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![Post {“author”: “mike”, “date”: datetime.datetime.utcnow(), “text”: “my blog post...”, “tags”: [“mongodb”, “python”]}](https://image.slidesharecdn.com/europython-090630095420-phpapp02/75/MongoDB-EuroPython-2009-24-2048.jpg)
![New post post = {“author”: “mike”, “date”: datetime.datetime.utcnow(), “text”: “my blog post...”, “tags”: [“mongodb”, “python”]} post_id = db.posts.save(post)](https://image.slidesharecdn.com/europython-090630095420-phpapp02/75/MongoDB-EuroPython-2009-26-2048.jpg)
![Last 10 posts query = db.posts.find() .sort(“date”, DESCENDING) .limit(10) for post in query: print post[“text”]](https://image.slidesharecdn.com/europython-090630095420-phpapp02/75/MongoDB-EuroPython-2009-28-2048.jpg)
![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)](https://image.slidesharecdn.com/europython-090630095420-phpapp02/75/MongoDB-EuroPython-2009-35-2048.jpg)
Uploaded byMike Dirolf
MongoDB EuroPython 2009
The document discusses NoSQL databases and describes MongoDB as an open-source, high-performance, schema-free, document-oriented database. It provides an overview of MongoDB's data model using documents and collections, and examples of common operations like creating, querying, and indexing documents.
MongoDB EuroPython 2009
- 1.
- 2. RDBMS • Great formany 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 masternode • Row / Column hybrid • Versioned
- 7. BigTable • Open-source clones: • HBase • Hypertable
- 8. Dynamo • Simple Key/Valuestore • 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 asa 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 lotin 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 onperformance • Rich dynamic queries • Secondary indexes • Replication / failover • Auto-sharding • Many platforms / languages supported
- 17. Good at • Theweb • Caching • High volume / low value • Scalability
- 18. Less good at •Highly transactional • Ad-hoc business intelligence • Problems that require SQL
- 19. PyMongo • Python driverfor MongoDB • Pure Python, with optional C extension • Installation (setuptools): easy_install pymongo
- 20. Document • Unit ofstorage (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 equivalentof 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.
- 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}, {“$push”: {“comments”: c}})
- 28. Last 10 posts query= db.posts.find() .sort(“date”, DESCENDING) .limit(10) for post in query: print post[“text”]
- 29.
- 30. Posts in thelast week last_week = datetime.datetime.utcnow() + datetime.timedelta(days=-7) db.posts.find({“date”: {“$gt”: last_week}})
- 31. Posts ending with ‘Python’ db.posts.find({“text”: re.compile(“Python$”)})
- 32. Posts with atag db.posts.find({“tag”: “mongodb”}) ... and fast db.posts.create_index(“tag”, ASCENDING)
- 33.
- 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