beam-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From da...@apache.org
Subject [2/3] beam-site git commit: Regenerate website
Date Wed, 22 Feb 2017 21:34:18 GMT
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/77d285ff
Tree: http://git-wip-us.apache.org/repos/asf/beam-site/tree/77d285ff
Diff: http://git-wip-us.apache.org/repos/asf/beam-site/diff/77d285ff

Branch: refs/heads/asf-site
Commit: 77d285ff8445e6a9902741f21c7a63bcd07ff47e
Parents: dca566f
Author: Davor Bonaci <davor@google.com>
Authored: Wed Feb 22 13:33:58 2017 -0800
Committer: Davor Bonaci <davor@google.com>
Committed: Wed Feb 22 13:33:58 2017 -0800

----------------------------------------------------------------------
 .../sdks/python-custom-io/index.html            | 48 ++++++++++----------
 content/documentation/sdks/python/index.html    |  4 +-
 2 files changed, 26 insertions(+), 26 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/beam-site/blob/77d285ff/content/documentation/sdks/python-custom-io/index.html
----------------------------------------------------------------------
diff --git a/content/documentation/sdks/python-custom-io/index.html b/content/documentation/sdks/python-custom-io/index.html
index c43f606..7592a4a 100644
--- a/content/documentation/sdks/python-custom-io/index.html
+++ b/content/documentation/sdks/python-custom-io/index.html
@@ -6,7 +6,7 @@
   <meta http-equiv="X-UA-Compatible" content="IE=edge">
   <meta name="viewport" content="width=device-width, initial-scale=1">
 
-  <title>Beam Custom Sources and Sinks for Python</title>
+  <title>Apache Beam: Creating New Sources and Sinks with the Python SDK</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 Languages (DSLs). Dataflow pipelines simplify the mechanics of large-scale batch
and streaming data processing and can run on a number of runtimes like Apache Flink, Apache
Spark, and Google Cloud Dataflow (a cloud service). Beam also brings DSL in different languages,
allowing users to easily implement their data integration processes.
 ">
 
@@ -146,24 +146,24 @@
     <div class="container" role="main">
 
       <div class="row">
-        <h1 id="beam-custom-sources-and-sinks-for-python">Beam Custom Sources and Sinks
for Python</h1>
+        <h1 id="creating-new-sources-and-sinks-with-the-python-sdk">Creating New Sources
and Sinks with the Python SDK</h1>
 
-<p>The Beam SDK for Python provides an extensible API that you can use to create custom
data sources and sinks. This tutorial shows how to create custom sources and sinks using <a
href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/iobase.py">Beam’s
Source and Sink API</a>.</p>
+<p>The Apache Beam SDK for Python provides an extensible API that you can use to create
new data sources and sinks. This tutorial shows how to create new sources and sinks using
<a href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/iobase.py">Beam’s
Source and Sink API</a>.</p>
 
 <ul>
-  <li>Create a custom source by extending the <code class="highlighter-rouge">BoundedSource</code>
and <code class="highlighter-rouge">RangeTracker</code> interfaces.</li>
-  <li>Create a custom sink by implementing the <code class="highlighter-rouge">Sink</code>
and <code class="highlighter-rouge">Writer</code> classes.</li>
+  <li>Create a new source by extending the <code class="highlighter-rouge">BoundedSource</code>
and <code class="highlighter-rouge">RangeTracker</code> interfaces.</li>
+  <li>Create a new sink by implementing the <code class="highlighter-rouge">Sink</code>
and <code class="highlighter-rouge">Writer</code> classes.</li>
 </ul>
 
-<h2 id="why-create-a-custom-source-or-sink">Why Create a Custom Source or Sink</h2>
+<h2 id="why-create-a-new-source-or-sink">Why Create a New Source or Sink</h2>
 
-<p>You’ll need to create a custom source or sink if you want your pipeline to read
data from (or write data to) a storage system for which the Beam SDK for Python does not provide
<a href="/documentation/programming-guide/#io">native support</a>.</p>
+<p>You’ll need to create a new source or sink if you want your pipeline to read data
from (or write data to) a storage system for which the Beam SDK for Python does not provide
<a href="/documentation/programming-guide/#io">native support</a>.</p>
 
