Apache Beam

Apache Beam is a unified model for defining both batch and streaming data-parallel processing pipelines, as well as a set of language-specific SDKs for constructing pipelines and Runners for executing them on distributed processing backends like Apache Spark, Apache Flink, and Google Cloud Dataflow.

Status

The Apache Beam project is in the process of bootstrapping. This includes the creation of project resources, the refactoring of the initial code submissions, and the formulation of project documentation, planning, and design documents. Please expect a significant amount of churn and breaking changes in the near future.

Overview

Beam provides a general approach to expressing embarrassingly parallel data processing pipelines and supports three categories of users, each of which have relatively disparate backgrounds and needs.

1. End Users: Writing pipelines with an existing SDK, running it on an existing runner. These users want to focus on writing their application logic and have everything else just work.
2. SDK Writers: Developing a Beam SDK targeted at a specific user community (Java, Python, Scala, Go, R, graphical, etc). These users are language geeks, and would prefer to be shielded from all the details of various runners and their implementations.
3. Runner Writers: Have an execution environment for distributed processing and would like to support programs written against the Beam Model. Would prefer to be shielded from details of multiple SDKs.

The Beam Model

The model behind Beam evolved from a number of internal Google data processing projects, including MapReduce, FlumeJava, and Millwheel. This model was originally known as the “Dataflow Model”.

To learn more about the Beam Model (though still under the original name of Dataflow), see the World Beyond Batch: Streaming 101 and Streaming 102 posts on O’Reilly’s Radar site, and the VLDB 2015 paper.

The key concepts in the Beam programming model are:

• PCollection: represents a collection of data, which could be bounded or unbounded in size.
• PTransform: represents a computation that transforms input PCollections into output PCollections.
• Pipeline: manages a directed acyclic graph of PTransforms and PCollections that is ready for execution.
• PipelineRunner: specifies where and how the pipeline should execute.

SDKs

Beam supports multiple language specific SDKs for writing pipelines against the Beam Model.

Currently, this repository contains the Beam Java SDK, which is in the process of evolving from the Dataflow Java SDK. The Dataflow Python SDK will also become part of Beam in the near future.

Have ideas for new SDKs or DSLs? See the Jira.

Runners

Beam supports executing programs on multiple distributed processing backends through PipelineRunners. Currently, the following PipelineRunners are available:

• The DirectRunner runs the pipeline on your local machine.
• The DataflowPipelineRunner submits the pipeline to the Google Cloud Dataflow.
• The FlinkRunner runs the pipeline on an Apache Flink cluster. The code has been donated from dataArtisans/flink-dataflow and is now part of Beam.
• The SparkRunner runs the pipeline on an Apache Spark cluster. The code has been donated from cloudera/spark-dataflow and is now part of Beam.

Have ideas for new Runners? See the Jira.

Getting Started

Coming soon!

Flink Runner

See the Flink Runner README.

Spark Runner

See the Spark Runner README.

Contact Us

To get involved in Apache Beam:

• Subscribe or mail the [email protected] list.
• Subscribe or mail the [email protected] list.
• Report issues on Jira.

More Information

• Apache Beam
• Apache Beam Documentation