Impala - is that the next analytics tool?

1. Impala - Turbocharge Your Big Data Access
Ophir Cohen,
Data Platform Group Leader,
ophirc@liveperson.com
Jan 2014

2. LivePerson Is...
Creating Meaningful
Customer Connections
8,500
customers
SaaS pioneer since 1998
Mission
Customers
Technology

3. Volumes
per month 20ME
13 TB
ngagements per month 1.8 B
Visits per month
VOLUME

4. Data challenges @ LP
1. ~ 13TB of data each month
2. > 1PB Hadoop cluster
3. Few clusters across the globe
4. ~ 15,000 MR jobs daily on our main cluster
5. Various heterogeneous users (RND projects, PS, analytics, scientists
and more…)

5. Data accessing challenges @ LP
1. One month can take few hours (or days!)
2. Complex data model
3. PSs and analytics does not know Java (or scala ;) )

6. Hadoop Recap
1. Created by Doug Cutting (Yahoo employee back then) at about 2005
2. HDFS - distributed, scalable, and portable file system
3. MapReduce - framework for easily writing applications which process
vast amounts of data (multi-terabyte data-sets) in-parallel on large
clusters (thousands of nodes) in a reliable, fault-tolerant manner.
4. The leading big data solution

7. Accessing Hadoop

8. Stone Age
Java Map/Reduce

9. Java Map/Reduce
Pros
➢ It has been there from the beginning
➢ Reliable
➢ Flexible
➢ Easy for Java developers
Cons
➢ You need to know Java
➢ You need to write in Java ;)
➢ Exhausting development cycle

10. Bronze Age
Hive

11. Hive
Pros
➢ Common SQL-like language (HQL)
➢ Running on the cluster - great for trial-and-error method
Cons
➢ Declarative language (and those limited)
➢ Each query takes time

12. Iron Age
Impala

13. Impala
1. Cloudera initiative
2. As Cloudera says: “Real-Time Queries in Apache Hadoop,
For Real”
3. scalable parallel database technology
4. Impala is integrated with Hadoop to use the same file and
data formats, metadata, security and resource management
frameworks used by MapReduce, Apache Hive, Apache Pig
and other Hadoop software

14. Impala

15. Impala
✓ Uses Hive interface (HQL)
➢ No new education needed
➢ No (or small) Hive queries rewrite needed
✓ 4 to 30 X faster than Hive
➢ Trial and error works!
✓ Bypass map/reduce

16. Modern History
What next?

17. What next???
➢ Security
➢ Security
➢ Security
➢ RDBMS like on top of Hadoop
■ ACID
➢ Faster and faster access
➢ Data serialization solutions

18. THANK YOU!
We are Hiring
Extended version of this presentation will be given soon
Look for it on IL-TeckTalks group on Meetup:
http://www.meetup.com/ILTechTalks/
Ophir Cohen,
ophchu@gmail.com
@ophchu