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The Beam Programming Guide is intended for Beam users who want to use the Beam SDKs to create data processing pipelines. It provides guidance for using the Beam SDK classes to build and test your pipeline. It is not intended as an exhaustive reference, but as a language-agnostic, high-level guide to programmatically building your Beam pipeline. As the programming guide is filled out, the text will include code samples in multiple languages to help illustrate how to implement Beam concepts in your pipelines.
To use Beam, you need to first create a driver program using the classes in one of the Beam SDKs. Your driver program defines your pipeline, including all of the inputs, transforms, and outputs; it also sets execution options for your pipeline (typically passed in using command-line options). These include the Pipeline Runner, which, in turn, determines what back-end your pipeline will run on.
The Beam SDKs provide a number of abstractions that simplify the mechanics of large-scale distributed data processing. The same Beam abstractions work with both batch and streaming data sources. When you create your Beam pipeline, you can think about your data processing task in terms of these abstractions. They include:
Pipeline: A Pipeline encapsulates your entire data processing task, from start to finish. This includes reading input data, transforming that data, and writing output data. All Beam driver programs must create a Pipeline. When you create the Pipeline, you must also specify the execution options that tell the Pipeline where and how to run.
PCollection: A PCollection represents a distributed data set that your Beam pipeline operates on. The data set can be bounded, meaning it comes from a fixed source like a file, or unbounded, meaning it comes from a continuously updating source via a subscription or other mechanism. Your pipeline typically creates an initial PCollection by reading data from an external data source, but you can also create a PCollection from in-memory data within your driver program. From there, PCollections are the inputs and outputs for each step in your pipeline.
PTransform: A PTransform represents a data processing operation, or a step, in your pipeline. Every PTransform takes one or more PCollection objects as input, performs a processing function that you provide on the elements of that PCollection, and produces zero or more output PCollection objects.
I/O transforms: Beam comes with a number of “IOs” - library PTransforms that read or write data to various external storage systems.
A typical Beam driver program works as follows:
Pipeline object and set the pipeline execution options, including the Pipeline Runner.PCollection for pipeline data, either using the IOs to read data from an external storage system, or using a Create transform to build a PCollection from in-memory data.PTransforms to each PCollection. Transforms can change, filter, group, analyze, or otherwise process the elements in a PCollection. A transform creates a new output PCollection without modifying the input collection. A typical pipeline applies subsequent transforms to the each new output PCollection in turn until processing is complete. However, note that a pipeline does not have to be a single straight line of transforms applied one after another: think of PCollections as variables and PTransforms as functions applied to these variables: the shape of the pipeline can be an arbitrarily complex processing graph.PCollection(s) to an external source.When you run your Beam driver program, the Pipeline Runner that you designate constructs a workflow graph of your pipeline based on the PCollection objects you‘ve created and transforms that you’ve applied. That graph is then executed using the appropriate distributed processing back-end, becoming an asynchronous “job” (or equivalent) on that back-end.
The Pipeline abstraction encapsulates all the data and steps in your data processing task. Your Beam driver program typically starts by constructing a [Pipeline](https://beam.apache.org/releases/javadoc/{{ site.release_latest }}/index.html?org/apache/beam/sdk/Pipeline.html) Pipeline object, and then using that object as the basis for creating the pipeline's data sets as PCollections and its operations as Transforms.
To use Beam, your driver program must first create an instance of the Beam SDK class Pipeline (typically in the main() function). When you create your Pipeline, you‘ll also need to set some configuration options. You can set your pipeline’s configuration options programatically, but it's often easier to set the options ahead of time (or read them from the command line) and pass them to the Pipeline object when you create the object.
// Start by defining the options for the pipeline. PipelineOptions options = PipelineOptionsFactory.create(); // Then create the pipeline. Pipeline p = Pipeline.create(options);
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:pipelines_constructing_creating %}
Use the pipeline options to configure different aspects of your pipeline, such as the pipeline runner that will execute your pipeline and any runner-specific configuration required by the chosen runner. Your pipeline options will potentially include information such as your project ID or a location for storing files.
When you run the pipeline on a runner of your choice, a copy of the PipelineOptions will be available to your code. For example, if you add a PipelineOptions parameter to a DoFn's @ProcessElement method, it will be populated by the system.
While you can configure your pipeline by creating a PipelineOptions object and setting the fields directly, the Beam SDKs include a command-line parser that you can use to set fields in PipelineOptions using command-line arguments.
To read options from the command-line, construct your PipelineOptions object as demonstrated in the following example code:
PipelineOptions options = PipelineOptionsFactory.fromArgs(args).withValidation().create();
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:pipelines_constructing_creating %}
This interprets command-line arguments that follow the format:
--<option>=<value>
Note: Appending the method
.withValidationwill check for required command-line arguments and validate argument values.
Building your PipelineOptions this way lets you specify any of the options as a command-line argument.
Note: The [WordCount example pipeline]({{ site.baseurl }}/get-started/wordcount-example) demonstrates how to set pipeline options at runtime by using command-line options.
You can add your own custom options in addition to the standard PipelineOptions. To add your own options, define an interface with getter and setter methods for each option, as in the following example:
public interface MyOptions extends PipelineOptions { String getMyCustomOption(); void setMyCustomOption(String myCustomOption); }
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:pipeline_options_define_custom %}
You can also specify a description, which appears when a user passes --help as a command-line argument, and a default value.
You set the description and default value using annotations, as follows:
public interface MyOptions extends PipelineOptions { @Description("My custom command line argument.") @Default.String("DEFAULT") String getMyCustomOption(); void setMyCustomOption(String myCustomOption); }
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:pipeline_options_define_custom_with_help_and_default %}
{:.language-java} It's recommended that you register your interface with PipelineOptionsFactory and then pass the interface when creating the PipelineOptions object. When you register your interface with PipelineOptionsFactory, the --help can find your custom options interface and add it to the output of the --help command. PipelineOptionsFactory will also validate that your custom options are compatible with all other registered options.
{:.language-java} The following example code shows how to register your custom options interface with PipelineOptionsFactory:
PipelineOptionsFactory.register(MyOptions.class); MyOptions options = PipelineOptionsFactory.fromArgs(args) .withValidation() .as(MyOptions.class);
Now your pipeline can accept --myCustomOption=value as a command-line argument.
The [PCollection](https://beam.apache.org/releases/javadoc/{{ site.release_latest }}/index.html?org/apache/beam/sdk/values/PCollection.html) PCollection abstraction represents a potentially distributed, multi-element data set. You can think of a PCollection as “pipeline” data; Beam transforms use PCollection objects as inputs and outputs. As such, if you want to work with data in your pipeline, it must be in the form of a PCollection.
After you‘ve created your Pipeline, you’ll need to begin by creating at least one PCollection in some form. The PCollection you create serves as the input for the first operation in your pipeline.
You create a PCollection by either reading data from an external source using Beam‘s Source API, or you can create a PCollection of data stored in an in-memory collection class in your driver program. The former is typically how a production pipeline would ingest data; Beam’s Source APIs contain adapters to help you read from external sources like large cloud-based files, databases, or subscription services. The latter is primarily useful for testing and debugging purposes.
To read from an external source, you use one of the Beam-provided I/O adapters. The adapters vary in their exact usage, but all of them read from some external data source and return a PCollection whose elements represent the data records in that source.
Each data source adapter has a Read transform; to read, you must apply that transform to the Pipeline object itself. TextIO.Read io.TextFileSource, for example, reads from an external text file and returns a PCollection whose elements are of type String, each String represents one line from the text file. Here's how you would apply TextIO.Read io.TextFileSource to your Pipeline to create a PCollection:
public static void main(String[] args) { // Create the pipeline. PipelineOptions options = PipelineOptionsFactory.fromArgs(args).create(); Pipeline p = Pipeline.create(options); // Create the PCollection 'lines' by applying a 'Read' transform. PCollection<String> lines = p.apply( "ReadMyFile", TextIO.read().from("protocol://path/to/some/inputData.txt")); }
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:pipelines_constructing_reading %}
See the section on I/O to learn more about how to read from the various data sources supported by the Beam SDK.
{:.language-java} To create a PCollection from an in-memory Java Collection, you use the Beam-provided Create transform. Much like a data adapter's Read, you apply Create directly to your Pipeline object itself.
{:.language-java} As parameters, Create accepts the Java Collection and a Coder object. The Coder specifies how the elements in the Collection should be encoded.
{:.language-py} To create a PCollection from an in-memory list, you use the Beam-provided Create transform. Apply this transform directly to your Pipeline object itself.
The following example code shows how to create a PCollection from an in-memory Listlist:
public static void main(String[] args) { // Create a Java Collection, in this case a List of Strings. static final List<String> LINES = Arrays.asList( "To be, or not to be: that is the question: ", "Whether 'tis nobler in the mind to suffer ", "The slings and arrows of outrageous fortune, ", "Or to take arms against a sea of troubles, "); // Create the pipeline. PipelineOptions options = PipelineOptionsFactory.fromArgs(args).create(); Pipeline p = Pipeline.create(options); // Apply Create, passing the list and the coder, to create the PCollection. p.apply(Create.of(LINES)).setCoder(StringUtf8Coder.of()) }
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:model_pcollection %}
A PCollection is owned by the specific Pipeline object for which it is created; multiple pipelines cannot share a PCollection. In some respects, a PCollection functions like a collection class. However, a PCollection can differ in a few key ways:
The elements of a PCollection may be of any type, but must all be of the same type. However, to support distributed processing, Beam needs to be able to encode each individual element as a byte string (so elements can be passed around to distributed workers). The Beam SDKs provide a data encoding mechanism that includes built-in encoding for commonly-used types as well as support for specifying custom encodings as needed.
A PCollection is immutable. Once created, you cannot add, remove, or change individual elements. A Beam Transform might process each element of a PCollection and generate new pipeline data (as a new PCollection), but it does not consume or modify the original input collection.
A PCollection does not support random access to individual elements. Instead, Beam Transforms consider every element in a PCollection individually.
A PCollection is a large, immutable “bag” of elements. There is no upper limit on how many elements a PCollection can contain; any given PCollection might fit in memory on a single machine, or it might represent a very large distributed data set backed by a persistent data store.
