Generate SQL tables, load and extract data, based on JSON Table Schema descriptors.
Project description
tableschema-sql-py
Generate and load SQL tables based on Table Schema descriptors.
Features
- implements
tableschema.Storage
interface - provides additional features like indexes and updating
Contents
Getting Started
Installation
The package use semantic versioning. It means that major versions could include breaking changes. It's highly recommended to specify package
version range in your setup/requirements
file e.g. package>=1.0,<2.0
.
pip install tableschema-sql
Examples
Code examples in this readme requires Python 3.3+ interpreter. You could see even more example in examples directory.
from tableschema import Table
from sqlalchemy import create_engine
# Load and save table to SQL
engine = create_engine('sqlite://')
table = Table('data.csv', schema='schema.json')
table.save('data', storage='sql', engine=engine)
Documentation
The whole public API of this package is described here and follows semantic versioning rules. Everyting outside of this readme are private API and could be changed without any notification on any new version.
Storage
Package implements Tabular Storage interface (see full documentation on the link):
This driver provides an additional API:
Storage(engine, dbschema=None, prefix='', reflect_only=None, autoincrement=False)
engine (object)
-sqlalchemy
enginedbschema (str)
- name of database schemaprefix (str)
- prefix for all bucketsreflect_only (callable)
- a boolean predicate to filter the list of table names when reflectingautoincrement (bool)
- add autoincrement column at the beginning
storage.create(..., indexes_fields=None)
indexes_fields (str[])
- list of tuples containing field names, or list of such lists
storage.write(..., keyed=False, as_generator=False, update_keys=None)
keyed (bool)
- accept keyed rowsas_generator (bool)
- returns generator to provide writing control to the clientupdate_keys (str[])
- update instead of inserting if key values match existent rows
Contributing
The project follows the Open Knowledge International coding standards.
Recommended way to get started is to create and activate a project virtual environment. To install package and development dependencies into active environment:
$ make install
To run tests with linting and coverage:
$ make test
For linting pylama
configured in pylama.ini
is used. On this stage it's already
installed into your environment and could be used separately with more fine-grained control
as described in documentation - https://pylama.readthedocs.io/en/latest/.
For example to sort results by error type:
$ pylama --sort <path>
For testing tox
configured in tox.ini
is used.
It's already installed into your environment and could be used separately with more fine-grained control as described in documentation - https://testrun.org/tox/latest/.
For example to check subset of tests against Python 2 environment with increased verbosity.
All positional arguments and options after --
will be passed to py.test
:
tox -e py27 -- -v tests/<path>
Under the hood tox
uses pytest
configured in pytest.ini
, coverage
and mock
packages. This packages are available only in tox envionments.
Changelog
Here described only breaking and the most important changes. The full changelog and documentation for all released versions could be found in nicely formatted commit history.
v1.0
- Added FK support for SQLite databases
v0.x
- Initial driver implementation.
Project details
Release history Release notifications | RSS feed
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