Simple GraphQL Client
sgqlc - Simple GraphQL Client
.. image:: https://github.com/profusion/sgqlc/actions/workflows/ci.yml/badge.svg
:target: https://github.com/profusion/sgqlc/actions/workflows/ci.yml
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:target: https://coveralls.io/github/profusion/sgqlc?branch=master
Introduction
------------
This package offers an easy to use `GraphQL <http://graphql.org>`_
client. It's composed of the following modules:
- ``sgqlc.types``: declare GraphQL in Python, base to generate and
interpret queries. Submodule ``sgqlc.types.datetime`` will
provide bindings for ``datetime`` and ISO 8601, while
``sgqlc.types.relay`` will expose ``Node``, ``PageInfo`` and
``Connection``.
- ``sgqlc.operation``: use declared types to generate and
interpret queries.
- ``sgqlc.endpoint``: provide access to GraphQL endpoints, notably
``sgqlc.endpoint.http`` provides ``HTTPEndpoint`` using
``urllib.request.urlopen()``.
What's GraphQL?
===============
Straight from http://graphql.org:
**A query language for your API**
GraphQL is a query language for APIs and a runtime for fulfilling
those queries with your existing data. GraphQL provides a complete
and understandable description of the data in your API, gives
clients the power to ask for exactly what they need and nothing
more, makes it easier to evolve APIs over time, and enables
powerful developer tools.
It was created by Facebook based on their problems and solutions using
`REST <https://en.wikipedia.org/wiki/Representational_state_transfer>`_
to develop applications to consume their APIs. It was publicly
announced at
`React.js Conf 2015 <https://reactjs.org/blog/2015/02/20/introducing-relay-and-graphql.html>`_
and started to gain traction since then. Right now there are big names
transitioning from REST to GraphQL:
`Yelp <https://www.yelp.com/developers/graphql/guides/intro>`_
`Shopify <https://help.shopify.com/api/storefront-api/graphql>`_
and `GitHub <https://developer.github.com/v4/>`_, that did an
excellent
`post <https://githubengineering.com/the-github-graphql-api/>`_
to explain why they changed.
A short list of advantages over REST:
- Built-in schema, with documentation, strong typing and
introspection. There is no need to use
`Swagger <https://swagger.io>`_ or any other external tools to play
with it. Actually GraphQL provides a standard in-browser IDE for
exploring GraphQL endpoints: https://github.com/graphql/graphiql;
- Only the fields that you want. The queries must explicitly select which
fields are required, and that's all you're getting. If more fields
are added to the type, they **won't break** the API, since the new
fields won't be returned to old clients, as they didn't ask for such
fields. This makes much easier to keep APIs stable and **avoids
versioning**. Standard REST usually delivers all available fields in
the results, and when new fields are to be included, a new API
version is added (reflected in the URL path, or in an HTTP header);
- All data in one request. Instead of navigating hypermedia-driven
RESTful services, like discovering new ``"_links": {"href"...`` and
executing a new HTTP request, with GraphQL you specify nested
queries and let the whole navigation be done by the server. This
reduces latency **a lot**;
- The resulting JSON object matches the given query exactly; if
you requested ``{ parent { child { info } } }``, you're going to
receive the JSON object ``{"parent": {"child": {"info": value }}}``.
From GitHub's
`Migrating from REST to GraphQL <https://developer.github.com/v4/guides/migrating-from-rest/>`_
one can see these in real life::
$ curl -v https://api.github.com/orgs/github/members
[
{
"login": "...",
"id": 1234,
"avatar_url": "https://avatars3.githubusercontent.com/u/...",
"gravatar_id": "",
"url": "https://api.github.com/users/...",
"html_url": "https://github.com/...",
"followers_url": "https://api.github.com/users/.../followers",
"following_url": "https://api.github.com/users/.../following{/other_user}",
"gists_url": "https://api.github.com/users/.../gists{/gist_id}",
"starred_url": "https://api.github.com/users/.../starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/.../subscriptions",
"organizations_url": "https://api.github.com/users/.../orgs",
"repos_url": "https://api.github.com/users/.../repos",
"events_url": "https://api.github.com/users/.../events{/privacy}",
"received_events_url": "https://api.github.com/users/.../received_events",
"type": "User",
"site_admin": true
},
...
