The Rough Guide to MongoDB
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The document presents a comprehensive overview of MongoDB's advantages and disadvantages, highlighting its speed, flexibility, and related technologies. It discusses application development with MongoDB, including how to manage relationships between data objects effectively, and presents various coding examples and strategies for enhancing functionality. Overall, the focus is on utilizing MongoDB for advertising optimization while acknowledging its limitations in enterprise scenarios.
The Rough Guide to MongoDB
- 1. The Rough Guide to MongoDB Simeon Simeonov @simeons
- 3. Why MongoDB?
- 5. recruit amazing people solve hard problems ship make users happy repeat
- 8. SQL is slow (for our business)
- 9. SQL is slow (for our developer workflow)
- 10. SQL is slow (for our analytics system)
- 11. So what’s Swoop?
- 13. Display Advertising Makes the Web Suck User-focused optimization Tens of millions of users 1000+% better than average 200+% better than Google Swoop Fixes That
- 14. Mobile SDKs iOS & Android Web SDK RequireJS & jQuery Components AngularJS NLP, etc. Python Targeting High-Perf Java Analytics Ruby 2.0 Internal Apps Ruby 2.0 / Rails 3 Pub Portal Ruby 2.0 / Rails 3 Ad Portal Ruby 2.0 / Rails 4
- 16. MongoDB: the Bad Not Quite Enterprise-Grade Not Quite Enterprise-Grade Not Cheap to Run Well
- 17. I will write more robust code I will write more robust code I will write more robust code I will write more robust code I will write more robust code I will write more robust code I will write more robust code I will write more robust code I will write more robust code
- 18. I will design a better map-reduce I will design a better map-reduce I will design a better map-reduce I will design a better map-reduce I will design a better map-reduce I will design a better map-reduce I will design a better map-reduce I will design a better map-reduce I will design a better map-reduce
- 19. RAM + locks == $$$
- 21. Five Steps to Happiness Sharding Native Relationships Atomic Update Buffering Content-Addressed Storage Shell Tricks
- 24. // Google AdWords object model Account Campaign AdGroup // this joins ads & keywords Ad Keyword // For example AdGroup has an Account AdGroup has a Campaign AdGroup has many Ads AdGroup has many Keywords Slam dunk for SQL
- 25. // Let’s play a bit Account Campaign AdGroup Ad Keyword
- 26. // Let’s play some more Account Campaign AdGroup Ad Keyword
- 27. // There is just one bit left Account Campaign AdGroup 1 Ad 0 Keyword
- 28. // build a hierarchical ID accountIDcampaignIDadGroupID((0keywordID)|(1adID)) // a binary ID! 10100100001100000000101001100110101010010100 < accountID >< campaignID >< … // Encode it in base 16, 32 or 64 {"_id" : "a4300a66a94d20f1", … }
- 29. // Example The 5th ad Of the 3rd ad group Of the 7th campaign Of the 255th account could have the _id 0x00ff000700031005 The _id for the 10th keyword of the same ad group would be 0x00ff00070003000a
- 30. // Neat: the ad’s and keyword’s _ids contain the // IDs of all of their ancestors in the hierarchy. keywordId = 0x00ff00070003000a adGroupId = keywordId & 0xffffffffffff0000 campaignId = keywordId & 0xffffffff00000000 accountId = keywordId & 0xffff000000000000 // has-a relationship is a simple lookup account = db.accounts.findOne({_id: accountId})
- 31. // Neater: has-many relationships are just // range queries on the _id index. adGroupId = keywordId & 0xffffffffffff0000 startOfAds = adGroupId + 0x1000 endOfAds = adGroupId + 0x1fff adsForKeyword = db.ads.find({ _id: {$gte: startOfAds, $lte: endOfAds} }) // Technically, that was a join via the ad group. // Who said Mongo can’t do joins???
- 36. > db.reports.findOne() { "_id" : …, "period" : "hour", "shard" : 0, // 16Mb doc limit protection "topic" : "ce-1", "ts" : ISODate("2012-06-12T05:00:00Z"), "variations" : { "2" : { // variationID (dimension set) "hint" : { "present" : 311, // hint.present is a metric "clicks" : 1 } }, "4" : { "hint" : { "present" : 331 } } } }
- 37. Content Addressed Storage Lazy join abstraction Very space efficient Extremely (pre-)cacheable Join only happens during reporting
- 38. // Step 1: take a set of dimensions worth tracking data = { "domain_id" : "SW-28077508-16444", "hint" : "Find an organic alternative", "theme" : "red" } // Step 2: compute a digital signature, e.g., MD5 sig = "000069569F4835D16E69DF704187AC2F” // Step 3: if new sig, increment a counter counter = 264034 // Step 4: create a document in the context- // addressed store collection for these
- 39. > db.cas.findOne() { "_id" : "000069569F4835D16E69DF704187AC2F", // MD5 hash "data" : { // data that was digested to the hash above "domain_id" : "SW-28077508-16444", "hint" : "Find an organic alternative", "theme” : "red" }, "meta_data" : { "id" : 264034 // variationID }, "created_at" : ISODate("2013-02-04T12:05:34.752Z") } // Elsewhere, in the reports collection… "variations" : { "264034" : { // metrics here }, … lazy join
- 41. // Use underscore.js in the shell // See http://underscorejs.org/ function underscore() { load("/mongo_hacks/underscore.js"); }
- 42. // Loads underscore.js on the MongoDB server function server_underscore(force) { force = force || false; if (force || typeof(underscoreLoaded) === 'undefined') { db.eval(cat("/mongo_hacks/underscore.js")); underscoreLoaded = true; } }
- 43. // Callstack printing on exception -- wraps a function function dbg(f) { try { f(); } catch (e) { print("n**** Exception: " + e.toString()); print("n"); print(e.stack); print("n"); if (arguments.length > 1) { printjson(arguments); print("n"); } throw e; } }
- 44. function minutesAgo(minutes, d) { d = d || new Date(); return new Date(d.valueOf() - minutes * 60 * 1000); } function hoursAgo(hours, d) { d = d || new Date(); return minutesAgo(60 * hours, d); } function daysAgo(days, d) { d = d || new Date(); return hoursAgo(24 * days, d); }
- 45. // Don’t write in the shell. // Use your fav editor, save & type t() in mongo function t() { load("/mongo_hacks/bag_of_tricks.js"); }