The Python driver for Gel

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gel-python is the official Gel driver for Python. It provides both blocking IO and asyncio implementations.

The library requires Python 3.9 or later.

Documentation

The project documentation can be found here.

Installation

The library is available on PyPI. Use pip to install it:

$ pip install gel

Basic Usage

importdatetimeimportgeldefmain():
client=gel.create_client()
# Create a User object typeclient.execute(''' CREATE TYPE User { CREATE REQUIRED PROPERTY name -> str; CREATE PROPERTY dob -> cal::local_date; } ''')
# Insert a new User objectclient.query(''' INSERT User { name := <str>$name, dob := <cal::local_date>$dob } ''', name='Bob', dob=datetime.date(1984, 3, 1))
# Select User objects.user_set=client.query(
'SELECT User {name, dob} FILTER .name = <str>$name', name='Bob')
# *user_set* now contains# Set{Object{name := 'Bob', dob := datetime.date(1984, 3, 1)}}# Close the client.client.close()
if__name__=='__main__':
main()

Using the `models` generator

We provide a models generator that lets you build queries programmatically, and generate Pydantic models directly from your schema.

To use, run the following command:

$ gel generate py/models

This will find your Python project and add a models package to it. Then you can use the generated models to build queries and mutate instances of your objects directly.

importdatetimefrommodelsimportUser, stdfromgelimportcreate_clientdefmain():
client=create_client()
# Create a new User instance and save it to the databasebob=User(name='Bob', dob=datetime.date(1984, 3, 1))
client.save(bob)
# Select all Usersusers=client.query(User)
# Select all users with names like "Bob"bob_like=client.query(User.filter(lambdau: std.ilike(u.name, '%bob%')))
# Update an objectbob.name='Robert'client.save(bob)
# Delete an objectclient.execute(User.filter(id=bob.id).delete())
client.close()
if__name__=='__main__':
main()

Development

Instructions for installing Gel and gel-python locally can be found at docs.geldata.com/resources/guides/contributing/code.

To run the test suite, run $ python setup.py test.

License

gel-python is developed and distributed under the Apache 2.0 license.

The Python driver for Gel

gel-python is the official Gel driver for Python. It provides both blocking IO and asyncio implementations.

The library requires Python 3.9 or later.

Documentation

The project documentation can be found here.

Installation

The library is available on PyPI. Use pip to install it:

$ pip install gel

Basic Usage

importdatetimeimportgeldefmain():
client=gel.create_client()
# Create a User object typeclient.execute(''' CREATE TYPE User { CREATE REQUIRED PROPERTY name -> str; CREATE PROPERTY dob -> cal::local_date; } ''')
# Insert a new User objectclient.query(''' INSERT User { name := <str>$name, dob := <cal::local_date>$dob } ''', name='Bob', dob=datetime.date(1984, 3, 1))
# Select User objects.user_set=client.query(
'SELECT User {name, dob} FILTER .name = <str>$name', name='Bob')
# *user_set* now contains# Set{Object{name := 'Bob', dob := datetime.date(1984, 3, 1)}}# Close the client.client.close()
if__name__=='__main__':
main()

Using the `models` generator

We provide a models generator that lets you build queries programmatically, and generate Pydantic models directly from your schema.

To use, run the following command:

$ gel generate py/models

This will find your Python project and add a models package to it. Then you can use the generated models to build queries and mutate instances of your objects directly.

importdatetimefrommodelsimportUser, stdfromgelimportcreate_clientdefmain():
client=create_client()
# Create a new User instance and save it to the databasebob=User(name='Bob', dob=datetime.date(1984, 3, 1))
client.save(bob)
# Select all Usersusers=client.query(User)
# Select all users with names like "Bob"bob_like=client.query(User.filter(lambdau: std.ilike(u.name, '%bob%')))
# Update an objectbob.name='Robert'client.save(bob)
# Delete an objectclient.execute(User.filter(id=bob.id).delete())
client.close()
if__name__=='__main__':
main()

Development

Instructions for installing Gel and gel-python locally can be found at docs.geldata.com/resources/guides/contributing/code.