]
brings the whole set of member information, however you just want name
and avatar URL::
query {
organization(login:"github") { # select the organization
members(first: 100) { # then select the organization's members
edges { # edges + node: convention for paginated queries
node {
name
avatarUrl
}
}
}
}
}
Likewise, instead of 4 HTTP requests::
curl -v https://api.github.com/repos/profusion/sgqlc/pulls/9
curl -v https://api.github.com/repos/profusion/sgqlc/pulls/9/commits
curl -v https://api.github.com/repos/profusion/sgqlc/issues/9/comments
curl -v https://api.github.com/repos/profusion/sgqlc/pulls/9/reviews
A single GraphQL query brings all the needed information, and just the
needed information::
query {
repository(owner: "profusion", name: "sgqlc") {
pullRequest(number: 9) {
commits(first: 10) { # commits of profusion/sgqlc PR #9
edges {
node { commit { oid, message } }
}
}
comments(first: 10) { # comments of profusion/sgqlc PR #9
edges {
node {
body
author { login }
}
}
}
reviews(first: 10) { # reviews of profusion/sgqlc/ PR #9
edges { node { state } }
}
}
}
}
Motivation to create `sgqlc`
============================
As seen above, writing GraphQL queries is very easy, and it is equally easy to
interpret the results. So **what was the rationale to create sgqlc?**
- GraphQL has its domain-specific language (DSL), and mixing two
languages is always painful, as seen with SQL + Python, HTML +
Python... Being able to write just Python in Python is much
better. Not to say that GraphQL naming convention is closer to
Java/JavaScript, using ``aNameFormat`` instead of Python's
``a_name_format``.
- Navigating dict-of-stuff is a bit painful:
``d["repository"]["pullRequest"]["commits"]["edges"]["node"]``,
since these are valid Python identifiers, we better write:
``repository.pull_request.commits.edges.node``.
- Handling new ``scalar`` types. GraphQL allows one to define new scalar
types, such as ``Date``, ``Time`` and ``DateTime``. Often these are
serialized as ISO 8601 strings and the user must parse them in their
application. We offer ``sgqlc.types.datetime`` to automatically
generate ``datetime.date``, ``datetime.time`` and
``datetime.datetime``.
- Make it easy to write dynamic queries, including nested. As seen,
GraphQL can be used to fetch lots of information in one go; however
if what you need (arguments and fields) changes based on some
variable, such as user input or cached data, then you need to
concatenate strings to compose the final query. This can be error
prone and servers may block you due to invalid queries. Some tools
"solve" this by parsing the query locally before sending it to
server. However usually the indentation is screwed and reviewing it
is painful. We change that approach: use
``sgqlc.operation.Operation`` and it will always generate valid
queries, which can be printed out and properly indented. Bonus point
is that it can be used to later interpret the JSON results into native
Python objects.
- Usability improvements whenever needed. For instance
`Relay <https://facebook.github.io/relay/>`_ published their
`Cursor Connections Specification <https://facebook.github.io/relay/graphql/connections.htm>`_
and its widely used. To load more data, you need to extend the
previous data with newly fetched information, updating not only the
nodes and edges, but also page information. This is done
automatically by ``sgqlc.types.relay.Connection``.
It also helps with code-generation, ``sgqlc-codegen`` can generate both
the classes matching a GraphQL Schema or functions to return
``sgqlc.operation.Operation`` based on executable documents
GraphQL Domain Specific Language (DSL).
