Building and Improving Products with Hadoop
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In many instances the terms `big data` and `Hadoop` are reserved for conversations on business analytics. Instead, I posit that these technologies are most powerful when they are deployed as a way to both build new products, and improve existing ones. Measurement is a fundamental part of the process, but more importantly I will walk through an effective tool-chain that can be used to: a) build unique new products, based on data. b) test improvements to a product At Foursquare, we`ve used a Hadoop-based tool chain to build new products (like social-recommendations), and to improve existing features through initiatives such as experimentation, and offline data generation. These products and improvements are fundamental to our core business, yet their existence would not be possible without Hadoop. I will pull examples from Foursquare and other companies to demonstrate these points, and outline the infrastructure components needed to accomplish them.
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Building and Improving Products with Hadoop
- 1. 2013 Building and Improving Products with Hadoop Matthew Rathbone
- 2. 2013 What is Foursquare Foursquare helps you explore the world around you. Meet up with friends, discover new places, and save money using your phone. 4bn check-ins 35mm users 50mm POI 150 employees 1tb+ a day of data
- 4. 2013 The Right Tool for the Job • Nginx – Serving static files • Perl – Regular expressions • XML – Frustrating people • Hadoop (Map Reduce) – Counting
- 5. 2013 COUNTING – WHAT IS IT GOOD FOR http://www.flickr.com/photos/blaahhi/
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- 11. 2013 Statistically Improbable Phrases Statistically Improbable Phrases
- 12. 2013 SIPS use cases • menu extraction • sentiment analysis • venue ratings • specific recommendations • search indexing • pricing data • facility information
- 13. 2013 How is SIPS built? Basically lots of counting.
- 14. 2013 SIPS • Tokenize data with a language model (into N- Grams) • built using tips, shouts, menu items, likes, etc • Apply a TF-IDF algorithm (Term frequency, inverse document frequency) • Global phrase count • Local phrase count ( in a venue ) • Some Filtering and ranking • Re-compute & deploy nightly
- 16. 2013 SIPS – Without Hadoop Potential Problems • Database Query Throttling • Venues are out of sync • Altering the algorithm could take forever to populate for all venues • Where would you store the results? • What about debug data? • Does it scale to 10x, 100x? • What about other, similar workflows?
- 17. 2013 SIPS – Hadoop Benefits • Quick Deployment • Modular & Reusable • Arbitrarily complex combination of many datasets • Every step of the workflow creates value
- 18. 2013 Apple Store - Downtown San Francisco 1 tip mentions "haircuts" Search for "haircuts" in "san francisco" Apple store??? Fixed by looking at % of tips and overall frequency “Hey Apple, how bout less shiny pizzazz and fancy haircuts and more fix- my-f!@#$-imac”
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- 23. 2013 ACTUALLY, IT’S A BIT MORE COMPLICATED http://www.flickr.com/photos/bfishadow
- 24. 2013 These benefits require infrastructure
- 25. 2013 Dependency Management Many options • Oozie (Apache) • Azkaban (LinkedIn) • Luigi ( Spotify, we <3 this ) • Hamake ( Codeminders ) • Chronos ( AirBNB)
- 26. 2013
- 27. 2013 Database / Log Ingestion • Sqoop • Mongo-Hadoop • Kafka • Flume • Scribe • etc
- 28. 2013
- 29. 2013 MapReduce Friendly Datastore A few obvious ones: • Hbase • Cassandra • Voldemort we built our own, it’s very similar to Voldemort and uses the Hfile API
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- 31. 2013 Getting started without all that stuff
- 32. 2013 Components you likely don’t have
- 33. 2013 The best way to start Don’t use Hadoop. *but pretend you do
- 34. 2013 Other reasons to not use Hadoop • Your idea might not be very good • Hadoop will slow you down to start with • You don’t have enough infrastructure yet • build it when you need it • V1 might not be that complex • V1 could be a spreadsheet
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- 37. 2013 SIPS Version 1 • Off the shelf language model • A subset of Venues & Tips • Did not use Map Reduce • Did not push to production at all
- 38. 2013 SIPS Version 2 • Started building our own language model • Rewritten as a Map Reduce • Manually loaded data to production • Filters for English data only. Tweak, improve, etc
- 39. 2013 SIPS Version 3 • Incorporated more data sources into our language model • Deployment to KV store (auto) • Incorporated lots of debug output • Language pipeline also feeds sentiment analysis Now we’re in the perfect place to iterate & improve
- 41. 2013 In Summary • Hadoop is good for counting, so use it for counting • Move quickly whenever possible and don’t worry about automation • Bring in new production services as you need them • Freedom!
- 42. 20132013 Thanks! matthew@foursquare.com @rathboma Bonus: http://hadoopweekly.com from my colleague, Joe Crobak (presenting later!)
Notas del editor
- Friend – financeSpent 2 years building management platformScrapped the projectFund manager hired kid to build excel macrosRight tool for the job
- Great for analyticsGreat for your products too
- - tf-idf : counting globally, counting locally
- Use lots of data sources without fearEach MR step outputs data to hdfs that can be used in other workflows.Makes the workflow naturally modulareasy to test isolated parts of the workflow
- Once you’ve solved the MR -> Datastore problem once, you’ve solved it for good.
- Every task has requirementsOther tasksDirectories with _SUCCESS flagsRun on cron
- - Hadoop-friendly Datastore-- we built our own (HFile Service)-- -- immutable-- -- downloads data from s3-- -- reads everything into memory (but doesn't need to)-- -- create X shards using map-reduce, swap these into X servers. They memory-map the files
- when in production hadoop lets you iterate quicklyright now, it slows you downstill work offlinedo it without any of the important components I just told you about
- build a MVP in a spreadsheet, webview, whatevereven if you deploy it, you can manually load data into a DB to start withIf you’re testing a v1 for a limited subset (employees), you probably don’t have much data anyway
- This didn’t need any of the key infrastructure components
- This needed database dumps.Ran on a cronLoaded manually
- Needed database dumpsRun with our dependency management engineLoads to our production datastore