To run the test suite, run $ python setup.py test.

License

gel-python is developed and distributed under the Apache 2.0 license.

The Python driver for Gel

gel-python is the official Gel driver for Python. It provides both blocking IO and asyncio implementations.

The library requires Python 3.9 or later.

Documentation

The project documentation can be found here.

Installation

The library is available on PyPI. Use pip to install it:

$ pip install gel

Basic Usage

importdatetimeimportgeldefmain():
client=gel.create_client()
# Create a User object typeclient.execute(''' CREATE TYPE User { CREATE REQUIRED PROPERTY name -> str; CREATE PROPERTY dob -> cal::local_date; } ''')
# Insert a new User objectclient.query(''' INSERT User { name := <str>$name, dob := <cal::local_date>$dob } ''', name='Bob', dob=datetime.date(1984, 3, 1))
# Select User objects.user_set=client.query(
'SELECT User {name, dob} FILTER .name = <str>$name', name='Bob')
# *user_set* now contains# Set{Object{name := 'Bob', dob := datetime.date(1984, 3, 1)}}# Close the client.client.close()
if__name__=='__main__':
main()

Using the `models` generator

We provide a models generator that lets you build queries programmatically, and generate Pydantic models directly from your schema.

To use, run the following command:

$ gel generate py/models

This will find your Python project and add a models package to it. Then you can use the generated models to build queries and mutate instances of your objects directly.

importdatetimefrommodelsimportUser, stdfromgelimportcreate_clientdefmain():
client=create_client()
# Create a new User instance and save it to the databasebob=User(name='Bob', dob=datetime.date(1984, 3, 1))
client.save(bob)
# Select all Usersusers=client.query(User)
# Select all users with names like "Bob"bob_like=client.query(User.filter(lambdau: std.ilike(u.name, '%bob%')))
# Update an objectbob.name='Robert'client.save(bob)
# Delete an objectclient.execute(User.filter(id=bob.id).delete())
client.close()
if__name__=='__main__':
main()

Development

Instructions for installing Gel and gel-python locally can be found at docs.geldata.com/resources/guides/contributing/code.

To run the test suite, run $ python setup.py test.

License

gel-python is developed and distributed under the Apache 2.0 license.

The Python driver for Gel

gel-python is the official Gel driver for Python. It provides both blocking IO and asyncio implementations.

The library requires Python 3.9 or later.

Documentation

The project documentation can be found here.

Installation

The library is available on PyPI. Use pip to install it:

$ pip install gel

Basic Usage

importdatetimeimportgeldefmain():
client=gel.create_client()
# Create a User object typeclient.execute(''' CREATE TYPE User { CREATE REQUIRED PROPERTY name -> str; CREATE PROPERTY dob -> cal::local_date; } ''')
# Insert a new User objectclient.query(''' INSERT User { name := <str>$name, dob := <cal::local_date>$dob } ''', name='Bob', dob=datetime.date(1984, 3, 1))
# Select User objects.user_set=client.query(
'SELECT User {name, dob} FILTER .name = <str>$name', name='Bob')
# *user_set* now contains# Set{Object{name := 'Bob', dob := datetime.date(1984, 3, 1)}}# Close the client.client.close()
if__name__=='__main__':
main()

Using the `models` generator

We provide a models generator that lets you build queries programmatically, and generate Pydantic models directly from your schema.

