This document discusses Hadoop usage at eBay over time from 2007 to 2015. It describes: - The growth of eBay's Hadoop clusters from 1-10 nodes in 2007 to over 10,000 nodes and 150,000 cores projected for 2015. - How the amount of data stored in Hadoop has grown from 1PB in 2010 to a projected 150+ PB in 2015. - The types of clusters eBay uses including dedicated, shared, and HAAS clusters. - Some key use cases for Hadoop at eBay like building a near real-time search index and processing 1.68 million items in 3 minutes. - Operational requirements for eBay's large Hadoop ecosystem like high availability, security,

1. at ebay
Aroop Maliakkal Padmanabhan
Rajesh Chandramohan
Vikas Kanth

2. 2
1-10 nodes1-10 nodes
20072007
100+ nodes100+ nodes
1000 + core1000 + core
1 PB1 PB
20102010
20112011
1000+ node1000+ node
10,000+ core10,000+ core
10+ PB10+ PB
4000+ node4000+ node
40,000+ core40,000+ core
50+ PB50+ PB
20132013
20152015
10,000+ nodes10,000+ nodes
150,000+ cores150,000+ cores
150+ PB150+ PB
20092009
10+ nodes10+ nodes
Hadoop @ ebay

3. Hadoop @ ebay
10+ large Hadoop clusters
10,000+ nodes
50,000+ jobs per day
50,000,000+ tasks per day
500+ types of Hadoop/Hbase metrics
Billions of audit events per day
3

4. Dedicated clusters
• Very specific use case like index building (Near Real Time Indexing)
• Tight SLAs for jobs (seconds to few minutes)
• Immediate revenue impact
• TSDB clusters for monitoring
Shared clusters
• Used primarily for analytics of user behavior and inventory
• Batch and ad-hoc jobs
• YARN, Hbase, Hive, Pig, Hue, Spark, etc.
• Security enabled with Kerberos
HAAS clusters
• Used primarily for DEV and QA
Hadoop @ ebay

5. 5
5.5PBof data generates a 650
million item index in only 2.5
hours
1.68 million items
processed in
3 minutes
Hadoop Platform Use Case: Search Backend

6. DB
(Oracle)
DB
(Oracle)
HBASE
Item +
Seller
Table
HBASE
Item +
Seller
Table
BESBES
BULK DATA
LOADER
BULK DATA
LOADER
A
G
E
N
T
S
Query
Nodes
Mini Index
(Built every few mins)
Bulk Index
(Built every 6 hours)
EBay World Wide
Event based
update
Batch Update
(Every few hours)
Indexing Pipeline Overview

7. • Cluster Availability
• Security
• Supporting NRT on commodity hardware
• Lights out Management.
• Organic growth leads to heterogeneous environment
(SKUs, operating systems, vendors, network topology etc)
• Resource Management: Quota management, Queue management
Ecosystem Requirements

8. Eagle Data Activity Monitoring
Perimeter Security
Enable 2FA
Data Loss Prevention
Authorization and Access Control
Deploy Ranger for centralized Access Control
Kerberoized cluster

9. Architecture

10.  Use Case:
 Analyze HDFS file/directory metadata to find anomalies
in users' HDFS usage patterns in pseudo real time
 Retrieve HDFS Metadata like permissions, size, block
level properties etc which are not visible in HDFS Audit
logs
 Block unauthorized operations and send alerts
 Challenges:
 RPC to Namenode for this is an overhead !
 OIV is SLOW !!
Real Time Incremental Hadoop Image Processing

11. Solution

12. Hadoop Robot is the action and auto-remediation center for Hadoop
maintenance
Hadoop Robot

13. Data Flow

14. What’s Eagle
The uniform monitoring and alerting framework
to monitor large-scale distributed system like
hadoop, spark, cloud, etc. in real time.

15. Eagle Ecosystem
Apps
DAM
JPA
Interface
Web Portal
REST Services
Ambari Plugin
Integration
Kafka
Storm
HBase
Druid
Elastic Search
Eagle Framework
Provide full-stack monitoring framework for efficiently
developing highly scalable real-time monitoring
applications.
Eagle Apps
Provide built-in monitoring applications for domains like
hadoop, storm and cloud.
Eagle Integration
Integrate with distributed real-time execution environment
like storm, message bus like kafka and storage layer like
hbase, and also support extensions.
Eagle Interface
Allow to access or manage eagle through REST service,
web UI or Ambari plugin.
Eagle
Framework

16. JPA: Job Performance Analyser
Historical job analysis
Running job analysis
Anomaly host detection
Job data skew detection
Job performance suggestion
Anomaly Prediction based on machine learning
Monitor and analyze job performance in real-time

17. JPA Solutions

18. HDFS Tiered Storage

19. Logs Visualizer

20. Hadoop ElasticSearch

21. THANK YOU