</span>
+<span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">GroupBy</span><span class="p">(</span><span class="s1">&#39;recipe&#39;</span><span class="p">)</span>
+<span class="o">.</span><span class="n">aggregate_field</span><span class="p">(</span><span class="s1">&#39;quantity&#39;</span><span class="p">,</span> <span class="nb">sum</span><span class="p">,</span> <span class="s1">&#39;total_quantity&#39;</span><span class="p">)</span>
+<span class="o">.</span><span class="n">aggregate_field</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">quantity</span> <span class="o">*</span> <span class="n">x</span><span class="o">.</span><span class="n">unit_price</span><span class="p">,</span> <span class="nb">sum</span [...]
+</div>
+<p class="notebook-skip">Output:</p>
+<div class=notebook-skip>
+<pre><code>NamedTuple(recipe=&#39;pie&#39;, total_quantity=6, price=14.00),
+NamedTuple(recipe=&#39;muffin&#39;, total_quantity=5, price=7.00),</code></pre>
+</div>
+<p>
+<table align="left" style="margin-right:1em" class=".language-py" >
+<td>
+<a class="button" target="_blank" href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" width="32px" height="32px" alt="View source code" /> View source code</a>
+</td>
+</table>
+<p><br><br><br></p>
+.</p>
+<p>One can, of course, aggregate the same field multiple times as well.
+This example also illustrates a global grouping, as the grouping key is empty.</p>
+<div class=language-py>
+<div class="highlight"><pre class="chroma"><code class="language-py" data-lang="py"><span class="k">with</span> <span class="n">beam</span><span class="o">.</span><span class="n">Pipeline</span><span class="p">()</span> <span class="k">as</span> <span class="n">p</span><span class="p">:</span>
+<span class="n">grouped</span> <span class="o">=</span> <span class="p">(</span>
+<span class="n">p</span>
+<span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">Create</span><span class="p">(</span><span class="n">GROCERY_LIST</span><span class="p">)</span>
+<span class="o">|</span> <span class="n">beam</span><span class="o">.</span><span class="n">GroupBy</span><span class="p">()</span>
+<span class="o">.</span><span class="n">aggregate_field</span><span class="p">(</span><span class="s1">&#39;unit_price&#39;</span><span class="p">,</span> <span class="nb">min</span><span class="p">,</span> <span class="s1">&#39;min_price&#39;</span><span class="p">)</span>
+<span class="o">.</span><span class="n">aggregate_field</span><span class="p">(</span><span class="s1">&#39;unit_price&#39;</span><span class="p">,</span> <span class="n">MeanCombineFn</span><span class="p">(),</span> <span class="s1">&#39;mean_price&#39;</span><span class="p">)</span>
+<span class="o">.</span><span class="n">aggregate_field</span><span class="p">(</span><span class="s1">&#39;unit_price&#39;</span><span class="p">,</span> <span class="nb">max</span><span class="p">,</span> <span class="s1">&#39;max_price&#39;</span><span class="p">))</span></code></pre></div>
+</div>
+<p class="notebook-skip">Output:</p>
+<div class=notebook-skip>
+<pre><code>NamedTuple(min_price=1.00, mean_price=7 / 3, max_price=4.00),</code></pre>
+</div>
+<p>
+<table align="left" style="margin-right:1em" class=".language-py" >
+<td>
+<a class="button" target="_blank" href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" width="32px" height="32px" alt="View source code" /> View source code</a>
+</td>
+</table>
+<p><br><br><br></p>
+.</p>
+<h2 id="related-transforms">Related transforms</h2>
+<ul>
+<li><a href="/documentation/transforms/python/aggregation/combineperkey">CombinePerKey</a> for combining with a single CombineFn.</li>