To use, run the following command:

$ gel generate py/models

This will find your Python project and add a models package to it. Then you can use the generated models to build queries and mutate instances of your objects directly.

importdatetimefrommodelsimportUser, stdfromgelimportcreate_clientdefmain():
client=create_client()
# Create a new User instance and save it to the databasebob=User(name='Bob', dob=datetime.date(1984, 3, 1))
client.save(bob)
# Select all Usersusers=client.query(User)
# Select all users with names like "Bob"bob_like=client.query(User.filter(lambdau: std.ilike(u.name, '%bob%')))
# Update an objectbob.name='Robert'client.save(bob)
# Delete an objectclient.execute(User.filter(id=bob.id).delete())
client.close()
if__name__=='__main__':
main()

Development

Instructions for installing Gel and gel-python locally can be found at docs.geldata.com/resources/guides/contributing/code.

To run the test suite, run $ python setup.py test.

License

gel-python is developed and distributed under the Apache 2.0 license.

The Python driver for Gel

gel-python is the official Gel driver for Python. It provides both blocking IO and asyncio implementations.

The library requires Python 3.9 or later.

Documentation

The project documentation can be found here.

Installation

The library is available on PyPI. Use pip to install it:

$ pip install gel

Basic Usage

importdatetimeimportgeldefmain():
client=gel.create_client()
# Create a User object typeclient.execute(''' CREATE TYPE User { CREATE REQUIRED PROPERTY name -> str; CREATE PROPERTY dob -> cal::local_date; } ''')
# Insert a new User objectclient.query(''' INSERT User { name := <str>$name, dob := <cal::local_date>$dob } ''', name='Bob', dob=datetime.date(1984, 3, 1))
# Select User objects.user_set=client.query(
'SELECT User {name, dob} FILTER .name = <str>$name', name='Bob')
# *user_set* now contains# Set{Object{name := 'Bob', dob := datetime.date(1984, 3, 1)}}# Close the client.client.close()
if__name__=='__main__':
main()

Using the `models` generator

We provide a models generator that lets you build queries programmatically, and generate Pydantic models directly from your schema.

To use, run the following command:

$ gel generate py/models

This will find your Python project and add a models package to it. Then you can use the generated models to build queries and mutate instances of your objects directly.

importdatetimefrommodelsimportUser, stdfromgelimportcreate_clientdefmain():
client=create_client()
# Create a new User instance and save it to the databasebob=User(name='Bob', dob=datetime.date(1984, 3, 1))
client.save(bob)
# Select all Usersusers=client.query(User)
# Select all users with names like "Bob"bob_like=client.query(User.filter(lambdau: std.ilike(u.name, '%bob%')))
# Update an objectbob.name='Robert'client.save(bob)
# Delete an objectclient.execute(User.filter(id=bob.id).delete())
client.close()
if__name__=='__main__':
main()

Development

Instructions for installing Gel and gel-python locally can be found at docs.geldata.com/resources/guides/contributing/code.

To run the test suite, run $ python setup.py test.

License

gel-python is developed and distributed under the Apache 2.0 license.

The Python driver for Gel

gel-python is the official Gel driver for Python. It provides both blocking IO and asyncio implementations.

The library requires Python 3.9 or later.

Documentation

The project documentation can be found here.

Installation

The library is available on PyPI. Use pip to install it:

$ pip install gel

Basic Usage

importdatetimeimportgeldefmain():
client=gel.create_client()
# Create a User object typeclient.execute(''' CREATE TYPE User { CREATE REQUIRED PROPERTY name -> str; CREATE PROPERTY dob -> cal::local_date; } ''')
# Insert a new User objectclient.query(''' INSERT User { name := <str>$name, dob := <cal::local_date>$dob } ''', name='Bob', dob=datetime.date(1984, 3, 1))
# Select User objects.user_set=client.query(
'SELECT User {name, dob} FILTER .name = <str>$name', name='Bob')
# *user_set* now contains# Set{Object{name := 'Bob', dob := datetime.date(1984, 3, 1)}}# Close the client.client.close()
if__name__=='__main__':
main()

Using the `models` generator

We provide a models generator that lets you build queries programmatically, and generate Pydantic models directly from your schema.

