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Posted to commits@beam.apache.org by da...@apache.org on 2017/05/12 23:18:42 UTC
[1/3] beam-site git commit: Revise WordCount Example Walkthrough
Repository: beam-site
Updated Branches:
refs/heads/asf-site 87b0fb5e4 -> 261e39777
Revise WordCount Example Walkthrough
Fiddle with monospace markers.
Update code snippets.
Reorder logging example and update "[Google ]Cloud Logging" ->
Stackdriver Logging
Project: http://git-wip-us.apache.org/repos/asf/beam-site/repo
Commit: http://git-wip-us.apache.org/repos/asf/beam-site/commit/9fb214fe
Tree: http://git-wip-us.apache.org/repos/asf/beam-site/tree/9fb214fe
Diff: http://git-wip-us.apache.org/repos/asf/beam-site/diff/9fb214fe
Branch: refs/heads/asf-site
Commit: 9fb214fe7ee5c662248a448d06ef30e3aa8eb82e
Parents: 87b0fb5
Author: Thomas Groh <tg...@google.com>
Authored: Fri May 12 14:01:06 2017 -0700
Committer: Davor Bonaci <da...@google.com>
Committed: Fri May 12 16:17:27 2017 -0700
----------------------------------------------------------------------
src/documentation/sdks/python-custom-io.md | 2 +-
src/get-started/wordcount-example.md | 86 ++++++++++++++-----------
2 files changed, 48 insertions(+), 40 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/beam-site/blob/9fb214fe/src/documentation/sdks/python-custom-io.md
----------------------------------------------------------------------
diff --git a/src/documentation/sdks/python-custom-io.md b/src/documentation/sdks/python-custom-io.md
index ee87e4e..8ce174e 100644
--- a/src/documentation/sdks/python-custom-io.md
+++ b/src/documentation/sdks/python-custom-io.md
@@ -228,7 +228,7 @@ The Beam SDK for Python contains some convenient abstract base classes to help y
#### FileSink
-If your data source uses files, you can derive your `Sink` and `Writer` classes from the `FileSink` and `FileSinkWriter` classes, which can be found in the [fileio.py](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/fileio.py) module. These classes implement code common sinks that interact with files, including:
+If your data source uses files, you can derive your `Sink` and `Writer` classes from the `FileBasedSink` and `FileBasedSinkWriter` classes, which can be found in the [filebasedsink.py](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/filebasedsink.py) module. These classes implement code common sinks that interact with files, including:
* Setting file headers and footers
* Sequential record writing
http://git-wip-us.apache.org/repos/asf/beam-site/blob/9fb214fe/src/get-started/wordcount-example.md
----------------------------------------------------------------------
diff --git a/src/get-started/wordcount-example.md b/src/get-started/wordcount-example.md
index 3572e76..19a82d7 100644
--- a/src/get-started/wordcount-example.md
+++ b/src/get-started/wordcount-example.md
@@ -47,7 +47,7 @@ The following sections explain these concepts in detail along with excerpts of t
### Creating the Pipeline
-The first step in creating a Beam pipeline is to create a `PipelineOptions object`. This object lets us set various options for our pipeline, such as the pipeline runner that will execute our pipeline and any runner-specific configuration required by the chosen runner. In this example we set these options programmatically, but more often command-line arguments are used to set `PipelineOptions`.
+The first step in creating a Beam pipeline is to create a `PipelineOptions` object. This object lets us set various options for our pipeline, such as the pipeline runner that will execute our pipeline and any runner-specific configuration required by the chosen runner. In this example we set these options programmatically, but more often command-line arguments are used to set `PipelineOptions`.
You can specify a runner for executing your pipeline, such as the `DataflowRunner` or `SparkRunner`. If you omit specifying a runner, as in this example, your pipeline will be executed locally using the `DirectRunner`. In the next sections, we will specify the pipeline's runner.
@@ -86,14 +86,14 @@ Pipeline p = Pipeline.create(options);
The Minimal WordCount pipeline contains several transforms to read data into the pipeline, manipulate or otherwise transform the data, and write out the results. Each transform represents an operation in the pipeline.
-Each transform takes some kind of input (data or otherwise), and produces some output data. The input and output data is represented by the SDK class `PCollection`. `PCollection` is a special class, provided by the Beam SDK, that you can use to represent a data set of virtually any size, including infinite data sets.
+Each transform takes some kind of input (data or otherwise), and produces some output data. The input and output data is represented by the SDK class `PCollection`. `PCollection` is a special class, provided by the Beam SDK, that you can use to represent a data set of virtually any size, including unbounded data sets.
<img src="{{ "/images/wordcount-pipeline.png" | prepend: site.baseurl }}" alt="Word Count pipeline diagram">
Figure 1: The pipeline data flow.
The Minimal WordCount pipeline contains five transforms:
-1. A text file `Read` transform is applied to the Pipeline object itself, and produces a `PCollection` as output. Each element in the output PCollection represents one line of text from the input file. This example happens to use input data stored in a publicly accessible Google Cloud Storage bucket ("gs://").
+1. A text file `Read` transform is applied to the Pipeline object itself, and produces a `PCollection` as output. Each element in the output PCollection represents one line of text from the input file. This example uses input data stored in a publicly accessible Google Cloud Storage bucket ("gs://").
```java
p.apply(TextIO.Read.from("gs://apache-beam-samples/shakespeare/*"))
@@ -109,7 +109,9 @@ The Minimal WordCount pipeline contains five transforms:
.apply("ExtractWords", ParDo.of(new DoFn<String, String>() {
@ProcessElement
public void processElement(ProcessContext c) {
- for (String word : c.element().split("[^a-zA-Z']+")) {
+ // \p{L} denotes the category of Unicode letters,
+ // so this pattern will match on everything that is not a letter.
