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Posted to commits@beam.apache.org by da...@apache.org on 2017/03/16 23:21:57 UTC
[1/3] beam-site git commit: Blog post for 0.6.0 release with python
sdk
Repository: beam-site
Updated Branches:
refs/heads/asf-site 2ccd76628 -> 3917f6e3d
Blog post for 0.6.0 release with python sdk
Project: http://git-wip-us.apache.org/repos/asf/beam-site/repo
Commit: http://git-wip-us.apache.org/repos/asf/beam-site/commit/be9e207d
Tree: http://git-wip-us.apache.org/repos/asf/beam-site/tree/be9e207d
Diff: http://git-wip-us.apache.org/repos/asf/beam-site/diff/be9e207d
Branch: refs/heads/asf-site
Commit: be9e207ddd881cf6beda26b811b20ba878def648
Parents: 2ccd766
Author: Ahmet Altay <al...@google.com>
Authored: Thu Mar 16 15:43:27 2017 -0700
Committer: Davor Bonaci <da...@google.com>
Committed: Thu Mar 16 16:20:42 2017 -0700
----------------------------------------------------------------------
src/_data/authors.yml | 18 +++---
src/_posts/2017-03-16-python-sdk-release.md | 72 ++++++++++++++++++++++++
2 files changed, 83 insertions(+), 7 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/beam-site/blob/be9e207d/src/_data/authors.yml
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diff --git a/src/_data/authors.yml b/src/_data/authors.yml
index e4aa332..f66a69a 100644
--- a/src/_data/authors.yml
+++ b/src/_data/authors.yml
@@ -2,6 +2,9 @@ aljoscha:
name: Aljoscha Krettek
email: aljoscha@apache.org
twitter: aljoscha
+altay:
+ name: Ahmet Altay
+ email: altay@apache.org
davor:
name: Davor Bonaci
email: davor@apache.org
@@ -18,6 +21,13 @@ jamesmalone:
name: James Malone
email: jamesmalone@apache.org
twitter: chimerasaurus
+jesseanderson:
+ name: Jesse Anderson
+ twitter: jessetanderson
+klk:
+ name: Kenneth Knowles
+ email: klk@apache.org
+ twitter: KennKnowles
robertwb:
name: Robert Bradshaw
email: robertwb@apache.org
@@ -29,14 +39,8 @@ takidau:
tgroh:
name: Thomas Groh
email: tgroh@google.com
-jesseanderson:
- name: Jesse Anderson
- twitter: jessetanderson
thw:
name: Thomas Weise
email: thw@apache.org
twitter: thweise
-klk:
- name: Kenneth Knowles
- email: klk@apache.org
- twitter: KennKnowles
+
http://git-wip-us.apache.org/repos/asf/beam-site/blob/be9e207d/src/_posts/2017-03-16-python-sdk-release.md
----------------------------------------------------------------------
diff --git a/src/_posts/2017-03-16-python-sdk-release.md b/src/_posts/2017-03-16-python-sdk-release.md
new file mode 100644
index 0000000..72f5209
--- /dev/null
+++ b/src/_posts/2017-03-16-python-sdk-release.md
@@ -0,0 +1,72 @@
+---
+layout: post
+title: "Python SDK released in Apache Beam 0.6.0"
+date: 2017-03-16 00:00:01 -0800
+excerpt_separator: <!--more-->
+categories: blog
+authors:
+ - altay
+---
+
+Apache Beam\u2019s latest release, version [0.6.0]({{ site.baseurl }}/get-started/downloads/), introduces a new SDK -- this time, for the Python programming language. The Python SDK joins the Java SDK as the second implementation of the Beam programming model.
+
+<!--more-->
+
+The Python SDK incorporates all of the main concepts of the Beam model, including ParDo, GroupByKey, Windowing, and others. It features extensible IO APIs for writing bounded sources and sinks, and provides built-in implementation for reading and writing Text, Avro, and TensorFlow record files, as well as connectors to Google BigQuery and Google Cloud Datastore.
