# Dataclasses Avro Schema CLI
Command line interface from [dataclasses-avroschema](https://github.com/marcosschroh/dataclasses-avroschema) to work with `avsc` resources
[![Tests](https://github.com/marcosschroh/dc-avro/actions/workflows/tests.yaml/badge.svg)](https://github.com/marcosschroh/dc-avro/actions/workflows/tests.yaml)
[![GitHub license](https://img.shields.io/github/license/marcosschroh/dc-avro.svg)](https://github.com/marcosschroh/dc-avro/blob/master/LICENSE)
[![codecov](https://codecov.io/gh/marcosschroh/dc-avro/branch/master/graph/badge.svg)](https://codecov.io/gh/marcosschroh/dc-avro)
![python version](https://img.shields.io/badge/python-3.8%2B-yellowgreen)
## Requirements
`python 3.8+`
## 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
```bash
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
```bash
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
```yaml
- repo: https://github.com/marcosschroh/dc-avro.git
rev: 0.7.0
hooks:
- id: lint-avsc
additional_dependencies: [typing_extensions]
```
## Features
* [x] Validate `schema`
* [x] Lint `schemas`
* [x] Generate `models` from `schemas`
* [x] Data deserialization with `schema`
* [x] Data serialization with `schema`
* [x] View diff between `schemas`
* [ ] Generate fake data from `schema`
## Development
1. Install requirements: `poetry install`
2. Code linting: `./scripts/format`
3. Run tests: `./scripts/test`
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"description": "# Dataclasses Avro Schema CLI\n\nCommand line interface from [dataclasses-avroschema](https://github.com/marcosschroh/dataclasses-avroschema) to work with `avsc` resources\n\n[![Tests](https://github.com/marcosschroh/dc-avro/actions/workflows/tests.yaml/badge.svg)](https://github.com/marcosschroh/dc-avro/actions/workflows/tests.yaml)\n[![GitHub license](https://img.shields.io/github/license/marcosschroh/dc-avro.svg)](https://github.com/marcosschroh/dc-avro/blob/master/LICENSE)\n[![codecov](https://codecov.io/gh/marcosschroh/dc-avro/branch/master/graph/badge.svg)](https://codecov.io/gh/marcosschroh/dc-avro)\n![python version](https://img.shields.io/badge/python-3.8%2B-yellowgreen)\n\n## Requirements\n\n`python 3.8+`\n\n## Documentation\n\nhttps://marcosschroh.github.io/dc-avro/\n\n## Usage\n\nYou can validate one `avro schema` either from a `local file` or `url`:\n\nAssuming that we have a local file `schema.avsc` that contains an `avro schema`, we can check whether it is valid\n\n```bash\ndc-avro validate-schema --path schema.avsc\n\nValid schema!! \ud83d\udc4d \n\n{\n 'type': 'record',\n 'name': 'UserAdvance',\n 'fields': [\n {'name': 'name', 'type': 'string'},\n {'name': 'age', 'type': 'long'},\n {'name': 'pets', 'type': {'type': 'array', 'items': 'string', 'name': 'pet'}},\n {'name': 'accounts', 'type': {'type': 'map', 'values': 'long', 'name': 'account'}},\n {'name': 'favorite_colors', 'type': {'type': 'enum', 'name': 'FavoriteColor', 'symbols': ['BLUE', 'YELLOW', 'GREEN']}},\n {'name': 'has_car', 'type': 'boolean', 'default': False},\n {'name': 'country', 'type': 'string', 'default': 'Argentina'},\n {'name': 'address', 'type': ['null', 'string'], 'default': None},\n {'name': 'md5', 'type': {'type': 'fixed', 'name': 'md5', 'size': 16}}\n ]\n}\n```\n\nYou can validate several `.avsc` files with `lint` command\n\n```bash\ndc-avro lint tests/schemas/example.avsc tests/schemas/example_v2.avsc\n\n\ud83d\udc4d Total valid schemas: 2\ntests/schemas/example.avsc\ntests/schemas/example_v2.avsc\n```\n\nTo see all the commands execute `dc-avro --help`\n\n## Usage in pre-commit\n\nAdd the following lines to your `.pre-commit-config.yaml` file to enable avro schemas linting\n\n```yaml\n - repo: https://github.com/marcosschroh/dc-avro.git\n rev: 0.7.0\n hooks:\n - id: lint-avsc\n additional_dependencies: [typing_extensions]\n```\n\n## Features\n\n* [x] Validate `schema`\n* [x] Lint `schemas`\n* [x] Generate `models` from `schemas`\n* [x] Data deserialization with `schema`\n* [x] Data serialization with `schema`\n* [x] View diff between `schemas`\n* [ ] Generate fake data from `schema`\n\n## Development\n\n1. Install requirements: `poetry install`\n2. Code linting: `./scripts/format`\n3. Run tests: `./scripts/test`\n",
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