A PCollection can be either bounded or unbounded in size. A bounded PCollection represents a data set of a known, fixed size, while an unbounded PCollection represents a data set of unlimited size. Whether a PCollection is bounded or unbounded depends on the source of the data set that it represents. Reading from a batch data source, such as a file or a database, creates a bounded PCollection. Reading from a streaming or continously-updating data source, such as Pub/Sub or Kafka, creates an unbounded PCollection (unless you explicitly tell it not to).
The bounded (or unbounded) nature of your PCollection affects how Beam processes your data. A bounded PCollection can be processed using a batch job, which might read the entire data set once, and perform processing in a job of finite length. An unbounded PCollection must be processed using a streaming job that runs continuously, as the entire collection can never be available for processing at any one time.
Beam uses windowing to divide a continuously updating unbounded PCollection into logical windows of finite size. These logical windows are determined by some characteristic associated with a data element, such as a timestamp. Aggregation transforms (such as GroupByKey and Combine) work on a per-window basis — as the data set is generated, they process each PCollection as a succession of these finite windows.
Each element in a PCollection has an associated intrinsic timestamp. The timestamp for each element is initially assigned by the Source that creates the PCollection. Sources that create an unbounded PCollection often assign each new element a timestamp that corresponds to when the element was read or added.
Note: Sources that create a bounded
PCollectionfor a fixed data set also automatically assign timestamps, but the most common behavior is to assign every element the same timestamp (Long.MIN_VALUE).
Timestamps are useful for a PCollection that contains elements with an inherent notion of time. If your pipeline is reading a stream of events, like Tweets or other social media messages, each element might use the time the event was posted as the element timestamp.
You can manually assign timestamps to the elements of a PCollection if the source doesn‘t do it for you. You’ll want to do this if the elements have an inherent timestamp, but the timestamp is somewhere in the structure of the element itself (such as a “time” field in a server log entry). Beam has Transforms that take a PCollection as input and output an identical PCollection with timestamps attached; see Adding Timestamps for more information about how to do so.
Transforms are the operations in your pipeline, and provide a generic processing framework. You provide processing logic in the form of a function object (colloquially referred to as “user code”), and your user code is applied to each element of an input PCollection (or more than one PCollection). Depending on the pipeline runner and back-end that you choose, many different workers across a cluster may execute instances of your user code in parallel. The user code running on each worker generates the output elements that are ultimately added to the final output PCollection that the transform produces.
The Beam SDKs contain a number of different transforms that you can apply to your pipeline‘s PCollections. These include general-purpose core transforms, such as ParDo or Combine. There are also pre-written composite transforms included in the SDKs, which combine one or more of the core transforms in a useful processing pattern, such as counting or combining elements in a collection. You can also define your own more complex composite transforms to fit your pipeline’s exact use case.
To invoke a transform, you must apply it to the input PCollection. Each transform in the Beam SDKs has a generic apply method (or pipe operator |). Invoking multiple Beam transforms is similar to method chaining, but with one slight difference: You apply the transform to the input PCollection, passing the transform itself as an argument, and the operation returns the output PCollection. This takes the general form:
[Output PCollection] = [Input PCollection].apply([Transform])
[Output PCollection] = [Input PCollection] | [Transform]
Because Beam uses a generic apply method for PCollection, you can both chain transforms sequentially and also apply transforms that contain other transforms nested within (called composite transforms in the Beam SDKs).
How you apply your pipeline's transforms determines the structure of your pipeline. The best way to think of your pipeline is as a directed acyclic graph, where the nodes are PCollections and the edges are transforms. For example, you can chain transforms to create a sequential pipeline, like this one:
[Final Output PCollection] = [Initial Input PCollection].apply([First Transform]) .apply([Second Transform]) .apply([Third Transform])
[Final Output PCollection] = ([Initial Input PCollection] | [First Transform] | [Second Transform] | [Third Transform])
The resulting workflow graph of the above pipeline looks like this.
Figure: A linear pipeline with three sequential transforms.
However, note that a transform does not consume or otherwise alter the input collection--remember that a PCollection is immutable by definition. This means that you can apply multiple transforms to the same input PCollection to create a branching pipeline, like so:
[Output PCollection 1] = [Input PCollection].apply([Transform 1]) [Output PCollection 2] = [Input PCollection].apply([Transform 2])
[Output PCollection 1] = [Input PCollection] | [Transform 1] [Output PCollection 2] = [Input PCollection] | [Transform 2]
The resulting workflow graph from the branching pipeline above looks like this.
Figure: A branching pipeline. Two transforms are applied to a single PCollection of database table rows.
You can also build your own composite transforms that nest multiple sub-steps inside a single, larger transform. Composite transforms are particularly useful for building a reusable sequence of simple steps that get used in a lot of different places.
Beam provides the following core transforms, each of which represents a different processing paradigm:
ParDoGroupByKeyCoGroupByKeyCombineFlattenPartitionParDo is a Beam transform for generic parallel processing. The ParDo processing paradigm is similar to the “Map” phase of a Map/Shuffle/Reduce-style algorithm: a ParDo transform considers each element in the input PCollection, performs some processing function (your user code) on that element, and emits zero, one, or multiple elements to an output PCollection.
ParDo is useful for a variety of common data processing operations, including:
ParDo to consider each element in a PCollection and either output that element to a new collection, or discard it.PCollection contains elements that are of a different type or format than you want, you can use ParDo to perform a conversion on each element and output the result to a new PCollection.PCollection of records with multiple fields, for example, you can use a ParDo to parse out just the fields you want to consider into a new PCollection.ParDo to perform simple or complex computations on every element, or certain elements, of a PCollection and output the results as a new PCollection.In such roles, ParDo is a common intermediate step in a pipeline. You might use it to extract certain fields from a set of raw input records, or convert raw input into a different format; you might also use ParDo to convert processed data into a format suitable for output, like database table rows or printable strings.
When you apply a ParDo transform, you'll need to provide user code in the form of a DoFn object. DoFn is a Beam SDK class that defines a distributed processing function.
When you create a subclass of
DoFn, note that your subclass should adhere to the Requirements for writing user code for Beam transforms.
Like all Beam transforms, you apply ParDo by calling the apply method on the input PCollection and passing ParDo as an argument, as shown in the following example code:
// The input PCollection of Strings. PCollection<String> words = ...; // The DoFn to perform on each element in the input PCollection. static class ComputeWordLengthFn extends DoFn<String, Integer> { ... } // Apply a ParDo to the PCollection "words" to compute lengths for each word. PCollection<Integer> wordLengths = words.apply( ParDo .of(new ComputeWordLengthFn())); // The DoFn to perform on each element, which // we define above.
# The input PCollection of Strings. words = ... # The DoFn to perform on each element in the input PCollection. {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_pardo_pardo %} {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_pardo_apply %}``` In the example, our input `PCollection` contains `String` values. We apply a `ParDo` transform that specifies a function (`ComputeWordLengthFn`) to compute the length of each string, and outputs the result to a new `PCollection` of `Integer` values that stores the length of each word. ##### 4.2.1.2. Creating a DoFn The `DoFn` object that you pass to `ParDo` contains the processing logic that gets applied to the elements in the input collection. When you use Beam, often the most important pieces of code you'll write are these `DoFn`s--they're what define your pipeline's exact data processing tasks. > **Note:** When you create your `DoFn`, be mindful of the [Requirements > for writing user code for Beam transforms](#requirements-for-writing-user-code-for-beam-transforms) > and ensure that your code follows them. {:.language-java} A `DoFn` processes one element at a time from the input `PCollection`. When you create a subclass of `DoFn`, you'll need to provide type parameters that match the types of the input and output elements. If your `DoFn` processes incoming `String` elements and produces `Integer` elements for the output collection (like our previous example, `ComputeWordLengthFn`), your class declaration would look like this: ```java static class ComputeWordLengthFn extends DoFn<String, Integer> { ... }
{:.language-java} Inside your DoFn subclass, you‘ll write a method annotated with @ProcessElement where you provide the actual processing logic. You don’t need to manually extract the elements from the input collection; the Beam SDKs handle that for you. Your @ProcessElement method should accept a parameter tagged with @Element, which will be populated with the input element. In order to output elements, the method can also take a parameter of type OutputReceiver which provides a method for emitting elements. The parameter types must match the input and output types of your DoFn or the framework will raise an error. Note: @Element and OutputReceiver were introduced in Beam 2.5.0; if using an earlier release of Beam, a ProcessContext parameter should be used instead.
{:.language-py} Inside your DoFn subclass, you‘ll write a method process where you provide the actual processing logic. You don’t need to manually extract the elements from the input collection; the Beam SDKs handle that for you. Your process method should accept an object of type element. This is the input element and output is emitted by using yield or return statement inside process method.
static class ComputeWordLengthFn extends DoFn<String, Integer> { @ProcessElement public void processElement(@Element String word, OutputReceiver<Integer> out) { // Use OutputReceiver.output to emit the output element. out.output(word.length()); } }
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_pardo_pardo %}
{:.language-java}
Note: If the elements in your input
PCollectionare key/value pairs, you can access the key or value by usingelement.getKey()orelement.getValue(), respectively.
A given DoFn instance generally gets invoked one or more times to process some arbitrary bundle of elements. However, Beam doesn't guarantee an exact number of invocations; it may be invoked multiple times on a given worker node to account for failures and retries. As such, you can cache information across multiple calls to your processing method, but if you do so, make sure the implementation does not depend on the number of invocations.
In your processing method, you'll also need to meet some immutability requirements to ensure that Beam and the processing back-end can safely serialize and cache the values in your pipeline. Your method should meet the following requirements:
{:.language-java}
@Element annotation or ProcessContext.sideInput() (the incoming elements from the input collection).OutputReceiver.output() you should not modify that value in any way.If your function is relatively straightforward, you can simplify your use of ParDo by providing a lightweight DoFn in-line, as an anonymous inner class instance a lambda function.
