Extension for boto3 to ease work with s3
pip install saritasa-s3-toolsor if you are using poetry
poetry add saritasa-s3-toolsS3ClientandAsyncS3Clientfor integrations with s3 buckets. This clients are extension to boto3 clients with proper typing, support for async and method to generate signed urls for file upload.S3FileTypeConfigfor defining configuration parameters for direct upload to s3. Check out moreS3Keyfor generating unique keys for s3 upload, used forS3FileTypeConfigS3FileFieldandS3ImageFileField- factory-boy fields for generating files and saving it ins3pytestplugin with fixtures forboto3,S3ClientandAsyncS3Clientand etcDjangoplugin for setting up models and api
saritasa-s3-tools comes with django plugin which helps with models
config and api(Django Rest Framework and drf_spectacular).
You can try it out in example folder.
First you need to add file/image fields to you model, like below
import saritasa_s3_tools.django
from django.db import models
class ModelWithFiles(models.Model):
"""Test model with different files configs."""
file = saritasa_s3_tools.django.S3FileField(
blank=True,
null=True,
# S3FileTypeConfig is needed so that we could understand where to save
# file, who can save file, what files can be save and what are size
# constraints
s3_config=saritasa_s3_tools.S3FileTypeConfig(
name="django-files",
key=saritasa_s3_tools.keys.WithPrefixUUIDFolder("django-files"),
allowed=("text/plain",),
auth=lambda user: bool(user and user.is_authenticated),
content_length_range=(1000, 20000000),
),
)
image = saritasa_s3_tools.django.S3ImageField(
blank=True,
null=True,
s3_config=saritasa_s3_tools.S3FileTypeConfig(
name="django-images",
key=saritasa_s3_tools.keys.WithPrefixUUIDFolder("django-images"),
allowed=("image/png",),
content_length_range=(5000, 20000000),
),
)Then add S3FieldsConfigMixin mixin to your serializer, like this
from rest_framework import serializers
import saritasa_s3_tools.django
from .. import models
class ModelWithFilesSerializer(
saritasa_s3_tools.django.S3FieldsConfigMixin,
serializers.ModelSerializer,
):
"""Serializer to show info model with files."""
class Meta:
model = models.ModelWithFiles
fields = "__all__"Then just add S3GetParamsView view to your project urls like that.
from django.urls import path
path(
"s3/",
include("saritasa_s3_tools.django.urls"),
name="saritasa-s3-tools",
),Just add this to core conftest.py file
import pytest
@pytest.fixture(scope="session", autouse=True)
def _adjust_s3_bucket(django_adjust_s3_bucket: None) -> None:
"""Set bucket to a test one."""AWS_S3_SIGNATURE_VERSION = "s3v4"By default we assume s3v4 version for signature. We recommend that you would
set AWS_S3_SIGNATURE_VERSION to s3v4. If you need other versions, set it in
AWS_S3_SIGNATURE_VERSION and update SARITASA_S3_TOOLS_UPLOAD_PARAMS
(defaults are here) setting
to reflect expected fields that would return.
[async]- Add this to enable async support[factory]- Add this to enable factory-boy fieldS3FileFieldandS3ImageFileFieldfromsaritasa_s3_tools.factory[testing]- Add this to enable testing helping functions fromsaritasa_s3_tools.testing.shortcuts[django]- Add this to enable django support[django-openapi]- Add this to enable drf-spectacular support
To install all optional dependencies add [all]
import saritasa_s3_tools
import pathlib
import xml.etree.ElementTree
s3_client = saritasa_s3_tools.S3Client(
boto3_client=boto3_client,
default_bucket=s3_bucket,
)
s3_params = s3_client.generate_params(
filename=pathlib.Path(__file__).name,
config=saritasa_s3_tools.S3FileTypeConfig.configs["files"],
content_type="application/x-python-code",
extra_metadata={
"test": "123",
},
)
with (
httpx.Client() as client,
pathlib.Path(__file__).open("rb") as upload_file,
):
upload_response = client.post(
url=s3_params.url,
data={
key: value
for key, value in s3_params.params.items()
if value is not None
},
files={"file": upload_file.read()},
)
parsed_response = xml.etree.ElementTree.fromstring( # noqa: S314
upload_response.content.decode(),
)
file_key = parsed_response[2].text
file_url = parsed_response[0].textsaritasa-s3-tools comes with pytest plugin which can setup boto3 instances
and create/clean up buckets for testing. Supports pytest-xdist.
access_key_getter,s3_endpoint_url_getter,s3_region- are used to configure boto3 session and clients/resources that are used in tests. You can override them or set values in ini file for pytest. Plugin will tell what you are missing.aws_session- Returnsboto3.Sessionaws_config- Returnsbotocore.config.Config, override if you need customization, None by default.boto3_resource- Returns s3 resource or in typingmypy_boto3_s3.S3ServiceResourceboto3_client- Returns s3 client or in typingmypy_boto3_s3.S3Clients3_bucket_name- Name of bucket for testing, default:saritasa-s3-toolsors3_bucket_namefrom ini file.s3_bucket_cleaner- Returns function which cleans all files from buckets3_bucket_factory- Returns manager which creates bucket, and when it's no longer needed deletes its3_bucket- Creates bucket vias3_bucket_factoryand return it's names3_client- Returnssaritasa_s3_tools.S3Clientasync_s3_client- Returnssaritasa_s3_tools.AsyncS3Client