Building a Cross Channel Content Delivery Platform with MongoDB
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The document outlines details of a webinar focused on building a cross-channel content delivery platform using MongoDB, including technical requirements for participants and a post-webinar Q&A session. It highlights 10gen, the company behind MongoDB, and presents challenges faced by organizations when using relational databases, such as slow onboarding and scalability issues. Additionally, it covers MongoDB's features like sharding to improve performance and scalability, along with statistics related to MongoDB's adoption and community engagement.
![Example Tracker
db.click.update(
{url: "http://www.mongodb.org" },
{$push: {tags: [“MongoDB”, “NoSQL”]}}
)
19](https://image.slidesharecdn.com/buildingacrosschannelcontentdeliveryplatformwithmongodb-121113104839-phpapp01/85/Building-a-Cross-Channel-Content-Delivery-Platform-with-MongoDB-19-320.jpg)
![Example Tracker
click = {user: "Chris",
date: new Date(),
url: "http://www.mongodb.org",
tags : [“MongoDB”, “NoSQL”]}
20](https://image.slidesharecdn.com/buildingacrosschannelcontentdeliveryplatformwithmongodb-121113104839-phpapp01/85/Building-a-Cross-Channel-Content-Delivery-Platform-with-MongoDB-20-320.jpg)
![Example - twitter
{
"result" :[
{
"_id" : "Far Far Away",
"friends" : 344,
"followers" : 789
},
...
],
"ok" : 1
}
27](https://image.slidesharecdn.com/buildingacrosschannelcontentdeliveryplatformwithmongodb-121113104839-phpapp01/85/Building-a-Cross-Channel-Content-Delivery-Platform-with-MongoDB-27-320.jpg)
Building a Cross Channel Content Delivery Platform with MongoDB
- 1. Building a Cross Channel Content Delivery Platform with MongoDB Webinar Building a Cross Channel Content Delivery Platform with MongoDB will begin at 6:00 AM EDT / 4:00 AM PDT / 11:00 AM UTC Audio should start immediately when you log into the event via Audio Broadcast. You will need a Voip headset and reliable internet connection for Audio Broadcast. If you are having issues connecting, please dial 1-877-668-4493; Access code: 666 687 901. There is a Q&A following the webinar. A recording of the webinar will be available 24 hours after the event is complete.
- 2. 10gen Overview 10gen is the company behind MongoDB – the leading NoSQL database 2
- 3. 10gen Overview 170+ employees 3
- 4. 10gen Overview 500+ customers 4
- 5. 10gen Overview $73M in funding from top investors 5
- 6. Leading Organizations Rely on MongoDB 6
- 7. Media Company Change Application RDBMS 7
- 8. Media Company Change Application RDBMS 8
- 9. Media Company Change Application RDBMS 9
- 10. Media Company Change Application RDBMS 10
- 11. Media Company Change Application RDBMS 11
- 12. So What? 1. Slow onboard of each channel due to complex schema changes. 2. Unable to scale database to understand our customer! 3. Reduce in user experience due to performance 12
- 13. Schema 1. Slow onboard of each channel due to complex schema changes. 13
- 14. Example? Real Twitter { "_id" : ObjectId("4f47b268fb1c80e141e9888c"), "user" : { "friends_count" : 73, "location" : "Brazil", "screen_name" : "Bia_cunha1", "name" : "Beatriz Helena Cunha", "followers_count" : 102, } } 14
- 15. Media Company Change JSON Application RDBMS 15
- 16. Media Company Change JSON JSON JSON JSON Application SQL! RDBMS 16
- 17. Media Company Change JSON JSON JSON JSON Application JSON 17
- 18. Example Tracker click = {user: "Chris", date: new Date(), url: "http://www.mongodb.org"} > db.tracker.save(click) 18
- 19. Example Tracker db.click.update( {url: "http://www.mongodb.org" }, {$push: {tags: [“MongoDB”, “NoSQL”]}} ) 19
- 20. Example Tracker click = {user: "Chris", date: new Date(), url: "http://www.mongodb.org", tags : [“MongoDB”, “NoSQL”]} 20
- 21. Now we can answer... What are our customers doing? 21
- 22. Example - twitter Real Twitter { "_id" : ObjectId("4f47b268fb1c80e141e9888c"), "user" : { "friends_count" : 73, "location" : "Brazil", "screen_name" : "Bia_cunha1", "name" : "Beatriz Helena Cunha", "followers_count" : 102, } } 22
- 23. Example - twitter db.tweets.aggregate( {$match: {"user.friends_count": { $gt: 0 }, "user.followers_count": { $gt: 0 } } }, {$project: { location: "$user.location", friends: "$user.friends_count", followers: "$user.followers_count" } }, {$group: {_id: "$location", friends: {$sum: "$friends"}, followers: {$sum: "$followers"} } } ); 23
- 24. Example - twitter db.tweets.aggregate( {$match: {"user.friends_count": { $gt: 0 }, Predicate "user.followers_count": { $gt: 0 } } }, {$project: { location: "$user.location", friends: "$user.friends_count", followers: "$user.followers_count" } }, {$group: {_id: "$location", friends: {$sum: "$friends"}, followers: {$sum: "$followers"} } } ); 24
