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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=#sources>Sources</a><ul><li><a href=#when-to-use-splittable-dofn>When to use the Splittable DoFn interface</a></li><li><a href=#io-examples-using-sdfs>I/O examples using SDFs</a></li><li><a href=#using-pardo-and-groupbykey>Using ParDo and GroupByKey</a></li></ul></li><li><a href=#sinks>Sinks</a></li></ul></nav></nav><div class="body__contained body__section-nav arrow-list arrow-list--no-mt"><h1 id=overview-developing-a-new-io-connector>Overview: Developing a new I/O connector</h1><p><em>A guide for users who need to connect to a data store that isn’t supported by |
| the <a href=/documentation/io/built-in/>Built-in I/O connectors</a></em></p><p>To connect to a data store that isn’t supported by Beam’s existing I/O |
| connectors, you must create a custom I/O connector. A connector usually consists |
| of a source and a sink. All Beam sources and sinks are composite transforms; |
| however, the implementation of your custom I/O depends on your use case. Here |
| are the recommended steps to get started:</p><ol><li><p>Read this overview and choose your implementation. You can email the |
| <a href=/get-started/support>Beam dev mailing list</a> with any |
| questions you might have. In addition, you can check if anyone else is |
| working on the same I/O connector.</p></li><li><p>If you plan to contribute your I/O connector to the Beam community, see the |
| <a href=/contribute/contribution-guide/>Apache Beam contribution guide</a>.</p></li><li><p>Read the <a href=/contribute/ptransform-style-guide/>PTransform style guide</a> |
| for additional style guide recommendations.</p></li></ol><h2 id=sources>Sources</h2><p>For <strong>bounded (batch) sources</strong>, there are currently two options for creating a |
| Beam source:</p><ol><li><p>Use <code>Splittable DoFn</code>.</p></li><li><p>Use <code>ParDo</code> and <code>GroupByKey</code>.</p></li></ol><p><code>Splittable DoFn</code> is the recommended option, as it’s the most recent source framework for both |
| bounded and unbounded sources. This is meant to replace the <code>Source</code> APIs( |
| <a href=https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/io/BoundedSource.html>BoundedSource</a> and |
| <a href=https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/io/UnboundedSource.html>UnboundedSource</a>) |
| in the new system. Read |
| <a href=/learn/programming-guide/#splittable-dofns>Splittable DoFn Programming Guide</a> for how to write one |
| Splittable DoFn. For more information, see the |
| <a href=/roadmap/connectors-multi-sdk/>roadmap for multi-SDK connector efforts</a>.</p><p>For Java and Python <strong>unbounded (streaming) sources</strong>, you must use the <code>Splittable DoFn</code>, which |
| supports features that are useful for streaming pipelines, including checkpointing, controlling |
| watermark, and tracking backlog.</p><h3 id=when-to-use-splittable-dofn>When to use the Splittable DoFn interface</h3><p>If you are not sure whether to use <code>Splittable DoFn</code>, feel free to email the |
| <a href=/get-started/support>Beam dev mailing list</a> and we can discuss the specific pros and cons of your |
| case.</p><p>In some cases, implementing a <code>Splittable DoFn</code> might be necessary or result in better performance:</p><ul><li><p><strong>Unbounded sources:</strong> <code>ParDo</code> does not work for reading from unbounded |
| sources. <code>ParDo</code> does not support checkpointing or mechanisms like de-duping |
| that are useful for streaming data sources.</p></li><li><p><strong>Progress and size estimation:</strong> <code>ParDo</code> can’t provide hints to runners about |
| progress or the size of data they are reading. Without size estimation of the |
| data or progress on your read, the runner doesn’t have any way to guess how |
| large your read will be. Therefore, if the runner attempts to dynamically |
| allocate workers, it does not have any clues as to how many workers you might |
| need for your pipeline.</p></li><li><p><strong>Dynamic work rebalancing:</strong> <code>ParDo</code> does not support dynamic work |
| rebalancing, which is used by some readers to improve the processing speed of |
| jobs. Depending on your data source, dynamic work rebalancing might not be |
| possible.</p></li><li><p><strong>Splitting initially to increase parallelism:</strong> <code>ParDo</code> |
| does not have the ability to perform initial splitting.</p></li></ul><p>For example, if you’d like to read from a new file format that contains many |
| records per file, or if you’d like to read from a key-value store that supports |
| read operations in sorted key order.</p><h3 id=io-examples-using-sdfs>I/O examples using SDFs</h3><p><strong>Java Examples</strong></p><ul><li><a href=https://github.com/apache/beam/blob/571338b0cc96e2e80f23620fe86de5c92dffaccc/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/ReadFromKafkaDoFn.java#L118>Kafka</a>: |
| An I/O connector for <a href=https://kafka.apache.org/>Apache Kafka</a> |
| (an open-source distributed event streaming platform).</li><li><a href=https://github.com/apache/beam/blob/571338b0cc96e2e80f23620fe86de5c92dffaccc/sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Watch.java#L787>Watch</a>: |
| Uses a polling function producing a growing set of outputs for each input until a per-input |
| termination condition is met.</li><li><a href=https://github.com/apache/beam/blob/571338b0cc96e2e80f23620fe86de5c92dffaccc/sdks/java/io/parquet/src/main/java/org/apache/beam/sdk/io/parquet/ParquetIO.java#L365>Parquet</a>: |
| An I/O connector for <a href=https://parquet.apache.org/>Apache Parquet</a> |
