Analyzing Your Data in MongoDB - Texas Linux Fest 2013

1. Solutions Architect, 10gen Sandeep Parikh #TXLF Analyzing Your MongoDB Data
2. My Background •  Solutions Architect at 10gen •  Background in software engineering •  Written code for –  Graph processing –  Social network analysis –  Sentiment analysis –  Text similarity •  @crcsmnky on Twitter
3. Modern Data •  Sick of saying “big data” •  Three main attributes –  Volume: •  The amount of data we work with daily is growing and is only getting larger –  Variety •  Data comes in all shapes, sizes, layouts, formats – often changing mid-stream –  Velocity •  Being collected at an astounding pace, need answers from it just as quickly
4. MongoDB
5. Background •  Scalability using commodity systems •  Rich data modeling, ad-hoc queries, full indexes •  No multi-row transactions, no joins •  Heterogeneous APIs •  Dynamic schemas for iterative development •  Elastic approaches to deployment
6. Features •  Data stored as JSON documents –  Each document has it’s own schema •  Create, Read, Update, Delete (CRUD) –  Ad-hoc queries: equality, range, regex –  Atomic in-place updates •  Secondary indexes –  Single, compound, geospatial, unique, sparse, TTL •  Replication: redundancy, failover, availability •  Sharding: auto-partitioning, linear r/w scale
8. Data Analysis
9. Analysis Types •  Aggregations •  Projections •  Transformations •  Statistics •  Reporting •  “Deeper” mining –  Recommendations, similarity, graph metrics
10. Analysis Approaches •  Custom application code –  You know your data but might not scale •  Aggregation framework –  Declarative, pipeline-based approach, ad-hoc •  Native Map-Reduce in MongoDB –  JS functions that run over your data •  Other systems –  Hadoop, R, ETL, Reporting
11. MongoDB Map-Reduce
12. > var map = function() { emit(this.language, this.pages); } > var reduce = function(key, values) { var sum = 0; values.forEach(function(val) { sum += val; }); return sum; } Map and Reduce Functions { _id: 375, title: "The Great Gatsby", ISBN: "9781857150193", available: true, pages: 218, chapters: 9, subjects: [ "Long Island", "New York", "1920s" ], language: "English" }
13. > db.books.mapReduce(map, reduce, {out: ”lang_pages"}) { "result" : ”lang_pages", "timeMillis" : 2042, "counts" : { "input" : 33142, "emit" : 33142, "reduce" : 5235, "output" : 16176 }, "ok" : 1, } Execute Map-Reduce
14. > db.books.mapReduce(map, reduce, {out: ”lang_pages”, query: {available: true}}) Seed With Query
15. > db.lang_pages.find() { “_id”: “English”, “value”: 5103 } { “_id”: “Russian”, “value”: 2309 } ... Query Results
16. Aggregation Framework
17. Aggregation Framework •  Processes documents as a “stream” –  Input is a collection, output is a document •  Pipeline is a series of operations –  Filter, transform data –  Output of one stage is input to the next –  $ ps ax | grep mongod | head -n 1
18. db.books.aggregate( { $match: { available: true }}, { $project: { language: 1, pages: 1 }}, { $group: { _id: “$language”, count: { $sum: “$pages” }} ); Aggregation Framework { _id: 375, title: "The Great Gatsby", ISBN: "9781857150193", available: true, pages: 218, chapters: 9, subjects: [ "Long Island", "New York", "1920s" ], language: "English" } //Operations: $project, $match, $limit, $skip, $unwind, $group, $sort, $geoNear
19. { title: "The Great Gatsby", pages: 218, language: "English" } { title: "War and Peace", pages: 1440, language: "Russian" } { title: "Atlas Shrugged", pages: 1088, language: "English" } Matching { $match: { language: "Russian" }} { title: "War and Peace", pages: 1440, language: "Russian" }
20. { _id: 375, title: "Great Gatsby", ISBN: "9781857150193", available: true, pages: 218, subjects: [ "Long Island", "New York", "1920s" ], language: "English" } Projections { $project: { _id: 0, title: 1, language: 1 }} { title: "Great Gatsby", language: "English" }
21. { _id: 375, title: "Great Gatsby", ISBN: "9781857150193", available: true, pages: 218, chapters: 9, subjects: [ "Long Island", "New York", "1920s" ], language: "English" } Projections (continued) { $project: { avgChapterLength: { $divide: ["$pages", "$chapters"] }, lang: "$language" }} { _id: 375, avgChapterLength: 24.2222, lang: "English" }
22. { title: "The Great Gatsby", pages: 218, language: "English" } { title: "War and Peace", pages: 1440, language: "Russian" } { title: "Atlas Shrugged", pages: 1088, language: "English" } Grouping { $group: { _id: "$language", avgPages: { $avg: "$pages" } }} { _id: "Russian", avgPages: 1440 } { _id: "English", avgPages: 653 }
23. { title: "The Great Gatsby", pages: 218, language: "English" } { title: "War and Peace", pages: 1440, language: "Russian” } { title: "Atlas Shrugged", pages: 1088, language: "English" } Grouping (continued) { $group: { _id: "$language", numTitles: { $sum: 1 }, sumPages: { $sum: "$pages" } }} { _id: "Russian", numTitles: 1, sumPages: 1440 } { _id: "English", numTitles: 2, sumPages: 1306 }
24. { title: "The Great Gatsby", ISBN: "9781857150193", subjects: [ "Long Island", "New York", "1920s" ] } Unwinding Arrays { $unwind: "$subjects" } { title: "The Great Gatsby", ISBN: "9781857150193", subjects: "Long Island" } { title: "The Great Gatsby", ISBN: "9781857150193", subjects: "New York" } { title: "The Great Gatsby", ISBN: "9781857150193", subjects: "1920s" }
25. Slides are great but Let’s do some live examples
26. Yelp Dataset Challenge •  http://www.yelp.com/dataset_challenge/ •  Data contains around –  11,000 business –  8,000 checkins –  43,000 users –  229,000 reviews •  Tweaked data model a bit from original form •  Script to process downloaded data –  https://gist.github.com/crcsmnky/5675588
27. Some Ideas… •  When are reviews posted? •  Most popular categories by city? •  Funniest users? Most helpful?
28. Pros and Cons •  For “simple” tasks, the aggregation framework is best –  Map-Reduce is slower and more work •  Aggregation Framework output limited to 16MB document –  Map-Reduce can output to a collection •  Vote on SERVER-3253 to bring $out to aggregation
29. Analysis Beyond MongoDB
30. MongoDB and Hadoop MongoDB
31. MongoDB-Hadoop Use Cases MongoDB warehouse MongoDB ETL
32. MongoDB-Hadoop Adapter •  MongoDB as input/output storage for Hadoop jobs •  Supports MapReduce, Pig, Streaming •  Batch, offline processing •  1.0 released, 1.1 active development •  Leverage Hadoop ecosystem against operational data in MongoDB
33. Other •  Business intelligence tools –  Jaspersoft –  Alteryx •  ETL tools –  Pentaho –  Talend
34. Questions
35. Thanks! •  Sandeep Parikh, @crcsmnky •  www.mongodb.org –  Downloads, docs, drivers, use cases –  @mongodb •  www.10gen.com –  Presentations, subscriptions, monitoring –  @10gen