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From James Malone <jamesmal...@google.com.INVALID>
Subject [DISCUSS] Apache Dataflow Incubator Proposal
Date Wed, 20 Jan 2016 16:32:12 GMT
Hello everyone,

Attached to this message is a proposed new project - Apache Dataflow, a
unified programming model for data processing and integration.

The text of the proposal is included below. Additionally, the proposal is
in draft form on the wiki where we will make any required changes:

https://wiki.apache.org/incubator/DataflowProposal

We look forward to your feedback and input.

Best,

James

----

= 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 control.

== 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 - http://research.google.com/archive/mapreduce.html

* Dataflow model  - http://www.vldb.org/pvldb/vol8/p1792-Akidau.pdf

* FlumeJava - http://notes.stephenholiday.com/FlumeJava.pdf

* MillWheel - http://research.google.com/pubs/pub41378.html

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 (https://github.com/GoogleCloudPlatform/DataflowJavaSDK),
on Stack Overflow (
http://stackoverflow.com/questions/tagged/google-cloud-dataflow) 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
processing.

== 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 (https://www.chromium.org) and Kubernetes (
http://kubernetes.io/). 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 (
http://www.vldb.org/pvldb/vol8/p1792-Akidau.pdf) 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 (
https://github.com/GoogleCloudPlatform/DataflowJavaSDK/tree/master/contrib)
contains items from free-time contributors. Moreover, seperate projects,
such as ScalaFlow (https://github.com/darkjh/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: https://cloud.google.com/dataflow

* Dataflow programming model:
https://cloud.google.com/dataflow/model/programming-model

* Codebases

** Dataflow Java SDK: https://github.com/GoogleCloudPlatform/DataflowJavaSDK

** Flink Dataflow runner: https://github.com/dataArtisans/flink-dataflow

** Spark Dataflow runner: https://github.com/cloudera/spark-dataflow

* Dataflow Java SDK issue tracker:
https://github.com/GoogleCloudPlatform/DataflowJavaSDK/issues

* google-cloud-dataflow tag on Stack Overflow:
http://stackoverflow.com/questions/tagged/google-cloud-dataflow

== 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 (https://github.com/GoogleCloudPlatform/DataflowJavaSDK)

* Flink Dataflow runner (https://github.com/dataArtisans/flink-dataflow)

* Spark Dataflow runner (https://github.com/cloudera/spark-dataflow)

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 -
https://github.com/GoogleCloudPlatform/DataflowJavaSDK/blob/master/LICENSE

* Flink runner -
https://github.com/dataArtisans/flink-dataflow/blob/master/LICENSE

* Spark runner -
https://github.com/cloudera/spark-dataflow/blob/master/LICENSE

Contributors to the Dataflow SDK have also signed the Google Individual
Contributor License Agreement (
https://cla.developers.google.com/about/google-individual) 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 following:

* dev@dataflow.incubator.apache.org

* user@dataflow.incubator.apache.org

* private@dataflow.incubator.apache.org

* commits@dataflow.incubator.apache.org

=== 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     [aljoscha@apache.org]

* Amit Sela            [amitsela33@gmail.com]

* Ben Chambers         [bchambers@google.com]

* Craig Chambers       [chambers@google.com]

* Dan Halperin         [dhalperi@google.com]

* Davor Bonaci         [davor@google.com]

* Frances Perry        [fjp@google.com]

* James Malone         [jamesmalone@google.com]

* Jean-Baptiste Onofré [jbonofre@apache.org]

* Josh Wills           [jwills@apache.org]

* Kostas Tzoumas       [kostas@data-artisans.com]

* Kenneth Knowles      [klk@google.com]

* Luke Cwik            [lcwik@google.com]

* Maximilian Michels   [mxm@apache.org]

* Stephan Ewen         [stephan@data-artisans.com]

* Tom White            [tom@cloudera.com]

* Tyler Akidau         [takidau@google.com]

== 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      [jbonofre@apache.org]

=== Nominated Mentors ===

* Jim Jagielski           [jim@apache.org]

* Venkatesh Seetharam     [venkatesh@apache.org]

* Bertrand Delacretaz     [bdelacretaz@apache.org]

* Ted Dunning             [tdunning@apache.org]

=== Sponsoring Entity ===

The Apache Incubator

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