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PyPI · #4290 most downloaded on PyPI
Prisma Client Python is an auto-generated and fully type-safe database client
Last release 2 years ago
no release in 18 months
Ships fairly regularly
a new release about every 2 months
Nearly every release is documented
notes for 30 of 30 stable releases
1 version withdrawn
withdrawn after publishing
5 years old
31 releases · first in 2021
One column per quarter.
The last release, v0.14.0, included a regression with the handling of optional values for BigInt, Decimal & Json fields where an error would be raised
The last release, v0.14.0, included a regression with the handling of optional values for BigInt, Decimal & Json fields where an error would be raised if the value was None.
Massive thank you to @AstreaTSS for a very detailed bug report and contributing a fix!
.create_many() for SQLiteThe create_many() method is now supported in SQLite!
total = await client.user.create_many([{'name': 'Robert'}, {'name': 'Tegan'}])
# 2
However the skip_duplicates argument is unfortunately not supported yet.
Massive thank you to @AdeelK93 for contributing this feature!
connect_or_createYou can now connect or create a relational record through the .create() method, for example:
post = await client.post.create(
data={
'title': 'Post 1',
'published': True,
'author': {
'connect_or_create': {
'where': {
'id': user.id,
},
'create': {
'name': 'Robert',
},
},
},
},
include={
'author': True,
},
)
Thanks to @AdeelK93 for contributing this feature!
A new search parameter has been added for string field filters in PostgreSQL and MySQL. The syntax within the string is entirely dependent on the underlying database.
For example, in PostgreSQL, a filter for all posts where the title contains "cat" or "dog" would look like this:
posts = await client.post.find_many(
where={
'title': {
'search': 'cats | dogs',
},
}
)
To use full text search you'll need to add it as a preview feature
generator client {
provider = "prisma-client-py"
previewFeatures = ["fullTextSearch"]
}
See these docs for more details on full text search.
Massive thank you again(!) to @AdeelK93 for contirbuting this feature.
Prisma now has preview support for splitting your schema.prisma file into multiple separate files!
Prisma now has preview support for splitting your schema.prisma file into multiple separate files!
For more information see their docs.
Python 3.7 has been EOL for a while now and accounts for less than 1% of the downloads of Prisma Client Python, as such, support was dropped in this release.
The internal Prisma version has been updated from v5.11.0 to v5.17.0 which includes a lot of improvements, see the Prisma release notes for more information.
This is a patch release to fix a minor regression with datasource env vars and datasource overriding.
This is a patch release to fix a minor regression with datasource env vars and datasource overriding.
The only user facing changes in this release are to the python -m prisma_cleanup script to avoid a deprecation warning.
Up until now, if you had a schema like this defined:
model OrgMember {
// ...
}
You would have to access it like this:
from prisma import Prisma
client = Prisma()
member = client.orgmember.find_unique(...)
With this release you can customise the instance name on the client, e.g. orgmember, to whatever you would like! For example:
/// @Python(instance_name: "org_member")
model OrgMember {
// ...
}
The OrgMember model can now be accessed through the client like so:
client = Prisma()
client.org_member.find_unique(...)
A future release will also add support for changing the default casing implementation used in the client!
The internal Prisma version has been bumped from 5.8.0 to 5.11.0, you can find the release notes for each version below:
Up until now Prisma Client Python didn't declare support for Python 3.12 but it will have worked at runtime. The only user facing changes in this release are to the python -m prisma_cleanup script to avoid a deprecation warning.
Some minor changes were made to modules that were intended to be private but were not marked with a preceding _.
prisma.builder has been moved to prisma._builderprisma.builder module, QueryBuilder, has new required constructor argumentsI've also started laying the ground work for usage of both the async client and the sync client at the same time and hope to support this in the next release!
This release bumps the internal Prisma version from 5.4.2 to 5.8.0 bringing major preview improvements to `distinct` & `joins`.
This release bumps the internal Prisma version from 5.4.2 to 5.8.0 bringing major preview improvements to distinct & joins.
This release also ensures we use a consistent enum format on Python 3.11 upwards, see this issue for more details.
This release bumps the internal Prisma version from 4.15.2 to 5.4.2 bringing major performance improvements.
This release bumps the internal Prisma version from 4.15.2 to 5.4.2 bringing major performance improvements.
This release also adds support for retrieving metrics, e.g.
from prisma import Prisma
client = Prisma()
metrics = client.get_metrics()
print(metrics.counters[0])
See the docs for more information.
…types, however support for int / float has been deprecated and will be removed in a future release.
This release adds support for Pydantic v2 while maintaining backwards compatibility with Pydantic v1.
This should be a completely transparent change for the majority of users with one exception regarding prisma.validate.
