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Subject [1/3] incubator-beam-site git commit: [BEAM-277] Add transforms section to the programming guide
Date Thu, 24 Nov 2016 06:07:49 GMT
Repository: incubator-beam-site
Updated Branches:
  refs/heads/asf-site 1c9f85626 -> 1b458f102

[BEAM-277] Add transforms section to the programming guide


Branch: refs/heads/asf-site
Commit: 3627a440d0156faa63e4f57ad46a5e79acea84f8
Parents: 1c9f856
Author: melissa <>
Authored: Mon Nov 21 11:22:04 2016 -0800
Committer: Davor Bonaci <>
Committed: Wed Nov 23 22:06:29 2016 -0800

 src/documentation/ | 496 +++++++++++++++++++++++++++-
 1 file changed, 492 insertions(+), 4 deletions(-)
diff --git a/src/documentation/ b/src/documentation/
index 648962a..18e7800 100644
--- a/src/documentation/
+++ b/src/documentation/
@@ -27,6 +27,7 @@ The **Beam Programming Guide** is intended for Beam users who want to use
the Be
   * [Using ParDo](#transforms-pardo)
   * [Using GroupByKey](#transforms-gbk)
   * [Using Combine](#transforms-combine)
+  * [Using Flatten and Partition](#transforms-flatten-partition)
   * [General Requirements for Writing User Code for Beam Transforms](#transforms-usercodereqs)
   * [Side Inputs and Side Outputs](#transforms-sideio)
 * [I/O](#io)
@@ -205,7 +206,7 @@ However, note that a transform *does not consume or otherwise alter* the
input c
 [Output PCollection 2] = [Input PCollection].apply([Transform 2])
-The resulting workflow graph from the branching pipeline abouve looks like this:
+The resulting workflow graph from the branching pipeline above looks like this:
 [Branching Graph Graphic]
@@ -222,7 +223,7 @@ Beam provides the following transforms, each of which represents a different
 * `ParDo`
 * `GroupByKey`
 * `Combine`
-* `Flatten`
+* `Flatten` and `Partition`
 #### <a name="transforms-pardo"></a>ParDo
@@ -374,10 +375,266 @@ 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).
 > **A Note on Key/Value Pairs:** Beam represents key/value pairs slightly differently
depending on the language and SDK you're using. In the Beam SDK for Java, you represent a
key/value pair with an object of type `KV<K, V>`. In Python, you represent key/value
pairs with 2-tuples.
 #### <a name="transforms-combine"></a>Using Combine
+<span class="language-java">[`Combine`]({{ site.baseurl }}/documentation/sdks/javadoc/{{
site.release_latest }}/index.html?org/apache/beam/sdk/transforms/Combine.html)</span><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.
+##### **Simple Combinations Using Simple Functions**
+The following example code shows a simple combine function.
+// 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;
+  }
+# A bounded sum of positive integers.
+def bounded_sum(values, bound=500):
+  return min(sum(values), bound)
+##### **Advanced Combinations using CombineFn**
+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
+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:
+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 = ...
+class AverageFn(beam.CombineFn):
+  def create_accumulator(self):
+    return (0.0, 0)
+  def add_input(self, (sum, count), input):
+    return sum + input, count + 1
+  def merge_accumulators(self, accumulators):
+    sums, counts = zip(*accumulators)
+    return sum(sums), sum(counts)
+  def extract_output(self, (sum, count)):
+    return sum / count if count else float('NaN')
+If you are combining a `PCollection` of key-value pairs, [per-key combining](#transforms-combine-per-key)
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.
+##### **Combining a PCollection into a Single Value**
+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.
+// 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 = ...
+result = pc | beam.CombineGlobally(sum)
+##### 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:
+PCollection<Integer> pc = ...;
+PCollection<Integer> sum = pc.apply(
+  Combine.globally(new Sum.SumIntegerFn()).withoutDefaults());
+pc = ...
+sum = pc | beam.CombineGlobally(sum).without_defaults()
+##### Non-Global Windowing:
+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`:
+* Specify `.withoutDefaults`, where windows that are empty in the input `PCollection` will
likewise be empty in the output collection.
+* Specify `.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.
+##### <a name="transforms-combine-per-key"></a>**Combining Values in a Key-Grouped
+After creating a key-grouped collection (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 = ...
