# Performance

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

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()

Update from January 2024

Pickling is currently broken and awaiting a pull request to fix it. For more information, see the pull request on GitHub.

## 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. If possible also make use of the bulk query methods suffixed with _v.