fastgpx 0.6.0

An experimental Python library for parsing GPX files fast.

fastgpx

An experimental Python library for parsing GPX files fast.

# Get the total length of the tracks in a GPX file:
import fastgpx

gpx = fastgpx.parse("example.gpx")
print(f'{gpx.length_2d()} m')

# Iterate over GPX file:
import fastgpx

gpx = fastgpx.parse("example.gpx")
for track in gpx.tracks:
    print(f'Track: {track.name}')
    print(f'Distance: {track.length_2d()} m')
    if not track.time_bounds.is_empty():
      print(f'Time: {track.time_bounds().start_time} - {track.time_bounds().end_time}')
    for segment in track.segments:
        print(f'Segment: {segment.name}')
        for point in segment.points:
            print(f'Point: {point.latitude}, {point.longitude}')

Documentation

Requirements

• Python 3.12+ (Tested with 3.12, 3.13, 3.14)
• C++23 Compiler (For building fastgpx)

Windows

• Tested with MSVC 17.12.4+ and Clang-cl 19+.

Linux (Tested on Ubuntu)

• C++23 compatible runtime (GCC libstdc++ 14+ or Clang libc++ 18.1+)

GPX/XML Performance (Background)

This library came out of the need to extract information from many GPX files fast.

gpxpy is the most popular GPX library for Python. It is very versatile in manipulating GPX files.

However in benchmarking it doesn't perform well.

gpxpy docs says (at time of writing) that it uses lxml is available because it is faster than "minidom" (etree).

When benchmarking that was not the case. It appear that the stdlib XML library has gotten much better since gpxpy was created.

Reference: Open ticket on making etree default: https://github.com/tkrajina/gpxpy/issues/248

Benchmarks

Test machine:

• AMD Ryzen 7 5800 8-Core, 3.80 GHz
• 32 GB memory
• m2 SSD storage

gpxpy benchmarks

Comparing getting the distance of a GPX file using gpxpy vs manually extracting the data using xml_etree, computing distance between points using gpxpy distance functions.

gpxpy without lxml

Running benchmark with 3 iterations...
gpxpy 5463041.784135511 meters
gpxpy 5463041.784135511 meters
gpxpy 5463041.784135511 meters
gpxpy: 11.497863 seconds (Average: 3.832621 seconds)

gpxpy with lxml

Running benchmark with 3 iterations...
gpxpy 5463041.784135511 meters
gpxpy 5463041.784135511 meters
gpxpy 5463041.784135511 meters
gpxpy: 37.803625 seconds (Average: 12.601208 seconds)

xml_etree data extraction

Running benchmark with 3 iterations...
xml_etree 5463043.740615641 meters
xml_etree 5463043.740615641 meters
xml_etree 5463043.740615641 meters
xml_etree: 2.333200 seconds (Average: 0.777733 seconds)

Even with gpxpy using etree to parse the XML it is paster to parse it directly with etree and use gpxpy.geo distance functions to compute the distance of a GPX file. Unclear what the extra overhead is, possibly the cost of extraction additional data. (Some minor difference in how the total distance is computed in this example. Using different options for computing the distance.)

C++ benchmarks

Since XML parsing itself appear to have a significant impact on performance some popular C++ XML libraries was tested:

tinyxml2

Total Length: 5456930.710560566
Elapsed time: 0.4980144 seconds

pugixml

Total Length: 5456930.710560566
Elapsed time: 0.1890089 seconds

C++ vs Python implementations

Running 5 benchmarks with 3 iterations...

Running gpxpy ...
gpxpy: 50.182288 seconds (Average: 16.727429 seconds)

Running xml_etree ...
xml_etree: 8.269050 seconds (Average: 2.756350 seconds)

Running lxml ...
lxml: 8.479702 seconds (Average: 2.826567 seconds)

Running tinyxml (C++) ...
tinyxml (C++): 2.699880 seconds (Average: 0.899960 seconds)

Running pugixml (C++) ...
pugixml (C++): 0.381095 seconds (Average: 0.127032 seconds)

For computing the length of a GPX file, pugixml in a Python C extension was ~140 times faster than using gpxpy.