Installation
------------
Automatic::
pip install sgqlc
From source using ``pip``::
pip install .
Usage
-----
To reach a GraphQL endpoint using synchronous `HTTPEndpoint` with a
hand-written query (see more at ``examples/basic/01_http_endpoint.py``):
.. code-block:: python
from sgqlc.endpoint.http import HTTPEndpoint
url = 'http://server.com/graphql'
headers = {'Authorization': 'bearer TOKEN'}
query = 'query { ... }'
variables = {'varName': 'value'}
endpoint = HTTPEndpoint(url, headers)
data = endpoint(query, variables)
However, writing GraphQL queries and later interpreting the results
may be cumbersome. That's solved by our ``sgqlc.types``, which is
usually paired with ``sgqlc.operation`` to generate queries and then
interpret results (see more at ``examples/basic/02_schema_types.py``). The
example below matches a subset of
`GitHub API v4 <https://developer.github.com/v4/query/>`_.
In GraphQL syntax it would be::
query {
repository(owner: "profusion", name: "sgqlc") {
issues(first: 100) {
nodes {
number
title
}
pageInfo {
hasNextPage
endCursor
}
}
}
}
The output JSON object is:
.. code-block:: json
{
"data": {
"repository": {
"issues": {
"nodes": [
{"number": 1, "title": "..."},
{"number": 2, "title": "..."}
]
},
"pageInfo": {
"hasNextPage": false,
"endCursor": "..."
}
}
}
}
.. code-block:: python
from sgqlc.endpoint.http import HTTPEndpoint
from sgqlc.types import Type, Field, list_of
from sgqlc.types.relay import Connection, connection_args
from sgqlc.operation import Operation
# Declare types matching GitHub GraphQL schema:
class Issue(Type):
number = int
title = str
class IssueConnection(Connection): # Connection provides page_info!
nodes = list_of(Issue)
class Repository(Type):
issues = Field(IssueConnection, args=connection_args())
class Query(Type): # GraphQL's root
repository = Field(Repository, args={'owner': str, 'name': str})
# Generate an operation on Query, selecting fields:
op = Operation(Query)
# select a field, here with selection arguments, then another field:
issues = op.repository(owner=owner, name=name).issues(first=100)
# select sub-fields explicitly: { nodes { number title } }
issues.nodes.number()
issues.nodes.title()
# here uses __fields__() to select by name (*args)
issues.page_info.__fields__('has_next_page')
# here uses __fields__() to select by name (**kwargs)
issues.page_info.__fields__(end_cursor=True)
# you can print the resulting GraphQL
print(op)
# Call the endpoint:
data = endpoint(op)
# Interpret results into native objects
repo = (op + data).repository
for issue in repo.issues.nodes:
print(issue)
Why double-underscore and overloaded arithmetic methods?
========================================================
Since we don't want to clobber GraphQL fields, we cannot provide
nicely named methods. Therefore we use overloaded methods such as
``__iadd__``, ``__add__``, ``__bytes__`` (compressed GraphQL
representation) and ``__str__`` (indented GraphQL representation).
To select fields by name, use ``__fields__(*names, **names_and_args)``.
This helps with repetitive situations and can be used to "include all
fields", or "include all except...":
.. code-block:: python
# just 'a' and 'b'
type_selection.__fields__('a', 'b')
type_selection.__fields__(a=True, b=True) # equivalent
# a(arg1: value1), b(arg2: value2):
type_selection.__fields__(
a={'arg1': value1},
b={'arg2': value2})
# selects all possible fields
type_selection.__fields__()
# all but 'a' and 'b'
type_selection.__fields__(__exclude__=('a', 'b'))
type_selection.__fields__(a=False, b=False)
Code Generator
--------------
Manually converting an existing GraphQL schema to ``sgqlc.types``
subclasses is boring and error prone. To aid such task we offer a code
generator that outputs a Python module straight from JSON of an
introspection call:
.. code-block:: console
user@host$ python3 -m sgqlc.introspection \
--exclude-deprecated \
--exclude-description \
-H "Authorization: bearer ${GH_TOKEN}" \
https://api.github.com/graphql \
github_schema.json
user@host$ sgqlc-codegen schema github_schema.json github_schema.py
This generates ``github_schema`` that provides the
``sgqlc.types.Schema`` instance of the same name ``github_schema``.
