Monary: Really fast analysis with MongoDB and NumPy04:45 PM - 05:15 PM on August 16, 2014, Room 704
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NumPy arrays combine the speed of C with the convenience of Python. It is the fundamental package for scientific and statistical computing in Python. MongoDB’s scale, speed, and flexibility make it ideal for storing large amounts of data. However, the official MongoDB driver is not optimized for loading MongoDB documents into NumPy arrays. Enter “Monary”, which allows you to easily examine and manipulate data using NumPy arrays. We will explore how Monary can accelerate your scientific analysis while providing you with the scale and flexibility of MongoDB and the ease of Python.
For scientists, mathematicians, and programmers concerned with data, using MongoDB provides scale and flexibility, but can pose a problem: MongoDB stores data as documents, so Python programmers often retrieve the data as dictionaries. This format is prohibitive for data scientists who want to perform MapReduce or other column-oriented operations. Of course, the data in the dictionaries could then be copied over into a list or array, but this doubles the amount of work and scales poorly. How can we use MongoDB and Python with larger data sets?
The answer is Monary, a library with a simple solution: take the MongoDB documents and copy data directly into NumPy arrays. This talk will walk through a tutorial on using Monary, and we will offer under-the-hood explanations of how it all works. We will also give practical demonstrations of Monary’s speed benefits and uses.