+<li><a href="/documentation/transforms/python/aggregation/groupbykey">GroupByKey</a> for grouping with a known key.</li>
+<li><a href="/documentation/transforms/python/aggregation/cogroupbykey">CoGroupByKey</a> for multiple input collections.</li>
+</ul>
+<table align="left" style="margin-right:1em">
+<td>
+<a
+class="button"
+target="_blank"
+href="https://beam.apache.org/releases/pydoc/current/apache_beam.transforms.core.html#apache_beam.transforms.core.GroupByKey"
+><img
+src="https://beam.apache.org/images/logos/sdks/python.png"
+width="32px"
+height="32px"
+alt="Pydoc"
+/>
+Pydoc</a
+>
+</td>
+</table>
+<p><br><br><br></p></description></item><item><title>Documentation: GroupByKey</title><link>/documentation/transforms/java/aggregation/groupbykey/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>/documentation/transforms/java/aggregation/groupbykey/</guid><description>
<!--
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@@ -11857,7 +12111,8 @@ where each element consists of a key and all values associated with that key.<
<p><br><br><br></p>
<h2 id="related-transforms">Related transforms</h2>
<ul>
-<li><a href="/documentation/transforms/python/aggregation/combineglobally">CombineGlobally</a> for combining all values associated with a key to a single result.</li>
+<li><a href="/documentation/transforms/python/aggregation/groupby">GroupBy</a> for grouping by arbitrary properties of the elements.</li>
+<li><a href="/documentation/transforms/python/aggregation/combineperkey">CombinePerKey</a> for combining all values associated with a key to a single result.</li>
<li><a href="/documentation/transforms/python/aggregation/cogroupbykey">CoGroupByKey</a> for multiple input collections.</li>
</ul>
<table align="left" style="margin-right:1em">
@@ -12221,38 +12476,4 @@ When you submit a topology with argument <code>&quot;--external-libs beam
<h2 id="additional-notes">Additional notes</h2>
<h3 id="monitoring-your-job">Monitoring your job</h3>
<p>You can monitor your job with the JStorm UI, which displays all JStorm system metrics and Beam metrics.
-For testing on local mode, you can retreive the Beam metrics with the metrics method of PipelineResult.</p></description></item><item><title>Documentation: Keys</title><link>/documentation/transforms/java/elementwise/keys/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>/documentation/transforms/java/elementwise/keys/</guid><description>
-<!--
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-<h1 id="keys">Keys</h1>
-<table align="left">
-<a target="_blank" class="button"
-href="https://beam.apache.org/releases/javadoc/current/index.html?org/apache/beam/sdk/transforms/Keys.html">
-<img src="https://beam.apache.org/images/logos/sdks/java.png" width="20px" height="20px"
-alt="Javadoc" />
-Javadoc
-</a>
-</table>
-<br><br>
-<p>Takes a collection of key-value pairs, and returns the key of each element.</p>
-<h2 id="examples">Examples</h2>
-<p><strong>Example</strong></p>
-<div class=language-java>
-<div class="highlight"><pre class="chroma"><code class="language-java" data-lang="java"><span class="n">PCollection</span><span class="o">&lt;</span><span class="n">KV</span><span class="o">&lt;</span><span class="n">String</span><span class="o">,</span> <span class="n">Integer</span><span class="o">&gt;&gt;</span> <span class="n">keyValuePairs</span> <span class="o">=</span> <span class="cm">/* ... * [...]
-<span class="n">PCollection</span><span class="o">&lt;</span><span class="n">String</span><span class="o">&gt;</span> <span class="n">keys</span> <span class="o">=</span> <span class="n">keyValuePairs</span><span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="n">Keys</span><span class="o">.</span><span class="na">create</span><span class="o">());</span></cod [...]
