In this post I’m going to look at a concrete example of building an in-memory proximity (aka, nearest neighbor) search web service using Python, SciPy and Heroku.
Later we can speculate on use cases for this approach as opposed to a geo-aware database.
Define Our Terms
So, let’s define our terms:
- In-memory: The web service process contains the data we will query.
- Proximity search: Given a latitude/longitude coordinate, return a set of results within a fixed distance from that location. Also called nearest neighbor search, closest point search, etc. I prefer “proximity search.”
- Web service: This will be a JSON web service.
- Python: We’ll use Python 2.7.
- SciPy: We’ll use a C component of SciPy to do the search, namely
- Heroku: We’ll deploy the project on Heroku using a custom Python build-pack to install SciPy.
The Example Project
All of the code I’ll discuss and quote is available in an example project on Github.
The proximity search that the example performs looks up statistics (nothing fancy now, just sums) about crimes that occurred in Portland, Oregon near a given location.
Also, a warning: this is by no means an attempt at production-ready code.
The Secret Sauce: SciPy’s K-D Tree
The fastest way to do a proximity search lookup in Python that I could find was SciPy’s implementation of a k-d tree. For more information, check the Wikipedia article.
In short, a k-d tree is a binary space partitioning tree, and SciPy’s C implementation is pretty fast. Here are the docs for the code we’ll use.
The class is pretty simple. According to the docs, we load in some data and get a
query method that we can use to perform a nearest-neighbor search.
Building the Proximity Search
So, let’s look at some example code that loads up the k-d tree. Then we’ll look at code that performs the query.
I’ve simplified part of a class I used in the example project to do this. The source is available on GitHub.
Here’s a Gist of the code we’ll look at first:
We have an
__init__ method that creates a
Let’s assume this code loads a file that contains crime data tagged with Mercator coordinates into a dict whose keys are coordinates and values are an array of crimes that occurred at that location. (Presumably you also have a file of geo-tagged data that you wish to offer a proximity search web service for.)
On line 13 we create the k-d tree of crime locations. What we’re doing here is taking the coordinates of all known crimes (not the crime objects — just the coordinates, which are stored as keys in the
self.crimes dict) and passing them into the
scipy.spatial.cKDTree constructor. The
cKDTree builds an index of the coordinates.
Next we have a
get_points_nearby method that performs the nearest-neighbor(s) query against the k-d tree. The call to
query is on line 24.
We sent a coordinate into
query and we get back a tuple containing the distances and indices of nearest neighbors within the maximum distance that we supplied (in this case, 1/2 a mile).
That’s the meat of the proximity search, just passing the buck to SciPy — we now have our coordinates and we can look up in
self.crimes the actual crime data that map to those coordinates.
Creating the Web Service
Assuming your source data is already in latitude and longitude form, you can already use
cKDTree in the fashion we’ve looked at to do proximity search. Now we just need to wrap it up as a web service.
The following is an example using Flask because it’s a pretty easy framework to deploy to Heroku. I’ve edited it to remove only a couple of lines from the real file.
This file defines two web services, one available at
/crimes/stats/<longitude>,<latitude> and one at
Assuming that the
PortlandCrimeTracker object is capable of giving us back sums by category for crimes discovered near a coordinate, the rest of the work done in these services is ceremony:
get_point tries to obtain a coordinate from the current request, and if it fails, causes Flask to return a 400 status code for the request. Meanwhile,
get_crimes passes a valid coordinate and any GET parameters found in the request to
PortlandCrimeTracker.get_crimes_nearby, which returns data on crimes near the coordinate.
Deploying SciPy on Heroku
The trick to deploying SciPy on Heroku is using a custom buildpack. Fortunately, someone already creating one of these for this purpose. Some details about using it are available in this Stack Overflow comment.
I forked the buildpack for the sole purpose of pointing all of the repo URLs at my GitHub account.
Deploying this code onto Heroku with a free account (1 dyno) using no cache, Gunicorn and two workers got me an average of 98 requests per second after around 6000 requests.
Why not PostGIS?
Well, for one, to have fun!
I like the idea of doing an in-memory search more than storing geo-data in a database when the dataset is frozen or it changes at regular and predictable intervals. So, I wouldn’t use this for an application that made user-entered locations searchable. In that case I would probably use PostGIS.
That said, we swapped a PostGIS dependency for a SciPy dependency, and with Heroku that turns out to be less than straightforward to deploy.