Validr

A simple, fast, extensible python library for data validation.

Simple and readable schema
10X faster than jsonschema, 40X faster than schematics
Can validate and serialize any object
Easy to create custom validators
Accurate and friendly error messages

简单，快速，可拓展的数据校验库。

简洁，易读的 Schema
比 jsonschema 快 10 倍，比 schematics 快 40 倍
能够校验&序列化任意类型对象
易于拓展自定义校验器
准确友好的错误提示

Overview

from validr import T, modelclass, asdict

@modelclass
class Model:
    """Base Model"""

class Person(Model):
    name=T.str.maxlen(16).desc('at most 16 chars')
    website=T.url.optional.desc('website is optional')

guyskk = Person(name='guyskk', website='https://github.com/guyskk')
print(asdict(guyskk))

Install

Note: Only support Python 3.5+

pip install validr

When you have c compiler in your system, validr will be c speedup mode. Otherwise validr will fallback to pure python mode.

To force c speedup mode:

VALIDR_SETUP_MODE=c pip install validr

To force pure python mode:

VALIDR_SETUP_MODE=py pip install validr

Document

https://github.com/guyskk/validr/wiki

Performance

benchmark result in Travis-CI:

--------------------------timeits---------------------------
  voluptuous:default             10000 loops cost 0.368s
      schema:default              1000 loops cost 0.318s
        json:loads-dumps        100000 loops cost 1.380s
      validr:default            100000 loops cost 0.719s
      validr:model              100000 loops cost 1.676s
  jsonschema:draft3              10000 loops cost 0.822s
  jsonschema:draft4              10000 loops cost 0.785s
  schematics:default              1000 loops cost 0.792s
---------------------------scores---------------------------
  voluptuous:default               375
      schema:default                43
        json:loads-dumps          1000
      validr:default              1918
      validr:model                 823
  jsonschema:draft3                168
  jsonschema:draft4                176
  schematics:default                17

Develop

Validr is implemented by Cython since v0.14.0, it's 5X faster than pure Python implemented.

setup:

It's better to use virtualenv or similar tools to create isolated Python environment for develop.

After that, install dependencys:

./bootstrap.sh

build, test and benchmark:

inv build
inv test
inv benchmark

License

The project is open source under Anti-996 License and GNU GPL License, you can choose one of them.

guyskk / validr