+
+There are two runners capable of executing pipelines written with the Python SDK today: [Direct Runner]({{ site.baseurl }}/documentation/runners/direct/) and [Dataflow Runner]({{ site.baseurl }}/documentation/runners/dataflow/), both of which are currently limited to batch execution only. Upcoming features will shortly bring the benefits of the Python SDK to additional runners.
+
+#### Try the Apache Beam Python SDK
+
+If you would like to try out the Python SDK, a good place to start is the [Quickstart]({{ site.baseurl }}/get-started/quickstart-py/). After that, you can take a look at additional [examples](https://github.com/apache/beam/tree/v0.6.0/sdks/python/apache_beam/examples), and deep dive into the [API reference]({{ site.baseurl }}/documentation/sdks/pydoc/).
+
+Let\u2019s take a look at a quick example together. First, install the `apache-beam` package from PyPI and start your Python interpreter.
+
+```
+$ pip install apache-beam
+$ python
+```
+
+We will harness the power of Apache Beam to estimate Pi in honor of the recently passed Pi Day.
+
+```
+import random
+import apache_beam as beam
+
+def run_trials(count):
+ """Throw darts into unit square and count how many fall into unit circle."""
+ inside = 0
+ for _ in xrange(count):
+ x, y = random.uniform(0, 1), random.uniform(0, 1)
+ inside += 1 if x*x + y*y <= 1.0 else 0
+ return count, inside
+
+def combine_results(results):
+ """Given all the trial results, estimate pi."""
+ total, inside = sum(r[0] for r in results), sum(r[1] for r in results)
+ return total, inside, 4 * float(inside) / total if total > 0 else 0
+
+p = beam.Pipeline()
+(p | beam.Create([500] * 10) # Create 10 experiments with 500 samples each.
+ | beam.Map(run_trials) # Run experiments in parallel.
+ | beam.CombineGlobally(combine_results) # Combine the results.
+ | beam.io.WriteToText('./pi_estimate.txt')) # Write PI estimate to a file.
+
+p.run()
+```
+
+This example estimates Pi by throwing random darts into the unit square and keeping track of the fraction of those darts that fell into the unit circle (see the full [example](https://github.com/apache/beam/blob/v0.6.0/sdks/python/apache_beam/examples/complete/estimate_pi.py) for details). If you are curious, you can check the result of our estimation by looking at the output file.
+
+```
+$ cat pi_estimate.txt*
+```
+
+#### Roadmap
+
+The first thing on the Python SDK\u2019s roadmap is to address two of its limitations. First, the existing runners are currently limited to bounded PCollections, and we are looking forward to extending the SDK to support unbounded PCollections (\u201cstreaming\u201d). Additionally, we are working on extending support to more Apache Beam runners, and the upcoming Fn API will do the heavy lifting.
+
+Both of these improvements will enable the Python SDK to fulfill the mission of Apache Beam: a unified programming model for batch and streaming data processing that can run on any execution engine.
+
+#### Join us!
+
+Please consider joining us, whether as a user or a contributor, as we work towards our first release with API stability. If you\u2019d like to try out Apache Beam today, check out the latest [0.6.0]({{ site.baseurl }}/get-started/downloads/) release. We welcome contributions and participation from anyone through our mailing lists, issue tracker, pull requests, and events.
[3/3] beam-site git commit: This closes #182
Posted by da...@apache.org.