+ for (String word : c.element().split("[^\\p{L}]+")) {
if (!word.isEmpty()) {
c.output(word);
}
@@ -137,7 +139,7 @@ The Minimal WordCount pipeline contains five transforms:
4. The next transform formats each of the key/value pairs of unique words and occurrence counts into a printable string suitable for writing to an output file.
- The map transform is a higher-level composite transform that encapsulates a simple `ParDo`; for each element in the input `PCollection`, the map transform applies a function that produces exactly one output element.
+ The map transform is a higher-level composite transform that encapsulates a simple `ParDo`. For each element in the input `PCollection`, the map transform applies a function that produces exactly one output element.
```java
.apply("FormatResults", MapElements.via(new SimpleFunction<KV<String, Long>, String>() {
@@ -260,7 +262,7 @@ public static void main(String[] args) throws IOException {
You can hard-code various execution options when you run your pipeline. However, the more common way is to define your own configuration options via command-line argument parsing. Defining your configuration options via the command-line makes the code more easily portable across different runners.
-Add arguments to be processed by the command-line parser, and specify default values for them. You can then access the options values in your pipeline code.
+Add arguments to be processed by the command-line parser, and specify default values for them. You can then access the options values in your pipeline code.
```java
public static interface WordCountOptions extends PipelineOptions {
@@ -298,15 +300,7 @@ The following sections explain these key concepts in detail, and break down the
### Logging
-Each runner may choose to handle logs in its own way.
-
-#### Direct Runner
-
-If you execute your pipeline using `DirectRunner`, it will print the log messages directly to your local console.
-
-#### Dataflow Runner
-
-If you execute your pipeline using `DataflowRunner`, you can use Google Cloud Logging. Google Cloud Logging (currently in beta) aggregates the logs from all of your Dataflow job's workers to a single location in the Google Cloud Platform Console. You can use Cloud Logging to search and access the logs from all of the Compute Engine instances that Dataflow has spun up to complete your Dataflow job. You can add logging statements into your pipeline's `DoFn` instances that will appear in Cloud Logging as your pipeline runs.
+Each runner may choose to handle logs in its own way.
```java
// This example uses .trace and .debug:
@@ -333,7 +327,16 @@ public class DebuggingWordCount {
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:example_wordcount_debugging_logging
%}```
-If you execute your pipeline using `DataflowRunner`, you can control the worker log levels. Dataflow workers that execute user code are configured to log to Cloud Logging by default at "INFO" log level and higher. You can override log levels for specific logging namespaces by specifying: `--workerLogLevelOverrides={"Name1":"Level1","Name2":"Level2",...}`. For example, by specifying `--workerLogLevelOverrides={"org.apache.beam.examples":"DEBUG"}` when executing this pipeline using the Dataflow service, Cloud Logging would contain only "DEBUG" or higher level logs for the package in addition to the default "INFO" or higher level logs.
+
+#### Direct Runner
+
+If you execute your pipeline using `DirectRunner`, it will print the log messages directly to your local console.
+
+#### Dataflow Runner
+
+If you execute your pipeline using `DataflowRunner`, you can use Stackdriver Logging. Stackdriver Logging aggregates the logs from all of your Dataflow job's workers to a single location in the Google Cloud Platform Console. You can use Stackdriver Logging to search and access the logs from all of the workers that Dataflow has spun up to complete your Dataflow job. Logging statements in your pipeline's `DoFn` instances will appear in Stackdriver Logging as your pipeline runs.
+
+If you execute your pipeline using `DataflowRunner`, you can control the worker log levels. Dataflow workers that execute user code are configured to log to Stackdriver Logging by default at "INFO" log level and higher. You can override log levels for specific logging namespaces by specifying: `--workerLogLevelOverrides={"Name1":"Level1","Name2":"Level2",...}`. For example, by specifying `--workerLogLevelOverrides={"org.apache.beam.examples":"DEBUG"}` when executing this pipeline using the Dataflow service, Stackdriver Logging would contain only "DEBUG" or higher level logs for the package in addition to the default "INFO" or higher level logs.
The default Dataflow worker logging configuration can be overridden by specifying `--defaultWorkerLogLevel=<one of TRACE, DEBUG, INFO, WARN, ERROR>`. For example, by specifying `--defaultWorkerLogLevel=DEBUG` when executing this pipeline with the Dataflow service, Cloud Logging would contain all "DEBUG" or higher level logs. Note that changing the default worker log level to TRACE or DEBUG will significantly increase the amount of logs output.
@@ -342,14 +345,18 @@ The default Dataflow worker logging configuration can be overridden by specifyin
> **Note:** This section is yet to be added. There is an open issue for this ([BEAM-792](https://issues.apache.org/jira/browse/BEAM-792)).
#### Apache Flink Runner
-
+
> **Note:** This section is yet to be added. There is an open issue for this ([BEAM-791](https://issues.apache.org/jira/browse/BEAM-791)).
+#### Apache Apex Runner
+
+> **Note:** This section is yet to be added. There is an open issue for this ([BEAM-2285](https://issues.apache.org/jira/browse/BEAM-2285)).
+
### Testing your Pipeline via PAssert
-`PAssert` is a set of convenient `PTransform`s in the style of Hamcrest's collection matchers that can be used when writing Pipeline level tests to validate the contents of PCollections. `PAssert` is best used in unit tests with small data sets, but is demonstrated here as a teaching tool.
+`PAssert` is a set of convenient PTransforms in the style of Hamcrest's collection matchers that can be used when writing Pipeline level tests to validate the contents of PCollections. `PAssert` is best used in unit tests with small data sets, but is demonstrated here as a teaching tool.
-Below, we verify that the set of filtered words matches our expected counts. Note that `PAssert` does not provide any output, and that successful completion of the pipeline implies that the expectations were met. See [DebuggingWordCountTest](https://github.com/apache/beam/blob/master/examples/java/src/test/java/org/apache/beam/examples/DebuggingWordCountTest.java) for an example unit test.