| (an open-source columnar storage format).</li><li><a href=https://github.com/apache/beam/blob/6fdde4f4eab72b49b10a8bb1cb3be263c5c416b5/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/healthcare/HL7v2IO.java#L493>HL7v2</a>: |
| An I/O connector for HL7v2 messages (a clinical messaging format that provides data about events |
| that occur inside an organization) part of |
| <a href=https://cloud.google.com/healthcare>Google’s Cloud Healthcare API</a>.</li><li><a href=https://github.com/apache/beam/blob/571338b0cc96e2e80f23620fe86de5c92dffaccc/sdks/java/core/src/main/java/org/apache/beam/sdk/io/Read.java#L248>BoundedSource wrapper</a>: |
| A wrapper which converts an existing <a href=https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/io/BoundedSource.html>BoundedSource</a> |
| implementation to a splittable DoFn.</li><li><a href=https://github.com/apache/beam/blob/571338b0cc96e2e80f23620fe86de5c92dffaccc/sdks/java/core/src/main/java/org/apache/beam/sdk/io/Read.java#L432>UnboundedSource wrapper</a>: |
| A wrapper which converts an existing <a href=https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/io/UnboundedSource.html>UnboundedSource</a> |
| implementation to a splittable DoFn.</li></ul><p><strong>Python Examples</strong></p><ul><li><a href=https://github.com/apache/beam/blob/571338b0cc96e2e80f23620fe86de5c92dffaccc/sdks/python/apache_beam/io/iobase.py#L1375>BoundedSourceWrapper</a>: |
| A wrapper which converts an existing <a href=https://beam.apache.org/releases/pydoc/current/apache_beam.io.iobase.html#apache_beam.io.iobase.BoundedSource>BoundedSource</a> |
| implementation to a splittable DoFn.</li></ul><h3 id=using-pardo-and-groupbykey>Using ParDo and GroupByKey</h3><p>For data stores or file types where the data can be read in parallel, you can |
| think of the process as a mini-pipeline. This often consists of two steps:</p><ol><li><p>Splitting the data into parts to be read in parallel</p></li><li><p>Reading from each of those parts</p></li></ol><p>Each of those steps will be a <code>ParDo</code>, with a <code>GroupByKey</code> in between. The |
| <code>GroupByKey</code> is an implementation detail, but for most runners <code>GroupByKey</code> |
| allows the runner to use different numbers of workers in some situations:</p><ul><li><p>Determining how to split up the data to be read into chunks</p></li><li><p>Reading data, which often benefits from more workers</p></li></ul><p>In addition, <code>GroupByKey</code> also allows dynamic work rebalancing to happen on |
| runners that support the feature.</p><p>Here are some examples of read transform implementations that use the “reading |
| as a mini-pipeline” model when data can be read in parallel:</p><ul><li><p><strong>Reading from a file glob</strong>: For example, reading all files in “~/data/**”.</p><ul><li>Get File Paths <code>ParDo</code>: As input, take in a file glob. Produce a |
| <code>PCollection</code> of strings, each of which is a file path.</li><li>Reading <code>ParDo</code>: Given the <code>PCollection</code> of file paths, read each one, |
| producing a <code>PCollection</code> of records.</li></ul></li><li><p><strong>Reading from a NoSQL database</strong> (such as Apache HBase): These databases |
| often allow reading from ranges in parallel.</p><ul><li>Determine Key Ranges <code>ParDo</code>: As input, receive connection information for |
| the database and the key range to read from. Produce a <code>PCollection</code> of key |
| ranges that can be read in parallel efficiently.</li><li>Read Key Range <code>ParDo</code>: Given the <code>PCollection</code> of key ranges, read the key |
| range, producing a <code>PCollection</code> of records.</li></ul></li></ul><p>For data stores or files where reading cannot occur in parallel, reading is a |
| simple task that can be accomplished with a single <code>ParDo</code>+<code>GroupByKey</code>. For |
| example:</p><ul><li><p><strong>Reading from a database query</strong>: Traditional SQL database queries often |
| can only be read in sequence. In this case, the <code>ParDo</code> would establish a |
| connection to the database and read batches of records, producing a |
| <code>PCollection</code> of those records.</p></li><li><p><strong>Reading from a gzip file</strong>: A gzip file must be read in order, so the read |
| cannot be parallelized. In this case, the <code>ParDo</code> would open the file and |
| read in sequence, producing a <code>PCollection</code> of records from the file.</p></li></ul><h2 id=sinks>Sinks</h2><p>To create a Beam sink, we recommend that you use a <code>ParDo</code> that writes the |
| received records to the data store. To develop more complex sinks (for example, |
| to support data de-duplication when failures are retried by a runner), use |
| <code>ParDo</code>, <code>GroupByKey</code>, and other available Beam transforms. |
| Many data services are optimized to write batches of elements at a time, |
| so it may make sense to group the elements into batches before writing. |
| Persistent connections can be initialized in a DoFn’s <code>setUp</code> or <code>startBundle</code> |
| method rather than upon the receipt of every element as well. |
| It should also be noted that in a large-scale, distributed system work can |
| <a href=/documentation/runtime/model/>fail and/or be retried</a>, so it is preferable to |
| make the external interactions idempotent when possible.</p><p>For <strong>file-based sinks</strong>, you can use the <code>FileBasedSink</code> abstraction that is |
| provided by both the Java and Python SDKs. Beam’s <code>FileSystems</code> utility classes |
| can also be useful for reading and writing files. See our language specific |
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