![NoORM
MongoDB document is a JSON object.
PyMongo driver exposes these as dictionaries
E.g. db.mycollection.insert(dict(myprop='foo!'))
You can even embed objects within a document:
E.g. db.mycollection.insert(
dict(emails=
[dict(email_lower='niallo@pywebsf.org')]
))](https://image.slidesharecdn.com/pywebsf-mongodb-pylons-ftw-2010-09-28-101022125443-phpapp02/85/MongoDB-Pylons-FTW-Scalable-Web-apps-with-Python-NoSQL-5-320.jpg)
![NoORM II
You can query across properties, embedded or
otherwise, easily.
E.g.
r = db.mycollection.find_one(
{emails.email_lower:'niallo@pywebsf.org'}
)
print r['emails'][0]['email_lower']](https://image.slidesharecdn.com/pywebsf-mongodb-pylons-ftw-2010-09-28-101022125443-phpapp02/85/MongoDB-Pylons-FTW-Scalable-Web-apps-with-Python-NoSQL-6-320.jpg)
![More on querying MongoDB
Sample rich query:
db.foo.find({'$or': [{'owner_account_id': Id},
{'shared_to': Id}], 'location': {'$near': [37.783,
122.393]}}.skip(offset).limit(PAGE_SIZE)](https://image.slidesharecdn.com/pywebsf-mongodb-pylons-ftw-2010-09-28-101022125443-phpapp02/85/MongoDB-Pylons-FTW-Scalable-Web-apps-with-Python-NoSQL-10-320.jpg)
MongoDB + Pylons FTW: Scalable Web apps with Python & NoSQL
- 2. MongoDB ?? Nonrelational (NoSQL) documentoriented database Rich query language Flexible data model (JSON) Sharding and replication with automaticfailover CAP write quorum support
- 3. MongoDB vs MySQL vs BDB Feature Comparison MongoDB MySQL BDB Master-Slave Replication Yes Yes No Automatic master failover Yes No No Ad-hoc Schemas Yes No No Manual index specification Yes Yes Yes Rich query language Yes Yes No Joins No Yes No
- 4. MongoDB: NoSQL? Closer to MySQL than to BDB/Tyrant/Redis Less emphasis on Googlescale scalability at this point compared to e.g. Cassandra/HBase Today, MongoDB scales slightly better than MySQL IMHO. Main advantage over RDBMS is ease of development!
- 5. NoORM MongoDB document is a JSON object. PyMongo driver exposes these as dictionaries E.g. db.mycollection.insert(dict(myprop='foo!')) You can even embed objects within a document: E.g. db.mycollection.insert( dict(emails= [dict(email_lower='[email protected]')] ))
- 7. NoORM III SQL queries can get very complicated (huge joins, aggregate queries, etc) Marshalling SQL to/from objects is painful, tedious. CrossRDBMS portability issues → ORMs can make sense with RDBMS At least in Python, I see absolutely no need for ORM with MongoDB.
- 8. More on querying MongoDB I alluded to a rich query language: $lt, $lte, $gt, $gte == <, <=, >, => $ne == <>, != $in == IN $or == OR Sort() == ORDER BY Limit() == LIMIT Skip() == OFFSET Group() == GROUP BY
- 9. Geo Features Geospatial querying out of the box: $near (x, y): Return documents sorted by distance from point. $within, $box/$center: Return documents within bounds of a rectangle/circle. Earth is flat in 1.6.x and lower. Spherical model in 1.7.0 and up. Other limits: Only one geo index per collection. No sharding support.
- 11. MongoDB Gotchas Obviously not as mature as any RDBMS on the market. You can find bugs, but developers are extremely responsive. 4MB limit per document. Cap on number of indexes per collection. No transactions, although some imperfect support for atomic operations. Race with e.g. Deleting single embedded object from list property. → Not the right fit for every system! → Think through your schema!
- 12. Pylons Elegant, lightweight but fullyfeatured framework. Perfect fit for mixed API and Web rendering environment we have at Catch.com Threaded design, easy to program. No callbacks needed. Works fine with synchronous libraries. Threads in Python suck due to the GIL. Multicore systems necessitate multiprocess operation. Options: Apache+mod_wsgi or multiple Pasters.
- 13. mod_wsgi vs multiple Pasters Apache+mod_wsgi is kind of a pain to setup, configure. Past production experiences with mod_wsgi include memory leaks, CPU spinning. Paster is simple, built into Pylons. One Paster server maps cleanly to one Python interpreter process. LOLapps serve highvolume production traffic with Paster just fine.
- 14. Catch.com Paster Multiple Pasters per machine, each listening on a separate port. Nginx load balances between them all. Nginx also terminates SSL. No issues whatsoever handling the load today.
- 15. Paster Debugging Tip Configure smtp_server Point it at a Google Mail list Set up a Gmail label to filter messages into Now you have nicely searchable, realtime stack traces aggregated from each Paster. Very useful for debugging distributed systems!