Gobblin' Big Data With Ease @ QConSF 2014

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

Overview
• Challenges
• What does Gobblin provide?
• How does Gobblin work?
• Retrospective and lookahead

Perception
Analytics Platform
Ingest
Framework
Primary
Data
Sources
Transformations Business
Facing
Insights
Member
Facing
Insights and
Data Products
Load
Load
Validation
Validation

Reality
Profile Data
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)
Databus
Changes
Core Data Set
(Tracking,
Database,
External)
Derived Data
Set
Computed Results for Member Facing Products
Enterprise
Products
Change
dump on filer
Ingest
utilities
Lassen
(facts and
dimensions)
Read store
(Voldemort)

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

Open source solutions
sqoopp
aegisthus
flumep morphlinep
logstash Camus
RDBMS vendor-specific
connectorsp

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

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

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.

Key Features
 Horizontally scalable and robust framework
 Unified computation paradigm
 Turn-key solution
 Customize your own Ingestion

Scalable and Robust Framework
Centralized
State Management
13
Scalable
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

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.

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.

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

Overview
• Challenges
• What does Gobblin provide?
• How does Gobblin work?
• Lookahead

Under the Hood

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

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

Sources
Source
Work
Unit Extractor Converter Publisher
• Determines how to partition work
- Partitioning algorithm can leverage source sharding
- Group partitions intelligently for performance
• Creates work-units to be scheduled
Quality
Checker
Writer

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

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

Extractors Source
Work
Quality
Checker
Writer
• Specifies how to get the schema and pull data from
the source
• Return ResultSet iterator
• Track high watermark
• Track extraction metrics

Converters
Source
Work
• 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
Quality
Checker
Writer

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
Quality
Checker
Writer

Writers
Source
Work
• 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)
Quality
Checker
Writer

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
Quality
Checker
Writer

Gobblin Compaction
Ingestion HDFS 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

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

Lesson Learned
• Data quality has a lot more work to do
• Small data problem is not small
• Performance optimization opportunities
• Operational traits

Gobblin Roadmap
• Gobblin on Yarn
• Streaming Sources
• Gobblin Workbench with ingestion DSL
• Data Profiling for richer quality checking
• Open source in Q4’14
33

Gobblin' Big Data With Ease @ QConSF 2014

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Editor's Notes