The document describes MongoDB, an open-source, high-performance, schema-free, document-oriented database that addresses some shortcomings of relational databases like scalability and flexibility. It discusses some key MongoDB concepts like documents, collections, indexing, embedding data, and querying capabilities. An example blog application is provided to illustrate common operations like creating, retrieving, and counting documents in a MongoDB deployment using PyMongo.

1. open-source, high-performance,
schema-free, document-oriented
database

2. RDBMS
• Great for many applications
• Shortcomings
• Scalability
• Flexibility

3. CAP Theorem
• Consistency
• Availability
• Tolerance to network Partitions
• Pick two
http://www.cs.berkeley.edu/~brewer/cs262b-2004/PODC-keynote.pdf

4. ACID vs BASE
• Atomicity
• Basically Available
• Consistency
• Soft state
• Isolation
• Eventually consistent
• Durability

5. Schema-free
• Loosening constraints - added flexibility
• Dynamically typed languages
• Migrations

6. • Focus on performance
• Rich dynamic queries
• Secondary indexes
• Replication / failover
• Auto-sharding
• Many platforms / languages supported

8. Good at
• The web
• Caching
• High volume / low value
• Scalability

9. Less good at
• Highly transactional
• Ad-hoc business intelligence
• Problems that require SQL

10. PyMongo
• Python driver for MongoDB
• Pure Python, with optional C extension
• Installation (setuptools):
easy_install pymongo

11. Document
• Unit of storage (think row)
• Just a dictionary
• Can store many Python types:
• None, bool, int, float, string / unicode,
dict, datetime.datetime, compiled re
• Some special types:
• SON, Binary, ObjectId, DBRef

12. Collection
• Schema-free equivalent of a table
• Logical groups of documents
• Indexes are per-collection

13. _id
• Special key
• Present in all documents
• Unique across a Collection
• Any type you want

14. Blog back-end

15. Post
{“author”: “mike”,
“date”: datetime.datetime.utcnow(),
“text”: “my blog post...”,
“tags”: [“mongodb”, “python”]}

16. Comment
{“author”: “eliot”,
“date”: datetime.datetime.utcnow(),
“text”: “great post!”}

17. New post
post = {“author”: “mike”,
“date”: datetime.datetime.utcnow(),
“text”: “my blog post...”,
“tags”: [“mongodb”, “python”]}
post_id = db.posts.save(post)

18. Embedding a comment
c = {“author”: “eliot”,
“date”: datetime.datetime.utcnow(),
“text”: “great post!”}
db.posts.update({“_id”: post_id},
{“$push”: {“comments”: c}})

19. Last 10 posts
query = db.posts.find()
.sort(“date”, DESCENDING)
.limit(10)
for post in query:
print post[“text”]

20. Posts by author
db.posts.find({“author”: “mike”})

21. Posts in the last week
last_week = datetime.datetime.utcnow() +
datetime.timedelta(days=-7)
db.posts.find({“date”: {“$gt”: last_week}})

22. Posts ending with
‘Python’
db.posts.find({“text”: re.compile(“Python$”)})

23. Posts with a tag
db.posts.find({“tag”: “mongodb”})
... and fast
db.posts.create_index(“tag”, ASCENDING)

24. Counting posts
db.posts.count()
db.posts.find({“author”: “mike”}).count()

25. Basic paging
page = 2
page_size = 15
db.posts.find().limit(page_size)
.skip(page * page_size)

26. Migration: adding titles
• Easy - just start adding them:
post = {“author”: “mike”,
“date”: datetime.datetime.utcnow(),
“text”: “another blog post...”,
“tags”: [“meetup”, “python”],
“title”: “Document Oriented Dbs”}
post_id = db.posts.save(post)

27. Advanced queries
• $gt, $lt, $gte, $lte, $ne, $all, $in, $nin
• where()
db.posts.find().where(“this.author == ‘mike’”)
• group()

28. Other cool stuff
• Capped collections
• Unique indexes
• Mongo shell
• GridFS
• MongoKit (on pypi)

29. • Download MongoDB
http://www.mongodb.org
• Install PyMongo
• Try it out!

30. • http://www.mongodb.org
• irc.freenode.net#mongodb
• mongodb-user on google groups
• @mongodb, @mdirolf
• mike@10gen.com
• http://www.slideshare.net/mdirolf

31. • Differences
• MVCC based
• Replication as path to scalability
• Query through predefined views
• ACID
• REST