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.

Build Status

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. After the Beam project's initial bootstrapping completes, it will include:

1. The DirectPipelineRunner runs the pipeline on your local machine.
2. The DataflowPipelineRunner submits the pipeline to the Google Cloud Dataflow.
3. The SparkPipelineRunner runs the pipeline on an Apache Spark cluster. See the code that will be donated at cloudera/spark-dataflow.
4. The FlinkPipelineRunner runs the pipeline on an Apache Flink cluster. See the code that will be donated at dataArtisans/flink-dataflow.

Have ideas for new Runners? See the Jira.

Getting Started

Coming soon!

Contact Us

To get involved in Apache Beam:

• Subscribe or mail the user@beam.incubator.apache.org list.
• Subscribe or mail the dev@beam.incubator.apache.org list.
• Report issues on Jira.

More Information

• Apache Beam
• Apache Beam Documentation