-<p>In simple cases, you may not need to create a custom source or sink. For example,
if you need to read data from an SQL database using an arbitrary query, none of the advanced
Source API features would benefit you. Likewise, if you’d like to write data to a third-party
API via a protocol that lacks deduplication support, the Sink API wouldn’t benefit you.
In such cases it makes more sense to use a <code class="highlighter-rouge">ParDo</code>.</p>
+<p>In simple cases, you may not need to create a new source or sink. For example, if
you need to read data from an SQL database using an arbitrary query, none of the advanced
Source API features would benefit you. Likewise, if you’d like to write data to a third-party
API via a protocol that lacks deduplication support, the Sink API wouldn’t benefit you.
In such cases it makes more sense to use a <code class="highlighter-rouge">ParDo</code>.</p>
 
-<p>However, if you’d like to use advanced features such as dynamic splitting and
size estimation, you should use Beam’s APIs and create a custom source or sink.</p>
+<p>However, if you’d like to use advanced features such as dynamic splitting and
size estimation, you should use Beam’s APIs and create a new source or sink.</p>
 
-<h2 id="a-namebasic-code-reqsabasic-code-requirements-for-custom-sources-and-sinks"><a
name="basic-code-reqs"></a>Basic Code Requirements for Custom Sources and Sinks</h2>
+<h2 id="a-namebasic-code-reqsabasic-code-requirements-for-new-sources-and-sinks"><a
name="basic-code-reqs"></a>Basic Code Requirements for New Sources and Sinks</h2>
 
 <p>Services use the classes you provide to read and/or write data using multiple worker
instances in parallel. As such, the code you provide for <code class="highlighter-rouge">Source</code>
and <code class="highlighter-rouge">Sink</code> subclasses must meet some basic
requirements:</p>
 
@@ -185,9 +185,9 @@
 
 <p>You can use test harnesses and utility methods available in the <a href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/source_test_utils.py">source_test_utils
module</a> to develop tests for your source.</p>
 
-<h2 id="a-namecreating-sourcesacreating-a-custom-source"><a name="creating-sources"></a>Creating
a Custom Source</h2>
+<h2 id="a-namecreating-sourcesacreating-a-new-source"><a name="creating-sources"></a>Creating
a New Source</h2>
 
-<p>You should create a custom source if you’d like to use the advanced features that
the Source API provides:</p>
+<p>You should create a new source if you’d like to use the advanced features that
the Source API provides:</p>
 
 <ul>
   <li>Dynamic splitting</li>
@@ -198,9 +198,9 @@
 
 <p>For example, if you’d like to read from a new file format that contains many records
per file, or if you’d like to read from a key-value store that supports read operations
in sorted key order.</p>
 
-<p>To create a custom data source for your pipeline, you’ll need to provide the format-specific
logic that tells the service how to read data from your input source, and how to split your
data source into multiple parts so that multiple worker instances can read your data in parallel.</p>
+<p>To create a new data source for your pipeline, you’ll need to provide the format-specific
logic that tells the service how to read data from your input source, and how to split your
data source into multiple parts so that multiple worker instances can read your data in parallel.</p>
 
-<p>You supply the logic for your custom source by creating the following classes:</p>
+<p>You supply the logic for your new source by creating the following classes:</p>
 
 <ul>
   <li>A subclass of <code class="highlighter-rouge">BoundedSource</code>,
which you can find in the <a href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/iobase.py">iobase.py</a>
module. <code class="highlighter-rouge">BoundedSource</code> is a source that
reads a finite amount of input records. The class describes the data you want to read, including
the data’s location and parameters (such as how much data to read).</li>
@@ -330,7 +330,7 @@
 
 <p>See <a href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/avroio.py">AvroSource</a>
for an example implementation of <code class="highlighter-rouge">FileBasedSource</code>.</p>
 
-<h2 id="a-namereading-sourcesareading-from-a-custom-source"><a name="reading-sources"></a>Reading
from a Custom Source</h2>
+<h2 id="a-namereading-sourcesareading-from-a-new-source"><a name="reading-sources"></a>Reading
from a New Source</h2>
 
 <p>The following example, <code class="highlighter-rouge">CountingSource</code>,
demonstrates an implementation of <code class="highlighter-rouge">BoundedSource</code>
and uses the SDK-provided <code class="highlighter-rouge">RangeTracker</code>
called <code class="highlighter-rouge">OffsetRangeTracker</code>.</p>
 
