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From BW <bw...@mysoftcloud.com>
Subject Re: Data processing pipeline workflow management
Date Thu, 12 Mar 2015 14:58:02 GMT
Any thoughts on integrating a plug in service with Marathon first then
layer Mesos on top?

On Wednesday, March 11, 2015, Mattmann, Chris A (3980) <
chris.a.mattmann@jpl.nasa.gov> wrote:

> Apache OODT now has a workflow plugin that connects to Mesos:
>
> http://oodt.apache.org/
>
> Cross posting this to dev@oodt.apache.org <javascript:;> so people like
> Mike Starch can chime in.
>
> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
> Chris Mattmann, Ph.D.
> Chief Architect
> Instrument Software and Science Data Systems Section (398)
> NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
> Office: 168-519, Mailstop: 168-527
> Email: chris.a.mattmann@nasa.gov <javascript:;>
> WWW:  http://sunset.usc.edu/~mattmann/
> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
> Adjunct Associate Professor, Computer Science Department
> University of Southern California, Los Angeles, CA 90089 USA
> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
>
>
>
>
>
>
> -----Original Message-----
> From: Zameer Manji <zmanji@apache.org <javascript:;>>
> Reply-To: "dev@aurora.incubator.apache.org <javascript:;>"
> <dev@aurora.incubator.apache.org <javascript:;>>
> Date: Wednesday, March 11, 2015 at 3:21 PM
> To: "dev@aurora.incubator.apache.org <javascript:;>" <
> dev@aurora.incubator.apache.org <javascript:;>>
> Subject: Re: Data processing pipeline workflow management
>
> >Hey,
> >
> >This is a great question. See my comments inline below.
> >
> >On Tue, Mar 10, 2015 at 8:28 AM, Lars Albertsson
> ><lars.albertsson@gmail.com <javascript:;>>
> >wrote:
> >
> >> We are evaluating Aurora as a workflow management tool for batch
> >> processing pipelines. We basically need a tool that regularly runs
> >> batch processes that are connected as producers/consumers of data,
> >> typically stored in HDFS or S3.
> >>
> >> The alternative tools would be Azkaban, Luigi, and Oozie, but I am
> >> hoping that building something built on Aurora would result in a
> >> better solution.
> >>
> >> Does anyone have experience with building workflows with Aurora? How
> >> is Twitter handling batch pipelines? Would the approach below make
> >> sense, or are there better suggestions? Is there anything related to
> >> this in the roadmap or available inside Twitter only?
> >>
> >
> >As far as I know, you are the first person to consider Aurora for workflow
> >management for batch processing. Currently Twitter does not use Aurora for
> >batch pipelines.
> >I'm not aware of the specifics of the design, but at Twitter there is an
> >internal solution for pipelines built upon Hadoop/YARN.
> >Currently Aurora is designed around being a service scheduler and I'm not
> >aware of any future plans to support workflows or batch computation.
> >
> >
> >> In our case, the batch processes will be a mix of cluster
> >> computation's with Spark, and single-node computations. We want the
> >> latter to also be scheduled on a farm, and this is why we are
> >> attracted to Mesos. In the text below, I'll call each part of a
> >> pipeline a 'step', in order to avoid confusion with Aurora jobs and
> >> tasks.
> >>
> >> My unordered wishlist is:
> >> * Data pipelines consist of DAGs, where steps take one or more inputs,
> >> and generate one or more outputs.
> >>
> >> * Independent steps in the DAG execute in parallel, constrained by
> >> resources.
> >>
> >> * Steps can be written in different languages and frameworks, some
> >> clustered.
> >>
> >> * The developer code/test/debug cycle is quick, and all functional
> >> tests can execute on a laptop.
> >>
> >> * Developers can test integrated data pipelines, consisting of
> >> multiple steps, on laptops.
> >>
> >> * Steps and their intputs and outputs are parameterised, e.g. by date.
> >> A parameterised step is typically independent from other instances of
> >> the same step, e.g. join one day's impressions log with user
> >> demographics. In some cases, steps depend on yesterday's results, e.g.
> >> apply one day's user management operation log to the user dataset from
> >> the day before.
> >>
> >> * Data pipelines are specified in embedded DSL files (e.g. aurora
> >> files), kept close to the business logic code.
> >>
> >> * Batch steps should be started soon after the input files become
> >> available.
> >>
> >> * Steps should gracefully avoid recomputation when output files exist.
> >>
> >> * Backfilling a window back in time, e.g. 30 days, should happen
> >> automatically if some earlier steps have failed, or if output files
> >> have been deleted manually.
> >>
> >> * Continuous deployment in the sense that steps are automatically
> >> deployed and scheduled after 'git push'.
> >>
> >> * Step owners can get an overview of step status and history, and
> >> debug step execution, e.g. by accessing log files.
> >>
> >>
> >> I am aware that no framework will give us everything. It is a matter
> >> of how much we need to live without or build ourselves.
> >>
> >
> >Your wishlist looks pretty reasonable for batch computation workflows.
> >
> >I'm not aware of any batch/workflow Mesos framework. If you want some or
> >all of the above features on top of Mesos, I think you would be venturing
> >into writing your own framework.
> >Aurora doesn't have the concept of DAG and it can't make scheduling
> >decisions based on job progress or HDFS state.
> >
> >--
> >Zameer Manji
>
>

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