Presented by: Ameya Kantikar, Groupon

1. HBase @ Groupon
Deal Personalization Systems with
HBase
Ameya Kanitkar
ameya@groupon.com

2. Relevance & Personalization
Systems @ Groupon

3. Relevance & Personalization

4. Scaling: Keeping Up With a Changing Business
ChicagoPittsburgh San Francisco
200+
400+
2000+
Growing Deals Growing Users
• 200 Million+ subscribers
• We need to store data
like, user click
history, email
records, service logs etc.
This tunes to billions of
data points and TB’s of
data

5. Deal Personalization Infrastructure Use Cases
Deliver Personalized
Emails
Deliver Personalized
Website & Mobile
Experience
Offline System Online System
Email
Personalize billions of emails for hundreds
of millions of users
Personalize one of the most popular
e-commerce mobile & web app
for hundreds of millions of users & page views

6. Earlier System
Offline
Personalization
Map/Reduce
Data Pipeline (User Logs, Email Records, User History etc)
Online Deal
Personalization
API
MySQL Store
Email

7. Earlier System
Email
Offline
Personalization
Map/Reduce
Data Pipeline
Online Deal
Personalization
API
MySQL Store
• Scaling MySQL for data
such as user click
history, email records
was painful (to say the
least)
• Need to maintain two
separate data pipelines
for essentially the same
data.

8. Email
Offline
Personalization
Map/Reduce
Data Pipeline
Online Deal
Personalization
API
Ideal Data Store
• Common data store that
serves data to both online and
offline systems
• Data store that scales to
hundreds of millions of
records
• Data store that plays well with
our existing hadoop based
systems
• Data store that supports get()
put() access patterns based
on a key (User ID).
Ideal System

9. Architecture Options
HBase System
Online
Personalization
Email
Offline
Personalization
Map/Reduce
Data Pipeline

10. Architecture Options
HBase System
Online
Personalization
Email
Offline
Personalization
Map/Reduce
Data Pipeline
• Simple design
• Consolidated system that
serves both online and offline
personalization
Pros

11. Architecture Options
HBase System
Online
Personalization
Email
Offline
Personalization
Map/Reduce
Data Pipeline
• We now have same uptime
SLA on both offline and online
system
• Maintaining online latency SLA
for bulk writes and bulk reads
is hard.
Cons
And here is why…

12. Architecture
HBase Offline
System
HBase for Online
System
Real Time
Relevance
Email
Relevance
Map/Reduce
Replication
Data Pipeline
• We can now
maintain different
SLA on online and
offline systems
• We can tune HBase
cluster differently
for online and
offline systems

13. HBase Schema Design
User ID Column Family 1 Column Family 2
Unique Identifier for
Users
User History and
Profile Information
Email History For Users
• Most of our data access patterns are via “User Key”
• This makes it easy to design HBase schema
• The actual data is kept in JSON
Overwrite user history
and profile info
Append email history for
each day as a separate
columns. (On avg each row
has over 200 columns)

14. Cluster Sizing
Hadoop +
HBase
Cluster
100+ machine Hadoop
cluster, this runs heavy map
reduce jobs
The same cluster also hosts
15 node HBase cluster
Online HBase
Cluster
HBase
Replication
10 Machine
dedicated HBase
cluster to serve real
time SLA
• Machine Profile
• 96 GB RAM (HBase 25
GB)
• 24 Virtual Cores CPU
• 8 2TB Disks
• Data Profile
• 100 Million+ Records
• 2TB+ Data
• Over 4.2 Billion Data
Points

15. Summary
SOLVED
Data store that
works for online
and offline use
cases

16. ameya@groupon.com
www.groupon.com/techjobs
Questions?
Thanks!

17. HBase Performance Tuning
• Pre Split Tables
• Increase Lease Timeouts
• Increase Scanner Timeouts
• Increase region sizes (Ours is 10 GB)
• Keep as few regions per region server as possible (We have around 15-35)
•For heavy write jobs:
• hbase.hregion.memstore.block.multiplier: 4
• hbase.hregion.memstore.flush.size: 134217728
• hbase.hstore.blockingStoreFiles: 100
• hbase.zookeeper.useMulti: false (This was necessary for us, we are on 0.94.2)
for stable replication
It took some performance tuning to get stability out of Hbase