This closes #182
Project: http://git-wip-us.apache.org/repos/asf/beam-site/repo
Commit: http://git-wip-us.apache.org/repos/asf/beam-site/commit/3917f6e3
Tree: http://git-wip-us.apache.org/repos/asf/beam-site/tree/3917f6e3
Diff: http://git-wip-us.apache.org/repos/asf/beam-site/diff/3917f6e3
Branch: refs/heads/asf-site
Commit: 3917f6e3de88d2a20449fdb2dbaaf08e14d9a7f0
Parents: 2ccd766 4acb641
Author: Davor Bonaci <da...@google.com>
Authored: Thu Mar 16 16:21:09 2017 -0700
Committer: Davor Bonaci <da...@google.com>
Committed: Thu Mar 16 16:21:09 2017 -0700
----------------------------------------------------------------------
content/blog/2017/03/16/python-sdk-release.html | 255 +++++++++++++++++++
content/blog/index.html | 16 ++
content/feed.xml | 166 ++++++------
content/index.html | 4 +-
src/_data/authors.yml | 18 +-
src/_posts/2017-03-16-python-sdk-release.md | 72 ++++++
6 files changed, 430 insertions(+), 101 deletions(-)
----------------------------------------------------------------------
[2/3] beam-site git commit: Regenerate website
Posted by da...@apache.org.
Regenerate website
Project: http://git-wip-us.apache.org/repos/asf/beam-site/repo
Commit: http://git-wip-us.apache.org/repos/asf/beam-site/commit/4acb6411
Tree: http://git-wip-us.apache.org/repos/asf/beam-site/tree/4acb6411
Diff: http://git-wip-us.apache.org/repos/asf/beam-site/diff/4acb6411
Branch: refs/heads/asf-site
Commit: 4acb6411a230a543930e2672f1181ea64ad49094
Parents: be9e207
Author: Davor Bonaci <da...@google.com>
Authored: Thu Mar 16 16:21:09 2017 -0700
Committer: Davor Bonaci <da...@google.com>
Committed: Thu Mar 16 16:21:09 2017 -0700
----------------------------------------------------------------------
content/blog/2017/03/16/python-sdk-release.html | 255 +++++++++++++++++++
content/blog/index.html | 16 ++
content/feed.xml | 166 ++++++------
content/index.html | 4 +-
4 files changed, 347 insertions(+), 94 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/beam-site/blob/4acb6411/content/blog/2017/03/16/python-sdk-release.html
----------------------------------------------------------------------
diff --git a/content/blog/2017/03/16/python-sdk-release.html b/content/blog/2017/03/16/python-sdk-release.html
new file mode 100644
index 0000000..cb1320c
--- /dev/null
+++ b/content/blog/2017/03/16/python-sdk-release.html
@@ -0,0 +1,255 @@
+<!DOCTYPE html>
+<html lang="en">
+
+ <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>Python SDK released in Apache Beam 0.6.0</title>
+ <meta name="description" content="Apache Beam\u2019s latest release, version 0.6.0, introduces a new SDK \u2013 this time, for the Python programming language. The Python SDK joins the Java SDK as the ...">
+
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+ <link rel="canonical" href="https://beam.apache.org/blog/2017/03/16/python-sdk-release.html" data-proofer-ignore>
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+ })(window,document,'script','//www.google-analytics.com/analytics.js','ga');
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+<article class="post" itemscope itemtype="http://schema.org/BlogPosting">
+
+ <header class="post-header">
+ <h1 class="post-title" itemprop="name headline">Python SDK released in Apache Beam 0.6.0</h1>
+ <p class="post-meta"><time datetime="2017-03-16T01:00:01-07:00" itemprop="datePublished">Mar 16, 2017</time> \u2022 Ahmet Altay
+</p>
+ </header>
+
+ <div class="post-content" itemprop="articleBody">
+ <p>Apache Beam\u2019s latest release, version <a href="/get-started/downloads/">0.6.0</a>, introduces a new SDK \u2013 this time, for the Python programming language. The Python SDK joins the Java SDK as the second implementation of the Beam programming model.</p>
+
+<!--more-->
+
+<p>The Python SDK incorporates all of the main concepts of the Beam model, including ParDo, GroupByKey, Windowing, and others. It features extensible IO APIs for writing bounded sources and sinks, and provides built-in implementation for reading and writing Text, Avro, and TensorFlow record files, as well as connectors to Google BigQuery and Google Cloud Datastore.</p>
+
+<p>There are two runners capable of executing pipelines written with the Python SDK today: <a href="/documentation/runners/direct/">Direct Runner</a> and <a href="/documentation/runners/dataflow/">Dataflow Runner</a>, both of which are currently limited to batch execution only. Upcoming features will shortly bring the benefits of the Python SDK to additional runners.</p>
+
+<h4 id="try-the-apache-beam-python-sdk">Try the Apache Beam Python SDK</h4>
+
+<p>If you would like to try out the Python SDK, a good place to start is the <a href="/get-started/quickstart-py/">Quickstart</a>. After that, you can take a look at additional <a href="https://github.com/apache/beam/tree/v0.6.0/sdks/python/apache_beam/examples">examples</a>, and deep dive into the <a href="/documentation/sdks/pydoc/">API reference</a>.</p>
+
+<p>Let\u2019s take a look at a quick example together. First, install the <code class="highlighter-rouge">apache-beam</code> package from PyPI and start your Python interpreter.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>$ pip install apache-beam
+$ python
+</code></pre>
+</div>
+
+<p>We will harness the power of Apache Beam to estimate Pi in honor of the recently passed Pi Day.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>import random
+import apache_beam as beam
+
+def run_trials(count):
+ """Throw darts into unit square and count how many fall into unit circle."""