+avg_accuracy_per_player = (player_accuracies
+                           | beam.CombinePerKey(
+                               beam.combiners.MeanCombineFn()))
+#### <a name="transforms-flatten-partition"></a>Using Flatten and Partition
+<span class="language-java">[`Flatten`]({{ site.baseurl }}/documentation/sdks/javadoc/{{
site.release_latest }}/index.html?org/apache/beam/sdk/transforms/Flatten.html)</span><span
and <span class="language-java">[`Partition`]({{ site.baseurl }}/documentation/sdks/javadoc/{{
site.release_latest }}/index.html?org/apache/beam/sdk/transforms/Partition.html)</span><span
are Beam transforms for `PCollection` objects that store the same data type. `Flatten` merges
multiple `PCollection` objects into a single logical `PCollection`, and `Partition` splits
a single `PCollection` into a fixed number of smaller collections.
+##### **Flatten**
+The following example shows how to apply a `Flatten` transform to merge multiple `PCollection`
+// 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.
+merged = (
+    (pcoll1, pcoll2, pcoll3)
+    # A list of tuples can be "piped" directly into a Flatten transform.
+    | beam.Flatten())
+##### Data Encoding in Merged Collections:
+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.
+##### Merging Windowed Collections:
+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**
+`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
+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.
+def partition_fn(student, num_partitions):
+  return int(get_percentile(student) * num_partitions / 100)
+by_decile = students | beam.Partition(partition_fn, 10)
+# You can extract each partition from the tuple of PCollection objects as follows:
+fortieth_percentile = by_decile[4]
 #### <a name="transforms-usercodereqs"></a>General Requirements for Writing User
Code for Beam Transforms
 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.
@@ -411,10 +668,241 @@ Your function object should be thread-compatible. Each instance of
your function
 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. 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.
+#### <a name="transforms-sideio"></a>Side Inputs and Side Outputs
+##### **Side Inputs**
+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.
+##### Passing Side Inputs to ParDo:
+  // 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.withSideInputs(maxWordLengthCutOffView)
+                    .of(new DoFn<String, String>() {
+      public void processElement(ProcessContext c) {
+        String word = c.element();
+        // In our DoFn, access the side input.
+        int lengthCutOff = c.sideInput(maxWordLengthCutOffView);
+        if (word.length() <= lengthCutOff) {
+          c.output(word);
+        }
+  }}));
+# Side inputs are available as extra arguments in the DoFn's process method or Map / FlatMap's
+# Optional, positional, and keyword arguments are all supported. Deferred arguments are unwrapped
into their actual values.
+# For example, using pvalue.AsIter(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.
+# Callable takes additional arguments.
+def filter_using_length(word, lower_bound, upper_bound=float('inf')):
+  if lower_bound <= len(word) <= upper_bound:
+    yield word
+# Construct a deferred side input.
+avg_word_len = (words
+                | beam.Map(len)
+                | beam.CombineGlobally(beam.combiners.MeanCombineFn()))
+# Call with explicit side inputs.
+small_words = words | 'small' >> beam.FlatMap(filter_using_length, 0, 3)
+# A single deferred side input.
+larger_than_average = (words | 'large' >> beam.FlatMap(
+    filter_using_length,
+    lower_bound=pvalue.AsSingleton(avg_word_len)))
+# Mix and match.
+small_but_nontrivial = words | beam.FlatMap(filter_using_length,
+                                            lower_bound=2,
+                                            upper_bound=pvalue.AsSingleton(
+                                                avg_word_len))
+# We can also pass side inputs to a ParDo transform, which will get passed to its process
+# The only change is that the first arguments are self and a context, rather than the PCollection
element itself.
+class FilterUsingLength(beam.DoFn):
+  def process(self, context, lower_bound, upper_bound=float('inf')):
+    if lower_bound <= len(context.element) <= upper_bound:
+      yield context.element
+small_words = words | beam.ParDo(FilterUsingLength(), 0, 3)
+##### Side Inputs and Windowing:
+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.
+##### **Side Outputs**
+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 `PCollection`s. If
you choose to have multiple outputs, your `ParDo` returns all of the output `PCollection`s
(including the main output) bundled together.
+##### Tags for Side Outputs:
+// To emit elements to a side output PCollection, 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
side outputs), you must create three TupleTags.
