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From Apache Wiki <>
Subject [Incubator Wiki] Update of "DataflowProposal" by jbonofre
Date Wed, 20 Jan 2016 15:42:43 GMT
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The "DataflowProposal" page has been changed by jbonofre:

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= Apache Dataflow =

== Abstract ==

Dataflow is an open source, unified model and set of language-specific SDKs for defining and
executing data processing workflows, and also data ingestion and integration flows, supporting
Enterprise Integration Patterns (EIPs) and Domain Specific Languages (DSLs). Dataflow pipelines
simplify the mechanics of large-scale batch and streaming data processing and can run on a
number of runtimes like Apache Flink, Apache Spark, and Google Cloud Dataflow (a cloud service).
Dataflow also brings DSL in different languages, allowing users to easily implement their
data integration processes.

== Proposal ==

Dataflow is a simple, flexible, and powerful system for distributed data processing at any
scale. Dataflow provides a unified programming model, a software development kit to define
and construct data processing pipelines, and runners to execute Dataflow pipelines in several
runtime engines, like Apache Spark, Apache Flink, or Google Cloud Dataflow. Dataflow can be
used for a variety of streaming or batch data processing goals including ETL, stream analysis,
and aggregate computation. The underlying programming model for Dataflow provides MapReduce-like
parallelism, combined with support for powerful data windowing, and fine-grained correctness

== Background ==

Dataflow started as a set of Google projects focused on making data processing easier, faster,
and less costly. The Dataflow model is a successor to MapReduce, FlumeJava, and Millwheel
inside Google and is focused on providing a unified solution for batch and stream processing.
These projects on which Dataflow is based have been published in several papers made available
to the public:

* MapReduce -
* Dataflow model  -
* FlumeJava -
* MillWheel -

Dataflow was designed from the start to provide a portable programming layer. When you define
a data processing pipeline with the Dataflow model, you are creating a job which is capable
of being processed by any number of Dataflow processing engines. Several engines have been
developed to run Dataflow pipelines in other open source runtimes, including a Dataflow runner
for Apache Flink and Apache Spark. There is also a “direct runner”, for execution on the
developer machine (mainly for dev/debug purposes). Another runner allows a Dataflow program
to run on a managed service, Google Cloud Dataflow, in Google Cloud Platform. The Dataflow
Java SDK is already available on GitHub, and independent from the Google Cloud Dataflow service.
Another Python SDK is currently in active development.

In this proposal, the Dataflow SDKs, model, and a set of runners will be submitted as an OSS
project under the ASF. The runners which are a part of this proposal include those for Spark
(from Cloudera), Flink (from data Artisans), and local development (from Google); the Google
Cloud Dataflow service runner is not included in this proposal. Further references to Dataflow
will refer to the Dataflow model, SDKs, and runners which are a part of this proposal (Apache
Dataflow) only. The initial submission will contain the already-released Java SDK; Google
intends to submit the Python SDK later in the incubation process. The Google Cloud Dataflow
service will continue to be one of many runners for Dataflow, built on Google Cloud Platform,
to run Dataflow pipelines. Necessarily, Cloud Dataflow will develop against the Apache project
additions, updates, and changes. Google Cloud Dataflow will become one user of Apache Dataflow
and will participate in the project openly and publicly. 

The Dataflow programming model has been designed with simplicity, scalability, and speed as
key tenants. In the Dataflow model, you only need to think about four top-level concepts when
constructing your data processing job:

* Pipelines - The data processing job made of a series of computations including input, processing,
and output
* PCollections - Bounded (or unbounded) datasets which represent the input, intermediate and
output data in pipelines
* PTransforms - A data processing step in a pipeline in which one or more PCollections are
an input and output
* I/O Sources and Sinks - APIs for reading and writing data which are the roots and endpoints
of the pipeline

== Rationale ==

With Dataflow, Google intended to develop a framework which allowed developers to be maximally
productive in defining the processing, and then be able to execute the program at various
levels of latency/cost/completeness without re-architecting or re-writing it. This goal was
informed by Google’s past experience  developing several models, frameworks, and tools useful
for large-scale and distributed data processing. While Google has previously published papers
describing some of its technologies, Google decided to take a different approach with Dataflow.
Google open-sourced the SDK and model alongside commercialization of the idea and ahead of
publishing papers on the topic. As a result, a number of open source runtimes exist for Dataflow,
such as the Apache Flink and Apache Spark runners.

We believe that submitting Dataflow as an Apache project will provide an immediate, worthwhile,
and substantial contribution to the open source community. As an incubating project, we believe
Dataflow will have a better opportunity to provide a meaningful contribution to OSS and also
integrate with other Apache projects.

In the long term, we believe Dataflow can be a powerful abstraction layer for data processing.
By providing an abstraction layer for data pipelines and processing, data workflows can be
increasingly portable, resilient to breaking changes in tooling, and compatible across many
execution engines, runtimes, and open source projects. 

