MongoDB SF Python
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The document discusses various types of databases including open-source, schema-free, document-oriented databases like MongoDB, as well as their strengths and weaknesses in terms of scalability, consistency, and flexibility. It also covers different storage models such as Bigtable and Dynamo, alongside caching solutions like Memcached and Redis. Key features of MongoDB and its Python driver Pymongo are highlighted, emphasizing its architecture, querying capabilities, and file storage options with GridFS.
MongoDB SF Python
- 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 • Use setuptools: easy_install pymongo
- 20. Basics • Connect: • Insert: • Query:
- 21. Document values • None • Special values: • bool • SON • int • Binary • float • ObjectId • string / unicode • DBRef • dict • datetime.datetime • compiled re
- 22. • Schema-free: • Query with selectors:
- 23. Advanced queries • Sorting: • Limit: • Range:
- 24. • $gt, $lt, $gte, $lte, $ne, $all, $in, $nin • where() • count() • group()
- 25. GridFS • File storage in MongoDB • File-like API for Python • Initialize: • Write: • Read:
- 26. Other cool stuff • Capped collections • Upserts • Multikeys
- 27. Demo
- 28. • Download MongoDB http://www.mongodb.org • Install PyMongo • Try it out!
- 29. • http://www.mongodb.org • irc.freenode.net#mongodb • mongodb-user on google groups • @mongodb • [email protected]