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class=section-nav-list-title>Concepts</span><ul class=section-nav-list><li><a href=/documentation/basics/>Basics of the Beam model</a></li><li><a href=/documentation/runtime/model/>How Beam executes a pipeline</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Beam programming guide</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/>Overview</a></li><li><a href=/documentation/programming-guide/#creating-a-pipeline>Pipelines</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>PCollections</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#pcollections>Creating a PCollection</a></li><li><a href=/documentation/programming-guide/#pcollection-characteristics>PCollection characteristics</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Transforms</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#applying-transforms>Applying transforms</a></li><li><span class=section-nav-list-title>Core Beam transforms</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#pardo>ParDo</a></li><li><a href=/documentation/programming-guide/#groupbykey>GroupByKey</a></li><li><a href=/documentation/programming-guide/#cogroupbykey>CoGroupByKey</a></li><li><a href=/documentation/programming-guide/#combine>Combine</a></li><li><a href=/documentation/programming-guide/#flatten>Flatten</a></li><li><a href=/documentation/programming-guide/#partition>Partition</a></li></ul></li><li><a href=/documentation/programming-guide/#requirements-for-writing-user-code-for-beam-transforms>Requirements for user code</a></li><li><a href=/documentation/programming-guide/#side-inputs>Side inputs</a></li><li><a href=/documentation/programming-guide/#additional-outputs>Additional outputs</a></li><li><a href=/documentation/programming-guide/#composite-transforms>Composite transforms</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Pipeline I/O</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#pipeline-io>Using I/O transforms</a></li><li><a href=/documentation/io/connectors/>I/O connectors</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>I/O connector guides</span><ul class=section-nav-list><li><a href=/documentation/io/built-in/parquet/>Apache Parquet I/O connector</a></li><li><a href=/documentation/io/built-in/hadoop/>Hadoop Input/Output Format IO</a></li><li><a href=/documentation/io/built-in/hcatalog/>HCatalog IO</a></li><li><a href=/documentation/io/built-in/google-bigquery/>Google BigQuery I/O connector</a></li><li><a href=/documentation/io/built-in/snowflake/>Snowflake I/O connector</a></li><li><a href=/documentation/io/built-in/cdap/>CDAP I/O connector</a></li><li><a href=/documentation/io/built-in/sparkreceiver/>Spark Receiver I/O connector</a></li><li><a href=/documentation/io/built-in/singlestore/>SingleStoreDB I/O connector</a></li><li><a href=/documentation/io/built-in/webapis/>Web APIs I/O connector</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Developing new I/O connectors</span><ul class=section-nav-list><li><a href=/documentation/io/developing-io-overview/>Overview: Developing connectors</a></li><li><a href=/documentation/io/developing-io-java/>Developing connectors (Java)</a></li><li><a href=/documentation/io/developing-io-python/>Developing connectors (Python)</a></li><li><a href=/documentation/io/io-standards/>I/O Standards</a></li></ul></li><li><a href=/documentation/io/testing/>Testing I/O transforms</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Schemas</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#what-is-a-schema>What is a schema</a></li><li><a href=/documentation/programming-guide/#schemas-for-pl-types>Schemas for programming language types</a></li><li><a href=/documentation/programming-guide/#schema-definition>Schema definition</a></li><li><a href=/documentation/programming-guide/#logical-types>Logical types</a></li><li><a href=/documentation/programming-guide/#creating-schemas>Creating schemas</a></li><li><a href=/documentation/programming-guide/#using-schemas>Using schemas</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Data encoding and type safety</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#data-encoding-and-type-safety>Data encoding basics</a></li><li><a href=/documentation/programming-guide/#specifying-coders>Specifying coders</a></li><li><a href=/documentation/programming-guide/#default-coders-and-the-coderregistry>Default coders and the CoderRegistry</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Windowing</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#windowing>Windowing basics</a></li><li><a href=/documentation/programming-guide/#provided-windowing-functions>Provided windowing functions</a></li><li><a href=/documentation/programming-guide/#setting-your-pcollections-windowing-function>Setting your PCollection’s windowing function</a></li><li><a href=/documentation/programming-guide/#watermarks-and-late-data>Watermarks and late data</a></li><li><a href=/documentation/programming-guide/#adding-timestamps-to-a-pcollections-elements>Adding timestamps to a PCollection’s elements</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Triggers</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#triggers>Trigger basics</a></li><li><a href=/documentation/programming-guide/#event-time-triggers>Event time triggers and the default trigger</a></li><li><a href=/documentation/programming-guide/#processing-time-triggers>Processing time triggers</a></li><li><a href=/documentation/programming-guide/#data-driven-triggers>Data-driven triggers</a></li><li><a href=/documentation/programming-guide/#setting-a-trigger>Setting a trigger</a></li><li><a href=/documentation/programming-guide/#composite-triggers>Composite triggers</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Metrics</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#metrics>Metrics basics</a></li><li><a href=/documentation/programming-guide/#types-of-metrics>Types of metrics</a></li><li><a href=/documentation/programming-guide/#querying-metrics>Querying metrics</a></li><li><a href=/documentation/programming-guide/#using-metrics>Using metrics in pipeline</a></li><li><a href=/documentation/programming-guide/#export-metrics>Export metrics</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>State and Timers</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#types-of-state>Types of state</a></li><li><a href=/documentation/programming-guide/#deferred-state-reads>Deferred state reads</a></li><li><a href=/documentation/programming-guide/#timers>Timers</a></li><li><a href=/documentation/programming-guide/#garbage-collecting-state>Garbage collecting state</a></li><li><a href=/documentation/programming-guide/#state-timers-examples>State and timers examples</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Splittable DoFns</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#sdf-basics>Basics</a></li><li><a href=/documentation/programming-guide/#sizing-and-progress>Sizing and progress</a></li><li><a href=/documentation/programming-guide/#user-initiated-checkpoint>User-initiated checkpoint</a></li><li><a href=/documentation/programming-guide/#runner-initiated-split>Runner initiated split</a></li><li><a