-</div>
-<h2 id="related-transforms">Related transforms</h2>
-<ul>
-<li><a href="/documentation/transforms/java/elementwise/kvswap">KvSwap</a> swaps key-value pair values.</li>
-<li><a href="/documentation/transforms/java/elementwise/values">Values</a> for extracting the value of each element.</li>
-<li><a href="/documentation/transforms/java/elementwise/withkeys">WithKeys</a> for adding a key to each element.</li>
-</ul></description></item></channel></rss>
\ No newline at end of file
+For testing on local mode, you can retreive the Beam metrics with the metrics method of PipelineResult.</p></description></item></channel></rss>
\ No newline at end of file
diff --git a/website/generated-content/documentation/transforms/python/aggregation/combineperkey/index.html b/website/generated-content/documentation/transforms/python/aggregation/combineperkey/index.html
index 185683f..71860c9 100644
--- a/website/generated-content/documentation/transforms/python/aggregation/combineperkey/index.html
+++ b/website/generated-content/documentation/transforms/python/aggregation/combineperkey/index.html
@@ -212,7 +212,7 @@ It allows to do additional calculations before extracting a result.</p></li></ul
<span class=o>|</span> <span class=s1>'Average'</span> <span class=o>>></span> <span class=n>beam</span><span class=o>.</span><span class=n>CombinePerKey</span><span class=p>(</span><span class=n>AverageFn</span><span class=p>())</span>
<span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>Map</span><span class=p>(</span><span class=k>print</span><span class=p>))</span></code></pre></div></div><p class=notebook-skip>Output:</p><div class=notebook-skip><pre><code>('🥕', 2.5)
('🍆', 1.0)
-('🍅', 4.0)</code></pre></div><table align=left style=margin-right:1em class=.language-py><td><a class=button target=_blank href=https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/combineperkey.py><img src=https://www.tensorflow.org/images/GitHub-Mark-32px.png width=32px height=32px alt="View source code"> View source code</a></td></table><p><br><br><br></p><h2 id=related-transforms>Related transforms</h2><p>You can use the [...]
+('🍅', 4.0)</code></pre></div><table align=left style=margin-right:1em class=.language-py><td><a class=button target=_blank href=https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/combineperkey.py><img src=https://www.tensorflow.org/images/GitHub-Mark-32px.png width=32px height=32px alt="View source code"> View source code</a></td></table><p><br><br><br></p><h2 id=related-transforms>Related transforms</h2><p>You can use the [...]
Pydoc</a></td></table><p><br><br><br></p></div></div><footer class=footer><div class=footer__contained><div class=footer__cols><div class=footer__cols__col><div class=footer__cols__col__logo><img src=/images/beam_logo_circle.svg class=footer__logo alt="Beam logo"></div><div class=footer__cols__col__logo><img src=/images/apache_logo_circle.svg class=footer__logo alt="Apache logo"></div></div><div class="footer__cols__col footer__cols__col--md"><div class=footer__cols__col__title>Start</di [...]
<a href=http://www.apache.org>The Apache Software Foundation</a>
| <a href=/privacy_policy>Privacy Policy</a>
diff --git a/website/generated-content/documentation/transforms/python/aggregation/groupby/index.html b/website/generated-content/documentation/transforms/python/aggregation/groupby/index.html
new file mode 100644
index 0000000..44e19cc
--- /dev/null
+++ b/website/generated-content/documentation/transforms/python/aggregation/groupby/index.html
@@ -0,0 +1,91 @@
+<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport content="width=device-width,initial-scale=1"><title>GroupBy</title><meta name=description content="Apache Beam is an open source, unified model and set of language-specific SDKs for defining and executing data processing workflows, and also data ingestion and integration flows, supporting Enterprise Integration Patterns (EIPs) and Domain Specific Langu [...]
+<span class=sr-only>Toggle navigation</span>
+<span class=icon-bar></span><span class=icon-bar></span><span class=icon-bar></span></button>
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask closed"></div><div id=navbar class="navbar-container closed"><ul class="nav navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a href=/documentation/>Documentation</a></li><li><a href=/documentation/sdks/java/>Languages</a></li><li><a href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a href=/roadmap/>Roadmap</a></li>< [...]
+Pydoc</a></td></table><p><br><br><br></p><p>Takes a collection of elements and produces a collection grouped,
+by properties of those elements.</p><p>Unlike <code>GroupByKey</code>, the key is dynamically created from the elements themselves.</p><h2 id=grouping-examples>Grouping Examples</h2><p>In the following example, we create a pipeline with a <code>PCollection</code> of fruits.</p><p>We use <code>GroupBy</code> to group all fruits by the first letter of their name.</p><div class=language-py><div class=highlight><pre class=chroma><code class=language-py data-lang=py><span class=k>with</span> [...]