+ inside = 0
+ for _ in xrange(count):
+ x, y = random.uniform(0, 1), random.uniform(0, 1)
+ inside += 1 if x*x + y*y <= 1.0 else 0
+ return count, inside
+
+def combine_results(results):
+ """Given all the trial results, estimate pi."""
+ total, inside = sum(r[0] for r in results), sum(r[1] for r in results)
+ return total, inside, 4 * float(inside) / total if total > 0 else 0
+
+p = beam.Pipeline()
+(p | beam.Create([500] * 10) # Create 10 experiments with 500 samples each.
+ | beam.Map(run_trials) # Run experiments in parallel.
+ | beam.CombineGlobally(combine_results) # Combine the results.
+ | beam.io.WriteToText('./pi_estimate.txt')) # Write PI estimate to a file.
+
+p.run()
+</code></pre>
+</div>
+
+<p>This example estimates Pi by throwing random darts into the unit square and keeping track of the fraction of those darts that fell into the unit circle (see the full <a href="https://github.com/apache/beam/blob/v0.6.0/sdks/python/apache_beam/examples/complete/estimate_pi.py">example</a> for details). If you are curious, you can check the result of our estimation by looking at the output file.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>$ cat pi_estimate.txt*
+</code></pre>
+</div>
+
+<h4 id="roadmap">Roadmap</h4>
+
+<p>The first thing on the Python SDK\u2019s roadmap is to address two of its limitations. First, the existing runners are currently limited to bounded PCollections, and we are looking forward to extending the SDK to support unbounded PCollections (\u201cstreaming\u201d). Additionally, we are working on extending support to more Apache Beam runners, and the upcoming Fn API will do the heavy lifting.</p>
+
+<p>Both of these improvements will enable the Python SDK to fulfill the mission of Apache Beam: a unified programming model for batch and streaming data processing that can run on any execution engine.</p>
+
+<h4 id="join-us">Join us!</h4>
+
+<p>Please consider joining us, whether as a user or a contributor, as we work towards our first release with API stability. If you\u2019d like to try out Apache Beam today, check out the latest <a href="/get-started/downloads/">0.6.0</a> release. We welcome contributions and participation from anyone through our mailing lists, issue tracker, pull requests, and events.</p>
+
+ </div>
+
+</article>
+
+ </div>
+
+
+ <hr>
+ <div class="row">
+ <div class="col-xs-12">
+ <footer>
+ <p class="text-center">
+ © Copyright
+ <a href="http://www.apache.org">The Apache Software Foundation</a>,
+ 2017. All Rights Reserved.
