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# Common pipeline patterns

Pipeline patterns demonstrate common Beam use cases. Pipeline patterns are based on real-world Beam deployments. Each pattern has a description, examples, and a solution or psuedocode.

**File processing patterns** - Patterns for reading from and writing to files
* [Processing files as they arrive](/documentation/patterns/file-processing/#processing-files-as-they-arrive)
* [Accessing filenames](/documentation/patterns/file-processing/#accessing-filenames)

**Side input patterns** - Patterns for processing supplementary data
* [Slowly updating global window side inputs](/documentation/patterns/side-inputs/#slowly-updating-global-window-side-inputs)

**Pipeline option patterns** - Patterns for configuring pipelines
* [Retroactively logging runtime parameters](/documentation/patterns/pipeline-options/#retroactively-logging-runtime-parameters)

**Custom I/O patterns** - Patterns for pipeline I/O
* [Choosing between built-in and custom connectors](/documentation/patterns/custom-io/#choosing-between-built-in-and-custom-connectors)

**Custom window patterns** - Patterns for windowing functions
* [Using data to dynamically set session window gaps](/documentation/patterns/custom-windows/#using-data-to-dynamically-set-session-window-gaps)

**BigQuery patterns** - Patterns for BigQueryIO
* [Google BigQuery patterns](/documentation/patterns/bigqueryio/#google-bigquery-patterns)

**AI Platform integration patterns** - Patterns for using Google Cloud AI Platform transforms
* [Analysing the structure and meaning of text](/documentation/patterns/ai-platform/#analysing-the-structure-and-meaning-of-text)
* [Getting predictions](/documentation/patterns/ai-platform/#getting-predictions)

**Schema patterns** - Patterns for using Schemas
* [Using Joins](/documentation/patterns/schema/#using-joins)

**BQML integration patterns** - Patterns on integrating BigQuery ML into your Beam pipeline
* [Model export and on-worker serving using BQML and TFX_BSL](/documentation/patterns/bqml/#bigquery-ml-integration)

**Cross-language patterns** - Patterns for creating cross-language pipelines
* [Cross-language patterns](/documentation/patterns/cross-language/#cross-language-transforms)

**State & timers patterns** - Patterns for using state & timers
* [Grouping elements for efficient external service calls](/documentation/patterns/grouping-elements-for-efficient-external-service-calls/#grouping-elements-for-efficient-external-service-calls-using-the-`GroupIntoBatches`-transform)
* [Dynamically grouping elements](/documentation/patterns/batch-elements/#dynamically-grouping-elements-using-the-`BatchElements`-transform)

**Cache with a shared object** - Patterns for using a shared object as a cache using the Python SDK
* [Create a cache on a batch pipeline](/documentation/patterns/shared-class/#create-a-cache-on-a-batch-pipeline)
* [Create a cache and update it regularly on a streaming pipeline](/documentation/patterns/shared-class/#create-a-cache-and-update-it-regularly-on-a-streaming-pipeline)

## Contributing a pattern

To contribute a new pipeline pattern, create [a feature request](https://github.com/apache/beam/issues/new?labels=new+feature%2Cawaiting+triage&template=feature.yml&title=%5BFeature+Request%5D%3A+) and add details to the issue description. See [Get started contributing](/contribute/) for more information.

## What's next

* Try an [end-to-end example](/get-started/try-apache-beam/)
* Execute your pipeline on a [runner](/documentation/runners/capability-matrix/)