+Below, we verify that the set of filtered words matches our expected counts. Note that `PAssert` does not produce any output, and pipeline will only succeed if all of the expectations are met. See [DebuggingWordCountTest](https://github.com/apache/beam/blob/master/examples/java/src/test/java/org/apache/beam/examples/DebuggingWordCountTest.java) for an example unit test.
```java
public static void main(String[] args) {
@@ -381,7 +388,7 @@ The following sections explain these key concepts in detail, and break down the
### Unbounded and bounded pipeline input modes
-Beam allows you to create a single pipeline that can handle both bounded and unbounded types of input. If the input is unbounded, then all `PCollections` of the pipeline will be unbounded as well. The same goes for bounded input. If your input has a fixed number of elements, it's considered a 'bounded' data set. If your input is continuously updating, then it's considered 'unbounded'.
+Beam allows you to create a single pipeline that can handle both bounded and unbounded types of input. If the input is unbounded, then all PCollections of the pipeline will be unbounded as well. The same goes for bounded input. If your input has a fixed number of elements, it's considered a 'bounded' data set. If your input is continuously updating, then it's considered 'unbounded'.
Recall that the input for this example is a a set of Shakespeare's texts, finite data. Therefore, this example reads bounded data from a text file:
@@ -415,20 +422,24 @@ Below is the code for `AddTimestampFn`, a `DoFn` invoked by `ParDo`, that sets t
```java
static class AddTimestampFn extends DoFn<String, String> {
- private static final Duration RAND_RANGE = Duration.standardHours(2);
private final Instant minTimestamp;
+ private final Instant maxTimestamp;
- AddTimestampFn() {
- this.minTimestamp = new Instant(System.currentTimeMillis());
+ AddTimestampFn(Instant minTimestamp, Instant maxTimestamp) {
+ this.minTimestamp = minTimestamp;
+ this.maxTimestamp = maxTimestamp;
}
@ProcessElement
public void processElement(ProcessContext c) {
- // Generate a timestamp that falls somewhere in the past two hours.
- long randMillis = (long) (Math.random() * RAND_RANGE.getMillis());
- Instant randomTimestamp = minTimestamp.plus(randMillis);
-
- // Set the data element with that timestamp.
+ Instant randomTimestamp =
+ new Instant(
+ ThreadLocalRandom.current()
+ .nextLong(minTimestamp.getMillis(), maxTimestamp.getMillis()));
+
+ /**
+ * Concept #2: Set the data element with that timestamp.
+ */
c.outputWithTimestamp(c.element(), new Instant(randomTimestamp));
}
}
@@ -440,9 +451,9 @@ This feature is not yet available in the Beam SDK for Python.
### Windowing
-Beam uses a concept called **Windowing** to subdivide a `PCollection` according to the timestamps of its individual elements. `PTransforms` that aggregate multiple elements, process each `PCollection` as a succession of multiple, finite windows, even though the entire collection itself may be of infinite size (unbounded).
+Beam uses a concept called **Windowing** to subdivide a `PCollection` according to the timestamps of its individual elements. PTransforms that aggregate multiple elements, process each `PCollection` as a succession of multiple, finite windows, even though the entire collection itself may be of infinite size (unbounded).
-The `WindowingWordCount` example applies fixed-time windowing, wherein each window represents a fixed time interval. The fixed window size for this example defaults to 1 minute (you can change this with a command-line option).
+The `WindowedWordCount` example applies fixed-time windowing, wherein each window represents a fixed time interval. The fixed window size for this example defaults to 1 minute (you can change this with a command-line option).
```java
PCollection<String> windowedWords = input
@@ -456,7 +467,7 @@ This feature is not yet available in the Beam SDK for Python.
### Reusing PTransforms over windowed PCollections
-You can reuse existing `PTransform`s, that were created for manipulating simple `PCollection`s, over windowed `PCollection`s as well.
+You can reuse existing PTransforms that were created for manipulating simple PCollections over windowed PCollections as well.
```java
PCollection<KV<String, Long>> wordCounts = windowedWords.apply(new WordCount.CountWords());
@@ -468,17 +479,14 @@ This feature is not yet available in the Beam SDK for Python.
### Write Results to an Unbounded Sink
-Since our input is unbounded, the same is true of our output `PCollection`. We need to make sure that we choose an appropriate, unbounded sink. Some output sinks support only bounded output, such as a text file. Google Cloud BigQuery is an output source that supports both bounded and unbounded input.
+When our input is unbounded, the same is true of our output `PCollection`. We need to make sure that we choose an appropriate, unbounded sink. Some output sinks support only bounded output, while others support both bounded and unbounded outputs. By using a `FilenamePolicy`, we can use `TextIO` to files that are partitioned by windows. We use a composite `PTransform` that uses such a policy internally to write a single sharded file per window.
-In this example, we stream the results to a BigQuery table. The results are then formatted for a BigQuery table, and then written to BigQuery using BigQueryIO.Write.
+In this example, we stream the results to a BigQuery table. The results are then formatted for a BigQuery table, and then written to BigQuery using BigQueryIO.Write.
```java
-wordCounts.apply(ParDo.of(new FormatAsTableRowFn()))
- .apply(BigQueryIO.Write
- .to(getTableReference(options))
- .withSchema(getSchema())
- .withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED)
- .withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_APPEND));
+ wordCounts
+ .apply(MapElements.via(new WordCount.FormatAsTextFn()))
+ .apply(new WriteOneFilePerWindow(output, options.getNumShards()));
```
```py
[3/3] beam-site git commit: This closes #241
Posted by da...@apache.org.
