• About Us
  • Blog
  • Basket
  • Account
  • Sign In
  •  

Python API

Benchmarks

This page provides guidance figures for detection speeds, memory usage and startup times for Pattern and Trie algorithms. Pattern is more memory efficient and can be run directly from disk while Trie requires considerably more main memory but delivers millions of detections per second. For more information check out the how device detection works page.

Results stated are detections per core on a quad core i7 2.2GHz CPU.

Pattern Benchmarks

Below is a table of performance metrics relating to the Python Pattern API. It shows the detection speed for a single request for each data set and where applicable also the mode of operation used.


LitePremiumEnterprise
Detections Per Second558654975143668
Time Per Detection (ms)0.01790.02010.0229
Pattern detection performance for Lite, Premium and Enterprise data files.
LitePremiumEnterprise
Startup Time (ms)16.63
29.95
34.35
Average Memory Usage (Mb)54
109
147
Pattern memory usage and startup times for Lite, Premium and Enterprise data files.

Trie Benchmarks

Below is a table of performance metrics relating to the Python Trie API. It shows the detection speed for a single request for each data set and where applicable also the mode of operation used.


LitePremiumEnterprise
Detections Per Second121951
243902
158730
Time Per Detection (ms)0.0082
0.0041
0.0063
Trie detection performance for Lite, Premium and Enterprise data files.
LitePremiumEnterprise
Startup Time (ms)419.15
7383.46
8294.48
Average Memory Usage (Mb)86
1029
1079
Trie memory usage and startup times for Lite, Premium and Enterprise data files.