Building a reliable pipeline of data ingress, batch computation, and data egress with Hadoop can be a major challenge. Most folks start out with cron to manage workflows, but soon discover that doesn't scale past a handful of jobs. There are a number of open-source workflow engines with support for Hadoop, including Azkaban (from LinkedIn), Luigi (from Spotify), and Apache Oozie. Having deployed all three of these systems in production, Joe will talk about what features and qualities are important for a workflow system.

1. Workflow Engines for
Hadoop
Joe Crobak
@joecrobak
NYC Data Engineering Meetup
September 5, 2013
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2. Intro
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3. 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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4. What is Apache Hadoop?
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5. What is a workflow?
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6. What is a workflow engine?
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7. Two Example Use-Cases
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8. 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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9. Analytics / Data Warehousing
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10. Analytics / Data Warehousing
• What happens if there’s a failure?
• rebuild the failed day
• ... and any downstream datasets
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11. Hadoop-Driven Features
• PeopleYou 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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12. Hadoop-Driven Features
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13. 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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14. 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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15. 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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16. 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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17. Apache
http://oozie.apache.org/
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18. Oozie - architecture
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19. 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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20. 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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21. Oozie - the bad
• Hello World in Oozie
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22. http://azkaban.github.io/azkaban2/
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23. Azkaban - architecture
Source: http://azkaban.github.io/azkaban2/overview.html
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24. 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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25. 25

26. Azkaban - the bad
• Representing data dependencies
• i.e. run job X when datasetY 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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27. 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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28. https://github.com/spotify/luigi
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29. Luigi - architecture
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30. 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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31. 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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32. Comparison matrix -
part 1
Lang
Code
Complexity
Frameworks Logs Community Docs
oozie java high - 105k
pig, hive, sqoop,
mapreduce
decentralized,
map tasks
Good - ASF in
many distros
excelle
nt
azkaban java moderate - 26k
pig, hive,
mapreduce
UI-accessible
few users,
responsive on
MLs
good
luigi python simple - 5.9k
hive, postgres,
scalding, python
streaming
decentral-ized
on workers
few users,
responsive on
github and MLs
good
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33. Comparison matrix -
part 2
property
configuration
Reruns
Customizat
ion (new
job type)
Testing User Auth
oozie
command-line,
properties file, xml
defaults
oozie job -
rerun
difficult MiniOozie
Kerberos, simple,
custom
azkaban
bundled inside
workflow zip, system
defaults
partial
reruns in UI
plugin
architecture
?
xml-based,
custom
luigi
command-line,
python ini file
remove
output,
idempotency
subclass
luigi.Task
python
unittests
linux-based
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34. Other workflow
engines
• Chronos
• EMR
• Mortar
• Qubole
• general purpose:
• kettle, spring batch
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35. 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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36. Less important
• High availability (cold failover with manual
intervention is OK)
• Multiple cluster support
• Security
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37. Best Practices
• Version datasets
• Backfilling datasets
• Monitor the absence of a job running
• Continuous deploy?
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38. 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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39. Thanks!
• Questions?
• shameless plug: Subscribe to my
newsletter: http://hadoopweekly.com
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