Here's the previous example, ParDo with ComputeLengthWordsFn, with the DoFn specified as an anonymous inner class instance a lambda function:
// The input PCollection. PCollection<String> words = ...; // Apply a ParDo with an anonymous DoFn to the PCollection words. // Save the result as the PCollection wordLengths. PCollection<Integer> wordLengths = words.apply( "ComputeWordLengths", // the transform name ParDo.of(new DoFn<String, Integer>() { // a DoFn as an anonymous inner class instance @ProcessElement public void processElement(@Element String word, OutputReceiver<Integer> out) { out.output(word.length()); } }));
# The input PCollection of strings. words = ... # Apply a lambda function to the PCollection words. # Save the result as the PCollection word_lengths. {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_pardo_using_flatmap %}``` If your `ParDo` performs a one-to-one mapping of input elements to output elements--that is, for each input element, it applies a function that produces *exactly one* output element, you can use the higher-level <span class="language-java">`MapElements`</span><span class="language-py">`Map`</span> transform. <span class="language-java">`MapElements` can accept an anonymous Java 8 lambda function for additional brevity.</span> Here's the previous example using <span class="language-java">`MapElements`</span> <span class="language-py">`Map`</span>: ```java // The input PCollection. PCollection<String> words = ...; // Apply a MapElements with an anonymous lambda function to the PCollection words. // Save the result as the PCollection wordLengths. PCollection<Integer> wordLengths = words.apply( MapElements.into(TypeDescriptors.integers()) .via((String word) -> word.length()));
# The input PCollection of string. words = ... # Apply a Map with a lambda function to the PCollection words. # Save the result as the PCollection word_lengths. {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_pardo_using_map %}``` {:.language-java} > **Note:** You can use Java 8 lambda functions with several other Beam > transforms, including `Filter`, `FlatMapElements`, and `Partition`. #### 4.2.2. GroupByKey {#groupbykey} `GroupByKey` is a Beam transform for processing collections of key/value pairs. It's a parallel reduction operation, analogous to the Shuffle phase of a Map/Shuffle/Reduce-style algorithm. The input to `GroupByKey` is a collection of key/value pairs that represents a *multimap*, where the collection contains multiple pairs that have the same key, but different values. Given such a collection, you use `GroupByKey` to collect all of the values associated with each unique key. `GroupByKey` is a good way to aggregate data that has something in common. For example, if you have a collection that stores records of customer orders, you might want to group together all the orders from the same postal code (wherein the "key" of the key/value pair is the postal code field, and the "value" is the remainder of the record). Let's examine the mechanics of `GroupByKey` with a simple example case, where our data set consists of words from a text file and the line number on which they appear. We want to group together all the line numbers (values) that share the same word (key), letting us see all the places in the text where a particular word appears. Our input is a `PCollection` of key/value pairs where each word is a key, and the value is a line number in the file where the word appears. Here's a list of the key/value pairs in the input collection:
cat, 1 dog, 5 and, 1 jump, 3 tree, 2 cat, 5 dog, 2 and, 2 cat, 9 and, 6 ...
`GroupByKey` gathers up all the values with the same key and outputs a new pair consisting of the unique key and a collection of all of the values that were associated with that key in the input collection. If we apply `GroupByKey` to our input collection above, the output collection would look like this:
cat, [1,5,9] dog, [5,2] and, [1,2,6] jump, [3] tree, [2] ...
Thus, `GroupByKey` represents a transform from a multimap (multiple keys to individual values) to a uni-map (unique keys to collections of values). ##### 4.2.2.1 GroupByKey and unbounded PCollections {#groupbykey-and-unbounded-pcollections} If you are using unbounded `PCollection`s, you must use either [non-global windowing](#setting-your-pcollections-windowing-function) or an [aggregation trigger](#triggers) in order to perform a `GroupByKey` or [CoGroupByKey](#cogroupbykey). This is because a bounded `GroupByKey` or `CoGroupByKey` must wait for all the data with a certain key to be collected, but with unbounded collections, the data is unlimited. Windowing and/or triggers allow grouping to operate on logical, finite bundles of data within the unbounded data streams. If you do apply `GroupByKey` or `CoGroupByKey` to a group of unbounded `PCollection`s without setting either a non-global windowing strategy, a trigger strategy, or both for each collection, Beam generates an IllegalStateException error at pipeline construction time. When using `GroupByKey` or `CoGroupByKey` to group `PCollection`s that have a [windowing strategy](#windowing) applied, all of the `PCollection`s you want to group *must use the same windowing strategy* and window sizing. For example, all of the collections you are merging must use (hypothetically) identical 5-minute fixed windows, or 4-minute sliding windows starting every 30 seconds. If your pipeline attempts to use `GroupByKey` or `CoGroupByKey` to merge `PCollection`s with incompatible windows, Beam generates an IllegalStateException error at pipeline construction time. #### 4.2.3. CoGroupByKey {#cogroupbykey} `CoGroupByKey` performs a relational join of two or more key/value `PCollection`s that have the same key type. [Design Your Pipeline]({{ site.baseurl }}/documentation/pipelines/design-your-pipeline/#multiple-sources) shows an example pipeline that uses a join. Consider using `CoGroupByKey` if you have multiple data sets that provide information about related things. For example, let's say you have two different files with user data: one file has names and email addresses; the other file has names and phone numbers. You can join those two data sets, using the user name as a common key and the other data as the associated values. After the join, you have one data set that contains all of the information (email addresses and phone numbers) associated with each name. If you are using unbounded `PCollection`s, you must use either [non-global windowing](#setting-your-pcollections-windowing-function) or an [aggregation trigger](#triggers) in order to perform a `CoGroupByKey`. See [GroupByKey and unbounded PCollections](#groupbykey-and-unbounded-pcollections) for more details. <span class="language-java"> In the Beam SDK for Java, `CoGroupByKey` accepts a tuple of keyed `PCollection`s (`PCollection<KV<K, V>>`) as input. For type safety, the SDK requires you to pass each `PCollection` as part of a `KeyedPCollectionTuple`. You must declare a `TupleTag` for each input `PCollection` in the `KeyedPCollectionTuple` that you want to pass to `CoGroupByKey`. As output, `CoGroupByKey` returns a `PCollection<KV<K, CoGbkResult>>`, which groups values from all the input `PCollection`s by their common keys. Each key (all of type `K`) will have a different `CoGbkResult`, which is a map from `TupleTag<T>` to `Iterable<T>`. You can access a specific collection in an `CoGbkResult` object by using the `TupleTag` that you supplied with the initial collection. </span> <span class="language-py"> In the Beam SDK for Python, `CoGroupByKey` accepts a dictionary of keyed `PCollection`s as input. As output, `CoGroupByKey` creates a single output `PCollection` that contains one key/value tuple for each key in the input `PCollection`s. Each key's value is a dictionary that maps each tag to an iterable of the values under they key in the corresponding `PCollection`. </span> The following conceptual examples use two input collections to show the mechanics of `CoGroupByKey`. <span class="language-java"> The first set of data has a `TupleTag<String>` called `emailsTag` and contains names and email addresses. The second set of data has a `TupleTag<String>` called `phonesTag` and contains names and phone numbers. </span> <span class="language-py"> The first set of data contains names and email addresses. The second set of data contains names and phone numbers. </span> ```java {% github_sample /apache/beam/blob/master/examples/java/src/test/java/org/apache/beam/examples/snippets/SnippetsTest.java tag:CoGroupByKeyTupleInputs %}``` ```py {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_group_by_key_cogroupbykey_tuple_inputs %}``` After `CoGroupByKey`, the resulting data contains all data associated with each unique key from any of the input collections. ```java {% github_sample /apache/beam/blob/master/examples/java/src/test/java/org/apache/beam/examples/snippets/SnippetsTest.java tag:CoGroupByKeyTupleOutputs %}``` ```py {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_group_by_key_cogroupbykey_tuple_outputs %}``` The following code example joins the two `PCollection`s with `CoGroupByKey`, followed by a `ParDo` to consume the result. Then, the code uses tags to look up and format data from each collection. ```java {% github_sample /apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/snippets/Snippets.java tag:CoGroupByKeyTuple %}``` ```py {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:model_group_by_key_cogroupbykey_tuple %}``` The formatted data looks like this: ```java {% github_sample /apache/beam/blob/master/examples/java/src/test/java/org/apache/beam/examples/snippets/SnippetsTest.java tag:CoGroupByKeyTupleFormattedOutputs %}``` ```py {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_group_by_key_cogroupbykey_tuple_formatted_outputs %}``` #### 4.2.4. Combine {#combine} <span class="language-java">[`Combine`](https://beam.apache.org/releases/javadoc/{{ site.release_latest }}/index.html?org/apache/beam/sdk/transforms/Combine.html)</span> <span class="language-py">[`Combine`](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/transforms/core.py)</span> is a Beam transform for combining collections of elements or values in your data. `Combine` has variants that work on entire `PCollection`s, and some that combine the values for each key in `PCollection`s of key/value pairs. When you apply a `Combine` transform, you must provide the function that contains the logic for combining the elements or values. The combining function should be commutative and associative, as the function is not necessarily invoked exactly once on all values with a given key. Because the input data (including the value collection) may be distributed across multiple workers, the combining function might be called multiple times to perform partial combining on subsets of the value collection. The Beam SDK also provides some pre-built combine functions for common numeric combination operations such as sum, min, and max. Simple combine operations, such as sums, can usually be implemented as a simple function. More complex combination operations might require you to create a subclass of `CombineFn` that has an accumulation type distinct from the input/output type. ##### 4.2.4.1. Simple combinations using simple functions {#simple-combines} The following example code shows a simple combine function. ```java // Sum a collection of Integer values. The function SumInts implements the interface SerializableFunction. public static class SumInts implements SerializableFunction<Iterable<Integer>, Integer> { @Override public Integer apply(Iterable<Integer> input) { int sum = 0; for (int item : input) { sum += item; } return sum; } }
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:combine_bounded_sum %}``` ##### 4.2.4.2. Advanced combinations using CombineFn {#advanced-combines} For more complex combine functions, you can define a subclass of `CombineFn`. You should use `CombineFn` if the combine function requires a more sophisticated accumulator, must perform additional pre- or post-processing, might change the output type, or takes the key into account. A general combining operation consists of four operations. When you create a subclass of `CombineFn`, you must provide four operations by overriding the corresponding methods: 1. **Create Accumulator** creates a new "local" accumulator. In the example case, taking a mean average, a local accumulator tracks the running sum of values (the numerator value for our final average division) and the number of values summed so far (the denominator value). It may be called any number of times in a distributed fashion. 2. **Add Input** adds an input element to an accumulator, returning the accumulator value. In our example, it would update the sum and increment the count. It may also be invoked in parallel. 3. **Merge Accumulators** merges several accumulators into a single accumulator; this is how data in multiple accumulators is combined before the final calculation. In the case of the mean average computation, the accumulators representing each portion of the division are merged together. It may be called again on its outputs any number of times. 4. **Extract Output** performs the final computation. In the case of computing a mean average, this means dividing the combined sum of all the values by the number of values summed. It is called once on the final, merged accumulator. The following example code shows how to define a `CombineFn` that computes a mean average: ```java public class AverageFn extends CombineFn<Integer, AverageFn.Accum, Double> { public static class Accum { int sum = 0; int count = 0; } @Override public Accum createAccumulator() { return new Accum(); } @Override public Accum addInput(Accum accum, Integer input) { accum.sum += input; accum.count++; return accum; } @Override public Accum mergeAccumulators(Iterable<Accum> accums) { Accum merged = createAccumulator(); for (Accum accum : accums) { merged.sum += accum.sum; merged.count += accum.count; } return merged; } @Override public Double extractOutput(Accum accum) { return ((double) accum.sum) / accum.count; } }