href=/documentation/programming-guide/#watermark-estimation>Watermark estimation</a></li><li><a href=/documentation/programming-guide/#truncating-during-drain>Truncating during drain</a></li><li><a href=/documentation/programming-guide/#bundle-finalization>Bundle finalization</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Multi-language Pipelines</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#create-x-lang-transforms>Creating cross-language transforms</a></li><li><a href=/documentation/programming-guide/#use-x-lang-transforms>Using cross-language transforms</a></li><li><a href=/documentation/programming-guide/#x-lang-transform-runner-support>Runner Support</a></li></ul></li><li><a href=/documentation/programming-guide/#batched-dofns>Batched DoFns</a></li><li><a href=/documentation/programming-guide/#transform-service>Transform service</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Pipeline development lifecycle</span><ul class=section-nav-list><li><a href=/documentation/pipelines/design-your-pipeline/>Design Your Pipeline</a></li><li><a href=/documentation/pipelines/create-your-pipeline/>Create Your Pipeline</a></li><li><a href=/documentation/pipelines/test-your-pipeline/>Test Your Pipeline</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Common pipeline patterns</span><ul class=section-nav-list><li><a href=/documentation/patterns/overview/>Overview</a></li><li><a href=/documentation/patterns/file-processing/>File processing</a></li><li><a href=/documentation/patterns/side-inputs/>Side inputs</a></li><li><a href=/documentation/patterns/pipeline-options/>Pipeline options</a></li><li><a href=/documentation/patterns/custom-io/>Custom I/O</a></li><li><a href=/documentation/patterns/custom-windows/>Custom windows</a></li><li><a href=/documentation/patterns/bigqueryio/>BigQueryIO</a></li><li><a href=/documentation/patterns/ai-platform/>AI Platform</a></li><li><a href=/documentation/patterns/schema/>Schema</a></li><li><a href=/documentation/patterns/bqml/>BigQuery ML</a></li><li><a href=/documentation/patterns/grouping-elements-for-efficient-external-service-calls/>Grouping elements for efficient external service calls</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>AI/ML pipelines</span><ul class=section-nav-list><li><a href=/documentation/ml/overview/>Get started with AI/ML</a></li><li><a href=/documentation/ml/about-ml/>About Beam ML</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Prediction and inference</span><ul class=section-nav-list><li><a href=/documentation/ml/inference-overview/>Overview</a></li><li><a href=/documentation/ml/multi-model-pipelines/>Build a pipeline with multiple models</a></li><li><a href=/documentation/ml/tensorrt-runinference>Build a custom model handler with TensorRT</a></li><li><a href=/documentation/ml/large-language-modeling>Use LLM inference</a></li><li><a href=/documentation/ml/multi-language-inference/>Build a multi-language inference pipeline</a></li><li><a href=/documentation/ml/side-input-updates/>Update your model in production</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Data processing</span><ul class=section-nav-list><li><a href=/documentation/ml/preprocess-data/>Preprocess data</a></li><li><a href=/documentation/ml/data-processing/>Explore your data</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Workflow orchestration</span><ul class=section-nav-list><li><a href=/documentation/ml/orchestration/>Use ML-OPS workflow orchestrators</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Model training</span><ul class=section-nav-list><li><a href=/documentation/ml/per-entity-training>Per-entity training</a></li><li><a href=/documentation/ml/online-clustering/>Online clustering</a></li><li><a href=/documentation/ml/model-evaluation/>ML model evaluation</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Use cases</span><ul class=section-nav-list><li><a href=/documentation/ml/anomaly-detection/>Build an anomaly detection pipeline</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Reference</span><ul class=section-nav-list><li><a href=/documentation/ml/runinference-metrics/>RunInference metrics</a></li><li><a href=/documentation/ml/model-evaluation/>Model validation</a></li></ul></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Runtime systems</span><ul class=section-nav-list><li><a href=/documentation/runtime/environments/>Container environments</a></li><li><a href=/documentation/runtime/resource-hints/>Resource hints</a></li><li><a href=/documentation/runtime/sdk-harness-config/>SDK Harness Configuration</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Transform catalog</span><ul class=section-nav-list><li class=section-nav-item--collapsible><span class=section-nav-list-title>Python</span><ul class=section-nav-list><li><a href=/documentation/transforms/python/overview/>Overview</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Element-wise</span><ul class=section-nav-list><li class=section-nav-item--collapsible><span class=section-nav-list-title>Enrichment</span><ul class=section-nav-list><li><a href=/documentation/transforms/python/elementwise/enrichment/>Overview</a></li><li><a href=/documentation/transforms/python/elementwise/enrichment-bigtable/>Bigtable example</a></li><li><a href=/documentation/transforms/python/elementwise/enrichment-vertexai/>Vertex AI Feature Store examples</a></li></ul></li><li><a href=/documentation/transforms/python/elementwise/filter/>Filter</a></li><li><a href=/documentation/transforms/python/elementwise/flatmap/>FlatMap</a></li><li><a href=/documentation/transforms/python/elementwise/keys/>Keys</a></li><li><a href=/documentation/transforms/python/elementwise/kvswap/>KvSwap</a></li><li><a href=/documentation/transforms/python/elementwise/map/>Map</a></li><li><a href=/documentation/transforms/python/elementwise/mltransform/>MLTransform</a></li><li><a href=/documentation/transforms/python/elementwise/pardo/>ParDo</a></li><li><a href=/documentation/transforms/python/elementwise/partition/>Partition</a></li><li><a href=/documentation/transforms/python/elementwise/regex/>Regex</a></li><li><a href=/documentation/transforms/python/elementwise/reify/>Reify</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>RunInference</span><ul class=section-nav-list><li><a href=/documentation/transforms/python/elementwise/runinference/>Overview</a></li><li><a href=/documentation/transforms/python/elementwise/runinference-pytorch/>PyTorch examples</a></li><li><a href=/documentation/transforms/python/elementwise/runinference-sklearn/>Sklearn examples</a></li></ul></li><li><a href=/documentation/transforms/python/elementwise/tostring/>ToString</a></li><li><a href=/documentation/transforms/python/elementwise/values/>Values</a></li><li><a href=/documentation/transforms/python/elementwise/withtimestamps/>WithTimestamps</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Aggregation</span><ul class=section-nav-list><li><a href=/documentation/transforms/python/aggregation/approximatequantiles/>ApproximateQuantiles</a></li><li><a href=/documentation/transforms/python/aggregation/approximateunique/>ApproximateUnique</a></li><li><a