Hadoop Summit 2014: Building a Self-Service Hadoop Platform at LinkedIn with Azkaban

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Building a Self-Service Hadoop Platform at LinkedIn
with Azkaban

Hadoop at LinkedIn
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Hadoop at LinkedIn
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Profile PageHome Page

Hadoop at LinkedIn
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Evolution of Workflows
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20092010201120122013

Azkaban 1.0
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Azkaban 1.0
 Run workflows
 Schedule jobs
 Job History
 Failure notification
 Easy to use web UI and
visualizations
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Azkaban 2.0
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Azkaban 2.0
 Major re-architecting
 Separate executor and web servers
 User authentication
 Pluggable database drivers
– H2
– MySQL
 Brand new UI
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Azkaban 2.0
 Jobtype plugins
– Built-in type: command
– Pluggable jobtypes:
 Java
 Pig
 Hive
– Non-Hadoop jobtypes:
 Teradata
 Voldemort
 Viewer plugins – extending the
Azkaban UI for other tools
– HDFS browser
– Reportal
 LinkedIn-specific code as plugins
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Azkaban 2.5
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Azkaban 2.5
 UI overhauled using Bootstrap
 Embedded flows
 New self-service tools
– Job Summary
– Flow Summary
– Pig Visualizer
 Jobtype-specific plugins
 HDFS viewer improvements
– Display file schema in addition to
content
– Parquet file viewer
 And more
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Who’s using Azkaban?
 Software Engineers
 Data Scientists
 Analysts
 Product Managers
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Azkaban Today
 Workflow manager and scheduler
 Integrated runtime environment
 Unified front-end for Hadoop tools
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Good News! Success!
 1000+ users
 Several clusters
 2,500 flows executing per day
 30,000 jobs executing per day
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Bad News! Success
 1000+ users
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Creating and Running Workflows
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Creating Workflows
 Add job “type” plugins
– hadoopJava
– Command
– Pig
– Hive
 Dependencies
– Determine the dependency graph
 Parameter passing
– Parameters can be passed to job
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type=pig
creamy.level=4
chunky.level=4
...
type=hadoopJava
jelly.type=grape
sugar=HFCS
...
type=command
bread.type=wheat
dependencies=peanutbutter,jelly
...
peanutbutter.job
bread.job
jelly.job

Embedded Flows
 Embed a flow as a node in another
flow.
– “flow” job type
– Set flow.name to name of the
embedded flow
– Parameters can be passed to flow
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peanutbutter jelly
bread
type=flow
flow.name=bread
dependencies=coffee,fruit
type=hive
coffee.decaf=false
coffee.cream=true
...
type=hadoopJava
fruit.type=apple
...
coffee.job fruit.job
sandwich.job

Project Management
Project Page
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Running Workflows
Flow Execution Panel
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Running Workflows
Notification Options
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Running Workflows
Failure Options
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 Finish Current
– Finishes current running flows, then stops
 Cancel All
– Kills all running jobs and finishes immediately
 Finish Possible
– Finish all possible jobs if their dependencies have met. Then it fails.
Running Workflows
Failure Options
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Running Workflows
Flow Parameters
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Running Workflows
Concurrent Execution Options
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 Skip Executions
– Prevent concurrent executions
 Run Concurrently
– Concurrently run the flow
 Pipeline
– Distance 1: jobA waits until concurrent jobA finishes
– Distance 2: jobA waits until concurrent jobA’s children finishes
Running Workflows
Concurrent Execution Options
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Running Workflows
Executing Flow Page
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Running Workflows
Flow Job List
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Scheduling Workflows
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Schedule Flow Panel
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Scheduled Flows
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Scheduling Flows
Setting SLAs
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Debugging and Tuning
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Hadoop at LinkedIn
 1000+ users
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Job Execution History
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Flow Execution History
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Running Workflows
Job Logs
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Job Summary
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Pig Visualizer
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Pig Visualizer
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Pig Visualizer
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Pig Visualizer
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Pig Visualizer
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Flow Summary
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Flow Summary
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Browsing HDFS
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HDFS Viewer
Browsing Files
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HDFS Viewer
Viewing Files
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HDFS Viewer
File Schema
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 Avro
 Parquet
 Binary JSON
 Sequence File
 Image
 Text
HDFS Viewer
Supported File Types
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Reportal
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Reportal
Dashboard
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Reportal
New Report
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Reportal
Viewing Results
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 Pig
 Hive
 Teradata
Reportal
Supported Query Types
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Upcoming Features
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Azkaban Gradle Plugin and DSL
 Describe Azkaban flow and deploy
with Gradle
 Single file (more if you want) to
describe all your workflows
– Compiles to .job files
 Static checker
 Valid Groovy code
– Add conditionals for deployment to
different clusters
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azkaban {
jobConfDir = ‘./jobs’
workflow(‘workflow2’) {
pigJob(‘job2’) {
script = ‘src/main/pig/count-by-country.job’
parameter ‘inputFile’, ‘/user/foo/sample’
reads ‘/data/databases/foo’, [as: ‘input’]
writes ‘/data/databases/bar’, [as: ‘output’]
}
hiveJob(‘job3’) {
query = ‘show tables’
}
workflowDepends ‘job2’, ‘job3’
}
}

Future Roadmap
 New visualizers (Hive, Tez, etc.)
 Support DSL from other tools
 Operationalization tooling
 Scalability improvements
 Improved plugin interfaces
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Future Discussions
 Conditional branching
 Hive Metastore browser
 Pluggable executors (e.g. YARN)
 Persistence storage server
 Launching and monitoring long-running YARN applications (Samza, Storm,
etc.)
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Main Contributors
 David Chen (LinkedIn)
 Hien Luu (LinkedIn)
 Anthony Hsu (LinkedIn)
 Alex Bain (LinkedIn)
 Richard Park (RelateIQ)
 Chenjie Yu (Tango)
 Shida Li (University of Waterloo)
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How to Contribute
Website: azkaban.github.io
GitHub: github.com/azkaban
LinkedIn’s Data Website: data.linkedin.com
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Hadoop Summit 2014: Building a Self-Service Hadoop Platform at LinkedIn with Azkaban

Hadoop Summit 2014: Building a Self-Service Hadoop Platform at LinkedIn with Azkaban

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