+ <span class=n>grouped</span> <span class=o>=</span> <span class=p>(</span>
+ <span class=n>p</span>
+ <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>Create</span><span class=p>([</span><span class=s1>'strawberry'</span><span class=p>,</span> <span class=s1>'raspberry'</span><span class=p>,</span> <span class=s1>'blueberry'</span><span class=p>,</span> <span class=s1>'blackberry'</span><span class=p>,</span> <span class=s1>'banana'</span><span class=p>])</span>
+ <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>GroupBy</span><span class=p>(</span><span class=k>lambda</span> <span class=n>s</span><span class=p>:</span> <span class=n>s</span><span class=p>[</span><span class=mi>0</span><span class=p>]))</span></code></pre></div></div><p class=notebook-skip>Output:</p><div class=notebook-skip><pre><code>('s', ['strawberry']),
+('r', ['raspberry']),
+('b', ['banana', 'blackberry', 'blueberry']),</code></pre></div><table align=left style=margin-right:1em class=.language-py><td><a class=button target=_blank href=https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py><img src=https://www.tensorflow.org/images/GitHub-Mark-32px.png width=32px height=32px alt="View source code"> View source code</a></td></table><p><br><br><br></p><p>We can [...]
+ <span class=n>grouped</span> <span class=o>=</span> <span class=p>(</span>
+ <span class=n>p</span>
+ <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>Create</span><span class=p>([</span><span class=s1>'strawberry'</span><span class=p>,</span> <span class=s1>'raspberry'</span><span class=p>,</span> <span class=s1>'blueberry'</span><span class=p>,</span> <span class=s1>'blackberry'</span><span class=p>,</span> <span class=s1>'banana'</span><span class=p>])</span>
+ <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>GroupBy</span><span class=p>(</span><span class=n>letter</span><span class=o>=</span><span class=k>lambda</span> <span class=n>s</span><span class=p>:</span> <span class=n>s</span><span class=p>[</span><span class=mi>0</span><span class=p>],</span> <span class=n>is_berry</span><span class=o>=</span><span class=k>lambda</span> <span class=n>s</span><span class=p>:</span> <span class=s1>'berry' [...]
+values are grouped accordingly.</p><p class=notebook-skip>Output:</p><div class=notebook-skip><pre><code>(NamedTuple(letter='s', is_berry=True), ['strawberry']),
+(NamedTuple(letter='r', is_berry=True), ['raspberry']),
+(NamedTuple(letter='b', is_berry=True), ['blackberry', 'blueberry']),
+(NamedTuple(letter='b', is_berry=False), ['banana']),</code></pre></div><table align=left style=margin-right:1em class=.language-py><td><a class=button target=_blank href=https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py><img src=https://www.tensorflow.org/images/GitHub-Mark-32px.png width=32px height=32px alt="View source code"> View source code</a></td></table><p><br><br><br></p><p>In the case tha [...]
+may be passed to <code>GroupBy</code> in the place of a callable expression. For example,
+suppose I have the following data</p><div class=language-py><div class=highlight><pre class=chroma><code class=language-py data-lang=py><span class=n>GROCERY_LIST</span> <span class=o>=</span> <span class=p>[</span>
+ <span class=n>beam</span><span class=o>.</span><span class=n>Row</span><span class=p>(</span><span class=n>recipe</span><span class=o>=</span><span class=s1>'pie'</span><span class=p>,</span> <span class=n>fruit</span><span class=o>=</span><span class=s1>'strawberry'</span><span class=p>,</span> <span class=n>quantity</span><span class=o>=</span><span class=mi>3</span><span class=p>,</span> <span class=n>unit_price</span><span class=o>=</span><span class=mf>1.50</span [...]
+ <span class=n>beam</span><span class=o>.</span><span class=n>Row</span><span class=p>(</span><span class=n>recipe</span><span class=o>=</span><span class=s1>'pie'</span><span class=p>,</span> <span class=n>fruit</span><span class=o>=</span><span class=s1>'raspberry'</span><span class=p>,</span> <span class=n>quantity</span><span class=o>=</span><span class=mi>1</span><span class=p>,</span> <span class=n>unit_price</span><span class=o>=</span><span class=mf>3.50</span> [...]