href=/documentation/transforms/python/aggregation/cogroupbykey/>CoGroupByKey</a></li><li><a href=/documentation/transforms/python/aggregation/combineglobally/>CombineGlobally</a></li><li><a href=/documentation/transforms/python/aggregation/combineperkey/>CombinePerKey</a></li><li><a href=/documentation/transforms/python/aggregation/combinevalues/>CombineValues</a></li><li><a href=/documentation/transforms/python/aggregation/count/>Count</a></li><li><a href=/documentation/transforms/python/aggregation/distinct/>Distinct</a></li><li><a href=/documentation/transforms/python/aggregation/groupby/>GroupBy</a></li><li><a href=/documentation/transforms/python/aggregation/groupbykey/>GroupByKey</a></li><li><a href=/documentation/transforms/python/aggregation/groupintobatches/>GroupIntoBatches</a></li><li><a href=/documentation/transforms/python/aggregation/latest/>Latest</a></li><li><a href=/documentation/transforms/python/aggregation/max/>Max</a></li><li><a href=/documentation/transforms/python/aggregation/mean/>Mean</a></li><li><a href=/documentation/transforms/python/aggregation/min/>Min</a></li><li><a href=/documentation/transforms/python/aggregation/sample/>Sample</a></li><li><a href=/documentation/transforms/python/aggregation/sum/>Sum</a></li><li><a href=/documentation/transforms/python/aggregation/top/>Top</a></li><li><a href=/documentation/transforms/python/aggregation/tolist/>ToList</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Other</span><ul class=section-nav-list><li><a href=/documentation/transforms/python/other/create/>Create</a></li><li><a href=/documentation/transforms/python/other/flatten/>Flatten</a></li><li><a href=/documentation/transforms/python/other/reshuffle/>Reshuffle</a></li><li><a href=/documentation/transforms/python/other/windowinto/>WindowInto</a></li></ul></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Java</span><ul class=section-nav-list><li><a href=/documentation/transforms/java/overview/>Overview</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Element-wise</span><ul class=section-nav-list><li><a href=/documentation/transforms/java/elementwise/filter/>Filter</a></li><li><a href=/documentation/transforms/java/elementwise/flatmapelements/>FlatMapElements</a></li><li><a href=/documentation/transforms/java/elementwise/keys/>Keys</a></li><li><a href=/documentation/transforms/java/elementwise/kvswap/>KvSwap</a></li><li><a href=/documentation/transforms/java/elementwise/mapelements/>MapElements</a></li><li><a href=/documentation/transforms/java/elementwise/pardo/>ParDo</a></li><li><a href=/documentation/transforms/java/elementwise/partition/>Partition</a></li><li><a href=/documentation/transforms/java/elementwise/regex/>Regex</a></li><li><a href=/documentation/transforms/java/elementwise/reify/>Reify</a></li><li><a href=/documentation/transforms/java/elementwise/tostring/>ToString</a></li><li><a href=/documentation/transforms/java/elementwise/values/>Values</a></li><li><a href=/documentation/transforms/java/elementwise/withkeys/>WithKeys</a></li><li><a href=/documentation/transforms/java/elementwise/withtimestamps/>WithTimestamps</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Aggregation</span><ul class=section-nav-list><li><a href=/documentation/transforms/java/aggregation/approximatequantiles/>ApproximateQuantiles</a></li><li><a href=/documentation/transforms/java/aggregation/approximateunique/>ApproximateUnique</a></li><li><a href=/documentation/transforms/java/aggregation/cogroupbykey/>CoGroupByKey</a></li><li><a href=/documentation/transforms/java/aggregation/combine/>Combine</a></li><li><a href=/documentation/transforms/java/aggregation/combinewithcontext/>CombineWithContext</a></li><li><a href=/documentation/transforms/java/aggregation/count/>Count</a></li><li><a href=/documentation/transforms/java/aggregation/distinct/>Distinct</a></li><li><a href=/documentation/transforms/java/aggregation/groupbykey/>GroupByKey</a></li><li><a href=/documentation/transforms/java/aggregation/groupintobatches/>GroupIntoBatches</a></li><li><a href=/documentation/transforms/java/aggregation/hllcount/>HllCount</a></li><li><a href=/documentation/transforms/java/aggregation/latest/>Latest</a></li><li><a href=/documentation/transforms/java/aggregation/max/>Max</a></li><li><a href=/documentation/transforms/java/aggregation/mean/>Mean</a></li><li><a href=/documentation/transforms/java/aggregation/min/>Min</a></li><li><a href=/documentation/transforms/java/aggregation/sample/>Sample</a></li><li><a href=/documentation/transforms/java/aggregation/sum/>Sum</a></li><li><a href=/documentation/transforms/java/aggregation/top/>Top</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Other</span><ul class=section-nav-list><li><a href=/documentation/transforms/java/other/create/>Create</a></li><li><a href=/documentation/transforms/java/other/flatten/>Flatten</a></li><li><a href=/documentation/transforms/java/other/passert/>PAssert</a></li><li><a href=/documentation/transforms/java/other/view/>View</a></li><li><a href=/documentation/transforms/java/other/window/>Window</a></li></ul></li></ul></li></ul></li><li><a href=/documentation/glossary/>Glossary</a></li><li><a href=https://cwiki.apache.org/confluence/display/BEAM/Apache+Beam>Beam Wiki <img src=/images/external-link-icon.png width=14 height=14 alt="External link."></a></li></ul></nav></div><nav class="page-nav clearfix" data-offset-top=90 data-offset-bottom=500><nav id=TableOfContents><ul><li><a href=#pipeline>Pipeline</a></li><li><a href=#pcollection>PCollection</a></li><li><a href=#ptransform>PTransform</a></li><li><a href=#aggregation>Aggregation</a></li><li><a href=#user-defined-function-udf>User-defined function (UDF)</a></li><li><a href=#schema>Schema</a></li><li><a href=#runner>Runner</a></li><li><a href=#window>Window</a></li><li><a href=#watermark>Watermark</a></li><li><a href=#trigger>Trigger</a></li><li><a href=#state-and-timers>State and timers</a></li><li><a href=#splittable-dofn>Splittable DoFn</a></li><li><a href=#whats-next>What’s next</a></li></ul></nav></nav><div class="body__contained body__section-nav arrow-list arrow-list--no-mt"><h1 id=basics-of-the-beam-model>Basics of the Beam model</h1><p>Apache Beam is a unified model for defining both batch and streaming |
| data-parallel processing pipelines. To get started with Beam, you’ll need to |
| understand an important set of core concepts:</p><ul><li><a href=#pipeline><em>Pipeline</em></a> - A pipeline is a user-constructed graph of |
| transformations that defines the desired data processing operations.</li><li><a href=#pcollection><em>PCollection</em></a> - A <code>PCollection</code> is a data set or data |
| stream. The data that a pipeline processes is part of a PCollection.</li><li><a href=#ptransform><em>PTransform</em></a> - A <code>PTransform</code> (or <em>transform</em>) represents a |
| data processing operation, or a step, in your pipeline. A transform is |
| applied to zero or more <code>PCollection</code> objects, and produces zero or more |
| <code>PCollection</code> objects.</li><li><a href=#aggregation><em>Aggregation</em></a> - Aggregation is computing a value from |
| multiple (1 or more) input elements.</li><li><a href=#user-defined-function-udf><em>User-defined function (UDF)</em></a> - Some Beam |
| operations allow you to run user-defined code as a way to configure the |
| transform.</li><li><a href=#schema><em>Schema</em></a> - A schema is a language-independent type definition for |
| a <code>PCollection</code>. The schema for a <code>PCollection</code> defines elements of that |
| <code>PCollection</code> as an ordered list of named fields.</li><li><a href=/documentation/sdks/java/><em>SDK</em></a> - A language-specific library that lets |
| pipeline authors build transforms, construct their pipelines, and submit |
| them to a runner.</li><li><a href=#runner><em>Runner</em></a> - A runner runs a Beam pipeline using the capabilities of |
| your chosen data processing engine.</li><li><a href=#window><em>Window</em></a> - A <code>PCollection</code> can be subdivided into windows based on |
| the timestamps of the individual elements. Windows enable grouping operations |
| over collections that grow over time by dividing the collection into windows |
| of finite collections.</li><li><a href=#watermark><em>Watermark</em></a> - A watermark is a guess as to when all data in a |
| certain window is expected to have arrived. This is needed because data isn’t |
| always guaranteed to arrive in a pipeline in time order, or to always arrive |
| at predictable intervals.</li><li><a href=#trigger><em>Trigger</em></a> - A trigger determines when to aggregate the results of |
| each window.</li><li><a href=#state-and-timers><em>State and timers</em></a> - Per-key state and timer callbacks |
| are lower level primitives that give you full control over aggregating input |
| collections that grow over time.</li><li><a href=#splittable-dofn><em>Splittable DoFn</em></a> - Splittable DoFns let you process |
| elements in a non-monolithic way. You can checkpoint the processing of an |
| element, and the runner can split the remaining work to yield additional |
| parallelism.</li></ul><p>The following sections cover these concepts in more detail and provide links to |
| additional documentation.</p><h2 id=pipeline>Pipeline</h2><p>A Beam pipeline is a graph (specifically, a |
| <a href=https://en.wikipedia.org/wiki/Directed_acyclic_graph>directed acyclic graph</a>) |
| of all the data and computations in your data processing task. This includes |
| reading input data, transforming that data, and writing output data. A pipeline |
| is constructed by a user in their SDK of choice. Then, the pipeline makes its |
| way to the runner either through the SDK directly or through the Runner API’s |
| RPC interface. For example, this diagram shows a branching pipeline:</p><p><img src=/images/design-your-pipeline-multiple-pcollections.svg alt="The pipeline applies two transforms to a single input collection. Eachtransform produces an output collection."></p><p>In this diagram, the boxes represent the parallel computations called |
| <a href=#ptransform><em>PTransforms</em></a> and the arrows with the circles represent the data |
| (in the form of <a href=#pcollection><em>PCollections</em></a>) that flows between the |
| transforms. The data might be bounded, stored, data sets, or the data might also |
| be unbounded streams of data. In Beam, most transforms apply equally to bounded |
| and unbounded data.</p><p>You can express almost any computation that you can think of as a graph as a |
| Beam pipeline. A Beam driver program typically starts by creating a <code>Pipeline</code> |
| object, and then uses that object as the basis for creating the pipeline’s data |
| sets and its transforms.</p><p>For more information about pipelines, see the following pages:</p><ul><li><a href=/documentation/programming-guide/#overview>Beam Programming Guide: Overview</a></li><li><a href=/documentation/programming-guide/#creating-a-pipeline>Beam Programming Guide: Creating a pipeline</a></li><li><a href=/documentation/pipelines/design-your-pipeline>Design your pipeline</a></li><li><a href=/documentation/pipelines/create-your-pipeline>Create your pipeline</a></li></ul><h2 id=pcollection>PCollection</h2><p>A <code>PCollection</code> is an unordered bag of elements. Each <code>PCollection</code> is a |
| potentially distributed, homogeneous data set or data stream, and is owned by |
| the specific <code>Pipeline</code> object for which it is created. Multiple pipelines |
| cannot share a <code>PCollection</code>. Beam pipelines process PCollections, and the |
| runner is responsible for storing these elements.</p><p>A <code>PCollection</code> generally contains “big data” (too much data to fit in memory on |
| a single machine). Sometimes a small sample of data or an intermediate result |
| might fit into memory on a single machine, but Beam’s computational patterns and |
| transforms are focused on situations where distributed data-parallel computation |
| is required. Therefore, the elements of a <code>PCollection</code> cannot be processed |
| individually, and are instead processed uniformly in parallel.</p><p>The following characteristics of a <code>PCollection</code> are important to know.</p><p><strong>Bounded vs. unbounded</strong>:</p><p>A <code>PCollection</code> can be either bounded or unbounded.</p><ul><li>A <em>bounded</em> <code>PCollection</code> is a dataset of a known, fixed size (alternatively, |
| a dataset that is not growing over time). Bounded data can be processed by |
| batch pipelines.</li><li>An <em>unbounded</em> <code>PCollection</code> is a dataset that grows over time, and the |
| elements are processed as they arrive. Unbounded data must be processed by |
| streaming pipelines.</li></ul><p>These two categories derive from the intuitions of batch and stream processing, |
| but the two are unified in Beam and bounded and unbounded PCollections can |
| coexist in the same pipeline. If your runner can only support bounded |
| PCollections, you must reject pipelines that contain unbounded PCollections. If |
| your runner is only targeting streams, there are adapters in Beam’s support code |
| to convert everything to APIs that target unbounded data.</p><p><strong>Timestamps</strong>:</p><p>Every element in a <code>PCollection</code> has a timestamp associated with it.</p><p>When you execute a primitive connector to a storage system, that connector is |
| responsible for providing initial timestamps. The runner must propagate and |
| aggregate timestamps. If the timestamp is not important, such as with certain |
| batch processing jobs where elements do not denote events, the timestamp will be |
| the minimum representable timestamp, often referred to colloquially as “negative |
| infinity”.</p><p><strong>Watermarks</strong>:</p><p>Every <code>PCollection</code> must have a <a href=#watermark>watermark</a> that estimates how |
| complete the <code>PCollection</code> is.</p><p>The watermark is a guess that “we’ll never see an element with an earlier |
| timestamp”. Data sources are responsible for producing a watermark. The runner |
| must implement watermark propagation as PCollections are processed, merged, and |
| partitioned.</p><p>The contents of a <code>PCollection</code> are complete when a watermark advances to |
| “infinity”. In this manner, you can discover that an unbounded PCollection is |
| finite.</p><p><strong>Windowed elements</strong>:</p><p>Every element in a <code>PCollection</code> resides in a <a href=#window>window</a>. No element |
| resides in multiple windows; two elements can be equal except for their window, |
| but they are not the same.</p><p>When elements are written to the outside world, they are effectively placed back |
| into the global window. Transforms that write data and don’t take this |
| perspective risk data loss.</p><p>A window has a maximum timestamp. When the watermark exceeds the maximum |
| timestamp plus the user-specified allowed lateness, the window is expired. All |
| data related to an expired window might be discarded at any time.</p><p><strong>Coder</strong>:</p><p>Every <code>PCollection</code> has a coder, which is a specification of the binary format |
| of the elements.</p><p>In Beam, the user’s pipeline can be written in a language other than the |
| language of the runner. There is no expectation that the runner can actually |
| deserialize user data. The Beam model operates principally on encoded data, |
| “just bytes”. Each <code>PCollection</code> has a declared encoding for its elements, |
| called a coder. A coder has a URN that identifies the encoding, and might have |
| additional sub-coders. For example, a coder for lists might contain a coder for |
| the elements of the list. Language-specific serialization techniques are |
| frequently used, but there are a few common key formats (such as key-value pairs |
| and timestamps) so the runner can understand them.</p><p><strong>Windowing strategy</strong>:</p><p>Every <code>PCollection</code> has a windowing strategy, which is a specification of |
| essential information for grouping and triggering operations. The <code>Window</code> |
| transform sets up the windowing strategy, and the <code>GroupByKey</code> transform has |
| behavior that is governed by the windowing strategy.</p><br><p>For more information about PCollections, see the following page:</p><ul><li><a href=/documentation/programming-guide/#pcollections>Beam Programming Guide: PCollections</a></li></ul><h2 id=ptransform>PTransform</h2><p>A <code>PTransform</code> (or transform) represents a data processing operation, or a step, |
| in your pipeline. A transform is usually applied to one or more input |
| <code>PCollection</code> objects. Transforms that read input are an exception; these |
| transforms might not have an input <code>PCollection</code>.</p><p>You provide transform 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 the input PCollection (or more than one PCollection). Depending on |
| the pipeline runner and backend that you choose, many different workers across a |
| cluster might execute instances of your user code in parallel. The user code |
| that runs on each worker generates the output elements that are added to zero or |
| more output <code>PCollection</code> objects.</p><p>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 <code>ParDo</code> or <code>Combine</code>. 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.</p><p>The following list has some common transform types:</p><ul><li>Source transforms such as <code>TextIO.Read</code> and <code>Create</code>. A source transform |
| conceptually has no input.</li><li>Processing and conversion operations such as <code>ParDo</code>, <code>GroupByKey</code>, |
| <code>CoGroupByKey</code>, <code>Combine</code>, and <code>Count</code>.</li><li>Outputting transforms such as <code>TextIO.Write</code>.</li><li>User-defined, application-specific composite transforms.</li></ul><p>For more information about transforms, see the following pages:</p><ul><li><a href=/documentation/programming-guide/#overview>Beam Programming Guide: Overview</a></li><li><a href=/documentation/programming-guide/#transforms>Beam Programming Guide: Transforms</a></li><li>Beam transform catalog (<a href=/documentation/transforms/java/overview/>Java</a>, |
| <a href=/documentation/transforms/python/overview/>Python</a>)</li></ul><h2 id=aggregation>Aggregation</h2><p>Aggregation is computing a value from multiple (1 or more) input elements. In |
| Beam, the primary computational pattern for aggregation is to group all elements |
| with a common key and window then combine each group of elements using an |
| associative and commutative operation. This is similar to the “Reduce” operation |
| in the <a href=https://en.wikipedia.org/wiki/MapReduce>MapReduce</a> model, though it is |
| enhanced to work with unbounded input streams as well as bounded data sets.</p><img src=/images/aggregation.png alt="Aggregation of elements." width=120px><p><em>Figure 1: Aggregation of elements. Elements with the same color represent those |
| with a common key and window.</em></p><p>Some simple aggregation transforms include <code>Count</code> (computes the count of all |
| elements in the aggregation), <code>Max</code> (computes the maximum element in the |
| aggregation), and <code>Sum</code> (computes the sum of all elements in the aggregation).</p><p>When elements are grouped and emitted as a bag, the aggregation is known as |
| <code>GroupByKey</code> (the associative/commutative operation is bag union). In this case, |
| the output is no smaller than the input. Often, you will apply an operation such |
| as summation, called a <code>CombineFn</code>, in which the output is significantly smaller |
| than the input. In this case the aggregation is called <code>CombinePerKey</code>.</p><p>In a real application, you might have millions of keys and/or windows; that is |
| why this is still an “embarrassingly parallel” computational pattern. In those |
| cases where you have fewer keys, you can add parallelism by adding a |
| supplementary key, splitting each of your problem’s natural keys into many |
| sub-keys. After these sub-keys are aggregated, the results can be further |
| combined into a result for the original natural key for your problem. The |
| associativity of your aggregation function ensures that this yields the same |
| answer, but with more parallelism.</p><p>When your input is unbounded, the computational pattern of grouping elements by |
| key and window is roughly the same, but governing when and how to emit the |
| results of aggregation involves three concepts:</p><ul><li><a href=#window>Windowing</a>, which partitions your input into bounded subsets that |
| can be complete.</li><li><a href=#watermark>Watermarks</a>, which estimate the completeness of your input.</li><li><a href=#trigger>Triggers</a>, which govern when and how to emit aggregated results.</li></ul><p>For more information about available aggregation transforms, see the following |
| pages:</p><ul><li><a href=/documentation/programming-guide/#core-beam-transforms>Beam Programming Guide: Core Beam transforms</a></li><li>Beam Transform catalog |
| (<a href=/documentation/transforms/java/overview/#aggregation>Java</a>, |
| <a href=/documentation/transforms/python/overview/#aggregation>Python</a>)</li></ul><h2 id=user-defined-function-udf>User-defined function (UDF)</h2><p>Some Beam operations allow you to run user-defined code as a way to configure |