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http://git-wip-us.apache.org/repos/asf/beam-site/blob/4acb6411/content/blog/index.html
----------------------------------------------------------------------
diff --git a/content/blog/index.html b/content/blog/index.html
index 73c3874..ca0732f 100644
--- a/content/blog/index.html
+++ b/content/blog/index.html
@@ -155,6 +155,22 @@
<p>This is the blog for the Apache Beam project. This blog contains news and updates
for the project.</p>
+<h3 id="a-classpost-link-hrefblog20170316python-sdk-releasehtmlpython-sdk-released-in-apache-beam-060a"><a class="post-link" href="/blog/2017/03/16/python-sdk-release.html">Python SDK released in Apache Beam 0.6.0</a></h3>
+<p><i>Mar 16, 2017 \u2022 Ahmet Altay
+</i></p>
+
+<p>Apache Beam\u2019s latest release, version <a href="/get-started/downloads/">0.6.0</a>, introduces a new SDK \u2013 this time, for the Python programming language. The Python SDK joins the Java SDK as the second implementation of the Beam programming model.</p>
+
+<!-- Render a "read more" button if the post is longer than the excerpt -->
+
+<p>
+<a class="btn btn-default btn-sm" href="/blog/2017/03/16/python-sdk-release.html" role="button">
+Read more <span class="glyphicon glyphicon-menu-right" aria-hidden="true"></span>
+</a>
+</p>
+
+<hr />
+
<h3 id="a-classpost-link-hrefblog20170213stateful-processinghtmlstateful-processing-with-apache-beama"><a class="post-link" href="/blog/2017/02/13/stateful-processing.html">Stateful processing with Apache Beam</a></h3>
<p><i>Feb 13, 2017 \u2022 Kenneth Knowles [<a href="https://twitter.com/KennKnowles">@KennKnowles</a>]
</i></p>
http://git-wip-us.apache.org/repos/asf/beam-site/blob/4acb6411/content/feed.xml
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--- a/content/feed.xml
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@@ -9,6 +9,80 @@
<generator>Jekyll v3.2.0</generator>
<item>
+ <title>Python SDK released in Apache Beam 0.6.0</title>
+ <description><p>Apache Beam\u2019s latest release, version <a href="/get-started/downloads/">0.6.0</a>, introduces a new SDK \u2013 this time, for the Python programming language. The Python SDK joins the Java SDK as the second implementation of the Beam programming model.</p>
+
+<!--more-->
+
+<p>The Python SDK incorporates all of the main concepts of the Beam model, including ParDo, GroupByKey, Windowing, and others. It features extensible IO APIs for writing bounded sources and sinks, and provides built-in implementation for reading and writing Text, Avro, and TensorFlow record files, as well as connectors to Google BigQuery and Google Cloud Datastore.</p>
+
+<p>There are two runners capable of executing pipelines written with the Python SDK today: <a href="/documentation/runners/direct/">Direct Runner</a> and <a href="/documentation/runners/dataflow/">Dataflow Runner</a>, both of which are currently limited to batch execution only. Upcoming features will shortly bring the benefits of the Python SDK to additional runners.</p>
+
+<h4 id="try-the-apache-beam-python-sdk">Try the Apache Beam Python SDK</h4>
+
+<p>If you would like to try out the Python SDK, a good place to start is the <a href="/get-started/quickstart-py/">Quickstart</a>. After that, you can take a look at additional <a href="https://github.com/apache/beam/tree/v0.6.0/sdks/python/apache_beam/examples">examples</a>, and deep dive into the <a href="/documentation/sdks/pydoc/">API reference</a>.</p>
+
+<p>Let\u2019s take a look at a quick example together. First, install the <code class="highlighter-rouge">apache-beam</code> package from PyPI and start your Python interpreter.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>$ pip install apache-beam
+$ python
+</code></pre>
+</div>
+
+<p>We will harness the power of Apache Beam to estimate Pi in honor of the recently passed Pi Day.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>import random
+import apache_beam as beam
+
+def run_trials(count):
+ """Throw darts into unit square and count how many fall into unit circle."""
+ inside = 0
+ for _ in xrange(count):
+ x, y = random.uniform(0, 1), random.uniform(0, 1)
+ inside += 1 if x*x + y*y &lt;= 1.0 else 0
+ return count, inside
+
+def combine_results(results):
+ """Given all the trial results, estimate pi."""