+ <span class=n>beam</span><span class=o>.</span><span class=n>Row</span><span class=p>(</span><span class=n>recipe</span><span class=o>=</span><span class=s1>'pie'</span><span class=p>,</span> <span class=n>fruit</span><span class=o>=</span><span class=s1>'blackberry'</span><span class=p>,</span> <span class=n>quantity</span><span class=o>=</span><span class=mi>1</span><span class=p>,</span> <span class=n>unit_price</span><span class=o>=</span><span class=mf>4.00</span [...]
+ <span class=n>beam</span><span class=o>.</span><span class=n>Row</span><span class=p>(</span><span class=n>recipe</span><span class=o>=</span><span class=s1>'pie'</span><span class=p>,</span> <span class=n>fruit</span><span class=o>=</span><span class=s1>'blueberry'</span><span class=p>,</span> <span class=n>quantity</span><span class=o>=</span><span class=mi>1</span><span class=p>,</span> <span class=n>unit_price</span><span class=o>=</span><span class=mf>2.00</span> [...]
+ <span class=n>beam</span><span class=o>.</span><span class=n>Row</span><span class=p>(</span><span class=n>recipe</span><span class=o>=</span><span class=s1>'muffin'</span><span class=p>,</span> <span class=n>fruit</span><span class=o>=</span><span class=s1>'blueberry'</span><span class=p>,</span> <span class=n>quantity</span><span class=o>=</span><span class=mi>2</span><span class=p>,</span> <span class=n>unit_price</span><span class=o>=</span><span class=mf>2.00</sp [...]
+ <span class=n>beam</span><span class=o>.</span><span class=n>Row</span><span class=p>(</span><span class=n>recipe</span><span class=o>=</span><span class=s1>'muffin'</span><span class=p>,</span> <span class=n>fruit</span><span class=o>=</span><span class=s1>'banana'</span><span class=p>,</span> <span class=n>quantity</span><span class=o>=</span><span class=mi>3</span><span class=p>,</span> <span class=n>unit_price</span><span class=o>=</span><span class=mf>1.00</span> [...]
+<span class=p>]</span></code></pre></div></div><p>We can then do</p><div class=language-py><div class=highlight><pre class=chroma><code class=language-py data-lang=py><span class=k>with</span> <span class=n>beam</span><span class=o>.</span><span class=n>Pipeline</span><span class=p>()</span> <span class=k>as</span> <span class=n>p</span><span class=p>:</span>
+ <span class=n>grouped</span> <span class=o>=</span> <span class=n>p</span> <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>Create</span><span class=p>(</span><span class=n>GROCERY_LIST</span><span class=p>)</span> <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>GroupBy</span><span class=p>(</span><span class=s1>'recipe'</span><span class=p>)</span></code></pre></div></div><p class=notebook-skip>Output:</p>< [...]
+ [
+ beam.Row(recipe='pie', fruit='strawberry', quantity=3, unit_price=1.50),
+ beam.Row(recipe='pie', fruit='raspberry', quantity=1, unit_price=3.50),
+ beam.Row(recipe='pie', fruit='blackberry', quantity=1, unit_price=4.00),
+ beam.Row(recipe='pie', fruit='blueberry', quantity=1, unit_price=2.00),
+ ]),
+('muffin',
+ [
+ beam.Row(recipe='muffin', fruit='blueberry', quantity=2, unit_price=2.00),
+ beam.Row(recipe='muffin', fruit='banana', quantity=3, unit_price=1.00),
+ ]),</code></pre></div><table align=left style=margin-right:1em class=.language-py><td><a class=button target=_blank href=https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py><img src=https://www.tensorflow.org/images/GitHub-Mark-32px.png width=32px height=32px alt="View source code"> View source code</a></td></table><p><br><br><br></p><p>It is possible to mix and match attributes and expressions, for example</p><div [...]