This closes #241
Project: http://git-wip-us.apache.org/repos/asf/beam-site/repo
Commit: http://git-wip-us.apache.org/repos/asf/beam-site/commit/261e3977
Tree: http://git-wip-us.apache.org/repos/asf/beam-site/tree/261e3977
Diff: http://git-wip-us.apache.org/repos/asf/beam-site/diff/261e3977
Branch: refs/heads/asf-site
Commit: 261e397777912fdf02e2b6282b6384f6e182e1a2
Parents: 87b0fb5 1212155
Author: Davor Bonaci <da...@google.com>
Authored: Fri May 12 16:17:50 2017 -0700
Committer: Davor Bonaci <da...@google.com>
Committed: Fri May 12 16:17:50 2017 -0700
----------------------------------------------------------------------
.../documentation/programming-guide/index.html | 2 +-
.../sdks/python-custom-io/index.html | 2 +-
.../get-started/wordcount-example/index.html | 80 ++++++++++--------
src/documentation/sdks/python-custom-io.md | 2 +-
src/get-started/wordcount-example.md | 86 +++++++++++---------
5 files changed, 95 insertions(+), 77 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/12121557
Tree: http://git-wip-us.apache.org/repos/asf/beam-site/tree/12121557
Diff: http://git-wip-us.apache.org/repos/asf/beam-site/diff/12121557
Branch: refs/heads/asf-site
Commit: 12121557b155ec7d0aea865afa5c6f2801217d56
Parents: 9fb214f
Author: Davor Bonaci <da...@google.com>
Authored: Fri May 12 16:17:49 2017 -0700
Committer: Davor Bonaci <da...@google.com>
Committed: Fri May 12 16:17:49 2017 -0700
----------------------------------------------------------------------
.../documentation/programming-guide/index.html | 2 +-
.../sdks/python-custom-io/index.html | 2 +-
.../get-started/wordcount-example/index.html | 80 +++++++++++---------
3 files changed, 47 insertions(+), 37 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/beam-site/blob/12121557/content/documentation/programming-guide/index.html
----------------------------------------------------------------------
diff --git a/content/documentation/programming-guide/index.html b/content/documentation/programming-guide/index.html
index 1e6e9a7..3b3cb14 100644
--- a/content/documentation/programming-guide/index.html
+++ b/content/documentation/programming-guide/index.html
@@ -1939,7 +1939,7 @@ Subsequent transforms, however, are applied to the result of the <code class="hi
<span class="n">unix_timestamp</span> <span class="o">=</span> <span class="n">extract_timestamp_from_log_entry</span><span class="p">(</span><span class="n">element</span><span class="p">)</span>
<span class="c"># Wrap and emit the current entry and new timestamp in a</span>
<span class="c"># TimestampedValue.</span>
- <span class="k">yield</span> <span class="n">beam</span><span class="o">.</span><span class="n">TimestampedValue</span><span class="p">(</span><span class="n">element</span><span class="p">,</span> <span class="n">unix_timestamp</span><span class="p">)</span>
+ <span class="k">yield</span> <span class="n">beam</span><span class="o">.</span><span class="n">window</span><span class="o">.</span><span class="n">TimestampedValue</span><span class="p">(</span><span class="n">element</span><span class="p">,</span> <span class="n">unix_timestamp</span><span class="p">)</span>
<span class="n">timestamped_items</span> <span class="o">=</span> <span class="n">items</span> <span class="o">|</span> <span class="s">'timestamp'</span> <span class="o">>></span> <span class="n">beam</span><span class="o">.</span><span class="n">ParDo</span><span class="p">(</span><span class="n">AddTimestampDoFn</span><span class="p">())</span>
http://git-wip-us.apache.org/repos/asf/beam-site/blob/12121557/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 629ef0f..fb2646f 100644
--- a/content/documentation/sdks/python-custom-io/index.html
+++ b/content/documentation/sdks/python-custom-io/index.html
@@ -464,7 +464,7 @@ numbers = p | 'ProduceNumbers' >> beam.io.Read(CountingSource(count))
<h4 id="filesink">FileSink</h4>
-<p>If your data source uses files, you can derive your <code class="highlighter-rouge">Sink</code> and <code class="highlighter-rouge">Writer</code> classes from the <code class="highlighter-rouge">FileSink</code> and <code class="highlighter-rouge">FileSinkWriter</code> classes, which can be found in the <a href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/fileio.py">fileio.py</a> module. These classes implement code common sinks that interact with files, including:</p>
+<p>If your data source uses files, you can derive your <code class="highlighter-rouge">Sink</code> and <code class="highlighter-rouge">Writer</code> classes from the <code class="highlighter-rouge">FileBasedSink</code> and <code class="highlighter-rouge">FileBasedSinkWriter</code> classes, which can be found in the <a href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/filebasedsink.py">filebasedsink.py</a> module. These classes implement code common sinks that interact with files, including:</p>
<ul>
<li>Setting file headers and footers</li>
http://git-wip-us.apache.org/repos/asf/beam-site/blob/12121557/content/get-started/wordcount-example/index.html
----------------------------------------------------------------------
diff --git a/content/get-started/wordcount-example/index.html b/content/get-started/wordcount-example/index.html
index 5cc32f3..333cfb9 100644
--- a/content/get-started/wordcount-example/index.html
+++ b/content/get-started/wordcount-example/index.html
@@ -172,6 +172,7 @@
<li><a href="#dataflow-runner" id="markdown-toc-dataflow-runner">Dataflow Runner</a></li>
<li><a href="#apache-spark-runner" id="markdown-toc-apache-spark-runner">Apache Spark Runner</a></li>
<li><a href="#apache-flink-runner" id="markdown-toc-apache-flink-runner">Apache Flink Runner</a></li>
+ <li><a href="#apache-apex-runner" id="markdown-toc-apache-apex-runner">Apache Apex Runner</a></li>
</ul>
</li>
<li><a href="#testing-your-pipeline-via-passert" id="markdown-toc-testing-your-pipeline-via-passert">Testing your Pipeline via PAssert</a></li>
@@ -228,7 +229,7 @@
<h3 id="creating-the-pipeline">Creating the Pipeline</h3>
-<p>The first step in creating a Beam pipeline is to create a <code class="highlighter-rouge">PipelineOptions object</code>. This object lets us set various options for our pipeline, such as the pipeline runner that will execute our pipeline and any runner-specific configuration required by the chosen runner. In this example we set these options programmatically, but more often command-line arguments are used to set <code class="highlighter-rouge">PipelineOptions</code>.</p>
