Introduction to MongoDB
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MongoDB is an open-source, document-oriented database that provides high performance and horizontal scalability. It uses a document-model where data is organized in flexible, JSON-like documents rather than rigidly defined rows and tables. Documents can contain multiple types of nested objects and arrays. MongoDB is best suited for applications that need to store large amounts of unstructured or semi-structured data and benefit from horizontal scalability and high performance.
![Flexible “Schemas”
{“author”:
“eliot”,
{“author”:
“mike”,
“text”:
“...”,
“text”:
“...”}
“tags”:
[“mongodb”]}](https://image.slidesharecdn.com/phillyete-100409154513-phpapp02/85/Introduction-to-MongoDB-10-320.jpg)
![Post
{author:
“mike”,
date:
new
Date(),
text:
“my
blog
post...”,
tags:
[“mongodb”,
“intro”]}](https://image.slidesharecdn.com/phillyete-100409154513-phpapp02/85/Introduction-to-MongoDB-20-320.jpg)
![New Post
post
=
{author:
“mike”,
date:
new
Date(),
text:
“my
blog
post...”,
tags:
[“mongodb”,
“intro”]}
db.posts.save(post)](https://image.slidesharecdn.com/phillyete-100409154513-phpapp02/85/Introduction-to-MongoDB-22-320.jpg)
![Migration: Adding Titles
(just
start
adding
them)
post
=
{author:
“mike”,
date:
new
Date(),
text:
“another
blog
post...”,
tags:
[“mongodb”],
title:
“MongoDB
for
Fun
and
Profit”}
post_id
=
db.posts.save(post)](https://image.slidesharecdn.com/phillyete-100409154513-phpapp02/85/Introduction-to-MongoDB-32-320.jpg)
Introduction to MongoDB
- 1. open-‐source, high-‐performance, document-‐oriented database
- 2. Non-relational Operational Stores (“NoSQL”) New Gen. OLAP RDBMS (vertica, aster, greenplum) (Oracle, MySQL)
- 3. NoSQL Really Means: non-‐relational, next-‐generation operational datastores and databases
- 4. no joins + no complex transactions Horizontally Scalable Architectures
- 5. no joins + no complex transactions New Data Models
- 6. New Data Models improved ways to develop applications?
- 7. Data Models Key / Value memcached, Dynamo Tabular BigTable Document Oriented MongoDB, CouchDB, JSON stores
- 8. • memcached scalability & performance • key/value • RDBMS depth of functionality
- 9. JSON-style Documents represented as BSON {“hello”: “world”} x16x00x00x00x02hello x00x06x00x00x00world x00x00 http://bsonspec.org
- 10. Flexible “Schemas” {“author”: “eliot”, {“author”: “mike”, “text”: “...”, “text”: “...”} “tags”: [“mongodb”]}
- 11. Dynamic Queries
- 12. Atomic Update Modifiers
- 14. Replication master slave master master slave slave slave slave master master slave master
- 15. Auto-sharding Shards mongod mongod mongod ... Config mongod mongod mongod Servers mongod mongod mongod mongos mongos ... client
- 16. Many Supported Platforms / Languages
- 17. Best Use Cases T Scaling Out Caching The Web High Volume
- 18. Less Good At highly transactional ad-‐hoc business intelligence problems that require SQL
- 19. A Quick Aside _id special key present in all documents unique across a Collection any type you want
- 20. Post {author: “mike”, date: new Date(), text: “my blog post...”, tags: [“mongodb”, “intro”]}
- 21. Comment {author: “eliot”, date: new Date(), text: “great post!”}
- 22. New Post post = {author: “mike”, date: new Date(), text: “my blog post...”, tags: [“mongodb”, “intro”]} db.posts.save(post)
- 23. Embedding a Comment c = {author: “eliot”, date: new Date(), text: “great post!”} db.posts.update({_id: post._id}, {$push: {comments: c}})
- 24. Posts by Author db.posts.find({author: “mike”})
- 25. Last 10 Posts db.posts.find() .sort({date: -‐1}) .limit(10)
- 26. Posts Since April 1 april_1 = new Date(2010, 3, 1) db.posts.find({date: {$gt: april_1}})
- 27. Posts Ending With ‘Tech’ db.posts.find({text: /Tech$/})
- 28. Posts With a Tag db.posts.find({tags: “mongodb”}) ...and Fast (multi-‐key indexes) db.posts.ensureIndex({tags: 1})
- 29. Indexing / Querying on Embedded Docs (dot notation) db.posts.ensureIndex({“comments.author”: 1}) db.posts.find({“comments.author”: “eliot”})
- 31. Basic Paging page = 2 page_size = 15 db.posts.find().limit(page_size) .skip(page * page_size)
- 32. Migration: Adding Titles (just start adding them) post = {author: “mike”, date: new Date(), text: “another blog post...”, tags: [“mongodb”], title: “MongoDB for Fun and Profit”} post_id = db.posts.save(post)
- 33. Advanced Queries $gt, $lt, $gte, $lte, $ne, $all, $in, $nin db.posts.find({$where: “this.author == ‘mike’ || this.title == ‘foo’”})
- 34. Other Cool Stuff aggregation and map/reduce capped collections unique indexes mongo shell GridFS geo
- 35. slides will be up on http://dirolf.com Download MongoDB http://www.mongodb.org and let us know what you think @mdirolf @mongodb