Given a latitude and longitude, I need to pull the nearest neighbors out of the database and return them in proximity order.  To get this to work we use:

• geohash – every entry in the database records its geohash code
• law of cosines - fast trig based solution to the distance problem
• python sorting

To do the query, we need to compute a bounding box:

swhash = Geohash.encode(latitude-delta, longitude-delta)
nehash = Geohash.encode(latitude+delta, longitude+delta)

This produced two hashcodes, one to the southwest of the orig, and one the northeast.  A delta of 0.5 is about 138 KM, in North America, at least.  Geohash codes are great because they can be sorted in lexical order, so my boto query now looks like this:

sdb = boto.connect_sdb(AWS_ACCESS_KEY, AWS_SECRET_KEY])
domain = sdb.get_domain(PLACES_DOMAIN)
places = domain.select("select * from `Places` where
    `geohash` >= '%s' and `geohash` <= '%s' LIMIT 10"
    %(swhash, nehash), max_items=10)

The places object is actually an iterator much thanks to Chris Moyer for documenting this. We want the array.  NOTE, the query is lazily performed when you interpret the first token from the iterator.

results = [place for place in places]

The distance function is very simple:

def distance(a,b):
    R = 6371 # radius of Earth in KM
    lat1 = radians(a[0])
    lon1 = radians(a[1])
    lat2 = radians(b[0])
    lon2 = radians(b[1])
    d = math.acos(math.sin(lat1)*math.sin(lat2) +
        math.cos(lat1)*math.cos(lat2) * math.cos(lon2-lon1)) * R;
    return d

Finally, python sorts the result:

results = sorted(results, key=lambda place: distance(orig,
    (float(place['latitude']), float(place['longitude']))))

This actually worked on the second try.

Notes: there are better distance functions, but this one works well and is quite fast.  I have not put in the error handling, or the back off code if you get too many or too few results.

My app gets images from various photo sharing sites.  After checking for copyright correctness, I download images onto my own machine for review and Photoshopping. Then I upload them into my S3/SimpleDB cloud storage.

The metadata I get back from the photo sharing sites is generally in JSON format, but I frequently want to edit this metadata and even though JSON is the bee’s knees, it’s not that great to edit, plus grepping for metadata kinda sucks too.

Enter Yaml:

city: ''
country: ''
filtered: 0
geohash: dhwfq5vbb02h
id: '3077164973'
kind: scene
latitude: 25.728738
license: '4'
longitude: -80.236244
owner: 11211909@N00
ownername: corsi photo
state: ''
tags: miami floridawaterbeachskylineshorelineoceanbuildings
title: Miami shoreline
url_z: http://farm4.staticflickr.com/3149/30771649c6a6c_z.jpg
vp_id: flickr.scene.11211909@N00-3077164973

Exit JSON:

{'tags': 'miami floridawaterbeachskylineshorelineoceanbuildings', 'vp_id': 'flickr.scene.11211909@N00-3077164973', 'owner': '11211909@N00', 'id': '3077164973', 'city': '', 'kind': 'scene', 'url_z': 'http://farm4.staticflickr.com/3149/3077164973_a31b4c6a6c_z.jpg', 'license': '4', 'title': 'Miami shoreline', 'country': '', 'longitude': -80.236244, 'state': '', 'geohash': 'dhwfq5vbb02h', 'ownername': 'corsi photo', 'latitude': 25.728738, 'filtered': 0}

PyYAML even has type inferencing, so when I reload the data, it seems to do just what I want.