eHarmony in Cloud

Subtitle

Brian Ko

eHarmony

• Online subscription-based matchmaking service

• Available in United States, Canada, Australia and United Kingdom.

• On average, 236 members in US marry every day.

• More than 20 million registered users.

1

Why Cloud?

• Problem exceeds the limits of the data center and data warehouse environment.

• Leverage EC2 and Hadoop to scale data

2

Finding match

3

• Model Creation

Find matching

• Matching

4

Find Matching

• Predicative Model Scores

5

Requirement

• All the matches, scores, and user information should be archived daily

• Ready for 10X growth• Possible O(n2) problem• Need to support set of models becoming

more complex

6

Challenge• Current architecture is multi-tiered with a

relational back-end• Scoring is DB join intensive• Data need constant archiving

– Matches, match scores, user attributes at time of match creation

– Model validation is done at a later time across many days

• Need a non-DB solution

7

Solution

• Open Source Java implementation of Google’s MapReduce framework

– Distributes work across vast amounts of data– Hadoop Distributed File System (HDFS)

provides reliability through replication– Automatic re-execution on failure/distribution– Scale horizontally on commodity hardware

8

Slide 9

• Simple Storage Service (S3) provides cheap unlimited storage.

• Elastic Cloud Computing (EC2) enables horizontal scaling by adding servers on demand.

9

MapReduce• A large server farm can use MapReduce to

process huge dataset.• Map step

– Master node takes the input– Chops it up into smaller sub-problems– Distributes those to worker nodes.

• Reduce step– Master node takes the answers to all the sub-

problems – Combines them in a way to get the output 

10

Why Hadoop

• Mapper and Reducer are written by you• Hadoop provides

– Parallelization– Shuffle and sort

11

Actual Process

• Upload to S3 and start EC2 Cluster

13

Actual Process

• Process and archive

14

Amazon Elastic MapReduce

• It is a web service• EC2 cluster is managed for you behind the

scenes• Starts Hadoop implementation of the

MapReduce framework on Amazon EC2• Each step can read and write data directly

from and to S3• Based on Hadoop 0.18.3

15

Elastic MapReduce• No need to explicitly allocate, start and

shutdown EC2 instances• Individual jobs were managed by a remote

script running on master node (no longer required)

• Jobs are arranged into a job flow, created with a single command

• Status of a job flow and all its steps are accessible by a REST service

16

Before Elastic Map Reduce

• Allocate/Verify cluster• Push application to cluster• Run a control script on the master• Kick off each job step on the master• Create and detect a job completion token• Shut the cluster down

17

After Elastic MapReduce

• With Elastic MapReduce we can do all this with a single local command

• Uses jar and conf files stored on S3• Various monitoring tools for EC2 and S3

are provided

18

Development Environment

• Cheap to set up on Amazon• Quick setup - Number of servers is

controlled by a config variable• Identical to production• Separate development account

recommended

19

Cost comparison• Average EC2 and S3 Cost

– Each run is 2 to 3 hours– $1200/month for EC2– $100/month for S3

• Projected in-house cost– $5000/month for a local cluster of 50 nodes

running 24/7– A new company needs to add data center and

operation personnel expense

20

Summary

• Dev tools really easy to work with and just work right out of the box

• Standard Hadoop AMI worked great• Easy to write unit tests for MapReduce• Hadoop community support is great.• EC2/S3/EMR are cost effective

The End

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