+ <span class=n>grouped</span> <span class=o>=</span> <span class=p>(</span>
+ <span class=n>p</span> <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>Create</span><span class=p>(</span><span class=n>GROCERY_LIST</span><span class=p>)</span>
+ <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>GroupBy</span><span class=p>(</span><span class=s1>'recipe'</span><span class=p>,</span> <span class=n>is_berry</span><span class=o>=</span><span class=k>lambda</span> <span class=n>x</span><span class=p>:</span> <span class=s1>'berry'</span> <span class=ow>in</span> <span class=n>x</span><span class=o>.</span><span class=n>fruit</span><span class=p>))</span></code></pre></div></div> [...]
+ [
+ beam.Row(recipe='pie', fruit='strawberry', quantity=3, unit_price=1.50),
+ beam.Row(recipe='pie', fruit='raspberry', quantity=1, unit_price=3.50),
+ beam.Row(recipe='pie', fruit='blackberry', quantity=1, unit_price=4.00),
+ beam.Row(recipe='pie', fruit='blueberry', quantity=1, unit_price=2.00),
+ ]),
+(NamedTuple(recipe='muffin', is_berry=True),
+ [
+ beam.Row(recipe='muffin', fruit='blueberry', quantity=2, unit_price=2.00),
+ ]),
+(NamedTuple(recipe='muffin', is_berry=False),
+ [
+ beam.Row(recipe='muffin', fruit='banana', quantity=3, unit_price=1.00),
+ ]),</code></pre></div><p><table align=left style=margin-right:1em class=.language-py><td><a class=button target=_blank href=https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py><img src=https://www.tensorflow.org/images/GitHub-Mark-32px.png width=32px height=32px alt="View source code"> View source code</a></td></table><p><br><br><br></p>.</p><h2 id=aggregation>Aggregation</h2><p>Grouping is often used in conjunctio [...]
+<code>aggregate_field</code> method of the ``GroupBy<code>transform can be used to accomplish this easily. This method takes three parameters: the field (or expression) which to aggregate, the</code>CombineFn<code>(or associative</code>callable`) with which to aggregate
+by, and finally a field name in which to store the result.
+For example, suppose one wanted to compute the amount of each fruit to buy.
+One could write</p><div class=language-py><div class=highlight><pre class=chroma><code class=language-py data-lang=py><span class=k>with</span> <span class=n>beam</span><span class=o>.</span><span class=n>Pipeline</span><span class=p>()</span> <span class=k>as</span> <span class=n>p</span><span class=p>:</span>
+ <span class=n>grouped</span> <span class=o>=</span> <span class=p>(</span>
+ <span class=n>p</span>
+ <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>Create</span><span class=p>(</span><span class=n>GROCERY_LIST</span><span class=p>)</span>
+ <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>GroupBy</span><span class=p>(</span><span class=s1>'fruit'</span><span class=p>)</span>
+ <span class=o>.</span><span class=n>aggregate_field</span><span class=p>(</span><span class=s1>'quantity'</span><span class=p>,</span> <span class=nb>sum</span><span class=p>,</span> <span class=s1>'total_quantity'</span><span class=p>))</span></code></pre></div></div><p class=notebook-skip>Output:</p><div class=notebook-skip><pre><code>NamedTuple(fruit='strawberry', total_quantity=3),
+NamedTuple(fruit='raspberry', total_quantity=1),
+NamedTuple(fruit='blackberry', total_quantity=1),
+NamedTuple(fruit='blueberry', total_quantity=3),
+NamedTuple(fruit='banana', total_quantity=3),</code></pre></div><p><table align=left style=margin-right:1em class=.language-py><td><a class=button target=_blank href=https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py><img src=https://www.tensorflow.org/images/GitHub-Mark-32px.png width=32px height=32px alt="View source code"> View source code</a></td></table><p><br><br><br></p>.</p><p>Similar to the paramete [...]