Due to this bug we can't use the new TypeAdapter concept in v2 and instead rely on the v1 re-exports, this has the implication that any errors raised by the validator will need to be caught using the v1 ValidationError type instead, e.g.
from pydantic.v1 import ValidationError
import prisma
try:
prisma.validate(...)
except ValidationError:
...
All timeout arguments now accept a datetime.timedelta instance as well as the previously accepted int / float types, however support for int / float has been deprecated and will be removed in a future release.
The previous values were used inconsistently, sometimes it meant seconds and sometimes it meant milliseconds. This new structure is more flexible and allows you to specify the time in whatever format you'd like. Thanks @jonathanblade!
from datetime import timedelta
from prisma import Prisma
db = Prisma(
connect_timeout=timedelta(seconds=20),
)
Thanks @zspine and @jonathanblade for contributing!
This release pins pydantic to < 2 as v2.0.0 is not yet compatible with Prisma.
This release pins pydantic to < 2 as v2.0.0 is not yet compatible with Prisma.
Thanks to @izeye and @Leon0824 for contributing!
This release adds support for interactive transactions! This means you can safely group sets of queries into a single atomic transaction that will be
This release adds support for interactive transactions! This means you can safely group sets of queries into a single atomic transaction that will be rolled back if any step fails.
Quick example:
from prisma import Prisma
prisma = Prisma()
await prisma.connect()
async with prisma.tx() as transaction:
user = await transaction.user.update(
where={'id': from_user_id},
data={'balance': {'decrement': 50}}
)
if user.balance < 0:
raise ValueError(f'{user.name} does not have enough balance')
await transaction.user.update(
where={'id': to_user_id},
data={'balance': {'increment': 50}}
)
For more information see the docs.
find_unique_or_raise & find_first_or_raiseThis release adds two new client methods, find_unique_or_raise & find_first_or_raise which are the exact same as the respective find_unique & find_first methods but will raise an error if a record could not be found.
This is useful when you know that you should always find a given record as you won't have to explicitly handle the case where it isn't found anymore:
user = await db.user.find_unique_or_raise(
where={
'id': '...',
},
include={
'posts': True,
},
)
This release bumps the internal Prisma version from v4.11.0 to v4.15.0.
Some of the highlights:
prisma db seedFor the full release notes, see the v4.12.0, v4.13.0, v4.14.0 and v4.15.0 release notes.
typing-extensions version from 3.7 to 4.0.1--generator option to prisma py generate as wellHuge thank you to @isometric & @techied for their continued support!
This release bumps the internal Prisma version from v4.10.1 to v4.11.0, although there aren't any major changes here for Prisma Client Python users.
This release bumps the internal Prisma version from v4.10.1 to v4.11.0, although there aren't any major changes here for Prisma Client Python users.
The full release notes can be found here.
In the next release the mypy plugin will be deprecated and later removed entirely. There is a bug in the plugin API in the latest versions of mypy tha…
This release adds support for selecting fields at the database level!
This currently only works for queries using model based access either by defining your own model classes or generating them using partial types.
Quick example:
from prisma.bases import BaseUser
class UserWithName(BaseUser):
name: str
# this query will only select the `name` field at the database level!
user = await UserWithName.prisma().find_first(
where={
'country': 'Scotland',
},
)
print(user.name)
For a more detailed guide see the docs.
You can now pass in a distinct filter to find_first() and find_many() queries.
For example, the following query will find all Profile records that have a distinct, or unique, city field.
profiles = await db.profiles.find_many(
distinct=['city'],
)
# [
# { city: 'Paris' },
# { city: 'Lyon' },
# ]
You can also filter by distinct combinations, for example the following query will return all records that have a distinct city and country combination.
profiles = await db.profiles.find_many(
distinct=['city', 'country'],
)
# [
# { city: 'Paris', country: 'France' },
# { city: 'Paris', country: 'Denmark' },
# { city: 'Lyon', country: 'France' },
# ]
Thanks to @yukukotani's great work on the CLI you can now ergonomically share the same schema between multiple languages, for example with the following schema:
datasource db {
provider = "sqlite"
url = "file:./dev.db"
}
generator node {
provider = "prisma-client-js"
}
generator python {
provider = "prisma-client-py"
}
model User {
id Int @id
name String
}
You can now skip the generation of the Node client with the --generator argument:
prisma generate --generator=python
See the generate documentation for more details.
This release bumps the internal Prisma version from v4.8.0 to v4.10.1
For the full release notes, see the v4.9.0 release notes and the v4.10.0 release notes.
Before this release there were no explicit compatibility requirements for type checkers. From now on we will only support the latest versions of Mypy and Pyright.