pc = ... {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:combine_custom_average_define %}``` If you are combining a `PCollection` of key-value pairs, [per-key combining](#combining-values-in-a-keyed-pcollection) is often enough. If you need the combining strategy to change based on the key (for example, MIN for some users and MAX for other users), you can define a `KeyedCombineFn` to access the key within the combining strategy. ##### 4.2.4.3. Combining a PCollection into a single value {#combining-pcollection} Use the global combine to transform all of the elements in a given `PCollection` into a single value, represented in your pipeline as a new `PCollection` containing one element. The following example code shows how to apply the Beam provided sum combine function to produce a single sum value for a `PCollection` of integers. ```java // Sum.SumIntegerFn() combines the elements in the input PCollection. The resulting PCollection, called sum, // contains one value: the sum of all the elements in the input PCollection. PCollection<Integer> pc = ...; PCollection<Integer> sum = pc.apply( Combine.globally(new Sum.SumIntegerFn()));
# sum combines the elements in the input PCollection. # The resulting PCollection, called result, contains one value: the sum of all # the elements in the input PCollection. pc = ... {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:combine_custom_average_execute %}``` ##### 4.2.4.4. Combine and global windowing {#combine-global-windowing} If your input `PCollection` uses the default global windowing, the default behavior is to return a `PCollection` containing one item. That item's value comes from the accumulator in the combine function that you specified when applying `Combine`. For example, the Beam provided sum combine function returns a zero value (the sum of an empty input), while the min combine function returns a maximal or infinite value. To have `Combine` instead return an empty `PCollection` if the input is empty, specify `.withoutDefaults` when you apply your `Combine` transform, as in the following code example: ```java PCollection<Integer> pc = ...; PCollection<Integer> sum = pc.apply( Combine.globally(new Sum.SumIntegerFn()).withoutDefaults());
pc = ... sum = pc | beam.CombineGlobally(sum).without_defaults()
If your PCollection uses any non-global windowing function, Beam does not provide the default behavior. You must specify one of the following options when applying Combine:
.withoutDefaults, where windows that are empty in the input PCollection will likewise be empty in the output collection..asSingletonView, in which the output is immediately converted to a PCollectionView, which will provide a default value for each empty window when used as a side input. You‘ll generally only need to use this option if the result of your pipeline’s Combine is to be used as a side input later in the pipeline.After creating a keyed PCollection (for example, by using a GroupByKey transform), a common pattern is to combine the collection of values associated with each key into a single, merged value. Drawing on the previous example from GroupByKey, a key-grouped PCollection called groupedWords looks like this:
cat, [1,5,9] dog, [5,2] and, [1,2,6] jump, [3] tree, [2] ...
In the above PCollection, each element has a string key (for example, “cat”) and an iterable of integers for its value (in the first element, containing [1, 5, 9]). If our pipeline‘s next processing step combines the values (rather than considering them individually), you can combine the iterable of integers to create a single, merged value to be paired with each key. This pattern of a GroupByKey followed by merging the collection of values is equivalent to Beam’s Combine PerKey transform. The combine function you supply to Combine PerKey must be an associative reduction function or a subclass of CombineFn.
// PCollection is grouped by key and the Double values associated with each key are combined into a Double. PCollection<KV<String, Double>> salesRecords = ...; PCollection<KV<String, Double>> totalSalesPerPerson = salesRecords.apply(Combine.<String, Double, Double>perKey( new Sum.SumDoubleFn())); // The combined value is of a different type than the original collection of values per key. PCollection has // keys of type String and values of type Integer, and the combined value is a Double. PCollection<KV<String, Integer>> playerAccuracy = ...; PCollection<KV<String, Double>> avgAccuracyPerPlayer = playerAccuracy.apply(Combine.<String, Integer, Double>perKey( new MeanInts())));
# PCollection is grouped by key and the numeric values associated with each key # are averaged into a float. player_accuracies = ... {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:combine_per_key %}
[Flatten](https://beam.apache.org/releases/javadoc/{{ site.release_latest }}/index.html?org/apache/beam/sdk/transforms/Flatten.html) Flatten and is a Beam transform for PCollection objects that store the same data type. Flatten merges multiple PCollection objects into a single logical PCollection.
The following example shows how to apply a Flatten transform to merge multiple PCollection objects.
// Flatten takes a PCollectionList of PCollection objects of a given type. // Returns a single PCollection that contains all of the elements in the PCollection objects in that list. PCollection<String> pc1 = ...; PCollection<String> pc2 = ...; PCollection<String> pc3 = ...; PCollectionList<String> collections = PCollectionList.of(pc1).and(pc2).and(pc3); PCollection<String> merged = collections.apply(Flatten.<String>pCollections());
# Flatten takes a tuple of PCollection objects. # Returns a single PCollection that contains all of the elements in the PCollection objects in that tuple. {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:model_multiple_pcollections_flatten %}
By default, the coder for the output PCollection is the same as the coder for the first PCollection in the input PCollectionList. However, the input PCollection objects can each use different coders, as long as they all contain the same data type in your chosen language.
When using Flatten to merge PCollection objects that have a windowing strategy applied, all of the PCollection objects you want to merge must use a compatible windowing strategy and window sizing. For example, all the collections you're merging must all use (hypothetically) identical 5-minute fixed windows or 4-minute sliding windows starting every 30 seconds.
If your pipeline attempts to use Flatten to merge PCollection objects with incompatible windows, Beam generates an IllegalStateException error when your pipeline is constructed.
[Partition](https://beam.apache.org/releases/javadoc/{{ site.release_latest }}/index.html?org/apache/beam/sdk/transforms/Partition.html) Partition is a Beam transform for PCollection objects that store the same data type. Partition splits a single PCollection into a fixed number of smaller collections.
Partition divides the elements of a PCollection according to a partitioning function that you provide. The partitioning function contains the logic that determines how to split up the elements of the input PCollection into each resulting partition PCollection. The number of partitions must be determined at graph construction time. You can, for example, pass the number of partitions as a command-line option at runtime (which will then be used to build your pipeline graph), but you cannot determine the number of partitions in mid-pipeline (based on data calculated after your pipeline graph is constructed, for instance).
The following example divides a PCollection into percentile groups.
// Provide an int value with the desired number of result partitions, and a PartitionFn that represents the // partitioning function. In this example, we define the PartitionFn in-line. Returns a PCollectionList // containing each of the resulting partitions as individual PCollection objects. PCollection<Student> students = ...; // Split students up into 10 partitions, by percentile: PCollectionList<Student> studentsByPercentile = students.apply(Partition.of(10, new PartitionFn<Student>() { public int partitionFor(Student student, int numPartitions) { return student.getPercentile() // 0..99 * numPartitions / 100; }})); // You can extract each partition from the PCollectionList using the get method, as follows: PCollection<Student> fortiethPercentile = studentsByPercentile.get(4);
# Provide an int value with the desired number of result partitions, and a partitioning function (partition_fn in this example). # Returns a tuple of PCollection objects containing each of the resulting partitions as individual PCollection objects. students = ... {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:model_multiple_pcollections_partition %} # You can extract each partition from the tuple of PCollection objects as follows: {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:model_multiple_pcollections_partition_40th %}
When you build user code for a Beam transform, you should keep in mind the distributed nature of execution. For example, there might be many copies of your function running on a lot of different machines in parallel, and those copies function independently, without communicating or sharing state with any of the other copies. Depending on the Pipeline Runner and processing back-end you choose for your pipeline, each copy of your user code function may be retried or run multiple times. As such, you should be cautious about including things like state dependency in your user code.
In general, your user code must fulfill at least these requirements:
In addition, it's recommended that you make your function object idempotent. Non-idempotent functions are supported by Beam, but require additional thought to ensure correctness when there are external side effects.
Note: These requirements apply to subclasses of
DoFn(a function object used with the ParDo transform),CombineFn(a function object used with the Combine transform), andWindowFn(a function object used with the Window transform).
Any function object you provide to a transform must be fully serializable. This is because a copy of the function needs to be serialized and transmitted to a remote worker in your processing cluster. The base classes for user code, such as DoFn, CombineFn, and WindowFn, already implement Serializable; however, your subclass must not add any non-serializable members.
Some other serializability factors you should keep in mind are:
Your function object should be thread-compatible. Each instance of your function object is accessed by a single thread at a time on a worker instance, unless you explicitly create your own threads. Note, however, that the Beam SDKs are not thread-safe. If you create your own threads in your user code, you must provide your own synchronization. Note that static members in your function object are not passed to worker instances and that multiple instances of your function may be accessed from different threads.
It‘s recommended that you make your function object idempotent--that is, that it can be repeated or retried as often as necessary without causing unintended side effects. Non-idempotent functions are supported, however the Beam model provides no guarantees as to the number of times your user code might be invoked or retried; as such, keeping your function object idempotent keeps your pipeline’s output deterministic, and your transforms' behavior more predictable and easier to debug.
In addition to the main input PCollection, you can provide additional inputs to a ParDo transform in the form of side inputs. A side input is an additional input that your DoFn can access each time it processes an element in the input PCollection. When you specify a side input, you create a view of some other data that can be read from within the ParDo transform's DoFn while procesing each element.
Side inputs are useful if your ParDo needs to inject additional data when processing each element in the input PCollection, but the additional data needs to be determined at runtime (and not hard-coded). Such values might be determined by the input data, or depend on a different branch of your pipeline.