+<p>The first step in creating a Beam pipeline is to create a <code class="highlighter-rouge">PipelineOptions</code> object. This object lets us set various options for our pipeline, such as the pipeline runner that will execute our pipeline and any runner-specific configuration required by the chosen runner. In this example we set these options programmatically, but more often command-line arguments are used to set <code class="highlighter-rouge">PipelineOptions</code>.</p>
<p>You can specify a runner for executing your pipeline, such as the <code class="highlighter-rouge">DataflowRunner</code> or <code class="highlighter-rouge">SparkRunner</code>. If you omit specifying a runner, as in this example, your pipeline will be executed locally using the <code class="highlighter-rouge">DirectRunner</code>. In the next sections, we will specify the pipeline’s runner.</p>
@@ -273,7 +274,7 @@
<p>The Minimal WordCount pipeline contains several transforms to read data into the pipeline, manipulate or otherwise transform the data, and write out the results. Each transform represents an operation in the pipeline.</p>
-<p>Each transform takes some kind of input (data or otherwise), and produces some output data. The input and output data is represented by the SDK class <code class="highlighter-rouge">PCollection</code>. <code class="highlighter-rouge">PCollection</code> is a special class, provided by the Beam SDK, that you can use to represent a data set of virtually any size, including infinite data sets.</p>
+<p>Each transform takes some kind of input (data or otherwise), and produces some output data. The input and output data is represented by the SDK class <code class="highlighter-rouge">PCollection</code>. <code class="highlighter-rouge">PCollection</code> is a special class, provided by the Beam SDK, that you can use to represent a data set of virtually any size, including unbounded data sets.</p>
<p><img src="/images/wordcount-pipeline.png" alt="Word Count pipeline diagram" />
Figure 1: The pipeline data flow.</p>
@@ -282,7 +283,7 @@ Figure 1: The pipeline data flow.</p>
<ol>
<li>
- <p>A text file <code class="highlighter-rouge">Read</code> transform is applied to the Pipeline object itself, and produces a <code class="highlighter-rouge">PCollection</code> as output. Each element in the output PCollection represents one line of text from the input file. This example happens to use input data stored in a publicly accessible Google Cloud Storage bucket (“gs://”).</p>
+ <p>A text file <code class="highlighter-rouge">Read</code> transform is applied to the Pipeline object itself, and produces a <code class="highlighter-rouge">PCollection</code> as output. Each element in the output PCollection represents one line of text from the input file. This example uses input data stored in a publicly accessible Google Cloud Storage bucket (“gs://”).</p>
<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="n">p</span><span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="n">TextIO</span><span class="o">.</span><span class="na">Read</span><span class="o">.</span><span class="na">from</span><span class="o">(</span><span class="s">"gs://apache-beam-samples/shakespeare/*"</span><span class="o">))</span>
</code></pre>
@@ -299,7 +300,9 @@ Figure 1: The pipeline data flow.</p>
<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="s">"ExtractWords"</span><span class="o">,</span> <span class="n">ParDo</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="k">new</span> <span class="n">DoFn</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">String</span><span class="o">>()</span> <span class="o">{</span>
<span class="nd">@ProcessElement</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">processElement</span><span class="o">(</span><span class="n">ProcessContext</span> <span class="n">c</span><span class="o">)</span> <span class="o">{</span>
- <span class="k">for</span> <span class="o">(</span><span class="n">String</span> <span class="n">word</span> <span class="o">:</span> <span class="n">c</span><span class="o">.</span><span class="na">element</span><span class="o">().</span><span class="na">split</span><span class="o">(</span><span class="s">"[^a-zA-Z']+"</span><span class="o">))</span> <span class="o">{</span>
+ <span class="c1">// \p{L} denotes the category of Unicode letters,</span>
+ <span class="c1">// so this pattern will match on everything that is not a letter.</span>
+ <span class="k">for</span> <span class="o">(</span><span class="n">String</span> <span class="n">word</span> <span class="o">:</span> <span class="n">c</span><span class="o">.</span><span class="na">element</span><span class="o">().</span><span class="na">split</span><span class="o">(</span><span class="s">"[^\\p{L}]+"</span><span class="o">))</span> <span class="o">{</span>
<span class="k">if</span> <span class="o">(!</span><span class="n">word</span><span class="o">.</span><span class="na">isEmpty</span><span class="o">())</span> <span class="o">{</span>
<span class="n">c</span><span class="o">.</span><span class="na">output</span><span class="o">(</span><span class="n">word</span><span class="o">);</span>
<span class="o">}</span>
@@ -330,7 +333,7 @@ Figure 1: The pipeline data flow.</p>
<li>
<p>The next transform formats each of the key/value pairs of unique words and occurrence counts into a printable string suitable for writing to an output file.</p>
- <p>The map transform is a higher-level composite transform that encapsulates a simple <code class="highlighter-rouge">ParDo</code>; for each element in the input <code class="highlighter-rouge">PCollection</code>, the map transform applies a function that produces exactly one output element.</p>
+ <p>The map transform is a higher-level composite transform that encapsulates a simple <code class="highlighter-rouge">ParDo</code>. For each element in the input <code class="highlighter-rouge">PCollection</code>, the map transform applies a function that produces exactly one output element.</p>
