MongoDB NYC Python
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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.
![Post
{“author”: “mike”,
“date”: datetime.datetime.utcnow(),
“text”: “my blog post...”,
“tags”: [“mongodb”, “python”]}](https://image.slidesharecdn.com/nycpython-090623164128-phpapp02/85/MongoDB-NYC-Python-15-320.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/nycpython-090623164128-phpapp02/85/MongoDB-NYC-Python-17-320.jpg)
![Last 10 posts
query = db.posts.find()
.sort(“date”, DESCENDING)
.limit(10)
for post in query:
print post[“text”]](https://image.slidesharecdn.com/nycpython-090623164128-phpapp02/85/MongoDB-NYC-Python-19-320.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/nycpython-090623164128-phpapp02/85/MongoDB-NYC-Python-26-320.jpg)
MongoDB NYC 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. • 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)
- 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 • [email protected] • http://www.slideshare.net/mdirolf
- 31. • Differences • MVCC based • Replication as path to scalability • Query through predefined views • ACID • REST