// Pass side inputs to your ParDo transform by invoking .withSideInputs. // Inside your DoFn, access the side input by using the method DoFn.ProcessContext.sideInput. // The input PCollection to ParDo. PCollection<String> words = ...; // A PCollection of word lengths that we'll combine into a single value. PCollection<Integer> wordLengths = ...; // Singleton PCollection // Create a singleton PCollectionView from wordLengths using Combine.globally and View.asSingleton. final PCollectionView<Integer> maxWordLengthCutOffView = wordLengths.apply(Combine.globally(new Max.MaxIntFn()).asSingletonView()); // Apply a ParDo that takes maxWordLengthCutOffView as a side input. PCollection<String> wordsBelowCutOff = words.apply(ParDo .of(new DoFn<String, String>() { @ProcessElement public void processElement(@Element String word, OutputReceiver<String> out, ProcessContext c) { // In our DoFn, access the side input. int lengthCutOff = c.sideInput(maxWordLengthCutOffView); if (word.length() <= lengthCutOff) { out.output(word); } } }).withSideInputs(maxWordLengthCutOffView) );
# Side inputs are available as extra arguments in the DoFn's process method or Map / FlatMap's callable. # Optional, positional, and keyword arguments are all supported. Deferred arguments are unwrapped into their # actual values. For example, using pvalue.AsIteor(pcoll) at pipeline construction time results in an iterable # of the actual elements of pcoll being passed into each process invocation. In this example, side inputs are # passed to a FlatMap transform as extra arguments and consumed by filter_using_length. words = ... {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_pardo_side_input %} # We can also pass side inputs to a ParDo transform, which will get passed to its process method. # The first two arguments for the process method would be self and element. {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_pardo_side_input_dofn %} ...
A windowed PCollection may be infinite and thus cannot be compressed into a single value (or single collection class). When you create a PCollectionView of a windowed PCollection, the PCollectionView represents a single entity per window (one singleton per window, one list per window, etc.).
Beam uses the window(s) for the main input element to look up the appropriate window for the side input element. Beam projects the main input element‘s window into the side input’s window set, and then uses the side input from the resulting window. If the main input and side inputs have identical windows, the projection provides the exact corresponding window. However, if the inputs have different windows, Beam uses the projection to choose the most appropriate side input window.
For example, if the main input is windowed using fixed-time windows of one minute, and the side input is windowed using fixed-time windows of one hour, Beam projects the main input window against the side input window set and selects the side input value from the appropriate hour-long side input window.
If the main input element exists in more than one window, then processElement gets called multiple times, once for each window. Each call to processElement projects the “current” window for the main input element, and thus might provide a different view of the side input each time.
If the side input has multiple trigger firings, Beam uses the value from the latest trigger firing. This is particularly useful if you use a side input with a single global window and specify a trigger.
While ParDo always produces a main output PCollection (as the return value from apply), you can also have your ParDo produce any number of additional output PCollections. If you choose to have multiple outputs, your ParDo returns all of the output PCollections (including the main output) bundled together.
// To emit elements to multiple output PCollections, create a TupleTag object to identify each collection // that your ParDo produces. For example, if your ParDo produces three output PCollections (the main output // and two additional outputs), you must create three TupleTags. The following example code shows how to // create TupleTags for a ParDo with three output PCollections. // Input PCollection to our ParDo. PCollection<String> words = ...; // The ParDo will filter words whose length is below a cutoff and add them to // the main ouput PCollection<String>. // If a word is above the cutoff, the ParDo will add the word length to an // output PCollection<Integer>. // If a word starts with the string "MARKER", the ParDo will add that word to an // output PCollection<String>. final int wordLengthCutOff = 10; // Create three TupleTags, one for each output PCollection. // Output that contains words below the length cutoff. final TupleTag<String> wordsBelowCutOffTag = new TupleTag<String>(){}; // Output that contains word lengths. final TupleTag<Integer> wordLengthsAboveCutOffTag = new TupleTag<Integer>(){}; // Output that contains "MARKER" words. final TupleTag<String> markedWordsTag = new TupleTag<String>(){}; // Passing Output Tags to ParDo: // After you specify the TupleTags for each of your ParDo outputs, pass the tags to your ParDo by invoking // .withOutputTags. You pass the tag for the main output first, and then the tags for any additional outputs // in a TupleTagList. Building on our previous example, we pass the three TupleTags for our three output // PCollections to our ParDo. Note that all of the outputs (including the main output PCollection) are // bundled into the returned PCollectionTuple. PCollectionTuple results = words.apply(ParDo .of(new DoFn<String, String>() { // DoFn continues here. ... }) // Specify the tag for the main output. .withOutputTags(wordsBelowCutOffTag, // Specify the tags for the two additional outputs as a TupleTagList. TupleTagList.of(wordLengthsAboveCutOffTag) .and(markedWordsTag)));
# To emit elements to multiple output PCollections, invoke with_outputs() on the ParDo, and specify the # expected tags for the outputs. with_outputs() returns a DoOutputsTuple object. Tags specified in # with_outputs are attributes on the returned DoOutputsTuple object. The tags give access to the # corresponding output PCollections. {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_pardo_with_tagged_outputs %} # The result is also iterable, ordered in the same order that the tags were passed to with_outputs(), # the main tag (if specified) first. {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_pardo_with_tagged_outputs_iter %}``` #### 4.5.2. Emitting to multiple outputs in your DoFn {#multiple-outputs-dofn} ```java // Inside your ParDo's DoFn, you can emit an element to a specific output PCollection by providing a // MultiOutputReceiver to your process method, and passing in the appropriate TupleTag to obtain an OutputReceiver. // After your ParDo, extract the resulting output PCollections from the returned PCollectionTuple. // Based on the previous example, this shows the DoFn emitting to the main output and two additional outputs. .of(new DoFn<String, String>() { public void processElement(@Element String word, MultiOutputReceiver out) { if (word.length() <= wordLengthCutOff) { // Emit short word to the main output. // In this example, it is the output with tag wordsBelowCutOffTag. out.get(wordsBelowCutOffTag).output(word); } else { // Emit long word length to the output with tag wordLengthsAboveCutOffTag. out.get(wordLengthsAboveCutOffTag).output(word.length()); } if (word.startsWith("MARKER")) { // Emit word to the output with tag markedWordsTag. out.get(markedWordsTag).output(word); } }}));
# Inside your ParDo's DoFn, you can emit an element to a specific output by wrapping the value and the output tag (str). # using the pvalue.OutputValue wrapper class. # Based on the previous example, this shows the DoFn emitting to the main output and two additional outputs. {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_pardo_emitting_values_on_tagged_outputs %} # Producing multiple outputs is also available in Map and FlatMap. # Here is an example that uses FlatMap and shows that the tags do not need to be specified ahead of time. {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_pardo_with_undeclared_outputs %}``` {:.language-java} #### 4.5.3. Accessing additional parameters in your DoFn {#other-dofn-parameters} {:.language-java} In addition to the element and the `OutputReceiver`, Beam will populate other parameters to your DoFn's `@ProcessElement` method. Any combination of these parameters can be added to your process method in any order. {:.language-java} **Timestamp:** To access the timestamp of an input element, add a parameter annotated with `@Timestamp` of type `Instant`. For example: ```java .of(new DoFn<String, String>() { public void processElement(@Element String word, @Timestamp Instant timestamp) { }})
{:.language-java} Window: To access the window an input element falls into, add a parameter of the type of the window used for the input PCollection. If the parameter is a window type (a subclass of BoundedWindow) that does not match the input PCollection, then an error will be raised. If an element falls in multiple windows (for example, this will happen when using SlidingWindows), then the @ProcessElement method will be invoked multiple time for the element, once for each window. For example, when fixed windows are being used, the window is of type IntervalWindow.
.of(new DoFn<String, String>() { public void processElement(@Element String word, IntervalWindow window) { }})
{:.language-java} PaneInfo: When triggers are used, Beam provides a PaneInfo object that contains information about the current firing. Using PaneInfo you can determine whether this is an early or a late firing, and how many times this window has already fired for this key.
.of(new DoFn<String, String>() { public void processElement(@Element String word, PaneInfo paneInfo) { }})
{:.language-java} PipelineOptions: The PipelineOptions for the current pipeline can always be accessed in a process method by adding it as a parameter:
.of(new DoFn<String, String>() { public void processElement(@Element String word, PipelineOptions options) { }})
{:.language-java} @OnTimer methods can also access many of these parameters. Timestamp, window, PipelineOptions, OutputReceiver, and MultiOutputReceiver parameters can all be accessed in an @OnTimer method. In addition, an @OnTimer method can take a parameter of type TimeDomain which tells whether the timer is based on event time or processing time.
Transforms can have a nested structure, where a complex transform performs multiple simpler transforms (such as more than one ParDo, Combine, GroupByKey, or even other composite transforms). These transforms are called composite transforms. Nesting multiple transforms inside a single composite transform can make your code more modular and easier to understand.
The Beam SDK comes packed with many useful composite transforms. See the API reference pages for a list of transforms:
The CountWords transform in the [WordCount example program]({{ site.baseurl }}/get-started/wordcount-example/) is an example of a composite transform. CountWords is a PTransform subclass that consists of multiple nested transforms.
In its expand method, the CountWords transform applies the following transform operations:
ParDo on the input PCollection of text lines, producing an output PCollection of individual words.Count on the PCollection of words, producing a PCollection of key/value pairs. Each key represents a word in the text, and each value represents the number of times that word appeared in the original data.Note that this is also an example of nested composite transforms, as Count is, by itself, a composite transform.
