Presented at Paris Mesos User group

1. Viadeo Segmentation
Platform with Spark
on Mesos
Paris Mesos User Group - 2014/09/10
@EugenCepoi - Viadeo

2. Two words on Spark
- A general purpose distributed computing framework
- Fast, testable, easy to code and deploy
- A strong ecosystem is being built around it

3. Two words on Mesos
- A cluster manager responsible of sharing resources (CPU & RAM) with
applications
- You can write your own framework for mesos, to run new kind of applications

4. Spark and Mesos at Viadeo
- Started using them together in mid 2013
- We started to use Spark mainly because of its usability and being a good
match to our data set sizes allowing to take full advantage of all its speed
- Mesos was the logical best way to run Spark and Hadoop jobs (and other kind
of software) on the same nodes and have dynamic resources sharing

5. Deploying Spark jobs on Mesos
Test, Build,
Package, Deploy
and run
Driver nodes
Mesos lib
Spark shell
Spark driver
Debian
packaged
Job code
A driver node is where we deploy the
code and from where we launch the
jobs on the cluster

6. Customer Segmentation
- Divide a population into a subset of customers that share common
characteristics
- Examples of segments: gender, industry, working in a big company…
- Used to achieve fine grained targeting (ads, new products, understand the
customer)
- Send IBM Ads to all male customers older than 40 years and working in the IT

7. The problem
- Business always needs new segments or wants to combine them
- The segmentation was computed through SQL queries on demand
- The raw data needs to be preprocessed (cleaned and computed)
- Many segments involve computations on the attributes they use, too
expensive on MySQL
- No way for non IT employees to have the segmentation

8. The goal
- Provide a solution that can compute even segments on complex data in
reasonable time
- Make them available to software components and humans (ex: Ad Targeting,
AB Testing, BI team)
- Expose a service that can answer to segmentation queries in real time
* Get live counters & and all the members belonging to a combination of segments
* A front-end allowing non IT people to combine segments and query it live

9. The pragmatic solution
- We don’t have the time to build a real data standardization layer
- The business team doing ad-hoc segmentation has “cleaning rules” and
knows the MySQL tables
- We can’t spend time to change segments definitions
- We have conventions
The idea: Delegate
- Implement the segments definitions as an SQL like DSL doing joins implicitly
and taking advantage of the conventions
- Let the business team create the segments definitions and define the cleaning
rules inside the segment definition

10. The big picture
Segment
definition
Segmentation Job
Stored with conventions:
/sqoop/Member/20140101/*
/sqoop/Skills/201401/02/*
/sqoop/Company/20140101/*
...
Members and their
segments
Real Time service
- Inverted index
- Query app
Member
Skills
Company
Daily MySQL
exports

11. The segment definition
- A DSL focused on expressing constraints on the data
- Doesn’t require the user to write JOINS, we imply it from the primary keys and
the column names (remember, conventions)
- A segment example:
● Define some variables (for ex. patterns you want to see in the data)
executiveKeywords: ["Dir.", "Resp.", Directeur, Directrice, Director, Dirigeant, Manager, Responsable, Chef, Chief, Head of]
● The segment itself (and reuse available variables)
Member.Headline = {executiveKeywords} or Position.StillInPosition=1 and Position.PositionTitle = {executiveKeywords} ...

12. Segments Definition
now()-Member.BirthDate > 30y
HDFS sqoops
/sqoop/Member/20140101/*
/sqoop/Skills/201401/02/*
...
Segmentation Job
Parse segment definition
using Scala combinators,
validate & broadcast to
all spark workers
Infer the input sources and
keep only the attributes we
need
Prune the data (rows) that
won’t change the result of
the expressions (reducing
the shuffled data size)
Join and evaluate each
expression (segment)
~ 30M Members
+ 80 segments
+10 Data sources
10g ~ 1g/source
Id: 1
Name: Lucas
BirthDate: 1986
Id: 2
Name: Joe
BirthDate: 1970
Id: 1
BirthDate: 1986
Id: 2
BirthDate: 1970
~ 2 min to complete
Each Member
segmentation

13. Querying & in memory index
Option 1 : Index in Elasticsearch and build an app to query it
- The most natural one, but at that moment we wanted to test hypotheses and
didn’t want to pollute our production Elasticsearch

14. Querying & in memory index
Option 2 : Use a long running spark job as a service (popular in the Spark
community)
● In a Spray app launch a spark job and load the data in memory
● Submit HTTP requests to the Spray app that will query the in memory RDD
- Increases possibility of problems/failures as the service would run 24/24 and
would have its data spread across N nodes
- Experienced blocking of offered resources by Mesos when running 2+ passive
spark shells

15. Querying & in memory index
Option 3 : A service using an in memory inverted index + a short spark job
● At startup, launch a job that will build the index
● Collect it on the driver node & stop the job
● Submit HTTP requests to the Spray app that will query the in memory index
- The quickest solution (for us) to get something running and collect feedback

16. Pattern Members
s1,s3 1, 2
s1,s2 3
s2 4
Member Segments
1 s1, s3
2 s1, s3
3 s1, s2
4 s2
Group by existing
segmentation
patterns
Fixed number of segments at
runtime, map each segment to
a position in a Bitset to compute
fast set intersections
Compute the intersections
of the query bitset and the
index bitsets
Raw Query: how many in s1 and s2? Query Bitset: 011
1
Inverted in memory index
Bitset Members
101 1, 2
011 3
010 4

17. Segmentation App
Segmentation App
Index construction
Job
In memory Index
~200 Mb
Spray Service
HDFS
/segmentation/20140101/...
< 200ms

18. Today...
- The segmentation job and the App, have been in production for 6 and 3
months, running every day without any trouble, nor requiring an intervention
- The computed segmentation is used to display targeted Ads in email
campaigns
- The Segmentation App, runs 24/24 7/7 and is mainly being used by the sales

19. Questions?

20. Thanks :)