Bigger Faster Easier: LinkedIn Hadoop Summit 2015

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We discuss LinkedIn's big data ecosystem and its evolution through the years. We introduce three open source projects, Gobblin for ingestion, Cubert for computation and Pinot for fast OLAP serving. We also showcase our in-house data discovery and lineage portal WhereHows.

Data & Analytics

The Data Driven Network
Kapil Surlaker
Director of Engineering
Powering the Data Driven Network
Kapil Surlaker and Shirshanka Das
Hadoop Summit 2015

Requirements
Source
Diversity
Batch
and
Streaming
Data
Quality

14
Source
Work
Unit
Work
Unit
Work
Unit
Extract
Extract
Extract
Convert
Convert
Convert
Quality
Quality
Quality
Write
Write
Write
Data
Publish
Task
Task
Task

Taming Source Diversity
REST
SFTP
JDBC
Protocol
Config
Source Extractor
checkpoint

Solving for real-time
Inefficiencies in batch
YARN based
Apache Helix
Continuous
Auto-scaling
YARN
Helix
Executor 1
Executor 2
Executor 3
HDFS
Stream Source

Data Quality
Per record, per task, or per
job
Composable quality checkers
Schema compatibility
Audit check
Sensitive fields
Unique key
Policy driven
Record
WriterJob
Task
Quality
Checker
FailQuarantine
Policy
Checker

Current Activity
Open source @ github.com/linkedin/gobblin
In production @ LinkedIn
Tens of TB per day
Hundreds of datasets
~20 different sources
Gobblin on YARN

Cubert: Converting hours to minutes
http://github.com/linkedin/cubert
Physical language
Block organization
Specialized operators

Where is my data?
How did it get here?
….
WhereHows
26

WhereHows: Roadmap
Streaming ecosystem integration
Kafka, Samza
Recommendations for Datasets, Metrics
Exploring Open Source

Precompute!
Device Geo View
Android US 1
Android IN 1
iOS US 1
Dimension View
Android 2
iOS 1
US 2
IN 1
Android,US 1
iOS,US 1
Android,IN 1

More dimensions!
Device Geo Carrier View
Android US ATT 1
Android IN Reliance 1
iOS US Verizon 1
Dimension View
Android 2
iOS 1
US 2
IN 1
ATT 1
Reliance 1
Verizon 1
Android,US 1
... ...

Challenges
Horizontally scalable
Low latency
Data freshness
Fault tolerance
OLAP features

Key features
SQL-like
interface
Columnar
storage and
indexing
Real-time
data load

(S)QL: Filters and Aggs
SELECT count(*)
FROM companyFollowHistoricalEvents
WHERE entityId = 121011 AND
'day' >= 15949 AND 'day' <= 15963 AND
paid = 'y’ AND
action = 'stop'

(S)QL: Group By
SELECT count(*)
FROM companyFollowHistoricalEvents
WHERE entityId = 121011 AND
'day' >= 15949 AND 'day' <= 15963 AND
paid = 'y’
GROUP BY action

(S)QL: ORDER BY and LIMIT
SELECT *
FROM companyFollowHistoricalEvents
WHERE entityId = 121011 AND
entityId = 1000 AND
action = 'start'
ORDER BY creationTime DESC LIMIT 1

Broker Helix
Real
time Historical
Kafka Hadoop
Pinot
Architecture
Queries
Raw
Data Samza

Pinot@LinkedIn
Site-‐facing
Apps Reporting
dashboards Monitoring

Breaking the cycle
Form hypothesis
Query
Repeat
OR …

Hmm... whats up with portugese and
spanish speaking countries?

Pinot Roadmap
Pinot is
Open Source !!!
github.com/linkedin/pinot
59

Kapil Surlaker
@kapilsurlaker
github.com/linkedin/
60
gobblin
cubert
pinot
Shirshanka Das
@shirshanka
Thanks!

