Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1wQh534. Lin Qiao discusses the architecture of Gobblin, LinkedIn’s framework for addressing the need of high quality and high velocity data ingestion. Filmed at qconsf.com. Lin Qiao is leading LinkedIn’s data lifecycle management for analytics, covering areas of data ingestion, data quality and workflow management.

1. ©2014 LinkedIn Corporation. All Rights Reserved.
Gobblin’ Big Data with Ease
Lin Qiao
Data Analytics Infra @ LinkedIn

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Watch the video with slide
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/gobblin-linkedin

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Strategy
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Highlights
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Presented at QCon San Francisco
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4. ©2014 LinkedIn Corporation. All Rights Reserved.
Overview
• Challenges
• What does Gobblin provide?
• How does Gobblin work?
• Retrospective and lookahead

5. ©2014 LinkedIn Corporation. All Rights Reserved.
Overview
• Challenges
• What does Gobblin provide?
• How does Gobblin work?
• Retrospective and lookahead

6. ©2014 LinkedIn Corporation. All Rights Reserved.
Perception
Analytics Platform
Ingest
Framework
Primary
Data
Sources
Transformations Business
Facing
Insights
Member
Facing
Insights and
Data Products
Load
Load
Validation
Validation

7. ©2014 LinkedIn Corporation. All Rights Reserved.
Reality
5
Hadoop
Camus
Lumos
Teradata
External
Partner
Data
Ingest
Framework
DWH ETL
(fact tables)
Product,
Sciences,
Enterprise
Analytics
Site
(Member
Facing
Products)
Kafka
Activity
(tracking)
Data
R/W store
(Oracle/
Espresso)
Profile Data
Databus
Changes
Derived Data
Set
Core Data Set
(Tracking,
Database,
External)
Computed Results for Member Facing Products
Enterprise
Products
Change
dump on filer
Ingest
utilities
Lassen
(facts and
dimensions)
Read store
(Voldemort)

8. ©2014 LinkedIn Corporation. All Rights Reserved.
Challenges @ LinkedIn
• Large variety of data sources
• Multi-paradigm: streaming data, batch data
• Different types of data: facts, dimensions, logs,
snapshots, increments, changelog
• Operational complexity of multiple pipelines
• Data quality
• Data availability and predictability
• Engineering cost

9. ©2014 LinkedIn Corporation. All Rights Reserved.
Open source solutions
sqoopp
flumep morphlinep
RDBMS vendor-
specific connectorsp
aegisthus
logstashCamus

10. ©2014 LinkedIn Corporation. All Rights Reserved.
Goals
• Unified and Structured Data Ingestion Flow
– RDBMS -> Hadoop
– Event Streams -> Hadoop
• Higher level abstractions
– Facts, Dimensions
– Snapshots, increments, changelog
• ELT oriented
– Minimize transformation in the ingest pipeline

11. ©2014 LinkedIn Corporation. All Rights Reserved.
Central Ingestion Pipeline
Hadoop
Teradata
External
Partner
Data
Gobblin
DWH ETL
(fact tables)
Product,
Sciences,
Enterprise
Analytics
Site
(Member
Facing
Products)
Kafka
Tracking
R/W store
(Oracle/
Espresso)
OLTP Data
Databus
Changes
Derived Data
Set
Core Data Set
(Tracking,
Database,
External)
Enterprise
Products
Change
dump on filer
REST
JDBC
SOAP
Custom
Compaction

12. ©2014 LinkedIn Corporation. All Rights Reserved.
Overview
• Challenges
• What does Gobblin provide?
• How does Gobblin work?
• Retrospective and lookahead

13. ©2014 LinkedIn Corporation. All Rights Reserved.
Gobblin Usage @ LinkedIn
• Business Analytics
– Source data for, sales analysis, product sentiment
analysis, etc.
• Engineering
– Source data for issue tracking, monitoring, product
release, security compliance, A/B testing
• Consumer product
– Source data for acquisition integration
– Performance analysis for email campaign, ads
campaign, etc.

14. ©2014 LinkedIn Corporation. All Rights Reserved.
Key Features
 Horizontally scalable and robust framework
 Unified computation paradigm
 Turn-key solution
 Customize your own Ingestion

15. ©2014 LinkedIn Corporation. All Rights Reserved.
Scalable and Robust Framework
13
Scalable
Centralized
State Management
State is carried over between jobs automatically, so metadata can be used
to track offsets, checkpoints, watermarks, etc.
Jobs are partitioned into tasks that run concurrently
Fault Tolerant Framework gracefully deals with machine and job failures
Query Assurance Baked in quality checking throughout the flow

16. ©2014 LinkedIn Corporation. All Rights Reserved.
Unified computation paradigm
Common execution
flow
Common execution flow between batch ingestion and streaming ingestion
pipelines
Shared infra
components
Shared job state management, job metrics store, metadata management.

17. ©2014 LinkedIn Corporation. All Rights Reserved.
Turn Key Solution
Built-in Exchange
Protocols
Existing adapters can easily be re-used for sources with common protocols
(e.g. JDBC, REST, SFTP, SOAP, etc.)
Built-in Source
Integration
Fully integrated with commonly used sources including MySQL, SQLServer,
Oracle, SalesForce, HDFS, filer, internal dropbox)
Built-in Data
Ingestion Semantics
Covers full dump and incremental ingestion for fact and dimension
datasets.
Policy driven flow
execution & tuning
Flow owners just need to specify pre-defined policy for handling job
failure, degree of parallelism, what data to publish, etc.