Your composite transform‘s parameters and return value must match the initial input type and final return type for the entire transform, even if the transform’s intermediate data changes type multiple times.
public static class CountWords extends PTransform<PCollection<String>, PCollection<KV<String, Long>>> { @Override public PCollection<KV<String, Long>> expand(PCollection<String> lines) { // Convert lines of text into individual words. PCollection<String> words = lines.apply( ParDo.of(new ExtractWordsFn())); // Count the number of times each word occurs. PCollection<KV<String, Long>> wordCounts = words.apply(Count.<String>perElement()); return wordCounts; } }
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:pipeline_monitoring_composite %}``` #### 4.6.2. Creating a composite transform {#composite-transform-creation} To create your own composite transform, create a subclass of the `PTransform` class and override the `expand` method to specify the actual processing logic. You can then use this transform just as you would a built-in transform from the Beam SDK. {:.language-java} For the `PTransform` class type parameters, you pass the `PCollection` types that your transform takes as input, and produces as output. To take multiple `PCollection`s as input, or produce multiple `PCollection`s as output, use one of the multi-collection types for the relevant type parameter. The following code sample shows how to declare a `PTransform` that accepts a `PCollection` of `String`s for input, and outputs a `PCollection` of `Integer`s: ```java static class ComputeWordLengths extends PTransform<PCollection<String>, PCollection<Integer>> { ... }
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_composite_transform %}``` Within your `PTransform` subclass, you'll need to override the `expand` method. The `expand` method is where you add the processing logic for the `PTransform`. Your override of `expand` must accept the appropriate type of input `PCollection` as a parameter, and specify the output `PCollection` as the return value. The following code sample shows how to override `expand` for the `ComputeWordLengths` class declared in the previous example: ```java static class ComputeWordLengths extends PTransform<PCollection<String>, PCollection<Integer>> { @Override public PCollection<Integer> expand(PCollection<String>) { ... // transform logic goes here ... }
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_composite_transform %}``` As long as you override the `expand` method in your `PTransform` subclass to accept the appropriate input `PCollection`(s) and return the corresponding output `PCollection`(s), you can include as many transforms as you want. These transforms can include core transforms, composite transforms, or the transforms included in the Beam SDK libraries. **Note:** The `expand` method of a `PTransform` is not meant to be invoked directly by the user of a transform. Instead, you should call the `apply` method on the `PCollection` itself, with the transform as an argument. This allows transforms to be nested within the structure of your pipeline. #### 4.6.3. PTransform Style Guide {#ptransform-style-guide} The [PTransform Style Guide]({{ site.baseurl }}/contribute/ptransform-style-guide/) contains additional information not included here, such as style guidelines, logging and testing guidance, and language-specific considerations. The guide is a useful starting point when you want to write new composite PTransforms. ## 5. Pipeline I/O {#pipeline-io} When you create a pipeline, you often need to read data from some external source, such as a file or a database. Likewise, you may want your pipeline to output its result data to an external storage system. 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/). ### 5.1. Reading input data {#pipeline-io-reading-data} Read transforms read data from an external source and return a `PCollection` representation of the data for use by your pipeline. You can use a read transform at any point while constructing your pipeline to create a new `PCollection`, though it will be most common at the start of your pipeline. ```java PCollection<String> lines = p.apply(TextIO.read().from("gs://some/inputData.txt"));
lines = pipeline | beam.io.ReadFromText('gs://some/inputData.txt')
Write transforms write the data in a PCollection to an external data source. You will most often use write transforms at the end of your pipeline to output your pipeline's final results. However, you can use a write transform to output a PCollection's data at any point in your pipeline.
output.apply(TextIO.write().to("gs://some/outputData"));
output | beam.io.WriteToText('gs://some/outputData')
Many read transforms support reading from multiple input files matching a glob operator you provide. Note that glob operators are filesystem-specific and obey filesystem-specific consistency models. The following TextIO example uses a glob operator (*) to read all matching input files that have prefix “input-” and the suffix “.csv” in the given location:
p.apply(“ReadFromText”, TextIO.read().from("protocol://my_bucket/path/to/input-*.csv");
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:model_pipelineio_read %}
To read data from disparate sources into a single PCollection, read each one independently and then use the Flatten transform to create a single PCollection.
For file-based output data, write transforms write to multiple output files by default. When you pass an output file name to a write transform, the file name is used as the prefix for all output files that the write transform produces. You can append a suffix to each output file by specifying a suffix.
The following write transform example writes multiple output files to a location. Each file has the prefix “numbers”, a numeric tag, and the suffix “.csv”.
records.apply("WriteToText", TextIO.write().to("protocol://my_bucket/path/to/numbers") .withSuffix(".csv"));
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py tag:model_pipelineio_write %}
See the [Beam-provided I/O Transforms]({{site.baseurl }}/documentation/io/built-in/) page for a list of the currently available I/O transforms.
When Beam runners execute your pipeline, they often need to materialize the intermediate data in your PCollections, which requires converting elements to and from byte strings. The Beam SDKs use objects called Coders to describe how the elements of a given PCollection may be encoded and decoded.
Note that coders are unrelated to parsing or formatting data when interacting with external data sources or sinks. Such parsing or formatting should typically be done explicitly, using transforms such as
ParDoorMapElements.
{:.language-java} In the Beam SDK for Java, the type Coder provides the methods required for encoding and decoding data. The SDK for Java provides a number of Coder subclasses that work with a variety of standard Java types, such as Integer, Long, Double, StringUtf8 and more. You can find all of the available Coder subclasses in the Coder package.
{:.language-py} In the Beam SDK for Python, the type Coder provides the methods required for encoding and decoding data. The SDK for Python provides a number of Coder subclasses that work with a variety of standard Python types, such as primitive types, Tuple, Iterable, StringUtf8 and more. You can find all of the available Coder subclasses in the apache_beam.coders package.
Note that coders do not necessarily have a 1:1 relationship with types. For example, the Integer type can have multiple valid coders, and input and output data can use different Integer coders. A transform might have Integer-typed input data that uses BigEndianIntegerCoder, and Integer-typed output data that uses VarIntCoder.
The Beam SDKs require a coder for every PCollection in your pipeline. In most cases, the Beam SDK is able to automatically infer a Coder for a PCollection based on its element type or the transform that produces it, however, in some cases the pipeline author will need to specify a Coder explicitly, or develop a Coder for their custom type.
{:.language-java} You can explicitly set the coder for an existing PCollection by using the method PCollection.setCoder. Note that you cannot call setCoder on a PCollection that has been finalized (e.g. by calling .apply on it).
{:.language-java} You can get the coder for an existing PCollection by using the method getCoder. This method will fail with an IllegalStateException if a coder has not been set and cannot be inferred for the given PCollection.
Beam SDKs use a variety of mechanisms when attempting to automatically infer the Coder for a PCollection.
{:.language-java} Each pipeline object has a CoderRegistry. The CoderRegistry represents a mapping of Java types to the default coders that the pipeline should use for PCollections of each type.
{:.language-py} The Beam SDK for Python has a CoderRegistry that represents a mapping of Python types to the default coder that should be used for PCollections of each type.
{:.language-java} By default, the Beam SDK for Java automatically infers the Coder for the elements of a PCollection produced by a PTransform using the type parameter from the transform's function object, such as DoFn. In the case of ParDo, for example, a DoFn<Integer, String> function object accepts an input element of type Integer and produces an output element of type String. In such a case, the SDK for Java will automatically infer the default Coder for the output PCollection<String> (in the default pipeline CoderRegistry, this is StringUtf8Coder).
{:.language-py} By default, the Beam SDK for Python automatically infers the Coder for the elements of an output PCollection using the typehints from the transform's function object, such as DoFn. In the case of ParDo, for example a DoFn with the typehints @beam.typehints.with_input_types(int) and @beam.typehints.with_output_types(str) accepts an input element of type int and produces an output element of type str. In such a case, the Beam SDK for Python will automatically infer the default Coder for the output PCollection (in the default pipeline CoderRegistry, this is BytesCoder).
NOTE: If you create your
PCollectionfrom in-memory data by using theCreatetransform, you cannot rely on coder inference and default coders.Createdoes not have access to any typing information for its arguments, and may not be able to infer a coder if the argument list contains a value whose exact run-time class doesn't have a default coder registered.
{:.language-java} When using Create, the simplest way to ensure that you have the correct coder is by invoking withCoder when you apply the Create transform.
Each Pipeline object has a CoderRegistry object, which maps language types to the default coder the pipeline should use for those types. You can use the CoderRegistry yourself to look up the default coder for a given type, or to register a new default coder for a given type.
CoderRegistry contains a default mapping of coders to standard JavaPython types for any pipeline you create using the Beam SDK for JavaPython. The following table shows the standard mapping:
{:.language-java}
{:.language-py}
{:.language-java} You can use the method CoderRegistry.getCoder to determine the default Coder for a Java type. You can access the CoderRegistry for a given pipeline by using the method Pipeline.getCoderRegistry. This allows you to determine (or set) the default Coder for a Java type on a per-pipeline basis: i.e. “for this pipeline, verify that Integer values are encoded using BigEndianIntegerCoder.”
{:.language-py} You can use the method CoderRegistry.get_coder to determine the default Coder for a Python type. You can use coders.registry to access the CoderRegistry. This allows you to determine (or set) the default Coder for a Python type.
To set the default Coder for a JavaPython type for a particular pipeline, you obtain and modify the pipeline's CoderRegistry. You use the method Pipeline.getCoderRegistry coders.registry to get the CoderRegistry object, and then use the method CoderRegistry.registerCoder CoderRegistry.register_coder to register a new Coder for the target type.
The following example code demonstrates how to set a default Coder, in this case BigEndianIntegerCoder, for Integerint values for a pipeline.
PipelineOptions options = PipelineOptionsFactory.create(); Pipeline p = Pipeline.create(options); CoderRegistry cr = p.getCoderRegistry(); cr.registerCoder(Integer.class, BigEndianIntegerCoder.class);
apache_beam.coders.registry.register_coder(int, BigEndianIntegerCoder)
{:.language-java} If your pipeline program defines a custom data type, you can use the @DefaultCoder annotation to specify the coder to use with that type. For example, let's say you have a custom data type for which you want to use SerializableCoder. You can use the @DefaultCoder annotation as follows:
@DefaultCoder(AvroCoder.class) public class MyCustomDataType { ... }
{:.language-java} If you've created a custom coder to match your data type, and you want to use the @DefaultCoder annotation, your coder class must implement a static Coder.of(Class<T>) factory method.
public class MyCustomCoder implements Coder { public static Coder<T> of(Class<T> clazz) {...} ... } @DefaultCoder(MyCustomCoder.class) public class MyCustomDataType { ... }
{:.language-py} The Beam SDK for Python does not support annotating data types with a default coder. If you would like to set a default coder, use the method described in the previous section, Setting the default coder for a type.
Windowing subdivides a PCollection according to the timestamps of its individual elements. Transforms that aggregate multiple elements, such as GroupByKey and Combine, work implicitly on a per-window basis — they process each PCollection as a succession of multiple, finite windows, though the entire collection itself may be of unbounded size.
A related concept, called triggers, determines when to emit the results of aggregation as unbounded data arrives. You can use triggers to refine the windowing strategy for your PCollection. Triggers allow you to deal with late-arriving data or to provide early results. See the triggers section for more information.
Some Beam transforms, such as GroupByKey and Combine, group multiple elements by a common key. Ordinarily, that grouping operation groups all of the elements that have the same key within the entire data set. With an unbounded data set, it is impossible to collect all of the elements, since new elements are constantly being added and may be infinitely many (e.g. streaming data). If you are working with unbounded PCollections, windowing is especially useful.
