1. Hadoop
(Shanghai Developer
Meetup – Sept 15, 2011)
余家昌 (Andrew Yu)
EMC Greenplum
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2. The Elephant Chase
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3. © Copyright 2011 EMC Corporation. All rights reserved. 3

4. Yahoo! Hadoop use cases
• Personalized Yahoo! Homepage
• Yahoo! Mail anti-spam
• Search and Ad pipelines
• Ad inventory prediction
• Data analytics
• etc
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5. Enterprise Use Case: “Big ETL”
Challenge: Transform Massive Data Solution: Hadoop/MapReduce as ETL
Flows Containing Data Needed for fabric to load to Analytic Database
Complex Analysis
• Examples: • Components:
– Web Traffic Reduction – Hadoop: Massively-parallel ingest, storage and
– Network Traffic & Performance Analysis analysis
– Location Analytics for People and Goods – MapReduce: Runs multiple cascaded custom
analysis / extraction on capture data
– Smart Electric Power Grid
– Connectors move structured data to Analytics
– Genome Analysis DB
– Clinical Outcome Research & Analysis
• Hadoop’s Roles:
• Data Sources: – Capture TBs/day of machine-generated data
– Web server & app server logs – Quality: Run data quality tasks in MapReduce
– CDR / xDRs – Execute MapReduce flows
– Router & Switching Subsystem Logs – Extract/Combine data/metadata
– Sensor networks – Move processed data to analytic DB
• Limitations & Cautions:
– Software development, More parts (Cascading/Flow), Maintainability
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6. Enterprise Use Case: Fraud Detection
Challenge: Identify & alert fraudulent Solution: Hadoop/MapReduce to filter
activity patterns & correlate communications
• Examples: • Components:
– ESP’s - Email Fraud – Hadoop: Massively-parallel ingest,
– Finance/Banking - Bank Fraud storage and analysis
– Advertising - Click Fraud – Mahout: Machine learning tool for building
– Telecom – Network fraud fraud algorithms
– MapReduce: Rapid analysis & algorithm
• Data Sources: deployment
– Web & app server logs
• Hadoop’s Role(s):
– IP/Call Records
– Massive ingest of historical/real-time data
– Email Traffic
– Build/Validate model for fraud detection
– Customer Transaction Data
manually or using Mahout
– Banking/Credit Data
– Parallel MapReduce jobs for near real-
time fraud detection
• Limitations & Cautions:
– Software development, Partial Solution (not Real-time, not Interactive)
–
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7. Enterprise Use Case: Cluster Analysis
Challenge: Grouping a collection of Solution: Process and Refine in
data according to common similarities Hadoop and load into Analytical DB
• Examples: • Components:
– Customer segmentation – Hadoop: Flexible data storage as volume
– Financial cost/risk analysis increases and structures vary
– Patient-centric healthcare – MapReduce: Cascading allows data
– Financial stock classification processing with minimal adjustments
– Social network analysis – Optional: Connectors to move results to
Analytic DB
• Data Sources:
• Hadoop’s Role(s):
– Health records
– Flexible: Allow agile implementation of
– Sales data
and unit testing of algorithms
– Human genome sequences
– Large scale analysis in Hadoop creates
– Financial trading data more accurate groupings
– Facebook/Twitter/LinkedIn – Rapid, parallel processing in MapReduce
• Limitations & Cautions:
– Software development, Complex Integration with Sources
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8. Greenplum HD:
Community Edition Stack
100%
APACHE
Hive
Pig
HBase
Zookeeper
MapReduce Framework (MapRed)
Hadoop Distributed File System (HDFS)
Currently supported
Future releases may include support for Oozie and Mahout
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9. Greenplum HD:
Enterprise Edition Stack
100%
APACHE
Enhanced Monitoring
INTERFACE
Hive
Pig
HBase
Zookeeper
MapReduce Framework (MapRed)
Hadoop Distributed File System (HDFS)
Currently supported
Future releases may include support for Oozie and Mahout
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10. Greenplum HD: Enterprise Edition
Enterprise-Ready Hadoop Platform for Unstructured Data
• 2 – 5x Faster than Apache
Faster Hadoop
• High Availability
Reliable • Mirroring
Easier to • NFS mountable
Use • System Management
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11. Greenplum Enterprise HD is Faster than
Other Distributions
DFSIO Terasort
(higher is better) (lower is better)
1000 250
Elapsed time in minutes
900
800 200
700
MB/sec
600 150
500
400 100
300
200 50
100
0 0
Read Write 3.5 TB
10 node cluster, 2x Quad-Core, 24G DRAM, 12 x 1TB SATA Drives @ 7200 rpm, Quad NICs
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12. Greenplum Enterprise HD
