Dataclasses Avro Schema CLI

Command line interface from dataclasses-avroschema to work with avsc resources

python version

Requirements

python 3.10+

Documentation

https://marcosschroh.github.io/dc-avro/

Usage

You can validate one avro schema either from a local file or url:

Assuming that we have a local file schema.avsc that contains an avro schema, we can check whether it is valid

dc-avro validate-schema --path schema.avsc

Valid schema!! 👍 

{
    'type': 'record',
    'name': 'UserAdvance',
    'fields': [
        {'name': 'name', 'type': 'string'},
        {'name': 'age', 'type': 'long'},
        {'name': 'pets', 'type': {'type': 'array', 'items': 'string', 'name': 'pet'}},
        {'name': 'accounts', 'type': {'type': 'map', 'values': 'long', 'name': 'account'}},
        {'name': 'favorite_colors', 'type': {'type': 'enum', 'name': 'FavoriteColor', 'symbols': ['BLUE', 'YELLOW', 'GREEN']}},
        {'name': 'has_car', 'type': 'boolean', 'default': False},
        {'name': 'country', 'type': 'string', 'default': 'Argentina'},
        {'name': 'address', 'type': ['null', 'string'], 'default': None},
        {'name': 'md5', 'type': {'type': 'fixed', 'name': 'md5', 'size': 16}}
    ]
}

You can validate several .avsc files with lint command

dc-avro lint tests/schemas/example.avsc tests/schemas/example_v2.avsc

👍 Total valid schemas: 2
tests/schemas/example.avsc
tests/schemas/example_v2.avsc

To see all the commands execute dc-avro --help

Usage in pre-commit

Add the following lines to your .pre-commit-config.yaml file to enable avro schemas linting

  - repo: https://github.com/marcosschroh/dc-avro.git
    rev: 0.7.0
    hooks:
      - id: lint-avsc
        additional_dependencies: [typing_extensions]

Features

Validate schema
Lint schemas
Generate models from schemas
Data deserialization with schema
Data serialization with schema
View diff between schemas
Generate fake data from schema

Development

Install requirements: poetry install
Code linting: ./scripts/format
Run tests: ./scripts/test

Metadata

Release files for dc-avro 0.13.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for dc-avro 0.13.0
File	Size	Uploaded
dc_avro-0.13.0.tar.gz	6.9 kB	Oct 16, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for dc-avro 0.13.0
File	Interpreter	ABI	Platform	Reset
dc_avro-0.13.0-py3-none-any.whl 8.5 kB Oct 16, 2025	Python 3	none	any	Details

Total release size: 15.5 kB

Release files / dc_avro-0.13.0.tar.gz

Download URL	dc_avro-0.13.0.tar.gz
Size	6.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`985c6c089cdf4201a31240642312c7e1aebf553890eaf7689cda3c5f305c90e1`
BLAKE2b-256 checksum How to use checksums	`b36196941cc1e2a4e875cf5c08c2ab9da7d3e4209cb08b7b8f0ef3bd6427f3c5`
Upload date	Oct 16, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/2.2.1 CPython/3.13.7 Linux/6.11.0-1018-azure`

Release files / dc_avro-0.13.0-py3-none-any.whl

Download URL	dc_avro-0.13.0-py3-none-any.whl
Size	8.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`2883cc563d631004296b0581deee635286b0f0c30e67f9a8d2a13f000ce916e5`
BLAKE2b-256 checksum How to use checksums	`0f27e566a63a03fec8be15e29c4172f8eb428f29d80bf9b2d672a12ea3c37d7a`
Upload date	Oct 16, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/2.2.1 CPython/3.13.7 Linux/6.11.0-1018-azure`