+ total, inside = sum(r[0] for r in results), sum(r[1] for r in results)
+ return total, inside, 4 * float(inside) / total if total &gt; 0 else 0
+
+p = beam.Pipeline()
+(p | beam.Create([500] * 10) # Create 10 experiments with 500 samples each.
+ | beam.Map(run_trials) # Run experiments in parallel.
+ | beam.CombineGlobally(combine_results) # Combine the results.
+ | beam.io.WriteToText('./pi_estimate.txt')) # Write PI estimate to a file.
+
+p.run()
+</code></pre>
+</div>
+
+<p>This example estimates Pi by throwing random darts into the unit square and keeping track of the fraction of those darts that fell into the unit circle (see the full <a href="https://github.com/apache/beam/blob/v0.6.0/sdks/python/apache_beam/examples/complete/estimate_pi.py">example</a> for details). If you are curious, you can check the result of our estimation by looking at the output file.</p>
+
+<div class="highlighter-rouge"><pre class="highlight"><code>$ cat pi_estimate.txt*
+</code></pre>
+</div>
+
+<h4 id="roadmap">Roadmap</h4>
+
+<p>The first thing on the Python SDK\u2019s roadmap is to address two of its limitations. First, the existing runners are currently limited to bounded PCollections, and we are looking forward to extending the SDK to support unbounded PCollections (\u201cstreaming\u201d). Additionally, we are working on extending support to more Apache Beam runners, and the upcoming Fn API will do the heavy lifting.</p>
+
+<p>Both of these improvements will enable the Python SDK to fulfill the mission of Apache Beam: a unified programming model for batch and streaming data processing that can run on any execution engine.</p>
+
+<h4 id="join-us">Join us!</h4>
+
+<p>Please consider joining us, whether as a user or a contributor, as we work towards our first release with API stability. If you\u2019d like to try out Apache Beam today, check out the latest <a href="/get-started/downloads/">0.6.0</a> release. We welcome contributions and participation from anyone through our mailing lists, issue tracker, pull requests, and events.</p>
+</description>
+ <pubDate>Thu, 16 Mar 2017 01:00:01 -0700</pubDate>
+ <link>https://beam.apache.org/blog/2017/03/16/python-sdk-release.html</link>
+ <guid isPermaLink="true">https://beam.apache.org/blog/2017/03/16/python-sdk-release.html</guid>
+
+
+ <category>blog</category>
+
+ </item>
+
+ <item>
<title>Stateful processing with Apache Beam</title>
<description><p>Beam lets you process unbounded, out-of-order, global-scale data with portable
high-level pipelines. Stateful processing is a new feature of the Beam model
@@ -1233,97 +1307,5 @@ to us via <a href="/use/mailing-lists/">user\u2019s mailing list<
</item>
- <item>
- <title>Where's my PCollection.map()?</title>
- <description><p>Have you ever wondered why Beam has PTransforms for everything instead of having methods on PCollection? Take a look at the history that led to this (and other) design decisions.</p>
-
-<!--more-->
-
-<p>Though Beam is relatively new, its design draws heavily on many years of experience with real-world pipelines. One of the primary inspirations is <a href="http://research.google.com/pubs/pub35650.html">FlumeJava</a>, which is Google\u2019s internal successor to MapReduce first introduced in 2009.</p>
-
-<p>The original FlumeJava API has methods like <code class="highlighter-rouge">count</code> and <code class="highlighter-rouge">parallelDo</code> on the PCollections. Though slightly more succinct, this approach has many disadvantages to extensibility. Every new user to FlumeJava wanted to add transforms, and adding them as methods to PCollection simply doesn\u2019t scale well. In contrast, a PCollection in Beam has a single <code class="highlighter-rouge">apply</code> method which takes any PTransform as an argument.</p>
-
-<table class="table">
- <tr>
- <th>FlumeJava</th>
- <th>Beam</th>
- </tr>
- <tr>
- <td><pre>
-PCollection&lt;T&gt; input = \u2026
-PCollection&lt;O&gt; output = input.count()
- .parallelDo(...);
- </pre></td>
- <td><pre>
-PCollection&lt;T&gt; input = \u2026
-PCollection&lt;O&gt; output = input.apply(Count.perElement())