+and by expressions.</p><div class=language-py><div class=highlight><pre class=chroma><code class=language-py data-lang=py><span class=k>with</span> <span class=n>beam</span><span class=o>.</span><span class=n>Pipeline</span><span class=p>()</span> <span class=k>as</span> <span class=n>p</span><span class=p>:</span>
+ <span class=n>grouped</span> <span class=o>=</span> <span class=p>(</span>
+ <span class=n>p</span>
+ <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>Create</span><span class=p>(</span><span class=n>GROCERY_LIST</span><span class=p>)</span>
+ <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>GroupBy</span><span class=p>(</span><span class=s1>'recipe'</span><span class=p>)</span>
+ <span class=o>.</span><span class=n>aggregate_field</span><span class=p>(</span><span class=s1>'quantity'</span><span class=p>,</span> <span class=nb>sum</span><span class=p>,</span> <span class=s1>'total_quantity'</span><span class=p>)</span>
+ <span class=o>.</span><span class=n>aggregate_field</span><span class=p>(</span><span class=k>lambda</span> <span class=n>x</span><span class=p>:</span> <span class=n>x</span><span class=o>.</span><span class=n>quantity</span> <span class=o>*</span> <span class=n>x</span><span class=o>.</span><span class=n>unit_price</span><span class=p>,</span> <span class=nb>sum</span><span class=p>,</span> <span class=s1>'price'</span><span class=p>))</span></code></pre></div></div>< [...]
+NamedTuple(recipe='muffin', total_quantity=5, price=7.00),</code></pre></div><p><table align=left style=margin-right:1em class=.language-py><td><a class=button target=_blank href=https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupby_test.py><img src=https://www.tensorflow.org/images/GitHub-Mark-32px.png width=32px height=32px alt="View source code"> View source code</a></td></table><p><br><br><br></p>.</p><p>One can, o [...]
+This example also illustrates a global grouping, as the grouping key is empty.</p><div class=language-py><div class=highlight><pre class=chroma><code class=language-py data-lang=py><span class=k>with</span> <span class=n>beam</span><span class=o>.</span><span class=n>Pipeline</span><span class=p>()</span> <span class=k>as</span> <span class=n>p</span><span class=p>:</span>
+ <span class=n>grouped</span> <span class=o>=</span> <span class=p>(</span>
+ <span class=n>p</span>
+ <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>Create</span><span class=p>(</span><span class=n>GROCERY_LIST</span><span class=p>)</span>
+ <span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>GroupBy</span><span class=p>()</span>
+ <span class=o>.</span><span class=n>aggregate_field</span><span class=p>(</span><span class=s1>'unit_price'</span><span class=p>,</span> <span class=nb>min</span><span class=p>,</span> <span class=s1>'min_price'</span><span class=p>)</span>
+ <span class=o>.</span><span class=n>aggregate_field</span><span class=p>(</span><span class=s1>'unit_price'</span><span class=p>,</span> <span class=n>MeanCombineFn</span><span class=p>(),</span> <span class=s1>'mean_price'</span><span class=p>)</span>
+ <span class=o>.</span><span class=n>aggregate_field</span><span class=p>(</span><span class=s1>'unit_price'</span><span class=p>,</span> <span class=nb>max</span><span class=p>,</span> <span class=s1>'max_price'</span><span class=p>))</span></code></pre></div></div><p class=notebook-skip>Output:</p><div class=notebook-skip><pre><code>NamedTuple(min_price=1.00, mean_price=7 / 3, max_price=4.00),</code></pre></div><p><table align=left style=margin-right:1em class= [...]
+Pydoc</a></td></table><p><br><br><br></p></div></div><footer class=footer><div class=footer__contained><div class=footer__cols><div class=footer__cols__col><div class=footer__cols__col__logo><img src=/images/beam_logo_circle.svg class=footer__logo alt="Beam logo"></div><div class=footer__cols__col__logo><img src=/images/apache_logo_circle.svg class=footer__logo alt="Apache logo"></div></div><div class="footer__cols__col footer__cols__col--md"><div class=footer__cols__col__title>Start</di [...]
