[BEAM-2039] Number programming guide chapters#320
[BEAM-2039] Number programming guide chapters#320asfgit merged 2 commits intoapache:asf-sitefrom melap:linecleanup
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R: @kennknowles |
tgroh
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Meaningful content changes are deferrable to future changes.
| @@ -1710,21 +2501,27 @@ If our trigger is set to `.accumulatingFiredPanes`, the trigger emits the follow | |||
| ``` | |||
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| ##### Discarding Mode | |||
| ##### 8.4.1.2. Discarding mode: | |||
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| ##### 4.2.4.6. Combining values in a key-grouped collection |
| * [Pre-written Beam transforms for Java]({{ site.baseurl }}/documentation/sdks/javadoc/{{ site.release_latest }}/index.html?org/apache/beam/sdk/transforms/package-summary.html) | ||
| * [Pre-written Beam transforms for Python]({{ site.baseurl }}/documentation/sdks/pydoc/{{ site.release_latest }}/apache_beam.transforms.html) | ||
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| ### An example of a composite transform | ||
| #### 4.6.1. Composite transform example |
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I think this is too robotic and the previous is too verbose; maybe
'An example [composite ]transform'
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| When you create a pipeline, you often need to read data from some external source, such as a file in external data sink or a database. Likewise, you may want your pipeline to output its result data to a similar external data sink. Beam provides read and write transforms for a [number of common data storage types]({{site.baseurl }}/documentation/io/built-in/). If you want your pipeline to read from or write to a data storage format that isn't supported by the built-in transforms, you can [implement your own read and write transforms]({{site.baseurl }}/documentation/io/io-toc/). | ||
| When you create a new `PTransform`, be sure to read the [PTransform Style | ||
| Guide]({{ site.baseurl }}/contribute/ptransform-style-guide/). The guide |
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So demanding!
Maybe "The PTransform style guide contains additional information not included here such as...", "It is a useful starting point when writing new composite PTransforms"?
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| ### Windowing functions | ||
| ### 7.2. Beam windowing functions |
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"Provided/Built-in window functions"?
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| #### Single global window | ||
| #### 7.2.4. Single global window |
| default trigger generally requires the entire data set to be available before | ||
| processing, which is not possible with continuously updating data. To perform | ||
| aggregations on an unbounded `PCollection` that uses global windowing, you | ||
| should specify a non-default trigger for that `PCollection`. |
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Maybe we should add (at some later point) a section on a custom window fn?
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| ### <a name="setwindowingfunction"></a>Setting your PCollection's windowing function | ||
| ### 7.3. Setting your PCollection's windowing function |
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Can we condense the "How to construct a window fn" to the above section, and have this be just how to apply a WindowFn to a PCollection?
(Feel free to JIRA and do later)
| ```py | ||
| # The Beam SDK for Python does not support triggers. | ||
| ``` | ||
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| ##### <a name="default-trigger"></a>**Default Trigger** | ||
| #### 8.1.1. Default trigger |
melap
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Updated from feedback and resolved merge conflicts
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| ##### 4.2.4.6. Combining values in a key-grouped collection |
| * [Pre-written Beam transforms for Java]({{ site.baseurl }}/documentation/sdks/javadoc/{{ site.release_latest }}/index.html?org/apache/beam/sdk/transforms/package-summary.html) | ||
| * [Pre-written Beam transforms for Python]({{ site.baseurl }}/documentation/sdks/pydoc/{{ site.release_latest }}/apache_beam.transforms.html) | ||
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| ### An example of a composite transform | ||
| #### 4.6.1. Composite transform example |
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| When you create a pipeline, you often need to read data from some external source, such as a file in external data sink or a database. Likewise, you may want your pipeline to output its result data to a similar external data sink. Beam provides read and write transforms for a [number of common data storage types]({{site.baseurl }}/documentation/io/built-in/). If you want your pipeline to read from or write to a data storage format that isn't supported by the built-in transforms, you can [implement your own read and write transforms]({{site.baseurl }}/documentation/io/io-toc/). | ||
| When you create a new `PTransform`, be sure to read the [PTransform Style | ||
| Guide]({{ site.baseurl }}/contribute/ptransform-style-guide/). The guide |
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| ### Windowing functions | ||
| ### 7.2. Beam windowing functions |
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| #### Single global window | ||
| #### 7.2.4. Single global window |
| default trigger generally requires the entire data set to be available before | ||
| processing, which is not possible with continuously updating data. To perform | ||
| aggregations on an unbounded `PCollection` that uses global windowing, you | ||
| should specify a non-default trigger for that `PCollection`. |
| ```py | ||
| # The Beam SDK for Python does not support triggers. | ||
| ``` | ||
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| ##### <a name="default-trigger"></a>**Default Trigger** | ||
| #### 8.1.1. Default trigger |
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| ### <a name="setwindowingfunction"></a>Setting your PCollection's windowing function | ||
| ### 7.3. Setting your PCollection's windowing function |
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Refer to this link for build results (access rights to CI server needed): Jenkins built the site at commit id c5d8da6 with Jekyll and staged it here. Happy reviewing. Note that any previous site has been deleted. This staged site will be automatically deleted after its TTL expires. Push any commit to the pull request branch or re-trigger the build to get it staged again. |
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LGTM |
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@asfgit merge |
Includes the following consistency/formatting changes: