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<script>function showSearch(){addPlaceholder();var e,t=document.querySelector(".searchBar");t.classList.remove("disappear"),e=document.querySelector("#iconsBar"),e.classList.add("disappear")}function addPlaceholder(){$("input:text").attr("placeholder","What are you looking for?")}function endSearch(){var e,t=document.querySelector(".searchBar");t.classList.add("disappear"),e=document.querySelector("#iconsBar"),e.classList.remove("disappear")}function blockScroll(){$("body").toggleClass("fixedPosition")}function openMenu(){addPlaceholder(),blockScroll()}</script><div class="clearfix container-main-content"><div class="section-nav closed" data-offset-top=90 data-offset-bottom=500><span class="section-nav-back glyphicon glyphicon-menu-left"></span><nav><ul class=section-nav-list data-section-nav><li><span class=section-nav-list-main-title>Documentation</span></li><li><a href=/documentation>Using the Documentation</a></li><li class=section-nav-item--collapsible><span 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=#processing-of-elements>Processing of elements</a><ul><li><a href=#serialization-and-communication>Serialization and communication</a></li><li><a href=#bundling-and-persistence>Bundling and persistence</a></li><li><a href=#data-partitioning-and-inter-stage-execution>Data partitioning and inter-stage execution</a><ul><li><a href=#data-ordering-in-a-pipeline-execution>Data ordering in a pipeline execution</a></li></ul></li></ul></li><li><a href=#parallelism>Failures and parallelism within and between transforms</a><ul><li><a href=#data-parallelism>Data-parallelism within one transform</a></li><li><a href=#dependent-parallellism>Dependent-parallelism between transforms</a></li><li><a href=#failures-within-one-transform>Failures within one transform</a></li><li><a href=#coupled-failure>Coupled failure: Failures between transforms</a></li></ul></li></ul></nav></nav><div class="body__contained body__section-nav arrow-list arrow-list--no-mt"><h1 id=execution-model>Execution model</h1><p>The Beam model allows runners to execute your pipeline in different ways. You
may observe various effects as a result of the runner’s choices. This page
describes these effects so you can better understand how Beam pipelines execute.</p><h2 id=processing-of-elements>Processing of elements</h2><p>The serialization and communication of elements between machines is one of the
most expensive operations in a distributed execution of your pipeline. Avoiding
this serialization may require re-processing elements after failures or may
limit the distribution of output to other machines.</p><h3 id=serialization-and-communication>Serialization and communication</h3><p>The runner might serialize elements between machines for communication purposes
and for other reasons such as persistence.</p><p>A runner may decide to transfer elements between transforms in a variety of
ways, such as:</p><ul><li>Routing elements to a worker for processing as part of a grouping operation.
This may involve serializing elements and grouping or sorting them by their
key.</li><li>Redistributing elements between workers to adjust parallelism. This may
involve serializing elements and communicating them to other workers.</li><li>Using the elements in a side input to a <code>ParDo</code>. This may require
serializing the elements and broadcasting them to all the workers executing
the <code>ParDo</code>.</li><li>Passing elements between transforms that are running on the same worker.