In the Beam model, any PCollection (including unbounded PCollections) can be subdivided into logical windows. Each element in a PCollection is assigned to one or more windows according to the PCollection's windowing function, and each individual window contains a finite number of elements. Grouping transforms then consider each PCollection's elements on a per-window basis. GroupByKey, for example, implicitly groups the elements of a PCollection by key and window.
Caution: Beam's default windowing behavior is to assign all elements of a PCollection to a single, global window and discard late data, even for unbounded PCollections. Before you use a grouping transform such as GroupByKey on an unbounded PCollection, you must do at least one of the following:
If you don't set a non-global windowing function or a non-default trigger for your unbounded PCollection and subsequently use a grouping transform such as GroupByKey or Combine, your pipeline will generate an error upon construction and your job will fail.
After you set the windowing function for a PCollection, the elements' windows are used the next time you apply a grouping transform to that PCollection. Window grouping occurs on an as-needed basis. If you set a windowing function using the Window transform, each element is assigned to a window, but the windows are not considered until GroupByKey or Combine aggregates across a window and key. This can have different effects on your pipeline. Consider the example pipeline in the figure below:
Figure: Pipeline applying windowing
In the above pipeline, we create an unbounded PCollection by reading a set of key/value pairs using KafkaIO, and then apply a windowing function to that collection using the Window transform. We then apply a ParDo to the the collection, and then later group the result of that ParDo using GroupByKey. The windowing function has no effect on the ParDo transform, because the windows are not actually used until they're needed for the GroupByKey. Subsequent transforms, however, are applied to the result of the GroupByKey -- data is grouped by both key and window.
You can use windowing with fixed-size data sets in bounded PCollections. However, note that windowing considers only the implicit timestamps attached to each element of a PCollection, and data sources that create fixed data sets (such as TextIO) assign the same timestamp to every element. This means that all the elements are by default part of a single, global window.
To use windowing with fixed data sets, you can assign your own timestamps to each element. To assign timestamps to elements, use a ParDo transform with a DoFn that outputs each element with a new timestamp (for example, the [WithTimestamps](https://beam.apache.org/releases/javadoc/{{ site.release_latest }}/index.html?org/apache/beam/sdk/transforms/WithTimestamps.html) transform in the Beam SDK for Java).
To illustrate how windowing with a bounded PCollection can affect how your pipeline processes data, consider the following pipeline:
Figure: GroupByKey and ParDo without windowing, on a bounded collection.
In the above pipeline, we create a bounded PCollection by reading a set of key/value pairs using TextIO. We then group the collection using GroupByKey, and apply a ParDo transform to the grouped PCollection. In this example, the GroupByKey creates a collection of unique keys, and then ParDo gets applied exactly once per key.
Note that even if you don’t set a windowing function, there is still a window -- all elements in your PCollection are assigned to a single global window.
Now, consider the same pipeline, but using a windowing function:
Figure: GroupByKey and ParDo with windowing, on a bounded collection.
As before, the pipeline creates a bounded PCollection of key/value pairs. We then set a windowing function for that PCollection. The GroupByKey transform groups the elements of the PCollection by both key and window, based on the windowing function. The subsequent ParDo transform gets applied multiple times per key, once for each window.
You can define different kinds of windows to divide the elements of your PCollection. Beam provides several windowing functions, including:
You can also define your own WindowFn if you have a more complex need.
Note that each element can logically belong to more than one window, depending on the windowing function you use. Sliding time windowing, for example, creates overlapping windows wherein a single element can be assigned to multiple windows.
The simplest form of windowing is using fixed time windows: given a timestamped PCollection which might be continuously updating, each window might capture (for example) all elements with timestamps that fall into a five minute interval.
A fixed time window represents a consistent duration, non overlapping time interval in the data stream. Consider windows with a five-minute duration: all of the elements in your unbounded PCollection with timestamp values from 0:00:00 up to (but not including) 0:05:00 belong to the first window, elements with timestamp values from 0:05:00 up to (but not including) 0:10:00 belong to the second window, and so on.
Figure: Fixed time windows, 30s in duration.
A sliding time window also represents time intervals in the data stream; however, sliding time windows can overlap. For example, each window might capture five minutes worth of data, but a new window starts every ten seconds. The frequency with which sliding windows begin is called the period. Therefore, our example would have a window duration of five minutes and a period of ten seconds.
Because multiple windows overlap, most elements in a data set will belong to more than one window. This kind of windowing is useful for taking running averages of data; using sliding time windows, you can compute a running average of the past five minutes' worth of data, updated every ten seconds, in our example.
Figure: Sliding time windows, with 1 minute window duration and 30s window period.
A session window function defines windows that contain elements that are within a certain gap duration of another element. Session windowing applies on a per-key basis and is useful for data that is irregularly distributed with respect to time. For example, a data stream representing user mouse activity may have long periods of idle time interspersed with high concentrations of clicks. If data arrives after the minimum specified gap duration time, this initiates the start of a new window.
Figure: Session windows, with a minimum gap duration. Note how each data key has different windows, according to its data distribution.
By default, all data in a PCollection is assigned to the single global window, and late data is discarded. If your data set is of a fixed size, you can use the global window default for your PCollection.
You can use the single global window if you are working with an unbounded data set (e.g. from a streaming data source) but use caution when applying aggregating transforms such as GroupByKey and Combine. The single global window with a 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.
You can set the windowing function for a PCollection by applying the Window transform. When you apply the Window transform, you must provide a WindowFn. The WindowFn determines the windowing function your PCollection will use for subsequent grouping transforms, such as a fixed or sliding time window.
When you set a windowing function, you may also want to set a trigger for your PCollection. The trigger determines when each individual window is aggregated and emitted, and helps refine how the windowing function performs with respect to late data and computing early results. See the triggers section for more information.
The following example code shows how to apply Window to divide a PCollection into fixed windows, each one minute in length:
PCollection<String> items = ...; PCollection<String> fixedWindowedItems = items.apply( Window.<String>into(FixedWindows.of(Duration.standardMinutes(1))));
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:setting_fixed_windows %}
The following example code shows how to apply Window to divide a PCollection into sliding time windows. Each window is 30 seconds in length, and a new window begins every five seconds:
PCollection<String> items = ...; PCollection<String> slidingWindowedItems = items.apply( Window.<String>into(SlidingWindows.of(Duration.standardSeconds(30)).every(Duration.standardSeconds(5))));
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:setting_sliding_windows %}
The following example code shows how to apply Window to divide a PCollection into session windows, where each session must be separated by a time gap of at least 10 minutes:
PCollection<String> items = ...; PCollection<String> sessionWindowedItems = items.apply( Window.<String>into(Sessions.withGapDuration(Duration.standardMinutes(10))));
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:setting_session_windows %}
Note that the sessions are per-key — each key in the collection will have its own session groupings depending on the data distribution.
If your PCollection is bounded (the size is fixed), you can assign all the elements to a single global window. The following example code shows how to set a single global window for a PCollection:
PCollection<String> items = ...; PCollection<String> batchItems = items.apply( Window.<String>into(new GlobalWindows()));
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:setting_global_window %}
In any data processing system, there is a certain amount of lag between the time a data event occurs (the “event time”, determined by the timestamp on the data element itself) and the time the actual data element gets processed at any stage in your pipeline (the “processing time”, determined by the clock on the system processing the element). In addition, there are no guarantees that data events will appear in your pipeline in the same order that they were generated.
For example, let‘s say we have a PCollection that’s using fixed-time windowing, with windows that are five minutes long. For each window, Beam must collect all the data with an event time timestamp in the given window range (between 0:00 and 4:59 in the first window, for instance). Data with timestamps outside that range (data from 5:00 or later) belong to a different window.
However, data isn‘t always guaranteed to arrive in a pipeline in time order, or to always arrive at predictable intervals. Beam tracks a watermark, which is the system’s notion of when all data in a certain window can be expected to have arrived in the pipeline. Data that arrives with a timestamp after the watermark is considered late data.
From our example, suppose we have a simple watermark that assumes approximately 30s of lag time between the data timestamps (the event time) and the time the data appears in the pipeline (the processing time), then Beam would close the first window at 5:30. If a data record arrives at 5:34, but with a timestamp that would put it in the 0:00-4:59 window (say, 3:38), then that record is late data.
Note: For simplicity, we‘ve assumed that we’re using a very straightforward watermark that estimates the lag time. In practice, your PCollection's data source determines the watermark, and watermarks can be more precise or complex.
Beam's default windowing configuration tries to determines when all data has arrived (based on the type of data source) and then advances the watermark past the end of the window. This default configuration does not allow late data. Triggers allow you to modify and refine the windowing strategy for a PCollection. You can use triggers to decide when each individual window aggregates and reports its results, including how the window emits late elements.
Note: Managing late data is not supported in the Beam SDK for Python.
You can allow late data by invoking the .withAllowedLateness operation when you set your PCollection's windowing strategy. The following code example demonstrates a windowing strategy that will allow late data up to two days after the end of a window.
PCollection<String> items = ...; PCollection<String> fixedWindowedItems = items.apply( Window.<String>into(FixedWindows.of(Duration.standardMinutes(1))) .withAllowedLateness(Duration.standardDays(2)));
When you set .withAllowedLateness on a PCollection, that allowed lateness propagates forward to any subsequent PCollection derived from the first PCollection you applied allowed lateness to. If you want to change the allowed lateness later in your pipeline, you must do so explictly by applying Window.configure().withAllowedLateness().
An unbounded source provides a timestamp for each element. Depending on your unbounded source, you may need to configure how the timestamp is extracted from the raw data stream.
However, bounded sources (such as a file from TextIO) do not provide timestamps. If you need timestamps, you must add them to your PCollection’s elements.
You can assign new timestamps to the elements of a PCollection by applying a ParDo transform that outputs new elements with timestamps that you set.
An example might be if your pipeline reads log records from an input file, and each log record includes a timestamp field; since your pipeline reads the records in from a file, the file source doesn't assign timestamps automatically. You can parse the timestamp field from each record and use a ParDo transform with a DoFn to attach the timestamps to each element in your PCollection.
