Matchmaking in the Cloud
A study on Amazon EC2, Elastic
MapReduce and Apache Hadoop at
eHarmony
Ben Hardy - Sr. Software Engineer

About eHarmony
Online subscription-based matchmaking service
Launched in 2000
Available in United States, Canada, Australia and United
Kingdom
On average, 236 members in US marry every day
More than 20 million registered users
Matching models are based on decades of research and
clinical experience in psychology
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Business use case
To combine match data, user feedback,
and outcomes to help research improve
success of future matching models

Scorer
• Each match is evaluated and given a
score
• Score serves as predictor of match quality
• Score is compared later with match
outcome to inform research team

Challenges to meet
All possible matches = O(n2) problem
Database is a bottleneck
Input: tens of GB of matches, scores and
constantly changing user features are archived
daily
Output: TB of data currently archived and
growing
Desire scalability to 10x our current user base
More complex, data heavy models being added

How Hadoop solved our problem
Our problem can be broken up into a series of
MapReduce steps
Join match data, user A attributes into one line
Join above with user B attributes and
calculate the match score
Group the match scores by user

Architecture Overview
Data
unload s3put
Warehouse
User and
Match data
Cluster control
EC2 S3
start
verify Hadoop
get job status
shutdown
Local store s3get
Store
Score Data
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How AWS solved our problem
Quick and easy prototyping
Cost savings
Reduced headache
Easier resource management
Scaling is a no-brainer
S3 provides cheap permanent storage

AWS Elastic MapReduceBETA
Only need to think in terms of Elastic
MapReduce job flow
EC2 cluster is managed for you behind the
scenes
Each job flow has one or more steps
Each step is a Hadoop MapReduce process
Each step can read and write data directly from
and to S3
Based on Hadoop 0.18.3
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Simplified Job Control
Before EMR, we had to explicitly:
Run a Kick off Create and
Push Shut the
Allocate Verify control each job detect a job
application cluster
cluster cluster script on step on the completion
to cluster down
the master master token
Over 150 lines of scripts just for management
After EMR, it was one single local command
#{ELASTIC_MR_UTIL} --create --name
#{JOB_NAME} --num-instances #{NUM_INSTANCES} --instance-type #{INSTANCE_TYPE} --key_pair
#{KEY_PAIR_NAME} --log-uri #{SCORER_LOG_BUCKET_URL} --jar #{SCORER_JAR_S3_PATH} --main-class
#{MR_JAVA_PACKAGE}.join.JoinJob --arg -xconf --arg #{MASTER_CONF_DIR}/join-config.xml --jar
#{SCORER_JAR_S3_PATH} --main-class #{MR_JAVA_PACKAGE}.scorer.ScorerJob --arg -xconf --arg
#{MASTER_CONF_DIR}/scorer-config.xml --jar #{SCORER_JAR_S3_PATH} --main-class
#{MR_JAVA_PACKAGE}.combiner.CombinerJob --arg -xconf --arg #{MASTER_CONF_DIR}/combiner-
config-#{TARGET_ENV}.xml
Jar and Config on S3 and Job status queried via REST interface
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AWS Management Console
Elastic MapReduceBETA
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Points of caution
Elastic MapReduceBETA
Provisioning of the servers is not yet stable
Frequently failed in the first two weeks of the
beta program
It blocks until provisioning is complete
Only billed for job execution time
Amazon is working on improving reliability
It’s handy to terminate a hanged job with
Amazon Web Services Management Console
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