== Initial Goals ==

We are breaking our initial goals into immediate (< 2 months), short-term (2-4 months),
and intermediate-term (> 4 months).

Our immediate goals include the following:

* Plan for reconciling the Dataflow Java SDK and various runners into one project
* Plan for refactoring the existing Java SDK for better extensibility by SDK and runner writers
* Validating all dependencies are ASL 2.0 or compatible
* Understanding and adapting to the Apache development process

Our short-term goals include:

* Moving the newly-merged lists, and build utilities to Apache
* Start refactoring codebase and move code to Apache Git repo
* Continue development of new features, functions, and fixes in the Dataflow Java SDK, and
Dataflow runners
* Cleaning up the Dataflow SDK sources and crafting a roadmap and plan for how to include
new major ideas, modules, and runtimes
* Establishment of easy and clear build/test framework for Dataflow and associated runtimes;
creation of testing, rollback, and validation policy
* Analysis and design for work needed to make Dataflow a better data processing abstraction
layer for multiple open source frameworks and environments

Finally, we have a number of intermediate-term goals:

* Roadmapping, planning, and execution of integrations with other OSS and non-OSS projects/products
* Inclusion of additional SDK for Python, which is under active development

== Current Status ==

=== Meritocracy ===

Dataflow was initially developed based on ideas from many employees within Google. As an ASL
OSS project on GitHub, the Dataflow SDK has received contributions from data Artisans, Cloudera
Labs, and other individual developers. As a project under incubation, we are committed to
expanding our effort to build an environment which supports a meritocracy. We are focused
on engaging the community and other related projects for support and contributions. Moreover,
we are committed to ensure contributors and committers to Dataflow come from a broad mix of
organizations through a merit-based decision process during incubation. We believe strongly
in the Dataflow model and are committed to growing an inclusive community of Dataflow contributors.

=== Community ===

The core of the Dataflow Java SDK has been developed by Google for use with Google Cloud Dataflow.
Google has active community engagement in the SDK GitHub repository (,
on Stack Overflow ( and has
had contributions from a number of organizations and indivuduals.

Everyday, Cloud Dataflow is actively used by a number of organizations and institutions for
batch and stream processing of data. We believe acceptance will allow us to consolidate existing
Dataflow-related work, grow the Dataflow community, and deepen connections between Dataflow
and other open source projects. 

=== Core Developers ===

The core developers for Dataflow and the Dataflow runners are:

* Frances Perry
* Tyler Akidau
* Davor Bonaci
* Luke Cwik
* Ben Chambers
* Kenn Knowles
* Dan Halperin
* Daniel Mills
* Mark Shields
* Craig Chambers
* Maximilian Michels
* Tom White
* Josh Wills

=== Alignment ===

The Dataflow SDK can be used to create Dataflow pipelines which can be executed on Apache
Spark or Apache Flink. Dataflow is also related to other Apache projects, such as Apache Crunch.
We plan on expanding functionality for Dataflow runners, support for additional domain specific
languages, and increased portability so Dataflow is a powerful abstraction layer for data

== Known Risks ==

=== Orphaned Products ===

The Dataflow SDK is presently used by several organizations, from small startups to Fortune
100 companies, to construct production pipelines which are executed in Google Cloud Dataflow.
Google has a long-term commitment to advance the Dataflow SDK; moreover, Dataflow is seeing
increasing interest, development, and adoption from organizations outside of Google. 

=== Inexperience with Open Source ===

Google believes strongly in open source and the exchange of information to advance new ideas
and work. Examples of this commitment are active OSS projects such as Chromium (
and Kubernetes ( With Dataflow, we have tried to be increasingly open
and forward-looking; we have published a paper in the VLDB conference describing the Dataflow
model ( and were quick to release the Dataflow
SDK as open source software with the launch of Cloud Dataflow. Our submission to the Apache
Software Foundation is a logical extension of our commitment to open source software.

=== Homogeneous Developers ===

The majority of committers in this proposal belong to Google due to the fact that Dataflow
has emerged from several internal Google projects. This proposal also includes committers
outside of Google who are actively involved with other Apache projects, such as Hadoop, Flink,
and Spark.  We expect our entry into incubation will allow us to expand the number of individuals
and organizations participating in Dataflow development. Additionally, separation of the Dataflow
SDK from Google Cloud Dataflow allows us to focus on the open source SDK and model and do
what is best for this project.

=== Reliance on Salaried Developers ===

The Dataflow SDK and Dataflow runners have been developed primarily by salaried developers
supporting the Google Cloud Dataflow project. While the Dataflow SDK and Cloud Dataflow have
been developed by different teams (and this proposal would reinforce that separation) we expect
our initial set of developers will still primarily be salaried. Contribution has not been
exclusively from salaried developers, however. For example, the contrib directory of the Dataflow
SDK ( contains
items from free-time contributors. Moreover, seperate projects, such as ScalaFlow (
have been created around the Dataflow model and SDK. We expect our reliance on salaried developers
will decrease over time during incubation.