In the next release the mypy plugin will be deprecated and later removed entirely. There is a bug in the plugin API in the latest versions of mypy that completely breaks the plugin and seems impossible to fix. See #683 for more information.
Massive thank you to @prisma, @techied, @exponential-hq and @danburonline for their continued support! Thank you to @paudrow for becoming a sponsor!
This release contains some changes to the format of the Prisma Schema.
This release contains some changes to the format of the Prisma Schema.
Most of these changes are due to a conceptual shift from allowing implicit behaviour to forcing verboseness to reduce the amount of under the hood magic that Prisma does, thankfully this means that a lot of the changes that you will be required to make should be pretty straightforward and easily fixable by running prisma format which will show you all the places that need changed in your schema.
Changes:
sqlite:// URL prefix, you should now use file:// insteadFor more details see the Prisma v4.0.0 upgrade path.
This release includes an internal restructuring of how raw queries are deserialized. While all these changes should be completely backwards compatible, there may be edge cases that have changed. If you encounter one of these edge cases please open an issue and it will be fixed ASAP.
For some additional context, this restructuring means that most fields will internally be returned as strings until Prisma Client Python deserializes them (previously this was done at the query engine level).
This release completely refactors how the Prisma CLI is downloaded and ran. The previous implementation relied on downloading a single pkg binary, this worked but had several limitations which means we now:
The new solution is involves directly downloading a Node.js binary (if you don't already have it installed) and directly installing the Prisma ClI through npm. Note that this does not pollute your userspace and does not make Node available to the rest of your system.
This will result in a small size increase (~150MB) in the case where Node is not already installed on your machine, if this matters to you you can install Prisma Client Python with the node extra, e.g. pip install prisma[node], which will install a Node binary to your site-packages that results in the same storage requirements as the previous pkg solution. You can also directly install nodejs-bin yourself. It's also worth noting that this release includes significant (~50%) reduction in the size of the Prisma Engine binaries which makes the default Node binary size increase less impactful.
With this release you can now run Prisma Studio from the CLI which makes it incredibly easy to view & edit the data in your database. Simply run the following command
$ prisma studio
Or
$ prisma studio --schema=backend/schema.prisma
Note that there is also a dark mode available
This release adds official support for CockroachDB. You could've used CockroachDB previously by setting provider to postgresql but now you can explicitly specify CockroachDB in your Prisma Schema:
datasource db {
provider = "cockroachdb"
url = env("COCKROACHDB_URL")
}
It should be noted that there are a couple of edge cases:
TL;DR for improvements made by Prisma that will now be in Prisma Client Python
Full list of changes:
/tmp by defaultGoing forward we will now use a GitHub Project to track state and relative priority of certain issues. If you'd like to increase the priority of issues that would benefit you please add 👍 reactions.
This is less of a roadmap per se but will hopefully give you some insight into the priority of given issues / features.
Thank you to @kfields for helping with raw query deserialization!
Massive thank you to @prisma & @techied for their continued support and @exponential-sponsorship for becoming a sponsor!
Argument list too long when connecting to a database with a large schema error
This release adds official support for the Windows platform!
The main fix that comes with this release is a workaround for the missing error messages issue that has plagued so many.
A lot of the effort that went into this release was improving our internal testing strategies. This involved a major overhaul of our testing suite so that we can easily test multiple different database providers. This means we will be less likely to ship bugs and will be able to develop database specific features much faster!
In addition to the refactored test suite we also have new docker-based tests for ensuring compatibility with multiple platforms and environments that were previously untested. @jacobdr deserves a massive thank you for this!
It should be noted that you may encounter some deprecation warnings from the transitive dependencies we use.
Previously there was a mismatch between the resolution algorithm for relative SQLite paths which could cause the Client and the CLI to point to different databases.
The mismatch is caused by the CLI using the path to the Prisma Schema file as the base path whereas the Client used the current working directory as the base path.
The Client will now use the path to the Prisma Schema file as the base path for all relative SQLite paths, absolute paths are unchanged.
pyproject.toml fileYou can now configure Prisma Client Python using an entry in your pyproject.toml file instead of having to set environment variables, e.g.
[tool.prisma]
binary_cache_dir = '.binaries'
It should be noted that you can still use environment variables if you so desire, e.g.
PRISMA_BINARY_CACHE_DIR=".binaries"
This will also be useful as a workaround for #413 until the default behaviour is changed in the next release.
See the documentation for more information.
.env files overriding environment variablesPreviously any environment variables present in the .env or prisma/.env file would take precedence over the environment variables set at the system level. This behaviour was not correct as it does not match what the Prisma CLI does. This has now been changed such that any environment variables in the .env file will only be set if there is not an environment variable already present.