@@ -374,7 +374,7 @@
 </code></pre>
 </div>
 
-<p>To read data from a custom source in your pipeline, use the <code class="highlighter-rouge">Read</code>
transform:</p>
+<p>To read data from the source in your pipeline, use the <code class="highlighter-rouge">Read</code>
transform:</p>
 
 <div class="highlighter-rouge"><pre class="highlight"><code>p = beam.Pipeline(options=PipelineOptions())
 numbers = p | 'ProduceNumbers' &gt;&gt; beam.io.Read(CountingSource(count))
@@ -383,9 +383,9 @@ numbers = p | 'ProduceNumbers' &gt;&gt; beam.io.Read(CountingSource(count))
 
 <p><strong>Note:</strong> When you create a source that end-users are going
to use, it’s recommended that you do not expose the code for the source itself as demonstrated
in the example above, but rather use a wrapping <code class="highlighter-rouge">PTransform</code>
instead. See <a href="#ptransform-wrappers">PTransform wrappers</a> to see how
and why to avoid exposing your sources.</p>
 
-<h2 id="a-namecreating-sinksacreating-a-custom-sink"><a name="creating-sinks"></a>Creating
a Custom Sink</h2>
+<h2 id="a-namecreating-sinksacreating-a-new-sink"><a name="creating-sinks"></a>Creating
a New Sink</h2>
 
-<p>You should create a custom sink if you’d like to use the advanced features that
the Sink API provides, such as global initialization and finalization that allow the write
operation to appear “atomic” (i.e. either all data is written or none is).</p>
+<p>You should create a new sink if you’d like to use the advanced features that the
Sink API provides, such as global initialization and finalization that allow the write operation
to appear “atomic” (i.e. either all data is written or none is).</p>
 
 <p>A sink represents a resource that can be written to using the <code class="highlighter-rouge">Write</code>
transform. A parallel write to a sink consists of three phases:</p>
 
@@ -397,7 +397,7 @@ numbers = p | 'ProduceNumbers' &gt;&gt; beam.io.Read(CountingSource(count))
 
 <p>For example, if you’d like to write to a new table in a database, you should use
the Sink API. In this case, the initializer will create a temporary table, the writer will
write rows to it, and the finalizer will rename the table to a final location.</p>
 
-<p>To create a custom data sink for your pipeline, you’ll need to provide the format-specific
logic that tells the sink how to write bounded data from your pipeline’s <code class="highlighter-rouge">PCollection</code>s
to an output sink. The sink writes bundles of data in parallel using multiple workers.</p>
+<p>To create a new data sink for your pipeline, you’ll need to provide the format-specific
logic that tells the sink how to write bounded data from your pipeline’s <code class="highlighter-rouge">PCollection</code>s
to an output sink. The sink writes bundles of data in parallel using multiple workers.</p>
 
 <p>You supply the writing logic by creating the following classes:</p>
 
@@ -465,7 +465,7 @@ numbers = p | 'ProduceNumbers' &gt;&gt; beam.io.Read(CountingSource(count))
   <li>Setting the output MIME type</li>
 </ul>
 
-<h2 id="a-namewriting-sinksawriting-to-a-custom-sink"><a name="writing-sinks"></a>Writing
to a Custom Sink</h2>
+<h2 id="a-namewriting-sinksawriting-to-a-new-sink"><a name="writing-sinks"></a>Writing
to a New Sink</h2>
 
 <p>Consider a simple key-value storage that writes a given set of key-value pairs to
a set of tables. The following is the key-value storage’s API:</p>
 
@@ -532,15 +532,15 @@ kvs | 'WriteToSimpleKV' &gt;&gt; beam.io.Write(
 
 <h2 id="a-nameptransform-wrappersaptransform-wrappers"><a name="ptransform-wrappers"></a>PTransform
Wrappers</h2>
 
-<p>If you create a custom source or sink for your own use, such as for learning purposes,
you should create them as explained in the sections above and use them as demonstrated in
the examples.</p>
+<p>If you create a new source or sink for your own use, such as for learning purposes,
you should create them as explained in the sections above and use them as demonstrated in
the examples.</p>
 
