convex 0.4.0

Python client for the reactive backend-as-a-service Convex.

Convex

The official Python client for Convex.

Write and read data from a Convex backend with queries, mutations, and actions. Get up and running at docs.convex.dev.

Installation:

pip install convex

Basic usage:

>>> from convex import ConvexClient
>>> client = ConvexClient('https://example-lion-123.convex.cloud')
>>> messages = client.query("listMessages")
>>> from pprint import pprint
>>> pprint(messages)
[{'_creationTime': 1668107495676.2854,
  '_id': Id(table_name='messages', id='c09S884lW4kTLdQMtu2ravf'),
  'author': 'Tom',
  'body': 'Have you tried Convex?'},
 {'_creationTime': 1668107497732.2295,
  '_id': Id(table_name='messages', id='G3m0cCQp65GQDfUjUDnTPEj'),
  'author': 'Sarah',
  'body': "Yeah, it's working pretty well for me."}]
>>> client.mutation("sendMessage", dict(author="Me", body="Hello!"))

To find the url of your convex backend, open the deployment you want to work with in the appropriate project in the Convex dashboard and click "Settings" where the Deployment URL should be visible. To find out which queries, mutations, and actions are available check the Functions pane in the dashboard.

To see logs emitted from Convex functions, set the debug mode to True.

>>> client.set_debug(True)

To provide authentication for function execution, call set_auth().

>>> client.set_auth("token-from-authetication-flow")

Join us on Discord to get your questions answered or share what you're doing with Convex. If you're just getting started, see https://docs.convex.dev to see how to quickly spin up a backend that does everything you need in the Convex cloud.

Convex types

Convex backend functions are written in JavaScript, so arguments passed to Convex RPC functions in Python are serialized, sent over the network, and deserialized into JavaScript objects. To learn about Convex's supported types see https://docs.convex.dev/using/types.

In order to call a function that expects a JavaScript type, use the corresponding Python type or any other type that coerces to it. Values returned from Convex will be of the corresponding Python type.

JavaScript Type	Python Type	Example	Other Python Types that Convert
Id	Id (see below)	Id(tableName, id)
null	None	None
bigint	ConvexBigInt (see below)	ConvexInt64(2**60)
number	float or int	3.1, 10
boolean	bool	True, False
string	str	'abc'
ArrayBuffer	bytes	b'abc'	ArrayBuffer
Array	list	[1, 3.2, "abc"]	tuple, collections.abc.Sequence
Set	ConvexSet (see below)	ConvexSet([1,2])	set, frozenset, collections.abc.Set
Map	ConvexMap (see below)	ConvexMap([('a', 1), ('b', 2)])
object	dict	{a: "abc"}	collections.abc.Mapping

Id

Id objects represent references to Convex documents. They contain a table_name string specifying a Convex table (tables can be viewed in the dashboard) and a globably unique id string. If you'd like to learn more about the id string's format, see our docs.

Ints and Floats

While Convex supports storing Int64s and Float64s, idiomatic JavaScript pervasively uses the (floating point) Number type. In Python floats are often understood to contain the ints: the float type annotation is generally understood as Union[int, float].

Therefore, the Python Convex client converts Python's floats and ints to a Float64 in Convex.

To specify a JavaScript BigInt, use the ConvexInt64 class. Functions which return JavaScript BigInts will return ConvexBigInt64 instances.

ConvexSet

Similar to a Python set, but any Convex values can be items.

ConvexSets are returned from Convex cloud function calls that return JavaScript Sets.

Generally when calling Convex functions from Python, a Python builtin set can be used instead of a ConvexSet. But for representing unusual types like sets containing objects, you'll have to use a ConvexSet:

>>> set([{'a': 1}])
Traceback (most recent call last):
    ...
TypeError: unhashable type: 'dict'
>>> ConvexSet([{'a': 1}])
ConvexSet([{'a': 1.0}])

ConvexSet instances are immutable so must be fully populated when being constructed. In order to store mutable items, ConvexSets store snapshots of data when it was added.

>>> mutable_dict = {'a': 1}
>>> s = ConvexSet([mutable_dict, 'hello', 1])
>>> mutable_dict in s
True
>>> mutable_dict['b'] = 2
>>> mutable_dict in s
False
>>> s
ConvexSet([{'a': 1.0}, 'hello', 1.0])

ConvexSets perform a copy of each inserted item, so they require more memory than Python's builtin sets.

ConvexMap

Similar to a Python map, but any Convex values can be keys.

ConvexMaps are returned from Convex cloud function calls that return JavaScript Maps.

ConvexMaps are useful when calling Convex functions that expect a Map because dictionaries correspond to JavaScript objects, not Maps.

ConvexMap instances are immutable so must be fully populated when being constructed. In order to store mutable items, ConvexMaps store snapshots of data when it was added.

>>> mutable_dict = {'a': 1}
>>> s = ConvexMap([(mutable_dict, 123), ('b', 456)])
>>> mutable_dict in s
True
>>> mutable_dict['b'] = 2
>>> mutable_dict in s
False
>>> s
ConvexMap([({'a': 1.0}, 123.0), ('b', 456.0)])

ConvexMaps perform a copy of each inserted key/value pair, so they require more memory than Python's builtin dictionaries.

Pagination

Paginated queries are queries that accept pagination options as an argument and can be called repeatedly to produce additional "pages" of results.

For a paginated query like this:

import { query } from "./_generated/server";

export default query(async ({ db }, { paginationOpts }) => {
  return await db.query("messages").order("desc").paginate(paginationOpts);
});

and returning all results 5 at a time in Python looks like this:

import convex
client = convex.ConvexClient('https://happy-animal-123.convex.cloud')

done = False
cursor = None
data = []

while not done:
    result = client.query('listMessages', {"paginationOpts": {"numItems": 5, "cursor": cursor}})
    cursor = result['continueCursor']
    done = result["isDone"]
    data.extend(result['page'])
    print('got', len(result['page']), 'results')

print('collected', len(data), 'results')

Versioning

While we are pre-1.0.0, we'll update the minor version for large changes, and the patch version for small bugfixes. We may make backwards incompatible changes to the python client's API, but we will limit those to minor version bumps.