Then it's a matter of using that in your Python code, as in the example below
from ``examples/github/github_agile_dashboard.py``:
.. code-block:: python
from sgqlc.operation import Operation
from github_schema import github_schema as schema
op = Operation(schema.Query) # note 'schema.'
# -- code below follows as the original usage example:
# select a field, here with selection arguments, then another field:
issues = op.repository(owner=owner, name=name).issues(first=100)
# select sub-fields explicitly: { nodes { number title } }
issues.nodes.number()
issues.nodes.title()
# here uses __fields__() to select by name (*args)
issues.page_info.__fields__('has_next_page')
# here uses __fields__() to select by name (**kwargs)
issues.page_info.__fields__(end_cursor=True)
# you can print the resulting GraphQL
print(op)
# Call the endpoint:
data = endpoint(op)
# Interpret results into native objects
repo = (op + data).repository
for issue in repo.issues.nodes:
print(issue)
You can also generate these operations given a GraphQL Domain Specific
Language (DSL) operation:
.. code-block::
# sample_operations.gql
query ListIssues($owner: String!, $name: String!) {
repository(owner: $owner, name: $name) {
issues(first: 100) {
nodes {
number
title
}
pageInfo {
hasNextPage
endCursor
}
}
}
}
.. code-block:: console
user@host$ sgqlc-codegen operation \
--schema github_schema.json \
github_schema \
sample_operations.py \
sample_operations.gql
This generates ``sample_operations.py`` that provides the ``Operation``.
Then it's a matter of using that in your Python code, as in the example below
from ``examples/github/github-agile-dashboard.py``:
.. code-block:: python
from sample_operations import Operations
op = Operations.query.list_issues
# you can print the resulting GraphQL
print(op)
# Call the endpoint:
data = endpoint(op, {'owner': owner, 'name': name})
# Interpret results into native objects
repo = (op + data).repository
for issue in repo.issues.nodes:
print(issue)
Authors
-------
- `Gustavo Sverzut Barbieri <barbieri@profusion.mobi>`_
License
-------
`sgqlc` is licensed under the `ISC <https://opensource.org/licenses/ISC>`_.
Getting started developing
--------------------------
You need to use `poetry <https://python-poetry.org/docs/#installation>`_.
::
poetry install --all-extras --with dev
poetry shell
Install the `pre-commit <https://pre-commit.com/index.html#install>`_:
::
pre-commit install -f
Run the tests (one of the below):
::
pre-commit run -a # run all tests: flake8, pytest, ...
pre-commit run -a flake8 # run only flake8
pre-commit run -a tests # run only pytest (unit tests)
Keep 100% coverage. You can look at the coverage report at
``cover/index.html``. To do that, prefer
`doctest <https://docs.python.org/3.7/library/doctest.html>`_
so it serves as
both documentation and test. However we use
`pytest <https://docs.pytest.org/>`_ to write explicit tests that would be
hard to express using ``doctest``.
Build and review the generated Sphinx documentation, and validate if your
changes look right:
::
sphinx-build doc/source doc/build
open doc/build/html/index.html
To integrate changes from another branch, please **rebase** instead of
creating merge commits (
`read more <https://git-scm.com/book/en/v2/Git-Branching-Rebasing>`_).
Public Schemas
--------------
The following repositories provides public schemas generated using ``sgqlc-codegen``:
- `Mogost/sgqlc-schemas <https://github.com/Mogost/sgqlc-schemas>`_ GitHub, Monday.com