This may allow the runner to avoid serializing elements; instead, the runner
can just pass the elements in memory. This is done as part of an
optimization that is known as
<a href=/documentation/glossary/#fusion>fusion</a>.</li></ul><p>Some situations where the runner may serialize and persist elements are:</p><ol><li>When used as part of a stateful <code>DoFn</code>, the runner may persist values to some
state mechanism.</li><li>When committing the results of processing, the runner may persist the outputs
as a checkpoint.</li></ol><h3 id=bundling-and-persistence>Bundling and persistence</h3><p>Beam pipelines often focus on &ldquo;<a href=https://en.wikipedia.org/wiki/embarrassingly_parallel>embarassingly parallel</a>&rdquo;
problems. Because of this, the APIs emphasize processing elements in parallel,
which makes it difficult to express actions like &ldquo;assign a sequence number to
each element in a PCollection&rdquo;. This is intentional as such algorithms are much
more likely to suffer from scalability problems.</p><p>Processing all elements in parallel also has some drawbacks. Specifically, it
makes it impossible to batch any operations, such as writing elements to a sink
or checkpointing progress during processing.</p><p>Instead of processing all elements simultaneously, the elements in a
<code>PCollection</code> are processed in <em>bundles</em>. The division of the collection into
bundles is arbitrary and selected by the runner. This allows the runner to
choose an appropriate middle-ground between persisting results after every
element, and having to retry everything if there is a failure. For example, a
streaming runner may prefer to process and commit small bundles, and a batch
runner may prefer to process larger bundles.</p><h3 id=data-partitioning-and-inter-stage-execution>Data partitioning and inter-stage execution</h3><p>Partitioning and parallelization of element processing within a Beam pipeline is
dependent on two things:</p><ul><li>Data source implementation</li><li>Inter-stage key parallelism</li></ul><p>Beam pipelines read data from a source (e.g. <code>KafkaIO</code>, <code>BigQueryIO</code>, <code>JdbcIO</code>,
or your own source implementation). To implement a Source in Beam one must
implement it as a Splittable <code>DoFn</code>. A Splittable <code>DoFn</code> provides the runner
with interfaces to facilitate the splitting of work.</p><p>When running key-based operations in Beam (e.g. <code>GroupByKey</code>, <code>Combine</code>,
<code>Reshuffle.perKey</code>, and stateful <code>DoFn</code>s), Beam runners perform serialization
and transfer of data known as <em>shuffle</em><sup>1</sup>. Shuffle allows data
elements of the same key to be processed together.</p><p>The way in which runners <em>shuffle</em> data may be slightly different for Batch and
Streaming execution modes.</p><p><sup>1</sup>Not to be confused with the <code>shuffle</code> operation in some runners.</p><h4 id=data-ordering-in-a-pipeline-execution>Data ordering in a pipeline execution</h4><p>The Beam model does not define strict guidelines regarding the order in which
runners process elements or transport them across <code>PTransforms</code>. Runners are
free to implement data transfer semantics in different forms.</p><p>Some use cases exist where user pipelines may need to rely on specific ordering
semantics in pipeline execution. The <a href=/documentation/runners/capability-matrix/additional-common-features-not-yet-part-of-the-beam-model/index.html>capability matrix documents</a>
runner behavior for <strong>key-ordered delivery</strong>.</p><p>Consider a single Beam worker processing a series of bundles from the same Beam
transform, and consider a <code>PTransform</code> that outputs data from this Stage into a
downstream <code>PCollection</code>. Finally, consider two events <em>with the same key</em>
emitted in a certain order by this worker (within the same bundle or as part of
different bundles).</p><p>We say that the Beam runner supports <strong>key-ordered delivery</strong> if it guarantees
that these two events will be observed in the same order by a PTransform that is
immediately downstream independently of the kind of data transmission method.</p><p>This characteristic will hold true in runners and operations that have
key-limited parallelism.</p><h2 id=parallelism>Failures and parallelism within and between transforms</h2><p>In this section, we discuss how elements in the input collection are processed
in parallel, and how transforms are retried when failures occur.</p><h3 id=data-parallelism>Data-parallelism within one transform</h3><p>When executing a single <code>ParDo</code>, a runner might divide an example input
collection of nine elements into two bundles as shown in figure 1.</p><p><img src=/images/execution_model_bundling.svg alt="Bundle A contains five elements. Bundle B contains four elements."></p><p><em>Figure 1: A runner divides an input collection into two bundles.</em></p><p>When the <code>ParDo</code> executes, workers may process the two bundles in parallel as
shown in figure 2.</p><p><img src=/images/execution_model_bundling_gantt.svg alt="Two workers process the two bundles in parallel. Worker one processes bundle A. Worker two processes bundle B."></p><p><em>Figure 2: Two workers process the two bundles in parallel.</em></p><p>Since elements cannot be split, the maximum parallelism for a transform depends
on the number of elements in the collection. In figure 3, the input collection