| the transform. For example, when using <code>ParDo</code>, user-defined code specifies what |
| operation to apply to every element. For <code>Combine</code>, it specifies how values |
| should be combined. By using <a href=/documentation/patterns/cross-language/>cross-language transforms</a>, |
| a Beam pipeline can contain UDFs written in a different language, or even |
| multiple languages in the same pipeline.</p><p>Beam has several varieties of UDFs:</p><ul><li><a href=/documentation/programming-guide/#pardo><em>DoFn</em></a> - per-element processing |
| function (used in <code>ParDo</code>)</li><li><a href=/documentation/programming-guide/#setting-your-pcollections-windowing-function><em>WindowFn</em></a> - |
| places elements in windows and merges windows (used in <code>Window</code> and |
| <code>GroupByKey</code>)</li><li><a href=/documentation/programming-guide/#side-inputs><em>ViewFn</em></a> - adapts a |
| materialized <code>PCollection</code> to a particular interface (used in side inputs)</li><li><a href=/documentation/programming-guide/#side-inputs-windowing><em>WindowMappingFn</em></a> - |
| maps one element’s window to another, and specifies bounds on how far in the |
| past the result window will be (used in side inputs)</li><li><a href=/documentation/programming-guide/#combine><em>CombineFn</em></a> - associative and |
| commutative aggregation (used in <code>Combine</code> and state)</li><li><a href=/documentation/programming-guide/#data-encoding-and-type-safety><em>Coder</em></a> - |
| encodes user data; some coders have standard formats and are not really UDFs</li></ul><p>Each language SDK has its own idiomatic way of expressing the user-defined |
| functions in Beam, but there are common requirements. 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. Each copy |
| of your user code function might be retried or run multiple times, depending on |
| the pipeline runner and the processing backend that you choose for your |
| pipeline. Beam also supports stateful processing through the |
| <a href=/blog/stateful-processing/>stateful processing API</a>.</p><p>For more information about user-defined functions, see the following pages:</p><ul><li><a href=/documentation/programming-guide/#requirements-for-writing-user-code-for-beam-transforms>Requirements for writing user code for Beam transforms</a></li><li><a href=/documentation/programming-guide/#pardo>Beam Programming Guide: ParDo</a></li><li><a href=/documentation/programming-guide/#setting-your-pcollections-windowing-function>Beam Programming Guide: WindowFn</a></li><li><a href=/documentation/programming-guide/#combine>Beam Programming Guide: CombineFn</a></li><li><a href=/documentation/programming-guide/#data-encoding-and-type-safety>Beam Programming Guide: Coder</a></li><li><a href=/documentation/programming-guide/#side-inputs>Beam Programming Guide: Side inputs</a></li></ul><h2 id=schema>Schema</h2><p>A schema is a language-independent type definition for a <code>PCollection</code>. The |
| schema for a <code>PCollection</code> defines elements of that <code>PCollection</code> as an ordered |
| list of named fields. Each field has a name, a type, and possibly a set of user |
| options.</p><p>In many cases, the element type in a <code>PCollection</code> has a structure that can be |
| introspected. Some examples are JSON, Protocol Buffer, Avro, and database row |
| objects. All of these formats can be converted to Beam Schemas. Even within a |
| SDK pipeline, Simple Java POJOs (or equivalent structures in other languages) |
| are often used as intermediate types, and these also have a clear structure that |
| can be inferred by inspecting the class. By understanding the structure of a |
| pipeline’s records, we can provide much more concise APIs for data processing.</p><p>Beam provides a collection of transforms that operate natively on schemas. For |
| example, <a href=/documentation/dsls/sql/overview/>Beam SQL</a> is a common transform |
| that operates on schemas. These transforms allow selections and aggregations in |
| terms of named schema fields. Another advantage of schemas is that they allow |
| referencing of element fields by name. Beam provides a selection syntax for |
| referencing fields, including nested and repeated fields.</p><p>For more information about schemas, see the following pages:</p><ul><li><a href=/documentation/programming-guide/#schemas>Beam Programming Guide: Schemas</a></li><li><a href=/documentation/patterns/schema/>Schema Patterns</a></li></ul><h2 id=runner>Runner</h2><p>A Beam runner runs a Beam pipeline on a specific platform. Most runners are |
| translators or adapters to massively parallel big data processing systems, such |
| as Apache Flink, Apache Spark, Google Cloud Dataflow, and more. For example, the |
| Flink runner translates a Beam pipeline into a Flink job. The Direct Runner runs |
| pipelines locally so you can test, debug, and validate that your pipeline |
| adheres to the Apache Beam model as closely as possible.</p><p>For an up-to-date list of Beam runners and which features of the Apache Beam |
| model they support, see the runner |
| <a href=/documentation/runners/capability-matrix/>capability matrix</a>.</p><p>For more information about runners, see the following pages:</p><ul><li><a href=/documentation/#choosing-a-runner>Choosing a Runner</a></li><li><a href=/documentation/runners/capability-matrix/>Beam Capability Matrix</a></li></ul><h2 id=window>Window</h2><p>Windowing subdivides a <code>PCollection</code> into <em>windows</em> according to the timestamps |
| of its individual elements. Windows enable grouping operations over unbounded |
| collections by dividing the collection into windows of finite collections.</p><p>A <em>windowing function</em> tells the runner how to assign elements to one or more |
| initial windows, and how to merge windows of grouped elements. Each element in a |
| <code>PCollection</code> can only be in one window, so if a windowing function specifies |
| multiple windows for an element, the element is conceptually duplicated into |
| each of the windows and each element is identical except for its window.</p><p>Transforms that aggregate multiple elements, such as <code>GroupByKey</code> and <code>Combine</code>, |
| work implicitly on a per-window basis; they process each <code>PCollection</code> as a |
| succession of multiple, finite windows, though the entire collection itself may |
| be of unbounded size.</p><p>Beam provides several windowing functions:</p><ul><li><strong>Fixed time windows</strong> (also known as “tumbling windows”) represent a consistent |
| duration, non-overlapping time interval in the data stream.</li><li><strong>Sliding time windows</strong> (also known as “hopping windows”) also represent time |
| intervals in the data stream; however, sliding time windows can overlap.</li><li><strong>Per-session windows</strong> define windows that contain elements that are within a |
| certain gap duration of another element.</li><li><strong>Single global window</strong>: by default, all data in a <code>PCollection</code> is assigned to |