To use, run the following command:

$ gel generate py/models

This will find your Python project and add a models package to it. Then you can use the generated models to build queries and mutate instances of your objects directly.

importdatetimefrommodelsimportUser, stdfromgelimportcreate_clientdefmain():
client=create_client()
# Create a new User instance and save it to the databasebob=User(name='Bob', dob=datetime.date(1984, 3, 1))
client.save(bob)
# Select all Usersusers=client.query(User)
# Select all users with names like "Bob"bob_like=client.query(User.filter(lambdau: std.ilike(u.name, '%bob%')))
# Update an objectbob.name='Robert'client.save(bob)
# Delete an objectclient.execute(User.filter(id=bob.id).delete())
client.close()
if__name__=='__main__':
main()

Development

Instructions for installing Gel and gel-python locally can be found at docs.geldata.com/resources/guides/contributing/code.

To run the test suite, run $ python setup.py test.

License

gel-python is developed and distributed under the Apache 2.0 license.

The Python driver for Gel

gel-python is the official Gel driver for Python. It provides both blocking IO and asyncio implementations.

The library requires Python 3.9 or later.

Documentation

The project documentation can be found here.

Installation

The library is available on PyPI. Use pip to install it:

$ pip install gel

Basic Usage

importdatetimeimportgeldefmain():
client=gel.create_client()
# Create a User object typeclient.execute(''' CREATE TYPE User { CREATE REQUIRED PROPERTY name -> str; CREATE PROPERTY dob -> cal::local_date; } ''')
# Insert a new User objectclient.query(''' INSERT User { name := <str>$name, dob := <cal::local_date>$dob } ''', name='Bob', dob=datetime.date(1984, 3, 1))
# Select User objects.user_set=client.query(
'SELECT User {name, dob} FILTER .name = <str>$name', name='Bob')
# *user_set* now contains# Set{Object{name := 'Bob', dob := datetime.date(1984, 3, 1)}}# Close the client.client.close()
if__name__=='__main__':
main()

Using the `models` generator

We provide a models generator that lets you build queries programmatically, and generate Pydantic models directly from your schema.

To use, run the following command:

$ gel generate py/models

This will find your Python project and add a models package to it. Then you can use the generated models to build queries and mutate instances of your objects directly.

importdatetimefrommodelsimportUser, stdfromgelimportcreate_clientdefmain():
client=create_client()
# Create a new User instance and save it to the databasebob=User(name='Bob', dob=datetime.date(1984, 3, 1))
client.save(bob)
# Select all Usersusers=client.query(User)
# Select all users with names like "Bob"bob_like=client.query(User.filter(lambdau: std.ilike(u.name, '%bob%')))
# Update an objectbob.name='Robert'client.save(bob)
# Delete an objectclient.execute(User.filter(id=bob.id).delete())
client.close()
if__name__=='__main__':
main()

Development

Instructions for installing Gel and gel-python locally can be found at docs.geldata.com/resources/guides/contributing/code.

To run the test suite, run $ python setup.py test.

License

gel-python is developed and distributed under the Apache 2.0 license.

The Python driver for Gel

gel-python is the official Gel driver for Python. It provides both blocking IO and asyncio implementations.

The library requires Python 3.9 or later.

Documentation

The project documentation can be found here.

Installation

The library is available on PyPI. Use pip to install it:

$ pip install gel

Basic Usage

importdatetimeimportgeldefmain():
client=gel.create_client()
# Create a User object typeclient.execute(''' CREATE TYPE User { CREATE REQUIRED PROPERTY name -> str; CREATE PROPERTY dob -> cal::local_date; } ''')
# Insert a new User objectclient.query(''' INSERT User { name := <str>$name, dob := <cal::local_date>$dob } ''', name='Bob', dob=datetime.date(1984, 3, 1))
# Select User objects.user_set=client.query(
'SELECT User {name, dob} FILTER .name = <str>$name', name='Bob')
# *user_set* now contains# Set{Object{name := 'Bob', dob := datetime.date(1984, 3, 1)}}# Close the client.client.close()
if__name__=='__main__':
main()

Using the `models` generator

We provide a models generator that lets you build queries programmatically, and generate Pydantic models directly from your schema.