<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="s">"FormatResults"</span><span class="o">,</span> <span class="n">MapElements</span><span class="o">.</span><span class="na">via</span><span class="o">(</span><span class="k">new</span> <span class="n">SimpleFunction</span><span class="o"><</span><span class="n">KV</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">>,</span> <span class="n">String</span><span class="o">>()</span> <span class="o">{</span>
<span class="nd">@Override</span>
@@ -526,14 +529,6 @@ Figure 1: The pipeline data flow.</p>
<p>Each runner may choose to handle logs in its own way.</p>
-<h4 id="direct-runner">Direct Runner</h4>
-
-<p>If you execute your pipeline using <code class="highlighter-rouge">DirectRunner</code>, it will print the log messages directly to your local console.</p>
-
-<h4 id="dataflow-runner">Dataflow Runner</h4>
-
-<p>If you execute your pipeline using <code class="highlighter-rouge">DataflowRunner</code>, you can use Google Cloud Logging. Google Cloud Logging (currently in beta) aggregates the logs from all of your Dataflow job’s workers to a single location in the Google Cloud Platform Console. You can use Cloud Logging to search and access the logs from all of the Compute Engine instances that Dataflow has spun up to complete your Dataflow job. You can add logging statements into your pipeline’s <code class="highlighter-rouge">DoFn</code> instances that will appear in Cloud Logging as your pipeline runs.</p>
-
<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="c1">// This example uses .trace and .debug:</span>
<span class="kd">public</span> <span class="kd">class</span> <span class="nc">DebuggingWordCount</span> <span class="o">{</span>
@@ -592,7 +587,15 @@ Figure 1: The pipeline data flow.</p>
</code></pre>
</div>
-<p>If you execute your pipeline using <code class="highlighter-rouge">DataflowRunner</code>, you can control the worker log levels. Dataflow workers that execute user code are configured to log to Cloud Logging by default at “INFO” log level and higher. You can override log levels for specific logging namespaces by specifying: <code class="highlighter-rouge">--workerLogLevelOverrides={"Name1":"Level1","Name2":"Level2",...}</code>. For example, by specifying <code class="highlighter-rouge">--workerLogLevelOverrides={"org.apache.beam.examples":"DEBUG"}</code> when executing this pipeline using the Dataflow service, Cloud Logging would contain only “DEBUG” or higher level logs for the package in addition to the default “INFO” or higher level logs.</p>
+<h4 id="direct-runner">Direct Runner</h4>
+
+<p>If you execute your pipeline using <code class="highlighter-rouge">DirectRunner</code>, it will print the log messages directly to your local console.</p>
+
+<h4 id="dataflow-runner">Dataflow Runner</h4>
+
+<p>If you execute your pipeline using <code class="highlighter-rouge">DataflowRunner</code>, you can use Stackdriver Logging. Stackdriver Logging aggregates the logs from all of your Dataflow job’s workers to a single location in the Google Cloud Platform Console. You can use Stackdriver Logging to search and access the logs from all of the workers that Dataflow has spun up to complete your Dataflow job. Logging statements in your pipeline’s <code class="highlighter-rouge">DoFn</code> instances will appear in Stackdriver Logging as your pipeline runs.</p>
+
+<p>If you execute your pipeline using <code class="highlighter-rouge">DataflowRunner</code>, you can control the worker log levels. Dataflow workers that execute user code are configured to log to Stackdriver Logging by default at “INFO” log level and higher. You can override log levels for specific logging namespaces by specifying: <code class="highlighter-rouge">--workerLogLevelOverrides={"Name1":"Level1","Name2":"Level2",...}</code>. For example, by specifying <code class="highlighter-rouge">--workerLogLevelOverrides={"org.apache.beam.examples":"DEBUG"}</code> when executing this pipeline using the Dataflow service, Stackdriver Logging would contain only “DEBUG” or higher level logs for the package in addition to the default “INFO” or higher level logs.</p>
<p>The default Dataflow worker logging configuration can be overridden by specifying <code class="highlighter-rouge">--defaultWorkerLogLevel=<one of TRACE, DEBUG, INFO, WARN, ERROR></code>. For example, by specifying <code class="highlighter-rouge">--defaultWorkerLogLevel=DEBUG</code> when executing this pipeline with the Dataflow service, Cloud Logging would contain all “DEBUG” or higher level logs. Note that changing the default worker log level to TRACE or DEBUG will significantly increase the amount of logs output.</p>
@@ -608,11 +611,17 @@ Figure 1: The pipeline data flow.</p>
<p><strong>Note:</strong> This section is yet to be added. There is an open issue for this (<a href="https://issues.apache.org/jira/browse/BEAM-791">BEAM-791</a>).</p>
</blockquote>
+<h4 id="apache-apex-runner">Apache Apex Runner</h4>
+
+<blockquote>
+ <p><strong>Note:</strong> This section is yet to be added. There is an open issue for this (<a href="https://issues.apache.org/jira/browse/BEAM-2285">BEAM-2285</a>).</p>
+</blockquote>
+
<h3 id="testing-your-pipeline-via-passert">Testing your Pipeline via PAssert</h3>
-<p><code class="highlighter-rouge">PAssert</code> is a set of convenient <code class="highlighter-rouge">PTransform</code>s in the style of Hamcrest’s collection matchers that can be used when writing Pipeline level tests to validate the contents of PCollections. <code class="highlighter-rouge">PAssert</code> is best used in unit tests with small data sets, but is demonstrated here as a teaching tool.</p>
+<p><code class="highlighter-rouge">PAssert</code> is a set of convenient PTransforms in the style of Hamcrest’s collection matchers that can be used when writing Pipeline level tests to validate the contents of PCollections. <code class="highlighter-rouge">PAssert</code> is best used in unit tests with small data sets, but is demonstrated here as a teaching tool.</p>
-<p>Below, we verify that the set of filtered words matches our expected counts. Note that <code class="highlighter-rouge">PAssert</code> does not provide any output, and that successful completion of the pipeline implies that the expectations were met. See <a href="https://github.com/apache/beam/blob/master/examples/java/src/test/java/org/apache/beam/examples/DebuggingWordCountTest.java">DebuggingWordCountTest</a> for an example unit test.</p>
