Project: Beam (App Definition and Development / Streaming & Messaging)
File: flatmap.md

title: "FlatMap" <!-- Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> {{< localstorage language language-py >}} {{< button-pydoc path="apache_beam.transforms.core" class="FlatMap" >}} Applies a simple 1-to-many mapping function over each element in the collection. The many elements are flattened into the resulting collection. In the following examples, we create a pipeline with a `PCollection` of produce with their icon, name, and duration. Then, we apply `FlatMap` in multiple ways to yield zero or more elements per each input element into the resulting `PCollection`. `FlatMap` accepts a function that returns an `iterable`, where each of the output `iterable`'s elements is an element of the resulting `PCollection`. We use the function `str.split` which takes a single `str` element and outputs a `list` of `str`s. This pipeline splits the input element using whitespaces, creating a list of zero or more elements. {{< playground height="700px" >}} {{< playgroundsnippet language="py" path="SDKPYTHONFlatMapSimple" show="flatmapsimple" >}} {{< /playground >}} We define a function `split_words` which splits an input `str` element using the delimiter `','` and outputs a `list` of `str`s. {{< playground height="700px" >}} {{< playgroundsnippet language="py" path="SDKPYTHONFlatMapFunction" show="flatmapfunction" >}} {{< /playground >}} A common use case of `FlatMap` is to flatten a `PCollection` of iterables into a `PCollection` of elements. To do that, don't specify the function argument to `FlatMap`, which uses the identity mapping function. {{< playground height="700px" >}} {{< playgroundsnippet language="py" path="SDKPYTHONFlatMapNoFunction" show="flatmapno_function" >}} {{< /playground >}} For this example, we want to flatten a `PCollection` of lists of `str`s into a `PCollection` of `str`s. Each input element is already an `iterable`, where each element is what we want in the resulting `PCollection`. We use a lambda function that returns the same input element it

{{< playground height="700px" >}} {{< playgroundsnippet language="py" path="SDKPYTHONFlatMapLambda" show="flatmaplambda" >}} {{< /playground >}} For this example, we want to flatten a `PCollection` of lists of `str`s into a `PCollection` of `str`s. We use a generator to iterate over the input list and yield each of the elements. Each yielded result in the generator is an element in the resulting `PCollection`. {{< playground height="700px" >}} {{< playgroundsnippet language="py" path="SDKPYTHONFlatMapGenerator" show="flatmapgenerator" >}} {{< /playground >}} If your `PCollection` consists of `(key, value)` pairs, you can use `FlatMapTuple` to unpack them into different function arguments. {{< playground height="700px" >}} {{< playgroundsnippet language="py" path="SDKPYTHONFlatMapTuple" show="flatmaptuple" >}} {{< /playground >}} You can pass functions with multiple arguments to `FlatMap`. They are passed as additional positional arguments or keyword arguments to the function. In this example, `split_words` takes `text` and `delimiter` as arguments. {{< playground height="700px" >}} {{< playgroundsnippet language="py" path="SDKPYTHONFlatMapMultipleArguments" show="flatmapmultiple_arguments" >}} {{< /playground >}} If the `PCollection` has a single value, such as the average from another computation, passing the `PCollection` as a singleton accesses that value. In this example, we pass a `PCollection` the value `','` as a singleton. We then use that value as the delimiter for the `str.split` method. {{< playground height="700px" >}} {{< playgroundsnippet language="py" path="SDKPYTHONFlatMapSideInputSingleton" show="flatmapsideinputssingleton" >}} {{< /playground >}} If the `PCollection` has multiple values, pass the `PCollection` as an iterator. This accesses elements lazily as they are needed, so it is possible to iterate over large `PCollection`s that won't fit into memory. {{< playground height="700px" >}} {{< playgroundsnippet language="py" path="SDKPYTHONFlatMapSideInputsIter" show="flatmapsideinputsiter" >}} {{< /playground >}} Note: You can pass the `PCollection` as a list with `beam.pvalue.AsList(pcollection)`, but this requires that all the elements fit into memory. If a `PCollection` is small enough to fit into memory, then that `PCollection` can be passed as a dictionary. Each element must be a `(key, value)` pair. Note that all the elements of the `PCollection` must fit into memory for this. If the `PCollection` won't fit into memory, use `beam.pvalue.AsIter(pcollection)` instead. {{< playground height="700px" >}} {{< playgroundsnippet language="py" path="SDKPYTHONFlatMapSideInputsDict" show="flatmapsideinputsdict" >}} {{< /playground >}} is useful if the function is just deciding whether to output an element or not. is the most general elementwise mapping operation, and includes other abilities such as multiple output collections and side-inputs. behaves the same, but produces exactly one output for each input. {{< button-pydoc path="apache_beam.transforms.core" class="FlatMap" >}}