-<p>However, when you create a source or sink that end-users are going to use, instead
of exposing the source or sink itself, you should create a wrapper <code class="highlighter-rouge">PTransform</code>.
Ideally, a custom source or sink should be exposed to users simply as “something that can
be applied in a pipeline”, which is actually a <code class="highlighter-rouge">PTransform</code>.
That way, its implementation can be hidden and arbitrarily complex or simple.</p>
+<p>However, when you create a source or sink that end-users are going to use, instead
of exposing the source or sink itself, you should create a wrapper <code class="highlighter-rouge">PTransform</code>.
Ideally, a source or sink should be exposed to users simply as “something that can be applied
in a pipeline”, which is actually a <code class="highlighter-rouge">PTransform</code>.
That way, its implementation can be hidden and arbitrarily complex or simple.</p>
 
 <p>The greatest benefit of not exposing the implementation details is that later on
you will be able to add additional functionality without breaking the existing implementation
for users.  For example, if your users’ pipelines read from your source using <code class="highlighter-rouge">beam.io.Read(...)</code>
and you want to insert a reshard into the pipeline, all of your users would need to add the
reshard themselves (using the <code class="highlighter-rouge">GroupByKey</code>
transform). To solve this, it’s recommended that you expose your source as a composite <code
class="highlighter-rouge">PTransform</code> that performs both the read operation
and the reshard.</p>
 
-<p>To avoid exposing your custom sources and sinks to end-users, it’s recommended
that you use the <code class="highlighter-rouge">_</code> prefix when creating
your custom source and sink classes. Then, create a wrapper <code class="highlighter-rouge">PTransform</code>.</p>
+<p>To avoid exposing your sources and sinks to end-users, it’s recommended that you
use the <code class="highlighter-rouge">_</code> prefix when creating your new
source and sink classes. Then, create a wrapper <code class="highlighter-rouge">PTransform</code>.</p>
 
-<p>The following examples change the custom source and sink from the above sections
so that they are not exposed to end-users. For the source, rename <code class="highlighter-rouge">CountingSource</code>
to <code class="highlighter-rouge">_CountingSource</code>. Then, create the wrapper
<code class="highlighter-rouge">PTransform</code>, called <code class="highlighter-rouge">ReadFromCountingSource</code>:</p>
+<p>The following examples change the source and sink from the above sections so that
they are not exposed to end-users. For the source, rename <code class="highlighter-rouge">CountingSource</code>
to <code class="highlighter-rouge">_CountingSource</code>. Then, create the wrapper
<code class="highlighter-rouge">PTransform</code>, called <code class="highlighter-rouge">ReadFromCountingSource</code>:</p>
 
 <div class="highlighter-rouge"><pre class="highlight"><code>class ReadFromCountingSource(PTransform):
 

http://git-wip-us.apache.org/repos/asf/beam-site/blob/77d285ff/content/documentation/sdks/python/index.html
----------------------------------------------------------------------
diff --git a/content/documentation/sdks/python/index.html b/content/documentation/sdks/python/index.html
index 24573cf..aa6eb71 100644
--- a/content/documentation/sdks/python/index.html
+++ b/content/documentation/sdks/python/index.html
@@ -164,9 +164,9 @@
 
 <p>When you run your pipeline locally, the packages that your pipeline depends on are
available because they are installed on your local machine. However, when you want to run
your pipeline remotely, you must make sure these dependencies are available on the remote
machines. <a href="/documentation/sdks/python-pipeline-dependencies">Managing Python
Pipeline Dependencies</a> shows you how to make your dependencies available to the remote
workers.</p>
 
-<h2 id="custom-sources-and-sinks">Custom Sources and Sinks</h2>
+<h2 id="creating-new-sources-and-sinks">Creating New Sources and Sinks</h2>
 
-<p>The Beam SDK for Python provides an extensible API that you can use to create custom
data sources and sinks. The <a href="/documentation/sdks/python-custom-io">Custom Sources
and Sinks for Python tutorial</a> shows how to create custom sources and sinks using
<a href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/iobase.py">Beam’s
Source and Sink API</a>.</p>
+<p>The Beam SDK for Python provides an extensible API that you can use to create new
data sources and sinks. <a href="/documentation/sdks/python-custom-io">Creating New
Sources and Sinks with the Python SDK</a> shows how to create new sources and sinks
using <a href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/iobase.py">Beam’s
Source and Sink API</a>.</p>
 
 
       </div>


Mime
View raw message