+<p>Below, we verify that the set of filtered words matches our expected counts. Note that <code class="highlighter-rouge">PAssert</code> does not produce any output, and pipeline will only succeed if all of the expectations are met. See <a href="https://github.com/apache/beam/blob/master/examples/java/src/test/java/org/apache/beam/examples/DebuggingWordCountTest.java">DebuggingWordCountTest</a> for an example unit test.</p>
<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">main</span><span class="o">(</span><span class="n">String</span><span class="o">[]</span> <span class="n">args</span><span class="o">)</span> <span class="o">{</span>
<span class="o">...</span>
@@ -646,7 +655,7 @@ Figure 1: The pipeline data flow.</p>
<h3 id="unbounded-and-bounded-pipeline-input-modes">Unbounded and bounded pipeline input modes</h3>
-<p>Beam allows you to create a single pipeline that can handle both bounded and unbounded types of input. If the input is unbounded, then all <code class="highlighter-rouge">PCollections</code> of the pipeline will be unbounded as well. The same goes for bounded input. If your input has a fixed number of elements, it’s considered a ‘bounded’ data set. If your input is continuously updating, then it’s considered ‘unbounded’.</p>
+<p>Beam allows you to create a single pipeline that can handle both bounded and unbounded types of input. If the input is unbounded, then all PCollections of the pipeline will be unbounded as well. The same goes for bounded input. If your input has a fixed number of elements, it’s considered a ‘bounded’ data set. If your input is continuously updating, then it’s considered ‘unbounded’.</p>
<p>Recall that the input for this example is a a set of Shakespeare’s texts, finite data. Therefore, this example reads bounded data from a text file:</p>
@@ -679,20 +688,24 @@ Figure 1: The pipeline data flow.</p>
<p>Below is the code for <code class="highlighter-rouge">AddTimestampFn</code>, a <code class="highlighter-rouge">DoFn</code> invoked by <code class="highlighter-rouge">ParDo</code>, that sets the data element of the timestamp given the element itself. For example, if the elements were log lines, this <code class="highlighter-rouge">ParDo</code> could parse the time out of the log string and set it as the element’s timestamp. There are no timestamps inherent in the works of Shakespeare, so in this case we’ve made up random timestamps just to illustrate the concept. Each line of the input text will get a random associated timestamp sometime in a 2-hour period.</p>
<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="kd">static</span> <span class="kd">class</span> <span class="nc">AddTimestampFn</span> <span class="kd">extends</span> <span class="n">DoFn</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">String</span><span class="o">></span> <span class="o">{</span>
- <span class="kd">private</span> <span class="kd">static</span> <span class="kd">final</span> <span class="n">Duration</span> <span class="n">RAND_RANGE</span> <span class="o">=</span> <span class="n">Duration</span><span class="o">.</span><span class="na">standardHours</span><span class="o">(</span><span class="mi">2</span><span class="o">);</span>
<span class="kd">private</span> <span class="kd">final</span> <span class="n">Instant</span> <span class="n">minTimestamp</span><span class="o">;</span>
+ <span class="kd">private</span> <span class="kd">final</span> <span class="n">Instant</span> <span class="n">maxTimestamp</span><span class="o">;</span>
- <span class="n">AddTimestampFn</span><span class="o">()</span> <span class="o">{</span>
- <span class="k">this</span><span class="o">.</span><span class="na">minTimestamp</span> <span class="o">=</span> <span class="k">new</span> <span class="n">Instant</span><span class="o">(</span><span class="n">System</span><span class="o">.</span><span class="na">currentTimeMillis</span><span class="o">());</span>
+ <span class="n">AddTimestampFn</span><span class="o">(</span><span class="n">Instant</span> <span class="n">minTimestamp</span><span class="o">,</span> <span class="n">Instant</span> <span class="n">maxTimestamp</span><span class="o">)</span> <span class="o">{</span>
+ <span class="k">this</span><span class="o">.</span><span class="na">minTimestamp</span> <span class="o">=</span> <span class="n">minTimestamp</span><span class="o">;</span>
+ <span class="k">this</span><span class="o">.</span><span class="na">maxTimestamp</span> <span class="o">=</span> <span class="n">maxTimestamp</span><span class="o">;</span>
<span class="o">}</span>
<span class="nd">@ProcessElement</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">processElement</span><span class="o">(</span><span class="n">ProcessContext</span> <span class="n">c</span><span class="o">)</span> <span class="o">{</span>
- <span class="c1">// Generate a timestamp that falls somewhere in the past two hours.</span>
- <span class="kt">long</span> <span class="n">randMillis</span> <span class="o">=</span> <span class="o">(</span><span class="kt">long</span><span class="o">)</span> <span class="o">(</span><span class="n">Math</span><span class="o">.</span><span class="na">random</span><span class="o">()</span> <span class="o">*</span> <span class="n">RAND_RANGE</span><span class="o">.</span><span class="na">getMillis</span><span class="o">());</span>
- <span class="n">Instant</span> <span class="n">randomTimestamp</span> <span class="o">=</span> <span class="n">minTimestamp</span><span class="o">.</span><span class="na">plus</span><span class="o">(</span><span class="n">randMillis</span><span class="o">);</span>
-
- <span class="c1">// Set the data element with that timestamp.</span>
+ <span class="n">Instant</span> <span class="n">randomTimestamp</span> <span class="o">=</span>
+ <span class="k">new</span> <span class="nf">Instant</span><span class="o">(</span>
+ <span class="n">ThreadLocalRandom</span><span class="o">.</span><span class="na">current</span><span class="o">()</span>
+ <span class="o">.</span><span class="na">nextLong</span><span class="o">(</span><span class="n">minTimestamp</span><span class="o">.</span><span class="na">getMillis</span><span class="o">(),</span> <span class="n">maxTimestamp</span><span class="o">.</span><span class="na">getMillis</span><span class="o">()));</span>
+
+ <span class="cm">/**
+ * Concept #2: Set the data element with that timestamp.
