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![MongoDB Database Architecture: Document
{
_id: ObjectId("5099803df3f4948bd2f98391"),
name: { first: "Alan", last: "Turing" },
birth: new Date('Jun 23, 1912'),
death: new Date('Jun 07, 1954'),
contribs: [ "Turing machine", "Turing test", "Turingery" ],
views : NumberLong(1250000)
}](https://image.slidesharecdn.com/techorama-mongodbv1-140530012739-phpapp02/75/Techorama-Evolvable-Application-Development-with-MongoDB-16-2048.jpg)
![Comparison
Album
- id
- artistid
- title
Track
- no
- name
- unitPrice
- popularity
Artist
- id
- name
Album
- _id
- title
- artist
- tracks[]
- _id
- name
Relational Document db](https://image.slidesharecdn.com/techorama-mongodbv1-140530012739-phpapp02/75/Techorama-Evolvable-Application-Development-with-MongoDB-26-2048.jpg)
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Techorama - Evolvable Application Development with MongoDB
- 1. 1 Evolvable Application Developmentwith MongoDB Gerd Teniers Bart Wullems for .NET developers
- 2. WARNING – Thissession is rated as a ‘Grandma session’ (=Level 200)
- 3. 3 goals ofthis presentations When you leave this presentation you should have learned How easy it is to get started using MongoDB How using MongoDB changes the way you design and build your applications How MongoDB’s flexibility supports evolutionary design That giving speakers beer before a session is never a good idea
- 4. What is notcool? White socks & sandals
- 5. What is notcool? Dancing like Miley Cyrus
- 6. What is notcool? Relational databases
- 7. What is cool? Shortpants and very large socks
- 8. What is cool? Dancinglike Psy
- 9. What is cool? NO-SQL(=Not Only SQL)
- 10.
- 11. Entity Framework 7will support No-SQL
- 12.
- 13.
- 14. MongoDB HuMongous General purpose database Documentoriented database using JSON document syntax Features: - Flexibility - Power - Scaling - Ease of Use - Built-in Javascript Users: Craigslist, eBay, Foursquare, SourceForge, and The New York Times.
- 15. Written in C++ Extensiveuse of memory-mapped files i.e. read-through write-through memory caching. Runs nearly everywhere Data serialized as BSON (fast parsing) Full support for primary & secondary indexes Document model = less work High Performance
- 16. MongoDB Database Architecture:Document { _id: ObjectId("5099803df3f4948bd2f98391"), name: { first: "Alan", last: "Turing" }, birth: new Date('Jun 23, 1912'), death: new Date('Jun 07, 1954'), contribs: [ "Turing machine", "Turing test", "Turingery" ], views : NumberLong(1250000) }
- 17. MongoDB Database Architecture:Collection Logical group of documents May or may not share same keys Schema is dynamic/application maintained
- 18. Why should Iuse it?(or how do I convince my boss?) Developer productivity Avoid ORM pain, no mapping needed Performance(again) Scaling out is easy(or at least easier) Optimized for reads Flexibility Dynamic schema
- 19. How to runit? Exe Windows service Azure 3rd party commercial hosting
- 20. How to talkto it? Mongo shell Official and non official drivers >12 languages supported
- 21. DEMO 1 -PROTOTYPING
- 22.
- 23. 23 First step inany application is determine your domain/entities
- 24. In a relationalbased app We would start by doing schema design
- 25. In a MongoDBbased app We start building our app and let the schema evolve
- 26. Comparison Album - id - artistid -title Track - no - name - unitPrice - popularity Artist - id - name Album - _id - title - artist - tracks[] - _id - name Relational Document db
- 27.
- 28. Modeling Start from application-specificqueries “What questions do I have?” vs “What answers” “Data like the application wants it” Base parent documents on The most common usage What do I want returned?