Python 3.11 is now officially supported and tested!
It should be noted that you may encounter some deprecation warnings from the transitive dependencies we use.
Bytes typesYou can now generate JSON Schemas / OpenAPI Schemas for models that use the Bytes type.
from prisma import Base64
from pydantic import BaseModel
class MyModel(BaseModel):
image: Base64
print(MyModel.schema_json(indent=2))
{
"title": "MyModel",
"type": "object",
"properties": {
"image": {
"title": "Image",
"type": "string",
"format": "byte"
}
},
"required": [
"image"
]
}
Base64 type in custom pydantic modelsYou can now use the Base64 type in your own Pydantic models and benefit from all the advanced type coercion that Pydantic provides! Previously you would have to manually construct the Base64 instances yourself, now Pydantic will do that for you!
from prisma import Base64
from pydantic import BaseModel
class MyModel(BaseModel):
image: Base64
# pass in a raw base64 encoded string and it will be transformed to a Base64 instance!
model = MyModel.parse_obj({'image': 'SGV5IHRoZXJlIGN1cmlvdXMgbWluZCA6KQ=='})
print(repr(model.image)) # Base64(b'SGV5IHRoZXJlIGN1cmlvdXMgbWluZCA6KQ==')
It should be noted that this assumes that the data you pass is a valid base64 string, it does not do any conversion or validation for you.
You can now unregister a client instance, this can be very useful for writing tests that interface with Prisma Client Python. However, you shouldn't ever have to use this outside of a testing context as you should only be creating a single Prisma instance for each Python process unless you are supporting multi-tenancy. Thanks @leejayhsu for this!
from prisma.testing import unregister_client
unregister_client()
You can now access the location of the Prisma Schema file used to generate Prisma Client Python.
from prisma import SCHEMA_PATH
print(SCHEMA_PATH) # Path('/absolute/path/prisma/schema.prisma')
builtins module, thanks @leejayhsu!*.pyc and __pycache__ files during client generation\exclude and exclude_relational_fields are givenMany thanks to @leejayhsu, @lewoudar, @tyteen4a03 and @nesb1 for contributing to this release!
A massive thank you to @prisma and @techied for their continued support! It is incredibly appreciated 💜
I'd also like to thank GitHub themselves for sponsoring me as part of Maintainer Month!
This release is a patch release to fix a regression, #402, introduced by the latest Pydantic release.
This release is a patch release to fix a regression, #402, introduced by the latest Pydantic release.
> This change is only applied when generating recursive types as mypy does not support LiteralString yet.
This change is only applied when generating recursive types as mypy does not support
LiteralStringyet.
PEP 675 introduces a new string type, LiteralString, this type is a supertype of literal string types that allows functions to accept any arbitrary literal string type such as 'foo' or 'bar' for example.
All raw query methods, namely execute_raw, query_raw and query_first now take the LiteralString type as the query argument instead of str. This change means that any static type checker thats supports PEP 675 will report an error if you try and pass a string that cannot be defined statically, for example:
await User.prisma().query_raw(f'SELECT * FROM User WHERE id = {user_id}')
This change has been made to help prevent SQL injection attacks.
Thank you to @leejayhsu for contributing this feature!
None valuesYou can now filter records to remove or include occurrences where a field is None or not. For example, the following query will return all User records with an email that is not None:
await client.user.find_many(
where={
'NOT': [{'email': None}]
},
)
It should be noted that nested None checks are not supported yet, for example this is not valid:
await client.user.find_many(
where={
'NOT': [{'email': {'equals': None}}]
},
)
It should also be noted that this does not change the return type and you will still have to perform not None checks to appease type checkers. e.g.
users = await client.user.find_many(
where={
'NOT': [{'email': None}]
},
)
for user in users:
assert user.email is not None
print(user.email.split('@'))
There are two new exception classes, ForeignKeyViolationError and FieldNotFoundError.
The ForeignKeyViolationError is raised when a foreign key field has been provided but is not valid, for example, trying to create a post and connecting it to a non existent user:
await client.post.create(
data={
'title': 'My first post!',
'published': True,
'author_id': '<unknown user ID>',
}
)
The FieldNotFoundError is raised when a field has been provided but is not valid in that context, for example, creating a record and setting a field that does not exist on that record:
await client.post.create(
data={
'title': 'foo',
'published': True,
'non_existent_field': 'foo',
}
)
The type definitions for creating records now contain the scalar relational fields as well as an alternative to the longer form for connecting relational fields, for example:
model User {
id String @id @default(cuid())
name String
email String @unique
posts Post[]
}
model Post {
id String @id @default(cuid())
author User? @relation(fields: [author_id], references: [id])
author_id String?