- 25. Example - twitter db.tweets.aggregate( {$match: {"user.friends_count": { $gt: 0 }, Predicate "user.followers_count": { $gt: 0 } } }, {$project: Parts of the { location: "$user.location", friends: "$user.friends_count", document you followers: "$user.followers_count" want to project } }, {$group: {_id: "$location", friends: {$sum: "$friends"}, followers: {$sum: "$followers"} } } ); 25
- 26. Example - twitter db.tweets.aggregate( {$match: Predicate {"user.friends_count": { $gt: 0 }, "user.followers_count": { $gt: 0 } } }, {$project: { location: "$user.location", Parts of the friends: "$user.friends_count", document you followers: "$user.followers_count" want to project } }, {$group: {_id: "$location", friends: {$sum: "$friends"}, Function to apply followers: {$sum: "$followers"} } to the result set } ); 26
- 27. Example - twitter { "result" :[ { "_id" : "Far Far Away", "friends" : 344, "followers" : 789 }, ... ], "ok" : 1 } 27
- 28. Scale! Let’s scale our database to understand ALL our customer! 28
- 29. Sharding - Range distribution sh.shardCollection("test.tweets", {_id: 1}) shard01 shard02 shard03 29
- 30. Sharding - Range distribution a-i j-r s-z shard01 shard02 shard03 30
- 31. Sharding - Splits a-i ja-jz s-z shard01 k-r shard02 shard03 31
- 32. Sharding - Splits a-i ja-ji s-z shard01 ji-js shard02 shard03 js-jw jz-r 32
- 33. Sharding - Auto Balancing a-i ja-ji s-z ji-js shard01 shard02 shard03 js-jw js-jw jz-r jz-r 33
- 34. Sharding - Auto Balancing a-i ja-ji s-z shard01 ji-js shard02 shard03 js-jw jz-r 34
- 35. Performance Improve user experience due to increased performance 35
- 36. Memory MongoDB revolves around memory mapped files
- 37. Operating System map files on the Filesystem to Virtual Memory • 200 gigs of MongoDB files creates 200 gigs of virtual memory • OS controls what data in RAM • When a piece of data isn't found in memory – OS goes to disk to fetch the data • Indexes are part of the Regular Database files • Deployment Trick: Pre-Warm your Database (PreWarming your cache) to prevent cold
- 38. A Few Words on OS Choice • For production: Use a 64 bit OS and a 64 bit MongoDB Build – 32 Bit has a 2 gig limit; imposed by the operating systems for memory mapped files – Clients can be 32 bit • MongoDB Supports (little endian only) – Linux, FreeBSD, OS X (on Intel, not PowerPC) – Windows – Solaris (Intel only, Joyent offers a cloud service which works for Mongo)
- 39. Sharding for caching 96 GB Mem 3:1 Data/Mem a-i 300 GB Data j-r shard01 s-z 300 GB 39
- 40. Aggregate Horizontal Resources 96 GB Mem 96 GB Mem 96 GB Mem 1:1 Data/Mem 1:1 Data/Mem 1:1 Data/Mem a-i j-r s-z 300 GB Data j-r shard02 shard03 s-z 100 GB 100 GB 100 GB 40
- 41. Sharding Features • Shard data without no downtime • Automatic balancing as data is written • Commands routed (switched) to correct node • Inserts - must have the Shard Key • Updates - can have the Shard Key • Queries • With Shard Key - routed to nodes • Without Shard Key - scatter gather • Indexed / Sorted Queries • With Shard Key - routed in order • Without Shard Key - distributed sort merge 41
- 42. Summary
- 43. Relational Database Challenges Data Types Agile Development • Unstructured data • Iterative • Semi-structured data • Short development cycles • Polymorphic data • New workloads Volume of Data New Architectures • Petabytes of data • Horizontal scaling • Trillions of records • Commodity servers • Tens of millions of queries per • Cloud computing second 43
- 44. mongoDB Adoption Resource User Data Management 44
- 45. Global MongoDB Community 41,000+ Monthly Unique Downloads 24,000+ Online Education Registrants 12,000+ MongoDB User Group Members 10,000+ Annual MongoDB Days Attendees
- 46. Leading Organizations Rely on MongoDB 46
- 47. Questions • 10Gen Services – Development Support – Consultancy – TAM – Production Support • Free online MongoDB training – Develop – Deploy – Classes start Oct. 2012 47
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- #5: Note: Growth refers to year-to-date revenue based on our fiscal years for 2011 and 2012, i.e., it compares Feb-Oct 2011 (calendar year) to Feb-Oct 2012 (calendar). These figures are unaudited and subject to change.\n
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- #42: Spend time on this slide, even whiteboard the different scenarios for routed versus scatter gather versus distributed sort merge\n
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