MongoDB in the "Real World" talk from EuroPython 2009 in birmingham

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. BigTable
• Single master node
• Row / Column hybrid
• Versioned

7. BigTable
• Open-source clones:
• HBase
• Hypertable

8. Dynamo
• Simple Key/Value store
• No master node
• Write to any (many) nodes
• Read from one or more nodes (balance
speed vs. consistency)
• Read repair

9. Dynamo
• Open-source clones
• Project Voldemort
• Cassandra - data model more like
BigTable
• Dynomite

10. memcached
• Used as a caching layer
• Essentially a key/value store
• RAM only - fast
• Does away with ACID

11. Redis
• Like memcached
• Different
• Values can be strings, lists, sets
• Non-volatile

12. Tokyo Cabinet + Tyrant
• Key/value store with focus on speed
• Some more advanced queries
• Sorting, range or prefix matching
• Multiple storage engines
• Hash, B-Tree, Fixed length and Table

13. • A lot in common with MongoDB:
• Document-oriented
• Schema-free
• JSON-style documents

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

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

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

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

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

20. 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

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

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

23. Blog back-end

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

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

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

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

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

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

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

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

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

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

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

35. 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)

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

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

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

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