To use, run the following command:

$ gel generate py/models

This will find your Python project and add a models package to it. Then you can use the generated models to build queries and mutate instances of your objects directly.

importdatetimefrommodelsimportUser, stdfromgelimportcreate_clientdefmain():
client=create_client()
# Create a new User instance and save it to the databasebob=User(name='Bob', dob=datetime.date(1984, 3, 1))
client.save(bob)
# Select all Usersusers=client.query(User)
# Select all users with names like "Bob"bob_like=client.query(User.filter(lambdau: std.ilike(u.name, '%bob%')))
# Update an objectbob.name='Robert'client.save(bob)
# Delete an objectclient.execute(User.filter(id=bob.id).delete())
client.close()
if__name__=='__main__':
main()

Development

Instructions for installing Gel and gel-python locally can be found at docs.geldata.com/resources/guides/contributing/code.

To run the test suite, run $ python setup.py test.

License

gel-python is developed and distributed under the Apache 2.0 license.

Name		Name	Last commit message	Last commit date
.github		.github
docs		docs
edgedb		edgedb
gel		gel
tests		tests
tools		tools
.editorconfig		.editorconfig
.gitignore		.gitignore
.gitmodules		.gitmodules
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
Makefile		Makefile
README.rst		README.rst
flake.lock		flake.lock
flake.nix		flake.nix
pyproject.toml		pyproject.toml
setup.py		setup.py

Name		Name	Last commit message	Last commit date
.github		.github
docs		docs
edgedb		edgedb
gel		gel
tests		tests
tools		tools
.editorconfig		.editorconfig
.gitignore		.gitignore
.gitmodules		.gitmodules
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
Makefile		Makefile
README.rst		README.rst
flake.lock		flake.lock
flake.nix		flake.nix
pyproject.toml		pyproject.toml
setup.py		setup.py

Name		Name	Last commit message	Last commit date
.github		.github
docs		docs
edgedb		edgedb
gel		gel
tests		tests
tools		tools
.editorconfig		.editorconfig
.gitignore		.gitignore
.gitmodules		.gitmodules
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
Makefile		Makefile
README.rst		README.rst
flake.lock		flake.lock
flake.nix		flake.nix
pyproject.toml		pyproject.toml
setup.py		setup.py

Name		Name	Last commit message	Last commit date
.github		.github
docs		docs
edgedb		edgedb
gel		gel
tests		tests
tools		tools
.editorconfig		.editorconfig
.gitignore		.gitignore
.gitmodules		.gitmodules
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
Makefile		Makefile
README.rst		README.rst
flake.lock		flake.lock
flake.nix		flake.nix
pyproject.toml		pyproject.toml
setup.py		setup.py

Name		Name	Last commit message	Last commit date
.github		.github
docs		docs
edgedb		edgedb
gel		gel
tests		tests
tools		tools
.editorconfig		.editorconfig
.gitignore		.gitignore
.gitmodules		.gitmodules
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
Makefile		Makefile
README.rst		README.rst
flake.lock		flake.lock
flake.nix		flake.nix
pyproject.toml		pyproject.toml
setup.py		setup.py

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The Python driver for Gel

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The Python driver for Gel

Documentation

Installation

Basic Usage

Using the models generator

Development

License

About

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Forks

Releases

Packages

Used by

Contributors

Languages

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Latest commit

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The Python driver for Gel

Documentation

Installation

Basic Usage

Using the models generator

Development

License

About

Topics

Resources

Stars

Watchers

Forks

Releases

Packages

Used by

Contributors

Languages

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Latest commit

History

Folders and files

Repository files navigation

The Python driver for Gel

Documentation

Installation

Basic Usage

Using the models generator

Development

Using the `models` generator

Using the `models` generator

Using the `models` generator

Using the `models` generator

Using the `models` generator

Using the `models` generator