MongoDB and Python: Patterns and Processes for the Popular by Niall O'Higgins

By Niall O'Higgins

The best way to leverage MongoDB together with your Python functions, utilizing the hands-on recipes during this e-book. You get whole code samples for projects comparable to making speedy geo queries for location-based apps, successfully indexing your consumer files for social-graph lookups, and plenty of different scenarios.

This consultant explains the fundamentals of the document-oriented database and indicates you ways to establish a Python atmosphere with it. methods to learn and write to MongoDB, observe idiomatic MongoDB and Python styles, and use the database with a number of well known Python internet frameworks. You'll detect find out how to version your information, write powerful queries, and steer clear of concurrency difficulties corresponding to race stipulations and deadlocks.

The recipes may help you:

*Read, write, count number, and type files in a MongoDB collection

*Learn find out how to use the wealthy MongoDB question language

*Maintain information integrity in replicated/distributed MongoDB environments

*Use embedding to successfully version your info with no joins

*Code defensively to prevent keyerrors and different bugs

*Apply atomic operations to replace video game ratings, billing platforms, and extra with the quick accounting pattern

*Use MongoDB with the Pylons 1.x, Django, and Pyramid

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Additional info for MongoDB and Python: Patterns and Processes for the Popular Document-Oriented Database

Example text

Py develop Your Pylons app is now ready to be configured with a MongoDB connection. ini in your favourite editor. ini You should see the following output: Starting subprocess with file monitor Starting server in PID 82946. 1:5000 If you open the URL http://localhost:5000/ in a web browser, you should see the default Pylons page. This means that you have correctly set up your project. However, we do not yet have a way to talk to MongoDB. x and MongoDB | 47 Now that the configuration is in place, we can tell Pylons how to connect to MongoDB and where to make the PyMongo connection available to our application.

There is no aggregation to perform whatsoever. With this pattern, we can also write very quickly and safely. Because we are counting, we can take advantage of MongoDB’s atomic increment and decrement update modifiers, $inc and $dec. Atomic update operators are great because they ensure the underlying 42 | Chapter 3: Common MongoDB and Python Patterns data is in a consistent state and help to avoid nasty race conditions. Especially when dealing with billing, accurate counts are very important. Imagine we wish to increment the user’s score for this week by 24.

9 and up, the $poly gon operator allows for convex and concave polygon boundaries. Before you can use the geospatial queries, you must have a geospatial index. x, geospatial indexes are limited to a single index per collection. This means that each document can have only one location property queried efficiently by MongoDB. This can have some important implications for schema design which is why it is good to know from the outset. Geospatial indexes by default limit acceptable values for the location property on documents to those within GPS.

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