| the single global window, and late data is discarded.</li><li><strong>Calendar-based windows</strong> (not supported by the Beam SDK for Python)</li></ul><p>You can also define your own windowing function if you have more complex |
| requirements.</p><p>For example, let’s say we have a <code>PCollection</code> that uses 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) belongs to a different window.</p><p>Two concepts are closely related to windowing and covered in the following |
| sections: <a href=#watermark>watermarks</a> and <a href=#trigger>triggers</a>.</p><p>For more information about windows, see the following page:</p><ul><li><a href=/documentation/programming-guide/#windowing>Beam Programming Guide: Windowing</a></li><li><a href=/documentation/programming-guide/#setting-your-pcollections-windowing-function>Beam Programming Guide: WindowFn</a></li></ul><h2 id=watermark>Watermark</h2><p>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, data isn’t always guaranteed to arrive in |
| a pipeline in time order, or to always arrive at predictable intervals. For |
| example, you might have intermediate systems that don’t preserve order, or you |
| might have two servers that timestamp data but one has a better network |
| connection.</p><p>To address this potential unpredictability, Beam tracks a <em>watermark</em>. A |
| watermark is a guess as to when all data in a certain window is expected to have |
| arrived in the pipeline. You can also think of this as “we’ll never see an |
| element with an earlier timestamp”.</p><p>Data sources are responsible for producing a watermark, and every <code>PCollection</code> |
| must have a watermark that estimates how complete the <code>PCollection</code> is. The |
| contents of a <code>PCollection</code> are complete when a watermark advances to |
| “infinity”. In this manner, you might discover that an unbounded <code>PCollection</code> |
| is finite. After the watermark progresses past the end of a window, any further |
| element that arrives with a timestamp in that window is considered <em>late data</em>.</p><p><a href=#trigger>Triggers</a> are a related concept that allow you to modify and refine |
| the windowing strategy for a <code>PCollection</code>. You can use triggers to decide when |
| each individual window aggregates and reports its results, including how the |
| window emits late elements.</p><p>For more information about watermarks, see the following page:</p><ul><li><a href=/documentation/programming-guide/#watermarks-and-late-data>Beam Programming Guide: Watermarks and late data</a></li></ul><h2 id=trigger>Trigger</h2><p>When collecting and grouping data into windows, Beam uses <em>triggers</em> to |
| determine when to emit the aggregated results of each window (referred to as a |
| <em>pane</em>). 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.</p><p>At a high level, triggers provide two additional capabilities compared to |
| outputting at the end of a window:</p><ol><li>Triggers allow Beam to emit early results, before all the data in a given |
| window has arrived. For example, emitting after a certain amount of time |
| elapses, or after a certain number of elements arrives.</li><li>Triggers allow processing of late data by triggering after the event time |
| watermark passes the end of the window.</li></ol><p>These capabilities allow you to control the flow of your data and also balance |
| between data completeness, latency, and cost.</p><p>Beam provides a number of pre-built triggers that you can set:</p><ul><li><strong>Event time triggers</strong>: These triggers operate on the event time, as |
| indicated by the timestamp on each data element. Beam’s default trigger is |
| event time-based.</li><li><strong>Processing time triggers</strong>: These triggers operate on the processing time, |
| which is the time when the data element is processed at any given stage in |
| the pipeline.</li><li><strong>Data-driven triggers</strong>: These triggers operate by examining the data as it |
| arrives in each window, and firing when that data meets a certain property. |
| Currently, data-driven triggers only support firing after a certain number of |
| data elements.</li><li><strong>Composite triggers</strong>: These triggers combine multiple triggers in various |
| ways. For example, you might want one trigger for early data and a different |
| trigger for late data.</li></ul><p>For more information about triggers, see the following page:</p><ul><li><a href=/documentation/programming-guide/#triggers>Beam Programming Guide: Triggers</a></li></ul><h2 id=state-and-timers>State and timers</h2><p>Beam’s windowing and triggers provide an abstraction for grouping and |
| aggregating unbounded input data based on timestamps. However, there are |
| aggregation use cases that might require an even higher degree of control. State |
| and timers are two important concepts that help with these uses cases. Like |
| other aggregations, state and timers are processed per window.</p><p><strong>State</strong>:</p><p>Beam provides the State API for manually managing per-key state, allowing for |
| fine-grained control over aggregations. The State API lets you augment |
| element-wise operations (for example, <code>ParDo</code> or <code>Map</code>) with mutable state. Like |
| other aggregations, state is processed per window.</p><p>The State API models state per key. To use the state API, you start out with a |
| keyed <code>PCollection</code>. A <code>ParDo</code> that processes this <code>PCollection</code> can declare |
| persistent state variables. When you process each element inside the <code>ParDo</code>, |
| you can use the state variables to write or update state for the current key or |
| to read previous state written for that key. State is always fully scoped only |
| to the current processing key.</p><p>Beam provides several types of state, though different runners might support a |
| different subset of these states.</p><ul><li><strong>ValueState</strong>: ValueState is a scalar state value. For each key in the |
| input, a ValueState stores a typed value that can be read and modified inside |
| the <code>DoFn</code>.</li><li>A common use case for state is to accumulate multiple elements into a group:<ul><li><strong>BagState</strong>: BagState allows you to accumulate elements in an unordered |
| bag. This lets you add elements to a collection without needing to read any |
| of the previously accumulated elements.</li><li><strong>MapState</strong>: MapState allows you to accumulate elements in a map.</li><li><strong>SetState</strong>: SetState allows you to accumulate elements in a set.</li><li><strong>OrderedListState</strong>: OrderedListState allows you to accumulate elements in |
| a timestamp-sorted list.</li></ul></li><li><strong>CombiningState</strong>: CombiningState allows you to create a state object that |
| is updated using a Beam combiner. Like BagState, you can add elements to an |
| aggregation without needing to read the current value, and the accumulator |
| can be compacted using a combiner.</li></ul><p>You can use the State API together with the Timer API to create processing tasks |
