##// END OF EJS Templates
exchange: improve computation of relevant markers for large repos...
exchange: improve computation of relevant markers for large repos Compute the candidate nodes with relevant markers directly from keys of the predecessors/successors/children dictionaries of obsstore. This is faster than iterating over all nodes directly. This test could be further improved for repositories with relative few markers compared to the repository size, but this is no longer hot already. With the current loop structure, the obshashrange use works as well as before as it passes lists with a single node. Adjust the interface by allowing revision lists as well as node lists. This helps cases that computes ancestors as it reduces the materialisation cost. Use this in _pushdiscoveryobsmarker and _getbundleobsmarkerpart. Improve the latter further by directly using ancestors(). Performance benchmarks show notable and welcome improvement to no-op push and pull (that would also apply to other push/pull). This apply to push and pull done without evolve. ### push/pull Benchmark parameter # bin-env-vars.hg.flavor = default # benchmark.variants.explicit-rev = none # benchmark.variants.protocol = ssh # benchmark.variants.revs = none ## benchmark.name = hg.command.pull # data-env-vars.name = mercurial-devel-2024-03-22-zstd-sparse-revlog before: 5.968537 seconds after: 5.668507 seconds (-5.03%, -0.30) # data-env-vars.name = tryton-devel-2024-03-22-zstd-sparse-revlog before: 1.446232 seconds after: 0.835553 seconds (-42.23%, -0.61) # data-env-vars.name = netbsd-src-draft-2024-09-19-zstd-sparse-revlog before: 5.777412 seconds after: 2.523454 seconds (-56.32%, -3.25) ## benchmark.name = hg.command.push # data-env-vars.name = mercurial-devel-2024-03-22-zstd-sparse-revlog before: 6.155501 seconds after: 5.885072 seconds (-4.39%, -0.27) # data-env-vars.name = tryton-devel-2024-03-22-zstd-sparse-revlog before: 1.491054 seconds after: 0.934882 seconds (-37.30%, -0.56) # data-env-vars.name = netbsd-src-draft-2024-09-19-zstd-sparse-revlog before: 5.902494 seconds after: 2.957644 seconds (-49.89%, -2.94) There is not notable different in these result using the "rust" flavor instead of the "default". The performance impact on the same operation when using evolve were also tested and no impact was noted.
Joerg Sonnenberger -
r52789:8583d138 default
Show More
Name Size Modified Last Commit Author
/ mercurial / thirdparty / tomli
LICENSE Loading ...
README.md Loading ...
__init__.py Loading ...
_parser.py Loading ...
_re.py Loading ...
_types.py Loading ...
py.typed Loading ...

Build Status
codecov.io
PyPI version

Tomli

A lil' TOML parser

Table of Contents generated with mdformat-toc

Intro

Tomli is a Python library for parsing TOML.
Tomli is fully compatible with TOML v1.0.0.

Installation

pip install tomli

Usage

Parse a TOML string

import tomli

toml_str = """
           gretzky = 99

           [kurri]
           jari = 17
           """

toml_dict = tomli.loads(toml_str)
assert toml_dict == {"gretzky": 99, "kurri": {"jari": 17}}

Parse a TOML file

import tomli

with open("path_to_file/conf.toml", "rb") as f:
    toml_dict = tomli.load(f)

The file must be opened in binary mode (with the "rb" flag).
Binary mode will enforce decoding the file as UTF-8 with universal newlines disabled,
both of which are required to correctly parse TOML.
Support for text file objects is deprecated for removal in the next major release.

Handle invalid TOML

import tomli

try:
    toml_dict = tomli.loads("]] this is invalid TOML [[")
except tomli.TOMLDecodeError:
    print("Yep, definitely not valid.")

Note that while the TOMLDecodeError type is public API, error messages of raised instances of it are not.
Error messages should not be assumed to stay constant across Tomli versions.

Construct decimal.Decimals from TOML floats

from decimal import Decimal
import tomli

toml_dict = tomli.loads("precision-matters = 0.982492", parse_float=Decimal)
assert toml_dict["precision-matters"] == Decimal("0.982492")

Note that decimal.Decimal can be replaced with another callable that converts a TOML float from string to a Python type.
The decimal.Decimal is, however, a practical choice for use cases where float inaccuracies can not be tolerated.

Illegal types include dict, list, and anything that has the append attribute.
Parsing floats into an illegal type results in undefined behavior.

FAQ

Why this parser?

  • it's lil'
  • pure Python with zero dependencies
  • the fastest pure Python parser *:
    15x as fast as tomlkit,
    2.4x as fast as toml
  • outputs basic data types only
  • 100% spec compliant: passes all tests in
    a test set
    soon to be merged to the official
    compliance tests for TOML
    repository
  • thoroughly tested: 100% branch coverage

Is comment preserving round-trip parsing supported?

No.

The tomli.loads function returns a plain dict that is populated with builtin types and types from the standard library only.
Preserving comments requires a custom type to be returned so will not be supported,
at least not by the tomli.loads and tomli.load functions.

Look into TOML Kit if preservation of style is what you need.

Is there a dumps, write or encode function?

Tomli-W is the write-only counterpart of Tomli, providing dump and dumps functions.

The core library does not include write capability, as most TOML use cases are read-only, and Tomli intends to be minimal.

How do TOML types map into Python types?

TOML type Python type Details
Document Root dict
Key str
String str
Integer int
Float float
Boolean bool
Offset Date-Time datetime.datetime tzinfo attribute set to an instance of datetime.timezone
Local Date-Time datetime.datetime tzinfo attribute set to None
Local Date datetime.date
Local Time datetime.time
Array list
Table dict
Inline Table dict

Performance

The benchmark/ folder in this repository contains a performance benchmark for comparing the various Python TOML parsers.
The benchmark can be run with tox -e benchmark-pypi.
Running the benchmark on my personal computer output the following:

foo@bar:~/dev/tomli$ tox -e benchmark-pypi
benchmark-pypi installed: attrs==19.3.0,click==7.1.2,pytomlpp==1.0.2,qtoml==0.3.0,rtoml==0.7.0,toml==0.10.2,tomli==1.1.0,tomlkit==0.7.2
benchmark-pypi run-test-pre: PYTHONHASHSEED='2658546909'
benchmark-pypi run-test: commands[0] | python -c 'import datetime; print(datetime.date.today())'
2021-07-23
benchmark-pypi run-test: commands[1] | python --version
Python 3.8.10
benchmark-pypi run-test: commands[2] | python benchmark/run.py
Parsing data.toml 5000 times:
------------------------------------------------------
    parser |  exec time | performance (more is better)
-----------+------------+-----------------------------
     rtoml |    0.901 s | baseline (100%)
  pytomlpp |     1.08 s | 83.15%
     tomli |     3.89 s | 23.15%
      toml |     9.36 s | 9.63%
     qtoml |     11.5 s | 7.82%
   tomlkit |     56.8 s | 1.59%

The parsers are ordered from fastest to slowest, using the fastest parser as baseline.
Tomli performed the best out of all pure Python TOML parsers,
losing only to pytomlpp (wraps C++) and rtoml (wraps Rust).