A Python engine for Liquid, the safe customer-facing template language for flexible web apps.
We follow Shopify/Liquid closely and test against the Golden Liquid test suite.
Table of Contents
Install Python Liquid using Pipenv:
$ pipenv install -u python-liquidOr pip:
$ pip install python-liquidOr from conda-forge:
$ conda install -c conda-forge python-liquid- Documentation: https://jg-rp.github.io/liquid/
- Change Log: https://github.com/jg-rp/liquid/blob/main/CHANGES.md
- PyPi: https://pypi.org/project/python-liquid/
- Source Code: https://github.com/jg-rp/liquid
- Issue Tracker: https://github.com/jg-rp/liquid/issues
from liquid import Template
template = Template("Hello, {{ you }}!")
print(template.render(you="World")) # "Hello, World!"
print(template.render(you="Liquid")) # "Hello, Liquid!"- liquid-babel Internationalization and localization for Liquid templates.
- LiquidScript: A JavaScript and TypeScript engine for Liquid with a similar high-level API to Python Liquid.
- django-liquid: A Django template backend for Liquid. Render Liquid templates in your Django apps.
- Flask-Liquid: A Flask extension for Liquid. Render Liquid templates in your Flask applications.
- golden-liquid: A test suite for Liquid. See how various Liquid template engines compare to the reference implementation.
We strive to be 100% compatible with the reference implementation of Liquid, written in Ruby. That is, given an equivalent render context, a template rendered with Python Liquid should produce the same output as when rendered with Ruby Liquid.
See the known issues page for details of known incompatibilities between Python Liquid and Ruby Liquid, and please help by raising an issue if you notice an incompatibility.
You can run the benchmark using hatch run benchmark (or python -O scripts/performance.py if you don't have make) from the root of the source tree. On my ropey desktop computer with a Ryzen 5 1500X and Python 3.11.0, we get the following results.
Best of 5 rounds with 100 iterations per round and 60 ops per iteration (6000 ops per round).
lex template (not expressions): 1.2s (5020.85 ops/s, 83.68 i/s)
lex and parse: 5.0s (1197.32 ops/s, 19.96 i/s)
render: 1.4s (4152.92 ops/s, 69.22 i/s)
lex, parse and render: 6.5s (922.08 ops/s, 15.37 i/s)
And PyPy3.7 gives us a decent increase in performance.
Best of 5 rounds with 100 iterations per round and 60 ops per iteration (6000 ops per round).
lex template (not expressions): 0.58s (10308.67 ops/s, 171.81 i/s)
lex and parse: 3.6s (1661.20 ops/s, 27.69 i/s)
render: 0.95s (6341.14 ops/s, 105.69 i/s)
lex, parse and render: 4.6s (1298.18 ops/s, 21.64 i/s)
On the same machine, running rake benchmark:run from the root of the reference implementation source tree gives us these results.
/usr/bin/ruby ./performance/benchmark.rb lax
Running benchmark for 10 seconds (with 5 seconds warmup).
Warming up --------------------------------------
parse: 3.000 i/100ms
render: 8.000 i/100ms
parse & render: 2.000 i/100ms
Calculating -------------------------------------
parse: 39.072 (± 0.0%) i/s - 393.000 in 10.058789s
render: 86.995 (± 1.1%) i/s - 872.000 in 10.024951s
parse & render: 26.139 (± 0.0%) i/s - 262.000 in 10.023365s
I've tried to match the benchmark workload to that of the reference implementation, so that we might compare results directly. The workload is meant to be representative of Shopify's use case, although I wouldn't be surprised if their usage has changed subtly since the benchmark fixture was designed.
Please see Contributing to Python Liquid.