}
With the above schema and an already existent User record. You can now create a new Post record and connect it to the user by directly setting the author_id field:
await Post.prisma().create(
data={
'author_id': '<existing user ID>',
'title': 'My first post!',
},
)
This is provided as an alternative to this query:
await Post.prisma().create(
data={
'title': 'My first post!',
'author': {
'connect': {
'id': '<existing user ID>'
}
}
},
)
Although the above query should be preferred as it also exposes other methods, such as creating the relational record inline or connecting based on other unique fields.
The internal Prisma binaries that Prisma Python makes use of have been upgraded from v3.11.1 to v3.13.0. For a full changelog see the v3.12.0 release notes and v3.13.0 release notes.
Many thanks to @q0w and @leejayhsu for their first contributions!
Experimental support for the Decimal type has been added. The reason that support for this type is experimental is due to a missing internal feature i
Decimal typeExperimental support for the Decimal type has been added. The reason that support for this type is experimental is due to a missing internal feature in Prisma that means we cannot provide the same guarantees when working with the Decimal API as we can with the API for other types. For example, we cannot:
Decimal value with a greater precision than the database supports, leading to implicit truncation which may cause confusing errorsdecimal.Decimal objects to match the database level, potentially leading to even more confusing errors.If you need to use Decimal and are happy to work around these potential footguns then you must explicitly specify that you are aware of the limitations by setting a flag in the Prisma Schema:
generator py {
provider = "prisma-client-py"
enable_experimental_decimal = true
}
model User {
id String @id @default(cuid())
balance Decimal
}
The Decimal type maps to the standard library's Decimal class. All available query operations can be found below:
from decimal import Decimal
from prisma import Prisma
prisma = Prisma()
user = await prisma.user.find_first(
where={
'balance': Decimal(1),
# or
'balance': {
'equals': Decimal('1.23823923283'),
'in': [Decimal('1.3'), Decimal('5.6')],
'not_in': [Decimal(10), Decimal(20)],
'gte': Decimal(5),
'gt': Decimal(11),
'lt': Decimal(4),
'lte': Decimal(3),
'not': Decimal('123456.28'),
},
},
)
Updates on the status of support for Decimal will be posted in #106.
You can now add comments to your Prisma Schema and have them appear in the docstring for models and fields! For example:
/// The User model
model User {
/// The user's email address
email String
}
Will generate a model that looks like this:
class User(BaseModel):
"""The User model"""
email: str
"""The user's email address"""
If you try to import Prisma or Client before you've run prisma generate then instead of getting an opaque error message:
>>> from prisma import Prisma
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: cannot import name 'Prisma' from 'prisma' (/prisma/__init__.py)
you will now get an error like this:
>>> from prisma import Prisma
Traceback (most recent call last):
...
RuntimeError: The Client hasn't been generated yet, you must run `prisma generate` before you can use the client.
See https://prisma-client-py.readthedocs.io/en/stable/reference/troubleshooting/#client-has-not-been-generated-yet
You can now import the query batcher directly from the root package, making it much easier to type hint and providing support for an alternative API style:
from prisma import Prisma, Batch
prisma = Prisma()
async with Batch(prisma) as batcher:
...
def takes_batcher(batcher: Batch) -> None:
...
The internal Prisma binaries that Prisma Python makes use of have been upgraded from v3.10.0 to v3.11.1. For a full changelog see the v3.11.0 release notes and v3.11.1 release notes.
This project is now being sponsored by @prisma and @techied. I am so incredibly grateful to both for supporting Prisma Client Python 💜
…and our version. In v3.10.0 Prisma made some breaking changes to MongoDB schemas, for example:
This release is a patch release to fix incompatibilities between the documented MongoDB Prisma Schema and our version. In v3.10.0 Prisma made some breaking changes to MongoDB schemas, for example:
@default(dbgenerated()) with @default(auto())@db.Array(ObjectId) with @db.ObjectIdThis caused some confusion as following an official Prisma guide in their documentation resulted in an error (#326).
In v0.6.0 we renamed Prisma to Client, in doing so we accidentally removed the export for the previous Client name which was kept for backwards compat
In v0.6.0 we renamed Prisma to Client, in doing so we accidentally removed the export for the previous Client name which was kept for backwards compatibility. This release re-exports it so the following code will no longer raise an error:
from prisma import Client
in and not_in for Bytes fieldsFor example the following query will find the first record where binary_data is either my binary data or my other binary data.
from prisma import Base64
from prisma.models import Data
await Data.prisma().find_first(
where={
'binary_data': {
'in': [
Base64.encode(b'my binary data'),
Base64.encode(b'my other binary data'),
],
},
},
)
And if you want to find a record that doesn't match any of the arguments you can use not_in
from prisma import Base64
from prisma.models import Data
await Data.prisma().find_first(
where={
'binary_data': {
'not_in': [
Base64.encode(b'my binary data'),
Base64.encode(b'my other binary data'),
],
},
},
)
__slots__ definitionsAll applicable classes now define the __slots__ attribute for improved performance and memory usage, for more information on what this means, see the Python documentation.