=== Relationship with other Apache products ===

Dataflow directly interoperates with or utilizes several existing Apache projects. 

* Build
** Apache Maven
* Data I/O, Libraries
** Apache Avro
** Apache Commons
* Dataflow runners
** Apache Flink
** Apache Spark

Dataflow when used in batch mode shares similarities with Apache Crunch; however, Dataflow
is focused on a model, SDK, and abstraction layer beyond Spark and Hadoop (MapReduce.) One
key goal of Dataflow is to provide an intermediate abstraction layer which can easily be implemented
and utilized across several different processing frameworks.

=== An excessive fascination with the Apache brand ===

With this proposal we are not seeking attention or publicity. Rather, we firmly believe in
the Dataflow model, SDK, and the ability to make Dataflow a powerful yet simple framework
for data processing. While the Dataflow SDK and model have been open source, we believe putting
code on GitHub can only go so far. We see the Apache community, processes, and mission as
critical for ensuring the Dataflow SDK and model are truly community-driven, positively impactful,
and innovative open source software. While Google has taken a number of steps to advance its
various open source projects, we believe Dataflow is a great fit for the Apache Software Foundation
due to its focus on data processing and its relationships to existing ASF projects.

== Documentation ==

The following documentation is relevant to this proposal. Relevant portion of the documentation
will be contributed to the Apache Dataflow project.

* Dataflow website:
* Dataflow programming model:
* Codebases
** Dataflow Java SDK:
** Flink Dataflow runner:
** Spark Dataflow runner:
* Dataflow Java SDK issue tracker:
* google-cloud-dataflow tag on Stack Overflow:

== Initial Source ==

The initial source for Dataflow which we will submit to the Apache Foundation will include
several related projects which are currently hosted on the GitHub repositories: 

* Dataflow Java SDK (
* Flink Dataflow runner ( 
* Spark Dataflow runner (

These projects have always been Apache 2.0 licensed. We intend to bundle all of these repositories
since they are all complimentary and should be maintained in one project. Prior to our submission,
we will combine all of these projects into a new git repository.  

== Source and Intellectual Property Submission Plan ==

The source for the Dataflow SDK and the three runners (Spark, Flink, Google Cloud Dataflow)
are already licensed under an Apache 2 license.

* Dataflow SDK -
* Flink runner -
* Spark runner -

Contributors to the Dataflow SDK have also signed the Google Individual Contributor License
Agreement ( in order to contribute
to the project.

With respect to trademark rights, Google does not hold a trademark on the phrase “Dataflow.”
Based on feedback and guidance we receive during the incubation process, we are open to renaming
the project if necessary for trademark or other concerns.

== External Dependencies ==

All external dependencies are licensed under an Apache 2.0 or Apache-compatible license. As
we grow the Dataflow community we will configure our build process to require and validate
all contributions and dependencies are licensed under the Apache 2.0 license or are under
an Apache-compatible license.

== Required Resources ==

=== Mailing Lists ===

We currently use a mix of mailing lists. We will migrate our existing mailing lists to the


=== Source Control ===

The Dataflow team currently uses Git and would like to continue to do so. We request a Git
repository for Dataflow with mirroring to GitHub enabled. 

=== Issue Tracking ===

We request the creation of an Apache-hosted JIRA. The Dataflow project is currently using
both a public GitHub issue tracker and internal Google issue tracking. We will migrate and
combine from these two sources to the Apache JIRA. 

== Initial Committers ==

* Aljoscha Krettek        []
* Amit Sela               []
* Ben Chambers            []
* Craig Chambers          []
* Dan Halperin            []
* Davor Bonaci            []
* Frances Perry           []
* James Malone            []
* Jean-Baptiste Onofré    []
* Josh Wills              []
* Kostas Tzoumas          []
* Kenneth Knowles         []
* Luke Cwik               []
* Maximilian Michels      []
* Stephan Ewen            []
* Tom White               []
* Tyler Akidau            []

== Affiliations ==

The initial committers are from six organizations. Google developed Dataflow and the Dataflow
SDK, data Artisans developed the Flink runner, and Cloudera (Labs) developed the Spark runner.

* Cloudera
** Tom White
* Data Artisans
** Aljoscha Krettek
** Kostas Tzoumas
** Maximilian Michels
** Stephan Ewen
* Google
** Ben Chambers
** Dan Halperin
** Davor Bonaci
** Frances Perry
** James Malone
** Kenneth Knowles
** Luke Cwik
** Tyler Akidau
* PayPal
** Amit Sela
* Slack
** Josh Wills
* Talend
** Jean-Baptiste Onofré

== Sponsors ==

=== Champion ===

* Jean-Baptiste Onofre         []

=== Nominated Mentors ===

* Jim Jagielski              []
* Venkatesh Seetharam        []
* Bertrand Delacretaz        []
* Ted Dunning                []

=== Sponsoring Entity ===

The Apache Incubator

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