By Niall O'Higgins

Tips on how to leverage MongoDB along with your Python functions, utilizing the hands-on recipes during this e-book. You get whole code samples for initiatives comparable to making quick geo queries for location-based apps, successfully indexing your consumer files for social-graph lookups, and lots of different scenarios.

This consultant explains the fundamentals of the document-oriented database and exhibits you ways to establish a Python atmosphere with it. the right way to learn and write to MongoDB, follow idiomatic MongoDB and Python styles, and use the database with numerous well known Python internet frameworks. You'll observe easy methods to version your info, write powerful queries, and stay away from concurrency difficulties corresponding to race stipulations and deadlocks.

The recipes can help you:

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

*Learn how you can 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 online game ratings, billing platforms, and extra with the short accounting pattern

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

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Read Online or Download MongoDB and Python: Patterns and Processes for the Popular Document-Oriented Database PDF

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

Example text

Each of these can be performed atomically with the provided update modifiers. com" in the “emails” field. $pull will remove the entire document from the array in an atomic fashion, meaning there is no opportunity for a race condition. update({"username":"foouser"}, {"$pull":{"emails":{"primary":{"$ne":True}}}, safe=True) The full range of query modifiers (see table in Chapter 2) are available for use, including $gt, $lt and so on. Additionally, $pull can be used with arrays containing atoms (numbers, strings, dates, ObjectIDs etc).

For large collections, index creation can be time consuming. To help mitigate the impact of these operations on live, production databases, MongoDB is capable of building indexes in the background, without blocking database access. Building an index in the background may take slightly longer, and will still cause additional load on the system, but the database should otherwise remain available. create_index("username", background=True) As mentioned earlier in this section, MongoDB btree indexes can be used to enforce a uniqueness constraint on a particular property.

Location-based Apps with MongoDB: GeoSpatial Indexing | 33 It should be noted that at present, MongoDB’s geospatial indexing support is limited purely to point-based querying. The supplied operators can only be used for finding individual points—not routes or sub-shapes. MongoDB provides the $near and $within operators which constitute the primary means for performing geospatial queries in the system. Using $near, you can efficiently sort documents in a collection by their proximity to a given point.

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