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Build Your Web Analytics with node.js, Amazon DynamoDB and Amazon EMR (BDT203) | AWS re:Invent 2013

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Want to learn how to build your own Google Analytics? Learn how to build a scalable architecture using node.js, Amazon DynamoDB, and Amazon EMR. This architecture is used by ScribbleLive to track billions of engagement minutes per month. In this session, we go over the code in node.js, how to store the data in Amazon DynamoDB, and how to roll-up the data using Hadoop and Hive. Attend this session to learn how to move data quickly at any scale as well as how to use genomic analysis tools and pipelines for next generation sequencers using Globus on AWS.

Publicado en: Tecnología, Empresariales

Build Your Web Analytics with node.js, Amazon DynamoDB and Amazon EMR (BDT203) | AWS re:Invent 2013

  1. 1. Building Your Own Web Analytics Service with node.js, Amazon DynamoDB, and Amazon Elastic MapReduce Jonathan Keebler - Founder, CTO - ScribbleLive November 13, 2013 © 2013, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of, Inc.
  2. 2. Who Am I? •Jonathan Keebler @keebler •Built video player for all CTV properties –Worked on news sites like CTV, TSN, CP24 •CTO, Founder of ScribbleLive •Bootstrapped a high scalability startup –Credit card limit wasn’t that high, had to find cheap ways to handle the load of top tier news sites
  3. 3. What is ScribbleLive? •Leading provider of real-time engagement management solutions •We enable real-time publication and syndication of digital content •Our platform is transforming the way the world’s largest brands and media approach communication and content creation, creating true real-time engagement
  4. 4. Some of our customers
  5. 5. Today •Learn to build your own analytics service – Seriously, we’re going to do it •node.js on Amazon EC2: web servers •Amazon DynamoDB: database •Hadoop/Hive on Amazon Elastic MapReduce (EMR): roll-up data
  6. 6. Why would we do this? •ScribbleLive tracks “engagement minutes” (EMs) across all customer sites – e.g.,,, – EM = 1 minute of a user watching a webpage – 2.5B per month, 120M+ per hour •Big analytics providers couldn’t do it – Didn’t have the features – Too inaccurate
  7. 7. How are we going to do this? Visitors Elastic Load Balancing node.js node.js node.js DynamoDB node.js
  8. 8. DynamoDB: data structure •Separate tables by timeframe – Minute (written by node.js directly) – Hour (EMR from minute data) – Day (EMR from hour data) – Month (EMR from day data) •Structure – Hash: Item (page id) – Range: Time (rounded to min, hour, day) – { Hits: 1 }
  9. 9. Elastic Load Balancing: AMI setup •Custom AMI – Loads source from SVN – Launches node.js
  10. 10. Elastic Load Balancing: Load balancing •1 load balancer •Cookies keep unique user on same instance •Auto-scaling – CPU >50% or network-in 50M bytes, triggers new servers coming online and added to Elastic Load Balancing
  11. 11. node.js: Overview of code •Accepts GET /?item={ID}&uid={UserID} •Dictionary/Array of how many GETs per item in this minute – Hits[Minute][“{ID}”]++ – Example: Hits[“1/1/2014 1:23:00”][“abcd”]++ •Dictionary/Array of Users already counted in Item:Minute (prevent double-counting) •At end of minute, write data back to DynamoDB
  12. 12. node.js: Bulk writing to DynamoDB •Writing all data back immediately in a loop = BAD! – Throughput would spike in that ~second – Would have to use higher throughput limit – More $$$$ •Instead, figure out how many writes need to happen / 60 seconds = how many writes per second you should do
  13. 13. node.js: Bulk writing to DynamoDB •Call to DynamoDB per item: – update: (atomic) add X to {ID}:{Minute}
  14. 14. Hadoop: What we map and reduce •To go from minute to hourly data – Round every minute down to the nearest hour (floor( Minute / 3600 ) * 3600) – Sum the # of “Hits” from each data point •Just look at the past 24 hours to save time •Do the same for hourly to daily, daily to monthly
  15. 15. Hadoop: Hive scripts INSERT OVERWRITE TABLE MetricsHourly SELECT Item, (floor( Time / 3600 ) * 3600) AS Time, SUM(Hits) AS Hits, from_unixtime(floor( Time / 3600 ) * 3600 ) AS TimeFriendly FROM Metrics WHERE Time >= floor( unix_timestamp() / 86400 ) * 86400 - ( 86400 * 1 ) GROUP BY Item, floor( Time / 3600 ) * 3600;
  16. 16. Hadoop: Setting Up EMR
  17. 17. Hadoop: Setting Up EMR • “Start an Interactive Hive Session” • Run a cron job every 15 minutes to check if the Hive job is complete • If complete, downloads newest Hive script and restarts the job • Amazon CloudWatch alarms if jobs taking longer than 12 hours
  18. 18. Hadoop: Cron Job #!/bin/sh JOBID=$(hadoop job -list | grep job_ | cut -f1) if [ -n "$JOBID" ];then echo "Another job already running"; else echo "Starting Hive job..." echo `date` starting >> /var/log/metricsdaily_starting wget -qO- http://DEPLOY/metrics/rollups.sql > /tmp/rollups.sql && hive -f /tmp/rollups.sql fi
  19. 19. Application API •RESTful API in the language of your choice •Calls to DynamoDB: –query: Hash:{ID} w/ Range:{Time A}-{Time B} •Since M-R could take a day to run, need to reconstruct hourly data from minutes for most recent 24 hours –e.g. if you want hourly data for last 2 days, take 24 hourly data pts from yesterday, and 24*60 minute data pts from today (convert to hourly data pts in code)
  20. 20. Performance
  21. 21. Performance
  22. 22. Please give us your feedback on this presentation BDT203 As a thank you, we will select prize winners daily for completed surveys!