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Matchmaking in the Cloud A study on Amazon EC2, Elastic MapReduce and Apache Hadoop at eHarmony  Ben Hardy - Sr. Software Engineer
About eHarmony ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Business use case ,[object Object]
Scorer ,[object Object],[object Object],[object Object]
Challenges to meet ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How Hadoop solved our problem ,[object Object],[object Object],[object Object],[object Object]
Architecture Overview Hadoop Data Warehouse Local Store unload s3put EC2 S3 s3get start verify get job status shutdown User and Match data  Cluster control Score Data store
How AWS solved our problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
AWS Elastic MapReduce BETA ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Simplified Job Control ,[object Object],[object Object],[object Object],Jar and Config on S3 and Job status queried via REST interface #{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
AWS Management Console Elastic MapReduce BETA
Points of caution Elastic MapReduce BETA ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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AWS Customer Presentation - eHarmony

  • 1. Matchmaking in the Cloud A study on Amazon EC2, Elastic MapReduce and Apache Hadoop at eHarmony Ben Hardy - Sr. Software Engineer
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  • 7. Architecture Overview Hadoop Data Warehouse Local Store unload s3put EC2 S3 s3get start verify get job status shutdown User and Match data Cluster control Score Data store
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  • 11. AWS Management Console Elastic MapReduce BETA
  • 12.

Editor's Notes

  1. Here are some facts and figures on us 2% of US Marriages
  2. Db joins etc, models are CPU and IO intensive and need to be tested, in offline system we can take advantage of aggregate data without constraining our online system
  3. Getting our data to and from EC2 is definitely non-trivial Steps 1,2,6, and 7 are outside the cloud
  4. EMR simplifies the process and scripting for us by consolidating the allocation, hadoop configuration and process control of the jobs
  5. Lots of steps before EMR. No fun. Lots of possible points of failure. No need to copy job to master, or even touch the master in any way. Uses Amazon’s elastic-mapreduce.rb utility script.
  6. Status of job flow Status of steps in each job flow
  7. Design for failure