MongoDB natively supports geospatial indexing and querying, and it integrates easily with open source visualization tools. In this presentation, learn high-performance techniques for querying and retrieving geospatial data, and how to create a rich visual representation of global weather data using Python, Monary, and Matplotlib.
33. import numpy!
import pymongo!
!
data = []!
db = pymongo.MongoClient().my_database!
!
for doc in db.collection.find(query):!
data.append((!
doc['position']['coordinates'][0],!
doc['position']['coordinates'][1],!
doc['airTemperature']['value']))!
!
arrays = numpy.array(data)!
Not terrifically fast
34. Analyzing large datasets
• Querying: 109k documents per second
• (On localhost)
• Can we go faster?
• Enter“Monary”
35. MongoDB PyMongo NumPy Matplotlib
Python
dicts
MongoDB Monary NumPy Matplotlib
Monary
by David Beach