- .apply(ParDo.of(...));
- </pre></td>
- </tr>
-</table>
-
-<p>This is a more scalable approach for several reasons.</p>
-
-<h2 id="where-to-draw-the-line">Where to draw the line?</h2>
-<p>Adding methods to PCollection forces a line to be drawn between operations that are \u201cuseful\u201d enough to merit this special treatment and those that are not. It is easy to make the case for flat map, group by key, and combine per key. But what about filter? Count? Approximate count? Approximate quantiles? Most frequent? WriteToMyFavoriteSource? Going too far down this path leads to a single enormous class that contains nearly everything one could want to do. (FlumeJava\u2019s PCollection class is over 5000 lines long with around 70 distinct operations, and it could have been <em>much</em> larger had we accepted every proposal.) Furthermore, since Java doesn\u2019t allow adding methods to a class, there is a sharp syntactic divide between those operations that are added to PCollection and those that aren\u2019t. A traditional way to share code is with a library of functions, but functions (in traditional languages like Java at least) are written prefix-style, which does
n\u2019t mix well with the fluent builder style (e.g. <code class="highlighter-rouge">input.operation1().operation2().operation3()</code> vs. <code class="highlighter-rouge">operation3(operation1(input).operation2())</code>).</p>
-
-<p>Instead in Beam we\u2019ve chosen a style that places all transforms\u2013whether they be primitive operations, composite operations bundled in the SDK, or part of an external library\u2013on equal footing. This also facilitates alternative implementations (which may even take different options) that are easily interchangeable.</p>
-
-<table class="table">
- <tr>
- <th>FlumeJava</th>
- <th>Beam</th>
- </tr>
- <tr>
- <td><pre>
-PCollection&lt;O&gt; output =
- ExternalLibrary.doStuff(
- MyLibrary.transform(input, myArgs)
- .parallelDo(...),
- externalLibArgs);
- </pre></td>
- <td><pre>
-PCollection&lt;O&gt; output = input
- .apply(MyLibrary.transform(myArgs))
- .apply(ParDo.of(...))
- .apply(ExternalLibrary.doStuff(externalLibArgs));
- &nbsp;
- </pre></td>
- </tr>
-</table>
-
-<h2 id="configurability">Configurability</h2>
-<p>It makes for a fluent style to let values (PCollections) be the objects passed around and manipulated (i.e. the handles to the deferred execution graph), but it is the operations themselves that need to be composable, configurable, and extendable. Using PCollection methods for the operations doesn\u2019t scale well here, especially in a language without default or keyword arguments. For example, a ParDo operation can have any number of side inputs and side outputs, or a write operation may have configurations dealing with encoding and compression. One option is to separate these out into multiple overloads or even methods, but that exacerbates the problems above. (FlumeJava evolved over a dozen overloads of the <code class="highlighter-rouge">parallelDo</code> method!) Another option is to pass each method a configuration object that can be built up using more fluent idioms like the builder pattern, but at that point one might as well make the configurati
on object the operation itself, which is what Beam does.</p>
-
-<h2 id="type-safety">Type Safety</h2>
-<p>Many operations can only be applied to collections whose elements are of a specific type. For example, the GroupByKey operation should only be applied to <code class="highlighter-rouge">PCollection&lt;KV&lt;K, V&gt;&gt;</code>s. In Java at least, it\u2019s not possible to restrict methods based on the element type parameter alone. In FlumeJava, this led us to add a <code class="highlighter-rouge">PTable&lt;K, V&gt;</code> subclassing <code class="highlighter-rouge">PCollection&lt;KV&lt;K, V&gt;&gt;</code> to contain all the operations specific to PCollections of key-value pairs. This leads to the same question of which element types are special enough to merit being captured by PCollection subclasses. It is not very extensible for third parties and often requires manual downcasts/conversions (which can\u2019t be safely chained in Java) and special operations that produce thes
