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Five Ways To Do Data Analytics "The Wrong Way"
The document discusses various approaches to data analytics and common pitfalls. It provides examples of recommendation systems at Netflix and Pandora that achieved success by focusing on the business goals rather than just the technology. It also warns against complexifying systems and architectures unnecessarily over time and refusing to remove outdated components. Overall it advocates embracing complexity but also avoiding duct tape solutions, and designing systems with the intended use and business goals in mind rather than getting attached to specific technologies.
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Five Ways To Do Data Analytics "The Wrong Way"
- 1. Five Ways to Do Data Analytics “The Wrong Way” Title of the talk, on August 6 2014, @ Pinterest Powered by the Wisconsin Idea: The Wisconsin Idea is the principle that the university should improve people’s lives beyond the classroom. It spans UW–Madison’s teaching, research, outreach and public service. Jignesh M. Patel jignesh@cs.wisc.edu 1
- 2. Definition: A computing or networking architecture suggested by the marketing department for sales purposes rather than for technical reasons. Cisco calls them "reference designs". http://www.urbandictionary.com Follow the markitecture 2
- 3. http://gridgaintech.wordpress.com Technology = In-‐memory file system https://spark.apache.org Technology = In-‐memory caching + language bindings http://hortonworks.com/blog/100x-‐faster-‐hive/ The Stinger Initiative: 100X Hive Technology = caching, vectorized query execution http://blog.cloudera.com Technology = pin files in memory 3
- 4. http://hortonworks.com/blog/stinger-‐phase-‐2-‐the-‐journey-‐to-‐100x-‐faster-‐hive/ Problem: Claims are too broad! https://spark.apache.org Problem: Claims are too broad Venkatraman et al. EuroSys’13 Presto (not the FB) v/s Spark: Big Wins an in the R framework 4
- 5. Never fix a duct-‐taped solution Embrace complexity 5
- 6. Image from: http://http:// thewaysleueslove.blogspot.com One has to apply duct tape to fix problems, but consider removing it later. Stonebraker and Cetintemel, ICDE 2005 Natural instinct is to build/deploy a specialized system for each application, but that approach blows up the operational complexity 6
- 7. Chasseur and Patel, WebDB’13 JSON JSON Web App Mapping Layer Rather than a specialized engine for JSON document store, a simple language translator to SQL has higher performance and better data integrity. Chasseur and Patel, WebDB’13 Similar story for graphs and linear ML models – can easily be supported on top of systems powered by relational algebra The network effect! But in a bad way! Complexity Growth = O(N2) 1 2 3 1 2 3 4 7
- 8. R v/s Python debate Complexity Growth = O(N2) Also applies to tools and programming languages in house R Python 5K CRAN statistically robust packages Linear algebra, clustering, … ETL 8
- 9. Never realize that technology is NOT the “end,” but simply the “means to a (business) end” Think of technology as the end 9
- 10. Netflix Challenge Example: Building a recommendation system 10
- 11. Figure from: Ricardo: Integrating R and Hadoop by Das et al. SIGMOD’10 Key approach: Latent-‐factor Modeling All Together Now: A Perspective on the Netflix Prize, by Bell, Koren and Volinsky Winning insights • Missing ratings are not missing by random! • Parameters (popularity, users standards for rating, user tastes, …) vary over time • Combining sets of predictors • Efficient computation critical 11
- 12. Pandora’s Music Recommender by Michael Howe Pandora: Music Genome • Content-‐filtering • Classification to pick the recommendation • Key is to “build up a neighborhood for a particular user’s preference” Pandora.com Pandora: Music Genome 12
- 13. Build before you analyze the technology trend Never use back-‐of-‐the envelope calculations 13
- 14. Motivation for the UW Quickstep project http://quickstep.cs.wisc.edu Hardware changes are far more non-‐linear than in the past L a ten cy(( cy c le s )( CPU$ $ DRAM$ caches$ Magnetic)Hard)Disk)Drives) ~1#10s! ~100! ~107!– !108! CPU$ $caches$ NVRAM)(e.g.)SSDs)) ~105) –)106! Cap a c ity( Co s t( Energy Efficiency for Large-‐Scale MapReduce Workloads with Significant Interactive Analysis, Chen et al. EuroSys’12 Most interactive jobs work on “small” data sets 14
- 15. 15 Patterson, CACM 2004 Latency lags bandwidth J. Dean, Latency numbers every programmer should know, 2012 0 10 1,000 100,000 10,000,000 1,000,000,000 L1 cache reference Branch mispredict L2 cache reference Mutex lock/unlock Main memory reference Compress 1K bytes with Zippy Send 1K bytes over 1 Gbps network Read 4K randomly from SSD* Read 1 MB sequentially from memory Round trip within same datacenter Read 1 MB sequentially from SSD* Disk seek Read 1 MB sequentially from disk Send packet CA-‐>Netherlands-‐>CA Time in ns (log scale)
- 16. Amazing way to reason about bottlenecks Little’s Law L = λW 16 Amdahl, AFIPS 1967 Amdahl's law DeWitt and Gray, CACM 1992 Parallel computing is hard Speedup = Old/New
- 17. Stubbornly refuse to throw away code and platform architecture. Fall in love with your architecture 17
- 18. Data from 2013 publicly reported numbers and Alexa 19# 29# 18#7# 9# 1" 2" 4" 8" 16" 32" 64" 0" 1" 2" 3" $/Active)User)(log)scale)) Revenue/Employee)($M)) Google YouTube Problem: It’s hard to throw away something that you built, even if it doesn’t fit anymore 18 Bubble volume based on daily time on the site
- 19. 19 Watch for claims that are too broad Markitecture Simple is beautiful – keep the building blocks of your architectural DNA simple Complexity Periodically re-‐evaluate your technology architecture. Also, people and processes. Architecture Technology must serve an end business goal Technology and Business Amazingly powerful – think hard before you build! Back-‐of-‐the envelope calculations doing it right … SSuummmmaarryy