- 29. Modeling Embedding vs Linkingvs Hybrid Album - _id - artist - cover - _id - name Artist - _id - name - photo
- 30. Product Single collection inheritance Product -_id - price Book - author - title Album - artist - title Jeans - size - color - _id - price - author - title Relational Document db - _id - price - size - color
- 31. Product Single collection inheritance Product -_id - price Book - author - title Album - artist - title Jeans - size - color _type: Book - _id - price - author - title Relational Document db _type: Jeans - _id - price - size - color
- 32. One-to-many Embedded array /array keys Some queries get harder You can index arrays! Normalized approach More flexibility A lot less performance BlogPost - _id - content - tags: {“foo”, “bar”} - comments: {“id1”, “id2”}
- 33. Demo 2 –MODELING
- 34.
- 35. CRUD operations Create: insert,save Read: find, findOne Update: update, save Delete: remove, drop
- 36. ACID Transactions No supportfor multi-document transactions commit/rollback Atomic operations on document level Multiple actions inside the same document Incl. embedded documents By keeping transaction support extremely simple, MongoDB can provide greater performance especially for partitioned or replicated systems
- 37. Demo 3 –CRUD
- 38.
- 39. Storing binary documents AlthoughMongoDB is a document database, it’s not good for documents :-S Document != .PNG & .PDF files Document size is limited Max document size is 16MB Recommended document size <250KB Solution is GridFS Mechanism for storing large binary files in MongoDB Stores metadata in a single document inside the fs.files collection Splits files into chunks and stores them inside the fs.chunks collection GridFS implementation is handled completely by the client driver
- 40. Demo 4 –Evolving your domain model ------------& GRIDFS
- 41. Evolving your domainmodel Great for small changes! Hot swapping Minimal impact on your application and database Avoid Migrations Handle changes in your application instead of your database
- 42.
- 43. Avoid table collectionsscans by using indexes > db.albums.ensureIndex({title: 1}) Compound indexes Index on multiple fields > db.albums.ensureIndex({title: 1, year: 1}) Indexes have their price Every write takes longer Max 64 indexes on a collection Try to limit them Indexes are useful as the number of records you want to return are limited If you return >30% of a collection, check if a table scan is faster Creating indexes
- 44. Aggregations with theAggregation Framework $project Select() $unwind SelectMany() $match Where() $group GroupBy() $sort OrderBy() $skip Skip() $limit Take() Largely replaces the original Map/Reduce Much faster! Implemented in a multi-threaded C ++ No support in LINQ-provider yet (but in development)
- 45. Demo 5 –Optimizations
- 46.
- 47. Benefits Scalable: good fora lot of data & traffic Horizontal scaling: to more nodes Good for web-apps Performance No joins and constraints Dev/user friendly Data is modeled to how the app is going to use it No conversion between object oriented > relational No static schema = agile Evolvable
- 48. Drawbacks Forget what youhave learned New way of building and designing your application Can collect garbage No data integrity checks Add a clean-up job Database model is determined by usage Requires insight in the usage
- 49.
- 50. Things we didn’ttalk about
- 51. Things we didn’ttalk about… Security - HTTPS/SSL Compile the code yourself Eventual Consistency Geospatial features Realtime Aggregation
- 52. Things we didn’ttalk about… Many to Many - Multiple approaches References on 1 site References on both sites
- 53. Things we didn’ttalk about… Write Concerns - Acknowledged vs Unacknowledged writes - Stick with acknowledged writes(=default)
- 54. Things we didn’ttalk about… GridFS disadvantages - Slower performance: accessing files from MongoDB will not be as fast as going directly through the filesystem. - You can only modify documents by deleting them and resaving the whole thing. - Drivers are required
- 55. Things we didn’ttalk about… Schema Migrations - Avoid it - Make your app backwards compatible - Add version field to your documents
- 56. Things we didn’ttalk about… Why you should not use regexes - Slow! Advanced Indexing - Indexing objects and Arrays - Unique vs Sparse Indexes - Geospatial Indexes - Full Text Indexes MapReduce - Avoid it - Very slow in MongoDB - Use Aggregation FW instead
- 57. Things we didn’ttalk about… Sharding Based on a shard key (= field) Commands are sent to the shard that includes the relevant range of the data Data is evenly distributed across the shards Automatic reallocation of data when adding or removing servers
Editor's Notes
- #27 Documents contain: Values Arrays Embedded docs