Thank you to @matyasrichter 💜
Although very rare, it is sometimes possible to get your Prisma Client Python installation into a corrupted state when upgrading to a newer version. I
Although very rare, it is sometimes possible to get your Prisma Client Python installation into a corrupted state when upgrading to a newer version. In this situation you could try uninstalling and reinstalling Prisma Client Python however doing so will not always fix the client state, in this case you have to remove all of the files that are auto-generated by Prisma Client Python. To achieve this you would either have to manually remove them or download and run a script that we use internally.
With this release you can now automatically remove all auto-generated files by running the following command:
python -m prisma_cleanup
This will find your installed prisma package and remove the auto-generated files.
If you're using a custom output location then all you need to do is pass the import path, the same way you do to use the client in your code, for example:
python -m prisma_cleanup app.prisma
The name change that occurred in the last release has been reverted, see #300 for reasoning.
Fix transformation of fields nested within a list
In order to improve readability, the recommended method to import the client has changed from Client to Prisma. However, for backwards compatibility you can still import Client.
from prisma import Prisma
prisma = Prisma()
By default a warning is raised when you attempt to subclass a model while using pseudo-recursive types, see the documentation for more information.
This warning was raised when using a Prisma model in a FastAPI response model as FastAPI implicitly subclasses the given model. This means that the warning was actually redundant and as such has been removed, the following code snippet will no longer raise a warning:
from fastapi import FastAPI
from prisma.models import User
app = FastAPI()
@app.get('/foo', response_model=User)
async def get_foo() -> User:
...
The internal Prisma binaries that Prisma Python makes use of have been upgraded from v3.8.1 to v3.9.1. For a full changelog see the v3.9.0 release notes and v3.9.1 release notes.
You can now completely remove the internal HTTP timeout
from prisma import Prisma
prisma = Prisma(
http={
'timeout': None,
},
)
This project has been renamed from Prisma Client Python to Prisma Python
Thanks to @ghandic for the bug report and thanks to @kivo360 for contributing!
There are two arguments that were deprecated in previous releases that have now been removed:
update_many mutation data, updating relational fields from update_many is not supported yet (https://github.com/prisma/prisma/issues/3143).Python 3.6 reached its end of life on the 23rd of December 2021. You now need Python 3.7 or higher to use Prisma Client Python.
You can now group records by one or more field values and perform aggregations on each group!
It should be noted that the structure of the returned data is different to most other action methods, returning a TypedDict instead of a BaseModel.
For example:
results = await Profile.prisma().group_by(
by=['country'],
sum={
'views': True,
},
)
# [
# {"country": "Canada", "_sum": {"views": 23}},
# {"country": "Scotland", "_sum": {"views": 143}},
# ]
For more examples see the documentation: https://prisma-client-py.readthedocs.io/en/stable/reference/operations/#grouping-records
While the syntax is slightly different the official Prisma documentation is also a good reference: https://www.prisma.io/docs/concepts/components/prisma-client/aggregation-grouping-summarizing#group-by
You can now easily (and with full type-safety) define custom options for your own Prisma Generators!
from pydantic import BaseModel
from prisma.generator import GenericGenerator, GenericData, Manifest
# custom options must be defined using a pydantic BaseModel
class Config(BaseModel):
my_option: int
# we don't technically need to define our own Data class
# but it makes typing easier
class Data(GenericData[Config]):
pass
# the GenericGenerator[Data] part is what tells Prisma Client Python to use our
# custom Data class with our custom Config class
class MyGenerator(GenericGenerator[Data]):
def get_manifest(self) -> Manifest:
return Manifest(
name='My Custom Generator Options',
default_output='schema.md',
)
def generate(self, data: Data) -> None:
# generate some assets here
pass
if __name__ == '__main__':
MyGenerator.invoke()
There are two arguments that were deprecated in previous releases that have now been removed:
encoding argument to Base64.decode()order argument to actions.count()You can now update fields that are marked as @unique or @id:
user = await User.prisma().update(
where={
'email': 'robert@craigie.dev',
},
data={
'email': 'new@craigie.dev',
},
)
You can now easily replace the Prisma CLI binary that Prisma Client Python makes use of by overriding the PRISMA_CLI_BINARY environment variable. This shouldn't be necessary for the vast majority of users however as some platforms are not officially supported yet, binaries must be built manually in these cases.
