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Workflow Engines for
Hadoop
Joe Crobak
@joecrobak
NYC Data Engineering Meetup
September 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
• 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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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 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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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
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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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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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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