See the tests/ file for a comparison of various query methods and how much acceleration can be obtained from using Rtree.

There are a few simple things that will improve performance.

Use stream loading

This will substantially (orders of magnitude in many cases) improve performance over insert() by allowing the data to be pre-sorted

>>> def generator_function():
...    for i, obj in enumerate(somedata):
...        yield (i, (obj.xmin, obj.ymin, obj.xmax, obj.ymax), obj)
>>> r = index.Index(generator_function())

After bulk loading the index, you can then insert additional records into the index using insert()

Override dumps to use the highest pickle protocol

>>> import cPickle, rtree
>>> class FastRtree(rtree.Rtree):
...     def dumps(self, obj):
...         return cPickle.dumps(obj, -1)
>>> r = FastRtree()

Use objects=’raw’

In any intersection() or nearest() or query, use objects=’raw’ keyword argument

>>> objs = r.intersection((xmin, ymin, xmax, ymax), objects="raw")

Adjust index properties

Adjust rtree.index.Property appropriate to your index.

  • Set your leaf_capacity to a higher value than the default 100. 1000+ is fine for the default pagesize of 4096 in many cases.
  • Increase the fill_factor to something near 0.9. Smaller fill factors mean more splitting, which means more nodes. This may be bad or good depending on your usage.

Limit dimensionality to the amount you need

Don’t use more dimensions than you actually need. If you only need 2, only use two. Otherwise, you will waste lots of storage and add that many more floating point comparisons for each query, search, and insert operation of the index.

Use the correct query method

Use count() if you only need a count and intersection() if you only need the ids. Otherwise, lots of data may potentially be copied.