PCollection<LogEntry> unstampedLogs = ...; PCollection<LogEntry> stampedLogs = unstampedLogs.apply(ParDo.of(new DoFn<LogEntry, LogEntry>() { public void processElement(@Element LogEntry element, OutputReceiver<LogEntry> out) { // Extract the timestamp from log entry we're currently processing. Instant logTimeStamp = extractTimeStampFromLogEntry(element); // Use OutputReceiver.outputWithTimestamp (rather than // OutputReceiver.output) to emit the entry with timestamp attached. out.outputWithTimestamp(element, logTimeStamp); } }));
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:setting_timestamp %}
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window (referred to as a pane). If you use Beam's default windowing configuration and default trigger, Beam outputs the aggregated result when it estimates all data has arrived, and discards all subsequent data for that window.
You can set triggers for your PCollections to change this default behavior. Beam provides a number of pre-built triggers that you can set:
At a high level, triggers provide two additional capabilities compared to simply outputting at the end of a window:
These capabilities allow you to control the flow of your data and balance between different factors depending on your use case:
For example, a system that requires time-sensitive updates might use a strict time-based trigger that emits a window every N seconds, valuing promptness over data completeness. A system that values data completeness more than the exact timing of results might choose to use Beam's default trigger, which fires at the end of the window.
You can also set a trigger for an unbounded PCollection that uses a single global window for its windowing function. This can be useful when you want your pipeline to provide periodic updates on an unbounded data set — for example, a running average of all data provided to the present time, updated every N seconds or every N elements.
The AfterWatermark trigger operates on event time. The AfterWatermark trigger emits the contents of a window after the watermark passes the end of the window, based on the timestamps attached to the data elements. The watermark is a global progress metric, and is Beam's notion of input completeness within your pipeline at any given point. AfterWatermark.pastEndOfWindow() AfterWatermark only fires when the watermark passes the end of the window.
In addition, you can configure triggers that fire if your pipeline receives data before or after the end of the window.
The following example shows a billing scenario, and uses both early and late firings:
// Create a bill at the end of the month. AfterWatermark.pastEndOfWindow() // During the month, get near real-time estimates. .withEarlyFirings( AfterProcessingTime .pastFirstElementInPane() .plusDuration(Duration.standardMinutes(1)) // Fire on any late data so the bill can be corrected. .withLateFirings(AfterPane.elementCountAtLeast(1))
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_early_late_triggers %}``` #### 8.1.1. Default trigger {#default-trigger} The default trigger for a `PCollection` is based on event time, and emits the results of the window when the Beam's watermark passes the end of the window, and then fires each time late data arrives. However, if you are using both the default windowing configuration and the default trigger, the default trigger emits exactly once, and late data is discarded. This is because the default windowing configuration has an allowed lateness value of 0. See the Handling Late Data section for information about modifying this behavior. ### 8.2. Processing time triggers {#processing-time-triggers} The `AfterProcessingTime` trigger operates on *processing time*. For example, the <span class="language-java">`AfterProcessingTime.pastFirstElementInPane()`</span> <span class="language-py">`AfterProcessingTime`</span> trigger emits a window after a certain amount of processing time has passed since data was received. The processing time is determined by the system clock, rather than the data element's timestamp. The `AfterProcessingTime` trigger is useful for triggering early results from a window, particularly a window with a large time frame such as a single global window. ### 8.3. Data-driven triggers {#data-driven-triggers} Beam provides one data-driven trigger, <span class="language-java">`AfterPane.elementCountAtLeast()`</span> <span class="language-py">`AfterCount`</span>. This trigger works on an element count; it fires after the current pane has collected at least *N* elements. This allows a window to emit early results (before all the data has accumulated), which can be particularly useful if you are using a single global window. It is important to note that if, for example, you specify <span class="language-java">`.elementCountAtLeast(50)`</span> <span class="language-py">AfterCount(50)</span> and only 32 elements arrive, those 32 elements sit around forever. If the 32 elements are important to you, consider using [composite triggers](#composite-triggers) to combine multiple conditions. This allows you to specify multiple firing conditions such as “fire either when I receive 50 elements, or every 1 second”. ### 8.4. Setting a trigger {#setting-a-trigger} When you set a windowing function for a `PCollection` by using the <span class="language-java">`Window`</span><span class="language-py">`WindowInto`</span> transform, you can also specify a trigger. {:.language-java} You set the trigger(s) for a `PCollection` by invoking the method `.triggering()` on the result of your `Window.into()` transform. This code sample sets a time-based trigger for a `PCollection`, which emits results one minute after the first element in that window has been processed. The last line in the code sample, `.discardingFiredPanes()`, sets the window's **accumulation mode**. {:.language-py} You set the trigger(s) for a `PCollection` by setting the `trigger` parameter when you use the `WindowInto` transform. This code sample sets a time-based trigger for a `PCollection`, which emits results one minute after the first element in that window has been processed. The `accumulation_mode` parameter sets the window's **accumulation mode**. ```java PCollection<String> pc = ...; pc.apply(Window.<String>into(FixedWindows.of(1, TimeUnit.MINUTES)) .triggering(AfterProcessingTime.pastFirstElementInPane() .plusDelayOf(Duration.standardMinutes(1))) .discardingFiredPanes());
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_setting_trigger %}``` #### 8.4.1. Window accumulation modes {#window-accumulation-modes} When you specify a trigger, you must also set the the window's **accumulation mode**. When a trigger fires, it emits the current contents of the window as a pane. Since a trigger can fire multiple times, the accumulation mode determines whether the system *accumulates* the window panes as the trigger fires, or *discards* them. {:.language-java} To set a window to accumulate the panes that are produced when the trigger fires, invoke`.accumulatingFiredPanes()` when you set the trigger. To set a window to discard fired panes, invoke `.discardingFiredPanes()`. {:.language-py} To set a window to accumulate the panes that are produced when the trigger fires, set the `accumulation_mode` parameter to `ACCUMULATING` when you set the trigger. To set a window to discard fired panes, set `accumulation_mode` to `DISCARDING`. Let's look an example that uses a `PCollection` with fixed-time windowing and a data-based trigger. This is something you might do if, for example, each window represented a ten-minute running average, but you wanted to display the current value of the average in a UI more frequently than every ten minutes. We'll assume the following conditions: * The `PCollection` uses 10-minute fixed-time windows. * The `PCollection` has a repeating trigger that fires every time 3 elements arrive. The following diagram shows data events for key X as they arrive in the PCollection and are assigned to windows. To keep the diagram a bit simpler, we'll assume that the events all arrive in the pipeline in order. ![Diagram of data events for acculumating mode example]({{ "/images/trigger-accumulation.png" | prepend: site.baseurl }} "Data events for accumulating mode example") ##### 8.4.1.1. Accumulating mode {#accumulating-mode} If our trigger is set to accumulating mode, the trigger emits the following values each time it fires. Keep in mind that the trigger fires every time three elements arrive:
First trigger firing: [5, 8, 3] Second trigger firing: [5, 8, 3, 15, 19, 23] Third trigger firing: [5, 8, 3, 15, 19, 23, 9, 13, 10]
##### 8.4.1.2. Discarding mode {#discarding-mode} If our trigger is set to discarding mode, the trigger emits the following values on each firing:
First trigger firing: [5, 8, 3] Second trigger firing: [15, 19, 23] Third trigger firing: [9, 13, 10]
#### 8.4.2. Handling late data {#handling-late-data} > The Beam SDK for Python does not currently support allowed lateness. If you want your pipeline to process data that arrives after the watermark passes the end of the window, you can apply an *allowed lateness* when you set your windowing configuration. This gives your trigger the opportunity to react to the late data. If allowed lateness is set, the default trigger will emit new results immediately whenever late data arrives. You set the allowed lateness by using `.withAllowedLateness()` when you set your windowing function: ```java PCollection<String> pc = ...; pc.apply(Window.<String>into(FixedWindows.of(1, TimeUnit.MINUTES)) .triggering(AfterProcessingTime.pastFirstElementInPane() .plusDelayOf(Duration.standardMinutes(1))) .withAllowedLateness(Duration.standardMinutes(30));
# The Beam SDK for Python does not currently support allowed lateness.
This allowed lateness propagates to all PCollections derived as a result of applying transforms to the original PCollection. If you want to change the allowed lateness later in your pipeline, you can apply Window.configure().withAllowedLateness() again, explicitly.
You can combine multiple triggers to form composite triggers, and can specify a trigger to emit results repeatedly, at most once, or under other custom conditions.
Beam includes the following composite triggers:
AfterWatermark.pastEndOfWindow via .withEarlyFirings and .withLateFirings.Repeatedly.forever specifies a trigger that executes forever. Any time the trigger's conditions are met, it causes a window to emit results and then resets and starts over. It can be useful to combine Repeatedly.forever with .orFinally to specify a condition that causes the repeating trigger to stop.AfterEach.inOrder combines multiple triggers to fire in a specific sequence. Each time a trigger in the sequence emits a window, the sequence advances to the next trigger.AfterFirst takes multiple triggers and emits the first time any of its argument triggers is satisfied. This is equivalent to a logical OR operation for multiple triggers.AfterAll takes multiple triggers and emits when all of its argument triggers are satisfied. This is equivalent to a logical AND operation for multiple triggers.orFinally can serve as a final condition to cause any trigger to fire one final time and never fire again.Some of the most useful composite triggers fire a single time when Beam estimates that all the data has arrived (i.e. when the watermark passes the end of the window) combined with either, or both, of the following:
Speculative firings that precede the watermark passing the end of the window to allow faster processing of partial results.
Late firings that happen after the watermark passes the end of the window, to allow for handling late-arriving data
You can express this pattern using AfterWatermark. For example, the following example trigger code fires on the following conditions:
On Beam's estimate that all the data has arrived (the watermark passes the end of the window)
Any time late data arrives, after a ten-minute delay
{:.language-java}
.apply(Window .configure() .triggering(AfterWatermark .pastEndOfWindow() .withLateFirings(AfterProcessingTime .pastFirstElementInPane() .plusDelayOf(Duration.standardMinutes(10)))) .withAllowedLateness(Duration.standardDays(2)));
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_composite_triggers %}``` #### 8.5.3. Other composite triggers {#other-composite-triggers} You can also build other sorts of composite triggers. The following example code shows a simple composite trigger that fires whenever the pane has at least 100 elements, or after a minute. ```java Repeatedly.forever(AfterFirst.of( AfterPane.elementCountAtLeast(100), AfterProcessingTime.pastFirstElementInPane().plusDelayOf(Duration.standardMinutes(1))))
{% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py tag:model_other_composite_triggers %}```