The internal Prisma binaries that Prisma Client Python makes use of have been upgraded from v3.7.0 to v3.8.1. For a full changelog see the v3.8.0 release notes.
<!-- LAST PR: * chore(tests): fix local no event loop error by @RobertCraigie in https://github.com/RobertCraigie/prisma-client-py/pull/254 -->
Correctly render Enum fields within compound keys
Subclassing pseudo-recursive models will now raise a warning instead of crashing, static types will still not respect the subclass, for example:
from prisma.models import User
class MyUser(User):
@property
def fullname(self) -> str:
return f'{self.name} {self.surname}'
# static type checkers will think that `user` is an instance of `User` when it is actually `MyUser` at runtime
# you can fix this by following the steps here:
# https://prisma-client-py.readthedocs.io/en/stable/reference/limitations/#removing-limitations
user = MyUser.prisma().create(
data={
'name': 'Robert',
'surname': 'Craigie',
},
)
For more details, see the documentation
The default HTTP timeout used to communicate with the internal Query Engine has been increased from 5 seconds to 30 seconds, this means you should no longer encounter timeout errors when executing very large queries.
You can now customise the HTTPX Client used to communicate with the internal query engine, this could be useful if you need to increase the http timeout, for full reference see the documentation.
client = Client(
http={
'timeout': 100,
},
)
The internal Prisma binaries that Prisma Client Python makes use of have been upgraded from v3.4.0 to v3.7.0 for a full changelog see:
Instead of having to manually update the list of excluded fields when creating partial models whenever a new relation is added you can now just use exclude_relational_fields=True!
from prisma.models import User
User.create_partial('UserWithoutRelations', exclude_relational_fields=True)
class UserWithoutRelations:
id: str
name: str
email: Optional[str]
This release is a patch release, fixing a bug introduced in the dev CLI in v0.4.1
This release is a patch release, fixing a bug introduced in the dev CLI in v0.4.1 (#182)
You can now easily write your own Prisma generators in Python!
You can now easily write your own Prisma generators in Python!
For example:
generator.py
from pathlib import Path
from prisma.generator import BaseGenerator, Manifest, models
class MyGenerator(BaseGenerator):
def get_manifest(self) -> Manifest:
return Manifest(
name='My Prisma Generator',
default_output=Path(__file__).parent / 'generated.md',
)
def generate(self, data: Data) -> None:
lines = [
'# My Prisma Models!\n',
]
for model in data.dmmf.datamodel.models:
lines.append(f'- {model.name}')
output = Path(data.generator.output.value)
output.write_text('\n'.join(lines))
if __name__ == '__main__':
MyGenerator.invoke()
Then you can add the generator to your Prisma Schema file like so:
generator custom {
provider = "python generator.py"
}
Your custom generator will then be invoked whenever you run prisma generate
$ prisma generate
Prisma schema loaded from tests/data/schema.prisma
✔ Generated My Prisma Generator to ./generated.md in 497ms
For more details see the documentation: https://prisma-client-py.readthedocs.io/en/latest/reference/custom-generators/
You can now use the Client as a context manager to automatically connect and disconnect from the database, for example:
from prisma import Client
async with Client() as client:
await client.user.create(
data={
'name': 'Robert',
},
)
For more information see the documentation: https://prisma-client-py.readthedocs.io/en/stable/reference/client/#context-manager
You can now automatically register the Client when it is created:
from prisma import Client
client = Client(auto_register=True)
Which is equivalent to:
from prisma import Client, register
client = Client()
register(client)
The default timeout used for connecting to the database can now be set at the client level, for example:
from prisma import Client
client = Client(connect_timeout=5)
You can still explicitly specify the timeout when connecting, for example:
from prisma import Client
client = Client(connect_timeout=5)
client.connect() # timeout: 5
client.connect(timeout=10) # timeout: 10
DateTime microsecond precision is now truncated to 3 places (#129)The order argument to the count() method has been deprecated, this will be removed in the next release.
The following field names are now restricted and attempting to generate the client with any of them will now raise an error:
startswithendswithorder_bynot_inis_notBytes typeYou can now create models that make use of binary data, this is stored in the underlying database as Base64 data, for example:
model User {
id Int @id @default(autoincrement())
name String
binary Bytes
}
from prisma import Base64
from prisma.models import User
user = await User.prisma().create(
data={
'name': 'Robert',
'binary': Base64.encode(b'my binary data'),
},
)
print(f'binary data: {user.binary.decode()}')
You can now query for and update scalar list fields, for example:
model User {
id Int @id @default(autoincrement())
emails String[]
}
user = await client.user.find_first(
where={
'emails': {
'has': 'robert@craigie.dev',
},
},
)
For more details, visit the documentation: https://prisma-client-py.readthedocs.io/en/latest/reference/operations/#lists-fields
The order argument to the count() method has been deprecated, this will be removed in the next release.
