Slides on Gobblin that I used for presentation at VLDB 2015 in the Industrial 2 track. Checkout Gobblin at https://github.com/linkedin/gobblin.

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GOBBLIN: UNIFYING DATA
INGESTION FOR HADOOP
Lin Qiao, Yinan Li, Sahil Takiar, Ziyang Liu, Narasimha
Veeramreddy, Min Tu, Ying Dai, Issac Buenrostro, Kapil
Surlaker, Shirshanka Das, Chavdar Botev
DataAnalytics Infrastructure @ LinkedIn

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Agenda
• Why Gobblin?
• Gobblin Overview
• Case Studies
• Gobblin in Details
• Gobblin in Production @ LinkedIn
• FutureWork
• Q&A
1

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Data Ingestion Challenges @ LinkedIn
2
BIG engineering and operational COST!
Data Sources DataTypes Operational Pain

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Pre-Gobblin Era
3
OLTP
Tracking
Snapshot
and delta
file dumps
Kafka
Databus
Changes
Pipeline #1
External
Partner Data
Pipeline #2
REST
JDBC
SOAP
...
Pipeline #3
Pipeline #4
Pipeline #5
Pipeline #n
Databases
(Oracle/Espresso)

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The Gobblin Era
4
OLTP
Tracking
Snapshot
and delta
file dumps
Kafka
Databus
Changes
External
Partner Data
REST
JDBC
SOAP
...
Databases
(Oracle/Espresso)

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Requirements
5
Multi-platform and
Scalability Support
Rich Source
Integration
Centralized State
Management
OperabilityExtensibility Self Service

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Architecture Overview
6
Constructs for Building Ingestion Flows
WorkUnit /Task
Execution Runtime
Deployment Mode
state store
compaction
retention
mgmt.
monitoring
Standalone Hadoop MR Yarn
Source Extractor Converter
Qlty. Chker. Writer Publisher
Task Executor Task StateTracker
Job Launcher Job Scheduler

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Case Study: Kafka Ingestion
7
KafkaAvroSource
KafkaAvroExtracto
r
WorkUnit 1
(Topic 1, Partition 1)
KafkaConverter
TimePartitioned
AvroWriter
Avro
/kafka/topic/hourly/yyyy/mm/dd/hh/*.avro
Compaction
/kafka/topic/daily/yyyy/mm/dd/*.avro
AuditCount
QualityChecker
KafkaAvroExtracto
r
WorkUnit 2
(Topic 1, Partition 2)
KafkaConverter
TimePartitioned
AvroWriter
Avro
AuditCount
QualityChecker
KafkaAvroExtracto
r
WorkUnit 3
(Topic 1, Partitions 1 & 2)
KafkaConverter
TimePartitioned
AvroWriter
Avro
AuditCount
QualityChecker
TimePartitioned
DataPublisher

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Case Study: Database Ingestion
8
JdbcSource
JdbcExtractor
WorkUnit 1
[2015090512, 2015090514)
ToAvroConverter
SnapshotAvroWriter
Row
/database/table/incremental/snapshot-ts/*.avro
Compaction
/database/table/full/snapshot-ts/*.avro
SchemaCompatibiliy
& Count Qlty. Chker
SnapshotDataPublisher
JdbcExtractor
WorkUnit 1
[2015090512, 2015090514)
ToAvroConverter
SnapshotAvroWriter
Row
SchemaCompatibiliy
& Count Qlty. Chker
JdbcExtractor
WorkUnit 1
[2015090512, 2015090514)
ToAvroConverter
SnapshotAvroWriter
Row
SchemaCompatibiliy
& Count Qlty. Chker

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Case Study – Filtering Sensitive Data
9
Has Sensitive
Data?
no
Source
Extractor
WorkUnit
Converter and
Quality Checker
Fork and Branching
Writer
DataPublisher
Writer
Sensitive Data
Filtering Converter
yes

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Data Quality Checking
10
Record-level
Policies
Writer
Task-level
Policies
Publisher
Quarantine
FailTask
Quality Checkers
- Per record or per task.
- Policy driven
- Composable
~ Schema compatibility
~ Audit check
~ Sensitive fields
~ Required fields
~ Unique key

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State and Metadata Mgmt.
11
State Store
- Stores runtime metadata, e.g., checkpoints
(a.k.a. watermarks)
~ Carried over between job runs
- Default impl: serializes job/task states into
files, one per run.
- Allows other implementations that conform
to the interface to be plugged in.
State Store
job run #2
job run #3job run #1
SEP
2
SEP
3
SEP
2 SEP
3
EXAMPLE

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Metrics / Events and Alerting
12
Kafka
MetricContext
Topic 1
MetricContext
Topic 2
MetricContext
Partition 1
MetricContext
Partition 2
MetricContext
20
12 8
6 6
Metric
Reporter
Event
ReporterMetrics / Events
Collection and Reporting
- Metrics for ingestion progress
~ supports tagging
~ real-time aggregation
- Events for major milestones
~ “fire-and-forget”
- Various built-in metric / event
reporters

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Running Modes
13
Standalone
Runs in a single
JVM; tasks run in a
thread pool.
Scale-out with
MapReduce
Each job run launches
a MR job, using
mappers as containers
to run tasks.
Scale-out with
General
Distributed
Resource Manager
Supports long-running
continuous ingestion,
with better resource
utilization and SLA
guarantees.
YARN
*in progress

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Gobblin in Production @ LinkedIn
• In production since 2014
• Usages
– Internal sources  HDFS
• Kafka, MySQL, Dropbox, etc.
– External sources  HDFS
• Salesforce, GoogleAnalytics, S3, etc.
– HDFS  HDFS
• Closed member data purging
– Egress from HDFS (future work)
• Data volume
– Over a dozen data sources,
– thousands of datasets,
– tens ofTBs,
… daily.
14

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FutureWork
• Gobblin onYarn (alpha-release)
• Real-time elastic ingestion
• Integration with
– Apache Sqoop: using Sqoop connectors
– Logstash: log ingestion
– Morphlines: using Morphline transformation
– Apache Spark
15

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Conclusions
16
Pain of
maintaining
multiple
ingestion
pipelines
Gobblin to the
rescue!
Data quality
assurance and
centralized state
management
Gobblin in
production for a
wide range of
data sources
Continuous real-
time ingestion

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ACKNOWLEDGEMENT
Pradhan Cadabam
Shrikanth Shankar
Suvodeep Pyne
Ray Ortigas
Henry Cai
Kenneth Goodhope
Erik Krogen

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Thanks.
18
Github https://github.com/linkedin/gobblin
Documentation https://github.com/linkedin/gobblin/wiki
User Group https://groups.google.com/forum/#!forum/gobblin-users