e PCollection specializations.</p>
-
-<p>This is particularly inconvenient for transforms that produce outputs whose element types are the same as (or related to) their input\u2019s element types, requiring extra support to generate the right subclasses (e.g. a filter on a PTable should produce another PTable rather than just a raw PCollection of key-value pairs).</p>
-
-<p>Using PTransforms allows us to sidestep this entire issue. We can place arbitrary constraints on the context in which a transform may be used based on the type of its inputs; for instance GroupByKey is statically typed to only apply to a <code class="highlighter-rouge">PCollection&lt;KV&lt;K, V&gt;&gt;</code>. The way this happens is generalizable to arbitrary shapes, without needing to introduce specialized types like PTable.</p>
-
-<h2 id="reusability-and-structure">Reusability and Structure</h2>
-<p>Though PTransforms are generally constructed at the site at which they\u2019re used, by pulling them out as separate objects one is able to store them and pass them around.</p>
-
-<p>As pipelines grow and evolve, it is useful to structure your pipeline into modular, often reusable components, and PTransforms allow one to do this nicely in a data-processing pipeline. In addition, modular PTransforms also expose the logical structure of your code to the system (e.g. for monitoring). Of the three different representations of the WordCount pipeline below, only the structured view captures the high-level intent of the pipeline. Letting even the simple operations be PTransforms means there\u2019s less of an abrupt edge to packaging things up into composite operations.</p>
-
-<p><img class="center-block" src="/images/blog/simple-wordcount-pipeline.png" alt="Three different visualizations of a simple WordCount pipeline" width="500" /></p>
-
-<div class="text-center">
-<i>Three different visualizations of a simple WordCount pipeline which computes the number of occurrences of every word in a set of text files. The flat view gives the full DAG of all operations performed. The execution view groups operations according to how they're executed, e.g. after performing runner-specific optimizations like function composition. The structured view nests operations according to their grouping in PTransforms.</i>
-</div>
-
-<h2 id="summary">Summary</h2>
-<p>Although it\u2019s tempting to add methods to PCollections, such an approach is not scalable, extensible, or sufficiently expressive. Putting a single apply method on PCollection and all the logic into the operation itself lets us have the best of both worlds, and avoids hard cliffs of complexity by having a single consistent style across simple and complex pipelines, and between predefined and user-defined operations.</p>
-</description>
- <pubDate>Fri, 27 May 2016 09:00:00 -0700</pubDate>
- <link>https://beam.apache.org/blog/2016/05/27/where-is-my-pcollection-dot-map.html</link>
- <guid isPermaLink="true">https://beam.apache.org/blog/2016/05/27/where-is-my-pcollection-dot-map.html</guid>
-
-
- <category>blog</category>
-
- </item>
-
</channel>
</rss>
http://git-wip-us.apache.org/repos/asf/beam-site/blob/4acb6411/content/index.html
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<h2>Blog</h2>
<div class="list-group">
+ <a class="list-group-item" href="/blog/2017/03/16/python-sdk-release.html">Mar 16, 2017 - Python SDK released in Apache Beam 0.6.0</a>
+
<a class="list-group-item" href="/blog/2017/02/13/stateful-processing.html">Feb 13, 2017 - Stateful processing with Apache Beam</a>
<a class="list-group-item" href="/blog/2017/02/01/graduation-media-recap.html">Feb 1, 2017 - Media recap of the Apache Beam graduation</a>
@@ -188,8 +190,6 @@
<a class="list-group-item" href="/beam/update/2016/10/11/strata-hadoop-world-and-beam.html">Oct 11, 2016 - Strata+Hadoop World and Beam</a>
- <a class="list-group-item" href="/blog/2016/08/03/six-months.html">Aug 3, 2016 - Apache Beam: Six Months in Incubation</a>
-
</div>
</div>
<div class="col-md-6">