| that give you fine-grained control over the workflow.</p><p><strong>Timers</strong>:</p><p>Beam provides a per-key timer callback API that enables delayed processing of |
| data stored using the State API. The Timer API lets you set timers to call back |
| at either an event-time or a processing-time timestamp. For more advanced use |
| cases, your timer callback can set another timer. Like other aggregations, |
| timers are processed per window. You can use the timer API together with the |
| State API to create processing tasks that give you fine-grained control over the |
| workflow.</p><p>The following timers are available:</p><ul><li><strong>Event-time timers</strong>: Event-time timers fire when the input watermark for |
| the <code>DoFn</code> passes the time at which the timer is set, meaning that the runner |
| believes that there are no more elements to be processed with timestamps |
| before the timer timestamp. This allows for event-time aggregations.</li><li><strong>Processing-time timers</strong>: Processing-time timers fire when the real wall-clock |
| time passes. This is often used to create larger batches of data before |
| processing. It can also be used to schedule events that should occur at a |
| specific time.</li><li><strong>Dynamic timer tags</strong>: Beam also supports dynamically setting a timer tag. This |
| allows you to set multiple different timers in a <code>DoFn</code> and dynamically |
| choose timer tags (for example, based on data in the input elements).</li></ul><p>For more information about state and timers, see the following pages:</p><ul><li><a href=/documentation/programming-guide/#state-and-timers>Beam Programming Guide: State and Timers</a></li><li><a href=/blog/stateful-processing/>Stateful processing with Apache Beam</a></li><li><a href=/blog/timely-processing/>Timely (and Stateful) Processing with Apache Beam</a></li></ul><h2 id=splittable-dofn>Splittable DoFn</h2><p>Splittable <code>DoFn</code> (SDF) is a generalization of <code>DoFn</code> that lets you process |
| elements in a non-monolithic way. Splittable <code>DoFn</code> makes it easier to create |
| complex, modular I/O connectors in Beam.</p><p>A regular <code>ParDo</code> processes an entire element at a time, applying your regular |
| <code>DoFn</code> and waiting for the call to terminate. When you instead apply a |
| splittable <code>DoFn</code> to each element, the runner has the option of splitting the |
| element’s processing into smaller tasks. You can checkpoint the processing of an |
| element, and you can split the remaining work to yield additional parallelism.</p><p>For example, imagine you want to read every line from very large text files. |
| When you write your splittable <code>DoFn</code>, you can have separate pieces of logic to |
| read a segment of a file, split a segment of a file into sub-segments, and |
| report progress through the current segment. The runner can then invoke your |
| splittable <code>DoFn</code> intelligently to split up each input and read portions |
| separately, in parallel.</p><p>A common computation pattern has the following steps:</p><ol><li>The runner splits an incoming element before starting any processing.</li><li>The runner starts running your processing logic on each sub-element.</li><li>If the runner notices that some sub-elements are taking longer than others, |
| the runner splits those sub-elements further and repeats step 2.</li><li>The sub-element either finishes processing, or the user chooses to |
| checkpoint the sub-element and the runner repeats step 2.</li></ol><p>You can also write your splittable <code>DoFn</code> so the runner can split the unbounded |
| processing. For example, if you write a splittable <code>DoFn</code> to watch a set of |
| directories and output filenames as they arrive, you can split to subdivide the |
| work of different directories. This allows the runner to split off a hot |
| directory and give it additional resources.</p><p>For more information about Splittable <code>DoFn</code>, see the following pages:</p><ul><li><a href=/documentation/programming-guide/#splittable-dofns>Splittable DoFns</a></li><li><a href=/blog/splittable-do-fn-is-available/>Splittable DoFn in Apache Beam is Ready to Use</a></li></ul><h2 id=whats-next>What’s next</h2><p>Take a look at our <a href=/documentation/>other documentation</a> such as the Beam |
| programming guide, pipeline execution information, and transform reference |
| catalogs.</p><div class=feedback><p class=update>Last updated on 2024/05/03</p><h3>Have you found everything you were looking for?</h3><p class=description>Was it all useful and clear? Is there anything that you would like to change? Let us know!</p><button class=load-button><a href="https://docs.google.com/forms/d/e/1FAIpQLSfID7abne3GE6k6RdJIyZhPz2Gef7UkpggUEhTIDjjplHuxSA/viewform?usp=header_link" target=_blank>SEND FEEDBACK</a></button></div></div></div><footer class=footer><div class=footer__contained><div class=footer__cols><div class="footer__cols__col footer__cols__col__logos"><div class=footer__cols__col__logo><img src=/images/beam_logo_circle.svg class=footer__logo alt="Beam logo"></div><div class=footer__cols__col__logo><img src=/images/apache_logo_circle.svg class=footer__logo alt="Apache logo"></div></div><div class=footer-wrapper><div class=wrapper-grid><div class=footer__cols__col><div class=footer__cols__col__title>Start</div><div class=footer__cols__col__link><a href=/get-started/beam-overview/>Overview</a></div><div class=footer__cols__col__link><a href=/get-started/quickstart-java/>Quickstart (Java)</a></div><div class=footer__cols__col__link><a href=/get-started/quickstart-py/>Quickstart (Python)</a></div><div class=footer__cols__col__link><a href=/get-started/quickstart-go/>Quickstart (Go)</a></div><div class=footer__cols__col__link><a href=/get-started/downloads/>Downloads</a></div></div><div class=footer__cols__col><div class=footer__cols__col__title>Docs</div><div class=footer__cols__col__link><a href=/documentation/programming-guide/>Concepts</a></div><div class=footer__cols__col__link><a href=/documentation/pipelines/design-your-pipeline/>Pipelines</a></div><div class=footer__cols__col__link><a href=/documentation/runners/capability-matrix/>Runners</a></div></div><div class=footer__cols__col><div class=footer__cols__col__title>Community</div><div class=footer__cols__col__link><a href=/contribute/>Contribute</a></div><div class=footer__cols__col__link><a href=https://projects.apache.org/committee.html?beam target=_blank>Team<img src=/images/external-link-icon.png width=14 height=14 alt="External link."></a></div><div class=footer__cols__col__link><a href=/community/presentation-materials/>Media</a></div><div class=footer__cols__col__link><a href=/community/in-person/>Events/Meetups</a></div><div class=footer__cols__col__link><a href=/community/contact-us/>Contact Us</a></div></div><div class=footer__cols__col><div class=footer__cols__col__title>Resources</div><div class=footer__cols__col__link><a href=/blog/>Blog</a></div><div class=footer__cols__col__link><a href=https://github.com/apache/beam>GitHub</a></div></div></div><div class=footer__bottom>© |
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