+ */</span>
<span class="n">c</span><span class="o">.</span><span class="na">outputWithTimestamp</span><span class="o">(</span><span class="n">c</span><span class="o">.</span><span class="na">element</span><span class="o">(),</span> <span class="k">new</span> <span class="n">Instant</span><span class="o">(</span><span class="n">randomTimestamp</span><span class="o">));</span>
<span class="o">}</span>
<span class="o">}</span>
@@ -705,9 +718,9 @@ Figure 1: The pipeline data flow.</p>
<h3 id="windowing">Windowing</h3>
-<p>Beam uses a concept called <strong>Windowing</strong> to subdivide a <code class="highlighter-rouge">PCollection</code> according to the timestamps of its individual elements. <code class="highlighter-rouge">PTransforms</code> that aggregate multiple elements, process each <code class="highlighter-rouge">PCollection</code> as a succession of multiple, finite windows, even though the entire collection itself may be of infinite size (unbounded).</p>
+<p>Beam uses a concept called <strong>Windowing</strong> to subdivide a <code class="highlighter-rouge">PCollection</code> according to the timestamps of its individual elements. PTransforms that aggregate multiple elements, process each <code class="highlighter-rouge">PCollection</code> as a succession of multiple, finite windows, even though the entire collection itself may be of infinite size (unbounded).</p>
-<p>The <code class="highlighter-rouge">WindowingWordCount</code> example applies fixed-time windowing, wherein each window represents a fixed time interval. The fixed window size for this example defaults to 1 minute (you can change this with a command-line option).</p>
+<p>The <code class="highlighter-rouge">WindowedWordCount</code> example applies fixed-time windowing, wherein each window represents a fixed time interval. The fixed window size for this example defaults to 1 minute (you can change this with a command-line option).</p>
<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="n">PCollection</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">windowedWords</span> <span class="o">=</span> <span class="n">input</span>
<span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="n">Window</span><span class="o">.<</span><span class="n">String</span><span class="o">></span><span class="n">into</span><span class="o">(</span>
@@ -721,7 +734,7 @@ Figure 1: The pipeline data flow.</p>
<h3 id="reusing-ptransforms-over-windowed-pcollections">Reusing PTransforms over windowed PCollections</h3>
-<p>You can reuse existing <code class="highlighter-rouge">PTransform</code>s, that were created for manipulating simple <code class="highlighter-rouge">PCollection</code>s, over windowed <code class="highlighter-rouge">PCollection</code>s as well.</p>
+<p>You can reuse existing PTransforms that were created for manipulating simple PCollections over windowed PCollections as well.</p>
<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="n">PCollection</span><span class="o"><</span><span class="n">KV</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Long</span><span class="o">>></span> <span class="n">wordCounts</span> <span class="o">=</span> <span class="n">windowedWords</span><span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="k">new</span> <span class="n">WordCount</span><span class="o">.</span><span class="na">CountWords</span><span class="o">());</span>
</code></pre>
@@ -733,16 +746,13 @@ Figure 1: The pipeline data flow.</p>
<h3 id="write-results-to-an-unbounded-sink">Write Results to an Unbounded Sink</h3>
-<p>Since our input is unbounded, the same is true of our output <code class="highlighter-rouge">PCollection</code>. We need to make sure that we choose an appropriate, unbounded sink. Some output sinks support only bounded output, such as a text file. Google Cloud BigQuery is an output source that supports both bounded and unbounded input.</p>
+<p>When our input is unbounded, the same is true of our output <code class="highlighter-rouge">PCollection</code>. We need to make sure that we choose an appropriate, unbounded sink. Some output sinks support only bounded output, while others support both bounded and unbounded outputs. By using a <code class="highlighter-rouge">FilenamePolicy</code>, we can use <code class="highlighter-rouge">TextIO</code> to files that are partitioned by windows. We use a composite <code class="highlighter-rouge">PTransform</code> that uses such a policy internally to write a single sharded file per window.</p>
<p>In this example, we stream the results to a BigQuery table. The results are then formatted for a BigQuery table, and then written to BigQuery using BigQueryIO.Write.</p>
-<div class="language-java highlighter-rouge"><pre class="highlight"><code><span class="n">wordCounts</span><span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="n">ParDo</span><span class="o">.</span><span class="na">of</span><span class="o">(</span><span class="k">new</span> <span class="n">FormatAsTableRowFn</span><span class="o">()))</span>
- <span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="n">BigQueryIO</span><span class="o">.</span><span class="na">Write</span>
- <span class="o">.</span><span class="na">to</span><span class="o">(</span><span class="n">getTableReference</span><span class="o">(</span><span class="n">options</span><span class="o">))</span>
- <span class="o">.</span><span class="na">withSchema</span><span class="o">(</span><span class="n">getSchema</span><span class="o">())</span>
- <span class="o">.</span><span class="na">withCreateDisposition</span><span class="o">(</span><span class="n">BigQueryIO</span><span class="o">.</span><span class="na">Write</span><span class="o">.</span><span class="na">CreateDisposition</span><span class="o">.</span><span class="na">CREATE_IF_NEEDED</span><span class="o">)</span>
- <span class="o">.</span><span class="na">withWriteDisposition</span><span class="o">(</span><span class="n">BigQueryIO</span><span class="o">.</span><span class="na">Write</span><span class="o">.</span><span class="na">WriteDisposition</span><span class="o">.</span><span class="na">WRITE_APPEND</span><span class="o">));</span>
+<div class="language-java highlighter-rouge"><pre class="highlight"><code> <span class="n">wordCounts</span>
+ <span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="n">MapElements</span><span class="o">.</span><span class="na">via</span><span class="o">(</span><span class="k">new</span> <span class="n">WordCount</span><span class="o">.</span><span class="na">FormatAsTextFn</span><span class="o">()))</span>
+ <span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="k">new</span> <span class="n">WriteOneFilePerWindow</span><span class="o">(</span><span class="n">output</span><span class="o">,</span> <span class="n">options</span><span class="o">.</span><span class="na">getNumShards</span><span class="o">()));</span>
</code></pre>
</div>