+<a href=http://www.apache.org>The Apache Software Foundation</a>
+| <a href=/privacy_policy>Privacy Policy</a>
+| <a href=/feed.xml>RSS Feed</a><br><br>Apache Beam, Apache, Beam, the Beam logo, and the Apache feather logo are either registered trademarks or trademarks of The Apache Software Foundation. All other products or name brands are trademarks of their respective holders, including The Apache Software Foundation.</div></footer></body></html>
\ No newline at end of file
diff --git a/website/generated-content/documentation/transforms/python/aggregation/groupbykey/index.html b/website/generated-content/documentation/transforms/python/aggregation/groupbykey/index.html
index 48e7a5a..327df09 100644
--- a/website/generated-content/documentation/transforms/python/aggregation/groupbykey/index.html
+++ b/website/generated-content/documentation/transforms/python/aggregation/groupbykey/index.html
@@ -24,7 +24,7 @@ where each element consists of a key and all values associated with that key.</p
<span class=o>|</span> <span class=n>beam</span><span class=o>.</span><span class=n>Map</span><span class=p>(</span><span class=k>print</span><span class=p>))</span></code></pre></div></div><p class=notebook-skip>Output:</p><div class=notebook-skip><pre><code>('spring', ['🍓', '🥕', '🍆', '🍅'])
('summer', ['🥕', '🍅', '🌽'])
('fall', ['🥕', '🍅'])
-('winter', ['🍆'])</code></pre></div><table align=left style=margin-right:1em class=.language-py><td><a class=button target=_blank href=https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupbykey.py><img src=https://www.tensorflow.org/images/GitHub-Mark-32px.png width=32px height=32px alt="View source code"> View source code</a></td></table><p><br><br><br></p><h2 id=related-transforms>Related transforms</h2><ul><li [...]
+('winter', ['🍆'])</code></pre></div><table align=left style=margin-right:1em class=.language-py><td><a class=button target=_blank href=https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/aggregation/groupbykey.py><img src=https://www.tensorflow.org/images/GitHub-Mark-32px.png width=32px height=32px alt="View source code"> View source code</a></td></table><p><br><br><br></p><h2 id=related-transforms>Related transforms</h2><ul><li [...]
Pydoc</a></td></table><p><br><br><br></p></div></div><footer class=footer><div class=footer__contained><div class=footer__cols><div class=footer__cols__col><div class=footer__cols__col__logo><img src=/images/beam_logo_circle.svg class=footer__logo alt="Beam logo"></div><div class=footer__cols__col__logo><img src=/images/apache_logo_circle.svg class=footer__logo alt="Apache logo"></div></div><div class="footer__cols__col footer__cols__col--md"><div class=footer__cols__col__title>Start</di [...]
<a href=http://www.apache.org>The Apache Software Foundation</a>
| <a href=/privacy_policy>Privacy Policy</a>
diff --git a/website/generated-content/sitemap.xml b/website/generated-content/sitemap.xml
index e9afa70..b71b6f7 100644
--- a/website/generated-content/sitemap.xml
+++ b/website/generated-content/sitemap.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:xhtml="http://www.w3.org/1999/xhtml"><url><loc>/categories/blog/</loc><lastmod>2020-08-26T13:09:05-05:00</lastmod></url><url><loc>/blog/</loc><lastmod>2020-08-26T13:09:05-05:00</lastmod></url><url><loc>/categories/</loc><lastmod>2020-08-26T13:09:05-05:00</lastmod></url><url><loc>/blog/python-improved-annotations/</loc><lastmod>2020-08-26T13:09:05-05:00</lastmod></url>< [...]
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:xhtml="http://www.w3.org/1999/xhtml"><url><loc>/categories/blog/</loc><lastmod>2020-08-26T13:09:05-05:00</lastmod></url><url><loc>/blog/</loc><lastmod>2020-08-26T13:09:05-05:00</lastmod></url><url><loc>/categories/</loc><lastmod>2020-08-26T13:09:05-05:00</lastmod></url><url><loc>/blog/python-improved-annotations/</loc><lastmod>2020-08-26T13:09:05-05:00</lastmod></url>< [...]
\ No newline at end of file