has nine elements, so the maximum parallelism is nine.</p><p><img src=/images/execution_model_bundling_gantt_max.svg alt="Nine workers process a nine element input collection in parallel."></p><p><em>Figure 3: Nine workers process a nine element input collection in parallel.</em></p><p>Note: Splittable ParDo allows splitting the processing of a single input across
multiple bundles. This feature is a work in progress.</p><h3 id=dependent-parallellism>Dependent-parallelism between transforms</h3><p><code>ParDo</code> transforms that are in sequence may be <em>dependently parallel</em> if the
runner chooses to execute the consuming transform on the producing transform&rsquo;s
output elements without altering the bundling. In figure 4, <code>ParDo1</code> and
<code>ParDo2</code> are <em>dependently parallel</em> if the output of <code>ParDo1</code> for a given
element must be processed on the same worker.</p><p><img src=/images/execution_model_bundling_multi.svg alt="ParDo1 processes an input collection that contains bundles A and B. ParDo2 then processes the output collection from ParDo1, which contains bundles C and D."></p><p><em>Figure 4: Two transforms in sequence and their corresponding input collections.</em></p><p>Figure 5 shows how these dependently parallel transforms might execute. The
first worker executes <code>ParDo1</code> on the elements in bundle A (which results in
bundle C), and then executes <code>ParDo2</code> on the elements in bundle C. Similarly,
the second worker executes <code>ParDo1</code> on the elements in bundle B (which results
in bundle D), and then executes <code>ParDo2</code> on the elements in bundle D.</p><p><img src=/images/execution_model_bundling_multi_gantt.svg alt="Worker one executes ParDo1 on bundle A and Pardo2 on bundle C. Worker two executes ParDo1 on bundle B and ParDo2 on bundle D."></p><p><em>Figure 5: Two workers execute dependently parallel ParDo transforms.</em></p><p>Executing transforms this way allows a runner to avoid redistributing elements
between workers, which saves on communication costs. However, the maximum parallelism
now depends on the maximum parallelism of the first of the dependently parallel
steps.</p><h3 id=failures-within-one-transform>Failures within one transform</h3><p>If processing of an element within a bundle fails, the entire bundle fails. The
elements in the bundle must be retried (otherwise the entire pipeline fails),
although they do not need to be retried with the same bundling.</p><p>For this example, we will use the <code>ParDo</code> from figure 1 that has an input
collection with nine elements and is divided into two bundles.</p><p>In figure 6, the first worker successfully processes all five elements in bundle
A. The second worker processes the four elements in bundle B: the first two
elements were successfully processed, the third element’s processing failed, and
there is one element still awaiting processing.</p><p>We see that the runner retries all elements in bundle B and the processing
completes successfully the second time. Note that the retry does not necessarily
happen on the same worker as the original processing attempt, as shown in the
figure.</p><p><img src=/images/execution_model_failure_retry.svg alt="Worker two fails to process an element in bundle B. Worker one finishes processing bundle A and then successfully retries to execute bundle B."></p><p><em>Figure 6: The processing of an element within bundle B fails, and another worker
retries the entire bundle.</em></p><p>Because we encountered a failure while processing an element in the input
bundle, we had to reprocess <em>all</em> of the elements in the input bundle. This means
the runner must throw away the entire output of the bundle (including any state
mutations and set timers) since all of the results it contains will be recomputed.</p><p>Note that if the failed transform is a <code>ParDo</code>, then the <code>DoFn</code> instance is torn
down and abandoned.</p><h3 id=coupled-failure>Coupled failure: Failures between transforms</h3><p>If a failure to process an element in <code>ParDo2</code> causes <code>ParDo1</code> to re-execute,
these two steps are said to be <em>co-failing</em>.</p><p>For this example, we will use the two <code>ParDo</code>s from figure 4.</p><p>In figure 7, worker two successfully executes <code>ParDo1</code> on all elements in bundle
B. However, the worker fails to process an element in bundle D, so <code>ParDo2</code>
fails (shown as the red X). As a result, the runner must discard and recompute
the output of <code>ParDo2</code>. Because the runner was executing <code>ParDo1</code> and <code>ParDo2</code>
together, the output bundle from <code>ParDo1</code> must also be thrown away, and all
elements in the input bundle must be retried. These two <code>ParDo</code>s are co-failing.</p><p><img src=/images/execution_model_bundling_coupled_failure.svg alt="Worker two fails to process en element in bundle D, so all elements in both bundle B and bundle D must be retried."></p><p><em>Figure 7: Processing of an element within bundle D fails, so all elements in
the input bundle are retried.</em></p><p>Note that the retry does not necessarily have the same processing time as the
original attempt, as shown in the diagram.</p><p>All <code>DoFns</code> that experience coupled failures are terminated and must be torn
down since they aren’t following the normal <code>DoFn</code> lifecycle .</p><p>Executing transforms this way allows a runner to avoid persisting elements
between transforms, saving on persistence costs.</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>&copy;
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