All query action methods now have auto-generated docstrings specific for each model, this means that additional documentation will be shown when you hover over the method call in your IDE, for example:
typing-extensions is now a required dependencyThe prisma field name is now reserved, trying to generate a model that has a field called prisma will raise an error.
The prisma field name is now reserved, trying to generate a model that has a field called prisma will raise an error.
You can, however, still create a model that uses the prisma field name at the database level.
model User {
id String @id @default(cuid())
prisma_field String @map("prisma")
}
You can now run prisma queries directly from model classes, for example:
from prisma.models import User
user = await User.prisma().create(
data={
'name': 'Robert',
},
)
This API is exactly the same as the previous client-based API.
To get starting running queries from model classes, you must first register the prisma client instance that will be used to communicate with the database.
from prisma import Client, register
client = Client()
register(client)
await client.connect()
For more details, visit the documentation.
You can now select which fields are returned by count().
This returns a dictionary matching the fields that are passed in the select argument.
from prisma.models import Post
results = await Post.prisma().count(
select={
'_all': True,
'description': True,
},
)
# {'_all': 3, 'description': 2}
Python 3.10 is now officially supported.
The internal Prisma binaries that Prisma Client Python uses have been upgraded from 3.3.0 to 3.4.0.
prisma db push support for MongoDBThe current version of the client will now be displayed post-generation:
Prisma schema loaded from schema.prisma
✔ Generated Prisma Client Python (v0.3.0) to ./.venv/lib/python3.9/site-packages/prisma in 765ms
An explicit and helpful message is now shown when attempting to generate the Python Client using an unexpected version of Prisma.
Environment variables loaded from .env
Prisma schema loaded from tests/data/schema.prisma
Error:
Prisma Client Python expected Prisma version: 1c9fdaa9e2319b814822d6dbfd0a69e1fcc13a85 but got: da6fafb57b24e0b61ca20960c64e2d41f9e8cff1
If this is intentional, set the PRISMA_PY_DEBUG_GENERATOR environment variable to 1 and try again.
Are you sure you are generating the client using the python CLI?
e.g. python3 -m prisma generate (type=value_error)
--type-depth option to prisma py generateThis release is a patch release, the v0.2.3 release erroneously contained auto-generated files.
This release is a patch release, the v0.2.3 release erroneously contained auto-generated files.
This release has been yanked from PyPi as it contained auto-generated files, please install using 0.2.4 or greater.
This release has been yanked from PyPi as it contained auto-generated files, please install using 0.2.4 or greater.
The internal Prisma binaries that Prisma Client Python uses have been upgraded from 3.1.1 to 3.3.0.
For a full list of changes see https://github.com/prisma/prisma/releases/tag/3.2.0 and https://github.com/prisma/prisma/releases/tag/3.3.0
The python package has been renamed from prisma-client to prisma!
The python package has been renamed from prisma-client to prisma!
You can now install the client like so:
pip install prisma
You can still install using the old package name, however no new releases will be published.
The datasource can be dynamically overriden when the client is instantiated:
from prisma import Client
client = Client(
datasource={
'url': 'file:./dev_qa.db',
},
)
This is especially useful for testing purposes.
This feature is only supported when using PostgreSQL and MongoDB.
This feature is only supported when using PostgreSQL and MongoDB.
user = await client.user.find_first(
where={
'name': {
'contains': 'robert',
'mode': 'insensitive',
},
},
)
The internal Prisma binaries that Prisma Client Python uses have been upgraded from 2.30.0 to 3.1.1.
This brings with it a lot of new features and improvements:
For a full list of changes see https://github.com/prisma/prisma/releases/tag/3.1.1 and https://github.com/prisma/prisma/releases/tag/3.0.1
Prisma Client Python now comes bundled with a type validator, this makes it much easier to pass untrusted / untyped arguments to queries in a robust and type safe manner:
import prisma
from prisma.types import UserCreateInput
def get_untrusted_input():
return {'points': input('Enter how many points you have: ')}
data = prisma.validate(UserCreateInput, get_untrusted_input())
await client.user.create(data=data)
Any invalid input would then raise an easy to understand error (note: edited for brevity):
Enter how many points you have: a lot
Traceback:
pydantic.error_wrappers.ValidationError: 1 validation error for UserCreateInput
points
value is not a valid integer (type=type_error.integer)
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