Distributed Name Node
• Fully distributed Hadoop Hadoop
Node Node
service running on NN NN
all Hadoop nodes Hadoop Hadoop
Node NN Node NN
• Automatic and Hadoop Hadoop
transparent failover Node NN Node NN
• Persistent metadata Hadoop
Node
Hadoop
Node
NN NN
• Highly scalable in Hadoop Hadoop
Node Node
number of files NN NN
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13. Greenplum Enterprise HD
Job Tracker High Availability
• Assures business
continuity
• Designed for mission Greenplum Enterprise HD
Distribution for Apache Hadoop
critical use
– Automatic stateful restart
– Task Tracker reconnects Enterprise HD MapReduce
without task loss Distributed
– Persistent completed task Job Tracker HA Name Node
state
Enterprise HD
Lockless Storage Services
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14. Greenplum Enterprise HD
Snapshots
• Intelligent Snapshots
– Automatic data deduplication Hadoop / HBASE NFS
APPLICATIONS APPLICATIONS
– Block sharing for space
READ / WRITE
savings
Enterprise HD Lockless Storage
• Fast and flexible Services
– Zero performance loss when
REDIRECT ON
WRITE
FOR SNAPSHOT
writing to the original A B C C’ D
• Easy to manage
– Scheduled or on-demand
– Drag and drop recovery Snapshot
Snapshot Snapshot
1 2 3
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15. Greenplum Enterprise HD
Mirroring
• Business Continuity
Production Research – Efficient design
– Differential deltas are
updated
– Data is compressed and
Datacenter 1 WAN Datacenter 2 check-summed
• Easy to manage
– Scheduled or on-demand
– Consistent point-in-time
Production WAN Cloud
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16. Greenplum Enterprise HD
Direct Access Using NFS
• Simple application
integration Greenplum Enterprise HD
Distribution for Apache Hadoop
– Leverage NFS for
random read/write
Enterprise HD MapReduce
access
• Direct access for Job Tracker HA
Distributed
Name Node
standard Hadoop tools
– Command line utilities Enterprise HD
Lockless Storage Services
– File browsers
– Desktop utilities
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17. Greenplum Enterprise HD
Simple Management
• Intuitive
• Insightful
• Complete
• One node
or
thousands
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18. Greenplum HD: Software Distributions
Features Community Edition Enterprise Edition
Apache Compatibility 100% Apache Open Source 100% API Compatible
Name Node High Availability Reference Implementation Distributed and High Avaiability
Job Tracker HA Reference Implementation HT High Availability
Name Node Scalability NN Metadata in Memory Distributed Name Node
Premium Support Yes Yes
Performance 2 - 5x than Community Edition
Snapshots No Yes
Mirrors No Yes
NFS Mounts No Yes
System Management No Yes
Available for Ordering May 9th 2011 Q3
Pricing Per Node Pricing Per Node Pricing
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19. Greenplum HD on
Data Computing Appliance
• Introducing the world’s first:
– High-performance
– Purpose-built
– Data co-processing Hadoop
appliance
• Combining Greenplum Database
and Greenplum Hadoop in one
appliance
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20. GPDB  GPHD Interoperability
GPHD data in/out GPHD
in GPDB Query
File on
HD
GPDB
External Tables
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21. Greenplum Database
External Tables for Hadoop
• Bring GPDB relational expressive
Example:
power to HDFS
– HDFS data presented as external tables Select count(*) from
– HDFS data supporting full SQL syntax HDFS_data h,
GPDB_data g
• Have ALL, PART or NONE of your where h.key = g.key;
data in HDFS
Insert into
• Leverage full parallelism of both HDFS_data select *
Hadoop and GPDB from GPDB_data;
– GPDB can read from/write to HDFS,
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22. Greenplum Enterprise HD
HDFS Integration – Parallelized Flow
• Reading:
– Each GPDB segment reads a portion of the file
• Segment i of n reads the i/n-th portion
– Access offset from HDFS namenode
– Read data directly from HDFS datanode
• Writing:
– Each GPDB segment writes a file
– HDFS balancing distributes the load evenly
across datanodes
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23. Big Data Analytics “Stack”
Analytic Toolsets
(Business Analytics, BI, Statistics, etc.)
Greenplum Chorus
Enterprise Collaboration Platform for Data
Greenplum Database Greenplum HD
World’s Most Scalable MPP Database Platform Enterprise Analytics Platform for Unstructured Data
Greenplum Data Computing Appliances
Purpose-built for Big Data Analytics
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24. THANK YOU
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