18. ©2014 LinkedIn Corporation. All Rights Reserved.
Customize Your Own Ingestion Pipeline
Extendable
Operators
Configurable
Operator Flow
Operators for doing extraction, conversion, quality checking, data
persistence, etc., can be implemented or extended against common API.
Configuration allows for multiple plugin points to add in customized logic
and code

19. ©2014 LinkedIn Corporation. All Rights Reserved.
Overview
• Challenges
• What does Gobblin provide?
• How does Gobblin work?
• Lookahead

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Under the Hood

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Computation Model
• Gobblin standalone
– single process, multi-threading
– Testing, small data, sampling
• Gobblin on Map/Reduce
– Large datasets, horizontally scalable
• Gobblin on Yarn
– Better resource utilization
– More scheduling flexibilities

22. ©2014 LinkedIn Corporation. All Rights Reserved.
Scalable Ingestion Flow
20
Source
Work
Unit
Work
Unit
Work
Unit
Data
Publisher
Extractor Converter
Quality
Checker
Writer
Extractor Converter
Quality
Checker
Writer
Extractor Converter
Quality
Checker
Writer
Task
Task
Task

23. ©2014 LinkedIn Corporation. All Rights Reserved.
Sources
• Determines how to partition work
- Partitioning algorithm can leverage source sharding
- Group partitions intelligently for performance
• Creates work-units to be scheduled
Source
Work
Unit PublisherExtractor Converter
Quality
Checker
Writer

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Job Management
• Job execution states
– Watermark
– Task state, job state, quality checker output, error code
• Job synchronization
• Job failure handling: policy driven
22
State Store
Job run 1 Job run 3Job run 2

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Gobblin Operator Flow
Extract
Schema
Extract
Record
Convert
Record
Check
Record Data
Quality
Write
Record
Convert
Schema
Check Task
Data
Quality
Commit
Task Data
23

26. ©2014 LinkedIn Corporation. All Rights Reserved.
Extractors Source
Work
Unit PublisherExtractor Converter
Quality
Checker
Writer
• Specifies how to get the schema and pull data from
the source
• Return ResultSet iterator
• Track high watermark
• Track extraction metrics

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Converters
• Allow for schema and data transformation
– Filtering
– projection
– type conversion
– Structural change
• Composable: can specify a list of converters to be applied in
the given order
Source
Work
Unit PublisherExtractor Converter
Quality
Checker
Writer

28. ©2014 LinkedIn Corporation. All Rights Reserved.
Quality
Checkers
• Ensure quality of any data produced by Gobblin
• Can be run on a per record, per task, or per job basis
• Can specify a list of quality checkers to be applied
– Schema compatibility
– Audit check
– Sensitive fields
– Unique key
• Policy driven
– FAIL – if the check fails then so does the job
– OPTIONAL – if the checks fails the job continues
– ERR_FILE – the offending row is written to an error file
26
Source
Work
Unit PublisherExtractor Converter
Quality
Checker
Writer

29. ©2014 LinkedIn Corporation. All Rights Reserved.
Writers
• Writing data in Avro format onto HDFS
– One writer per task
• Flexibility
– Configurable compression codec (Deflate, Snappy)
– Configurable buffer size
• Plan to support other data format (Parquet, ORC)
Source
Work
Unit PublisherExtractor Converter
Quality
Checker
Writer

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Publishers
• Determines job success based on Policy.
- COMMIT_ON_FULL_SUCCESS
- COMMIT_ON_PARTIAL_SUCCESS
• Commits data to final directories based on job success.
Task 1
Task 2
Task 3
File 1
File 2
File 3
Tmp Dir
File 1
File 2
File 3
Final Dir
File 1
File 2
File 3
Source
Work
Unit PublisherExtractor Converter
Quality
Checker
Writer

31. ©2014 LinkedIn Corporation. All Rights Reserved.
Gobblin Compaction
• Dimensions:
– Initial full dump followed by incremental extracts in
Gobblin
– Maintain a consistent snapshot by doing regularly
scheduled compaction
• Facts:
– Merge small files
29
Ingestion HDFS Compaction

32. ©2014 LinkedIn Corporation. All Rights Reserved.
Overview
• Challenges
• What does Gobblin provide?
• How does Gobblin work?
• Retrospective and lookahead

33. ©2014 LinkedIn Corporation. All Rights Reserved.
Gobblin in Production
• > 350 datasets
• ~ 60 TB per day
• Salesforce
• Responsys
• RightNow
• Timeforce
• Slideshare
• Newsle
• A/B testing
• LinkedIn JIRA
• Data retention
31
Production
Instances
Data Volume

34. ©2014 LinkedIn Corporation. All Rights Reserved.
Lesson Learned
• Data quality has a lot more work to do
• Small data problem is not small
• Performance optimization opportunities
• Operational traits

35. ©2014 LinkedIn Corporation. All Rights Reserved.
Gobblin Roadmap
• Gobblin on Yarn
• Streaming Sources
• Gobblin Workbench with ingestion DSL
• Data Profiling for richer quality checking
• Open source in Q4’14
33

36. ©2014 LinkedIn Corporation. All Rights Reserved.

37. Watch the video with slide synchronization on
InfoQ.com!
http://www.infoq.com/presentations/gobblin-
linkedin