Workflow Engines for Hadoop

Joe Crobak@joecrobak

NYC Data Engineering MeetupSeptember 5, 2013

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Intro

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Background

• Devops/Infra for Hadoop

• ~4 years with Hadoop

• Have done two migrations from EMR to the colo.

• Formerly Data/Analytics Infrastructure @

• worked with Apache Oozie and Luigi

• Before that, Hadoop @

• worked with Azkaban 1.0

Disclosure: I’ve contributed to Luigi and Azkaban 1.0

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What is Apache Hadoop?

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What is a workflow?

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What is a workflow engine?

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Two Example Use-Cases

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Analytics / Data Warehousing

• logs -> fact table(s).

• database backups -> dimension tables.

• Compute rollups/cubes.

• Load data into a low-latency store (e.g. Redshift, Vertica, HBase).

• Dashboarding & BI tools hit database.

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Analytics / Data Warehousing

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Analytics / Data Warehousing

• What happens if there’s a failure?

• rebuild the failed day

• ... and any downstream datasets

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Hadoop-Driven Features

• People You May Know

• Amazon-style “People that buy this often by that”

• SPAM detection

• logs, databases -> machine learning / collaborative filtering

• derivative datasets -> production database (often k/v store)

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Hadoop-Driven Features

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Hadoop-Driven Features

• What happens if there’s a failure?

• possibly OK to skip a day.

• Workflow tends to be self-contained, so you don’t need to rerun downstream.

• Sanity check your data before pushing to production.

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Workflow Engine Evolution

• Usually start with cron

• at 01:00 import data

• at 02:00 run really expensive query A

• at 03:00 run query B, C, D

• ...

• This goes on until you have ~10 jobs or so.

• It’s hard to debug and rerun.

• Doesn’t scale to many people.

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Workflow Engine Evolution

• Two possibilities:

1. “a workflow engine can’t be too hard, let’s write our own”

2. spend weeks evaluating all the options out there. Try to shoehorn your workflow into each one.

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Workflow Engine Considerations

How do I...

• Deploy and Upgrade

• workflows and the workflow engine

• Test

• Detect Failure

• Debug/find logs

• Rebuild/backfill datasets

• Load data to/from a RDBMS

• Manage a set of similar tasks

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Apache

http://oozie.apache.org/

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Oozie - architecture

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Oozie - the good• Great community support

• Integrated with HUE, Cloudera Manager, Apache Ambari

• HCatalog integration

• SLA alerts (new in Oozie 4)

• Ecosystem support: Pig, Hive, Sqoop, etc.

• Very detailed documentation

• Launcher jobs as map tasks

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Oozie - the bad

• Launcher jobs as map tasks.

• UI - but HUE, oozie-web (and good API)

• Confusing object model (bundles, coordinators, workflows) - high barrier to entry.

• Setup - extjs, hadoop proxy user, RDBMS.

• XML!

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Oozie - the bad

• Hello World in Oozie

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http://azkaban.github.io/azkaban2/

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Azkaban - architecture

Source: http://azkaban.github.io/azkaban2/overview.html

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Azkaban - the good• Great UI

• DAG visualization

• Task history

• Easy access to log files

• Plugin architecture

• Pig, Hive, etc. Also, voldemort “build and push” integration

• SLA Alerting

• HDFS Browser

• User Authentication/Authorization and auditing.

• Reportal: https://github.com/azkaban/azkaban-plugins/pull/6

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Azkaban - the bad

• Representing data dependencies

• i.e. run job X when dataset Y is available.

• Executors run on separate workers, can be under-utilized (YARN anyone?).

• Community - mostly just LinkedIn, and they rewrote it in isolation.

• mailing list responsiveness is good.

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Azkaban - good and bad

• Job definitions as java properties

• Web uploads/deploy

• Running jobs, scheduling jobs.

• nearly impossible to integrate with configuration management

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https://github.com/spotify/luigi

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Luigi - architecture

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Luigi - the good• Task definitions are code.

• Tasks are idempotent.

• Workflow defines data (and task) dependencies.

• Growing community.

• Easy to hack on the codebase (<6k LoC).

• Postgres integration

• Foursquare got this working with Redshift and Vertica.

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Luigi - the bad

• Missing some key features, e.g. Pig support

• but this is easy to add

• Deploy situation is confusing (but easy to automate)

• visualizer scaling

• no persistent backing

• JVM overhead

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Comparison matrix - part 1

LangCode

ComplexityFrameworks Logs Community Docs

oozie java high - 105kpig, hive, sqoop,

mapreducedecentralized,

map tasksGood - ASF in many distros

excellent

azkaban java moderate - 26kpig, hive,

mapreduceUI-accessible

few users, responsive on

MLsgood

luigi python simple - 5.9khive, postgres,

scalding, python streaming

decentral-ized on workers

few users, responsive on github and MLs

good

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Comparison matrix - part 2

property configuration

RerunsCustomizat

ion (new job type)

Testing User Auth

ooziecommand-line,

properties file, xml defaults

oozie job -rerun

difficult MiniOozieKerberos, simple,

custom

azkabanbundled inside

workflow zip, system defaults

partial reruns in UI

plugin architecture

?xml-based,

custom

luigicommand-line, python ini file

remove output,

idempotency

subclass luigi.Task

python unittests

linux-based

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Other workflow engines

• Chronos

• EMR

• Mortar

• Qubole

• general purpose:

• kettle, spring batch

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Qualities I like in a workflow engine

• scripting language

• you end up writing scripts to run your job anyway

• custom logic, e.g. representing a dep on 7-days of data or run only every week

• Less property propagation

• Idempotency

• WYSIWYG

• It shouldn't be hard to take my existing job and move it to the workflow engine (it should just work).

• Easy to hack on

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Less important

• High availability (cold failover with manual intervention is OK)

• Multiple cluster support

• Security

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Best Practices

• Version datasets

• Backfilling datasets

• Monitor the absence of a job running

• Continuous deploy?

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Resources

• Azkaban talk at Hadoop User Group: http://www.youtube.com/watch?v=rIUlh33uKMU

• PyData talk on Luigi: http://vimeo.com/63435580

• Oozie talk at Hadoop user Group: http://www.slideshare.net/mislam77/oozie-hug-may12

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Thanks!

• Questions?

• shameless plug: Subscribe to my newsletter: http://hadoopweekly.com

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