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https://github.com/twidi/py-dataql
Python backend for "Data Query Languages" (like GraphQL and others)
https://github.com/twidi/py-dataql
Last synced: 3 months ago
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Python backend for "Data Query Languages" (like GraphQL and others)
- Host: GitHub
- URL: https://github.com/twidi/py-dataql
- Owner: twidi
- License: bsd-2-clause
- Created: 2015-06-15T21:41:36.000Z (over 9 years ago)
- Default Branch: develop
- Last Pushed: 2015-08-23T11:50:39.000Z (over 9 years ago)
- Last Synced: 2024-08-18T10:15:22.615Z (4 months ago)
- Language: Python
- Homepage:
- Size: 274 KB
- Stars: 24
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# py-dataql
Python backend for "Data Query Languages" (like GraphQL and others)
## What is it?
`dataql` is for "Data Query Language". It allows to query data in a simple way.
It is heavily inspired by [GraphQL, from Facebook](https://facebook.github.io/react/blog/2015/05/01/graphql-introduction.html)
I didn't want to force people to think "graph", and I chose a language that is different in some
ways. But this library is written with a base, and we provide a generic parser, but other parsers
could easily be written!
But I have an [implementation of a GraphQL server in python](https://github.com/twidi/py-graphql-server),
using `dataql`. The main difference is that it's strongly typed compared to `dataql`.
## How it works?
For example, the default generic parser included, `DataQLParser`, allows to ask for data
with this example query:
```
User.get('Elon Musk') {
name,
birthday.strftime('%x'),
companies[{
name,
year:created_year,
}],
company_names: companies[name],
first_company:companies.0.name,
}
```
And to get data like that:
```python
{
'name': 'Elon Musk',
'birthday': '06/28/71',
'companies': [
{
'name': 'Paypal',
'year': 1999
},
{
'name': 'Space X',
'year': 2002
}
],
'company_names': ['Paypal', 'Space X'],
'first_company': 'Paypal',
}
```
The main use is for an API, letting the client asking what it really needs, in only one http query,
without having to update the API endpoints.
The only thing to do on the server side is to define, via a registry, what objects and attributes
are allowed.
## Full example
This repository provides a [full, but simple, example](example.py)
## Installing
This library is available on pypi, but still in alpha version.
```
pip install dataql
```
## Documentation
No external documentation yet, but the code is heavily documented
using "[Numpydoc](https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt)":
```
---------------------------------------------------------------------------
File blank comment code
---------------------------------------------------------------------------
./__init__.py 2 1 6
./exceptions.py 5 5 3
./parsers/__init__.py 4 4 1
./parsers/base.py 142 383 120
./parsers/exceptions.py 13 13 15
./parsers/generic.py 150 477 86
./parsers/mixins.py 275 794 113
./resources.py 149 261 161
./solvers/exceptions.py 91 112 140
./solvers/filters.py 62 146 30
./solvers/registry.py 259 914 214
./solvers/resources.py 112 339 58
./utils.py 6 20 5
---------------------------------------------------------------------------
SUM: 1270 3469 952
---------------------------------------------------------------------------
```
You may refer to the [example.py](example.py) file for usage.
## TODO
This prototype is working as expected, but there is a lot of things to do:
- ~~create subclasses for exceptions~~
- ~~allow the use of filters that are not attributes of instances, but simple functions~~
- ~~allow the retrieval of list entries or dict entries, not only instance attributes~~
- ~~allow returning lists of values, not only objects~~
- write documentation about how to use `dataql` to get data from a query
- write documentation about how to create its own parser
- ~~test on the the Django ORM~~ (works)
- create an advanced solver for Django (using `select_related` and `prefetch_related`)
- tell me
## Tests
There are no external tests for now but there are a lot of examples in the classes and method
docstrings that are valid (and passing!) doctests.
You can launch all these doctests this way (at the root of the repository) :
```sh
./run_tests.sh
Ran 110 tests in 0.207s
OK
```
## Python version
It's 2015. So `dataql` is written with python 3.4 in mind. No plan to port it to python 2.
## License
The `dataql` library is under the BSD License. See [LICENSE file](LICENSE)
## Contributing
I created the base for a tool that could be really useful. I may have chose the wrong way for some
things, or maybe the whole project. Tell me. Add issues, create pull requests, define new
languages, or simply enhance the parser.... Feel free to contribute!
## Internals
This library is split in two main parts:
### parsers
`dataql.parsers` include everything that is needed to create a parser to parse a query into what
I named "resources" (for example a field, a list, an object, with filter, arguments...)
Each parser (only one for now) returns the same kind of resources. So there is no need to write
a new solver for a new parser
### solvers
`dataql.solvers` is the place where, with a base value and a resource, we can have a result
matching the query.
### Etc
More to come, folks!