+// The following example code shows how to create TupleTags for a ParDo with a main output
and two side outputs:
+  // 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 a side output
+  // PCollection<Integer>.
+  // If a word starts with the string "MARKER", the ParDo will add that word to a different
+  // side output PCollection<String>.
+  final int wordLengthCutOff = 10;
+  // Create the TupleTags for the main and side outputs.
+  // Main output.
+  final TupleTag<String> wordsBelowCutOffTag =
+      new TupleTag<String>(){};
+  // Word lengths side output.
+  final TupleTag<Integer> wordLengthsAboveCutOffTag =
+      new TupleTag<Integer>(){};
+  // "MARKER" words side output.
+  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 side outputs in
a TupleTagList.
+// Building on our previous example, we pass the three TupleTags (one for the main output
and two for the side outputs) 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
+          // Specify the tag for the main output, wordsBelowCutoffTag.
+          .withOutputTags(wordsBelowCutOffTag,
+          // Specify the tags for the two side outputs as a TupleTagList.
+                          TupleTagList.of(wordLengthsAboveCutOffTag)
+                                      .and(markedWordsTag))
+          .of(new DoFn<String, String>() {
+            // DoFn continues here.
+            ...
+          }
+# To emit elements to a side output PCollection, invoke with_outputs() on the ParDo, optionally
specifying the expected tags for the output.
+# 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.
+results = (words | beam.ParDo(ProcessWords(), cutoff_length=2, marker='x')
+           .with_outputs('above_cutoff_lengths', 'marked strings',
+                         main='below_cutoff_strings'))
+below = results.below_cutoff_strings
+above = results.above_cutoff_lengths
+marked = results['marked strings']  # indexing works as well
+# The result is also iterable, ordered in the same order that the tags were passed to with_outputs(),
the main tag (if specified) first.
+below, above, marked = (words
+                        | beam.ParDo(
+                            ProcessWords(), cutoff_length=2, marker='x')
+                        .with_outputs('above_cutoff_lengths',
+                                      'marked strings',
+                                      main='below_cutoff_strings'))
+##### Emitting to Side Outputs in your DoFn:
+// Inside your ParDo's DoFn, you can emit an element to a side output by using the method
+// Pass the appropriate TupleTag for the target side output collection when you call ProcessContext.sideOutput.
+// After your ParDo, extract the resulting main and side output PCollections from the returned
+// Based on the previous example, this shows the DoFn emitting to the main and side outputs.
+  .of(new DoFn<String, String>() {
+     public void processElement(ProcessContext c) {
+       String word = c.element();
+       if (word.length() <= wordLengthCutOff) {
+         // Emit this short word to the main output.
+         c.output(word);
+       } else {
+         // Emit this long word's length to a side output.
+         c.sideOutput(wordLengthsAboveCutOffTag, word.length());
+       }
+       if (word.startsWith("MARKER")) {
+         // Emit this word to a different side output.
+         c.sideOutput(markedWordsTag, word);
+       }
+     }}));
+# Inside your ParDo's DoFn, you can emit an element to a side output by wrapping the value
and the output tag (str).
+# using the pvalue.SideOutputValue wrapper class.
+# Based on the previous example, this shows the DoFn emitting to the main and side outputs.
+class ProcessWords(beam.DoFn):
+  def process(self, context, cutoff_length, marker):
+    if len(context.element) <= cutoff_length:
+      # Emit this short word to the main output.
+      yield context.element
+    else:
+      # Emit this word's long length to a side output.
+      yield pvalue.SideOutputValue(
+          'above_cutoff_lengths', len(context.element))
+    if context.element.startswith(marker):
+      # Emit this word to a different side output.
+      yield pvalue.SideOutputValue('marked strings', context.element)
+# Side outputs are 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.
+def even_odd(x):
+  yield pvalue.SideOutputValue('odd' if x % 2 else 'even', x)
+  if x % 10 == 0:
+    yield x
+results = numbers | beam.FlatMap(even_odd).with_outputs()
+evens = results.even
+odds = results.odd
+tens = results[None]  # the undeclared main output
 <a name="io"></a>
 <a name="running"></a>
 <a name="transforms-composite"></a>
-<a name="transforms-sideio"></a>
 <a name="coders"></a>
 <a name="windowing"></a>
 <a name="triggers"></a>

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