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Bigger Faster Easier: LinkedIn Hadoop Summit 2015

1. The Data Driven Network Kapil Surlaker Director of Engineering Powering the Data Driven Network Kapil Surlaker and Shirshanka Das Hadoop Summit 2015

2. 2

5. How does PYMK work? 5

7. Houston we have a problem

8. Step 1 Central transport pipeline

9. Still have a problem

10. Hadoop Ingest Pipeline Complexity

11. Step 2 Central Ingestion Framework 11

12. Requirements Source Diversity Batch and Streaming Data Quality

13. Gobblin Architecture

14. 14 Source Work Unit Work Unit Work Unit Extract Extract Extract Convert Convert Convert Quality Quality Quality Write Write Write Data Publish Task Task Task

15. Taming Source Diversity REST SFTP JDBC Protocol Config Source Extractor checkpoint

16. Solving for real-time Inefficiencies in batch YARN based Apache Helix Continuous Auto-scaling YARN Helix Executor 1 Executor 2 Executor 3 HDFS Stream Source

17. Data Quality Per record, per task, or per job Composable quality checkers Schema compatibility Audit check Sensitive fields Unique key Policy driven Record WriterJob Task Quality Checker FailQuarantine Policy Checker

18. Current Activity Open source @ github.com/linkedin/gobblin In production @ LinkedIn Tens of TB per day Hundreds of datasets ~20 different sources Gobblin on YARN

19.

20. Transformation: No one size fits all

21. Cubert: Converting hours to minutes http://github.com/linkedin/cubert Physical language Block organization Specialized operators

22.

23. Got Diversity?

24. Where is the billings data? How did it get here? What data is used to create inferred skills data? Who owns that flow? When will the latest profile data show up? 24

25. 25

26. Where is my data? How did it get here? …. WhereHows 26

27. WhereHows architecture

28. 28

29. 29

30.

31. 31

32.

33. Lineage

34. WhereHows: Roadmap Streaming ecosystem integration Kafka, Samza Recommendations for Datasets, Metrics Exploring Open Source

35.

36. Real-time. Interactive.

37. Slice and Dice metrics

38. Precompute! Device Geo View Android US 1 Android IN 1 iOS US 1 Dimension View Android 2 iOS 1 US 2 IN 1 Android,US 1 iOS,US 1 Android,IN 1

39. More dimensions! Device Geo Carrier View Android US ATT 1 Android IN Reliance 1 iOS US Verizon 1 Dimension View Android 2 iOS 1 US 2 IN 1 ATT 1 Reliance 1 Verizon 1 Android,US 1 ... ...

40. Challenges Horizontally scalable Low latency Data freshness Fault tolerance OLAP features

41. Introducing Pinot

42. Key features SQL-like interface Columnar storage and indexing Real-time data load

43. (S)QL: Filters and Aggs SELECT count(*) FROM companyFollowHistoricalEvents WHERE entityId = 121011 AND 'day' >= 15949 AND 'day' <= 15963 AND paid = 'y’ AND action = 'stop'

44. (S)QL: Group By SELECT count(*) FROM companyFollowHistoricalEvents WHERE entityId = 121011 AND 'day' >= 15949 AND 'day' <= 15963 AND paid = 'y’ GROUP BY action

45. (S)QL: ORDER BY and LIMIT SELECT * FROM companyFollowHistoricalEvents WHERE entityId = 121011 AND entityId = 1000 AND action = 'start' ORDER BY creationTime DESC LIMIT 1

46. Columnar Storage

47. Forward Index

48. Broker Helix Real time Historical Kafka Hadoop Pinot Architecture Queries Raw Data Samza

49. Fast but needs a ton of RAM

50. To pre-compute or not?

51. Data aware pre-computation

52. Pinot@LinkedIn Site-‐facing Apps Reporting dashboards Monitoring

53. Breaking the cycle Form hypothesis Query Repeat OR …

54. Hmm... whats up with portugese and spanish speaking countries?

55. Brazil?

56. 56

57. 57 Holidays in Brazil 2015