After reaching out to AWS about releasing a stubs only package as detailed in PEP-561, they asked me to hold off on releasing it as they were working typing support. Then I got bogged down at work for about four months and didn't have time to work on this at all. And now I'm deprecating it. Check out this fork for a maintained version. They also uploaded a boto3-stubs package.
A programmatically created package that defines
boto3 services as stand in classes with type annotations.
an incredibly useful, well designed interface to the AWS API. However, we live in an age where even free IDEs like
PyCharm CE have full code completion (IntelliSense). Because
boto3's services are created at runtime, IDEs aren't
able to index its code in order to provide code completion or infer the type of these services or of the objects created
by them. Even if it was able to do so, clients and service resources are created using a service agnostic factory method
and are only identified by a string argument of that method. IDEs don't parse arguments to infer the return type of a
method, and they probably shouldn't. Meaning that the only way for an IDE to know the type of a client created by
boto3.client('<service>') is for it to be explicitly declared in type annotations, type comments, or docstrings, which
brings us back to the original problem of services being defined at runtime. All of that to say that working with
boto3 can be very frustrating at times.
To reduce this frustration,
boto3_type_annotations defines stand in classes for the clients, service resources,
paginators, and waiters provided by
boto3's services. Even though these services are created by
boto3 are created at
runtime, they are still full fledged Python objects, and AWS has been nice enough to include documentation in the
docstrings of these objects' methods. By parsing those docstrings, we can retrieve the types of method
arguments--also, which arguments are required and which may be omitted--and the types of their return
values. With that, we have everything we need to create objects which mimic the class structure of
And with Python's
typing module, we can annotate the methods of the stand in objects with the types which we've
parsed. What this means is that we can use these stand in objects to declare the type of
boto3 service objects in our
With or Without Docstrings
This package is available both with docstrings, named
boto3_type_annotations_with_docs on PyPi (which contains the
same documentation you'll find online), and without, named
boto3_type_annotations on PyPi. The reason for this is
that, for a python package,
boto3_type_annotations_with_docs is HUGE.
boto3_type_annotations is pretty large itself
at 2.2 MB, but
boto3_type_annotations_with_docs dwarfs it at 41 MB. Being that
botocore add up to be 34
MB, this is likely not ideal for many use cases. However, there are use cases in which you may want documentation in
your IDE, during development for example. A possible workflow for this use case is detailed below.
pip install boto3_type_annotations
pip install boto3_type_annotations_with_docs
Regardless of which deployment package you install, you'll still import the same package,
Its constituent packages and modules can be used to declare the type of
boto3 objects. For instance, everybody's
import boto3 from boto3_type_annotations.s3 import Client, ServiceResource from boto3_type_annotations.s3.waiter import BucketExists from boto3_type_annotations.s3.paginator import ListObjectsV2 # With type annotations client: Client = boto3.client('s3') client.create_bucket(Bucket='foo') # Not only does your IDE knows the name of this method, # it knows the type of the `Bucket` argument too! # It also, knows that `Bucket` is required, but `ACL` isn't! # Waiters and paginators and defined also... waiter: BucketExists = client.get_waiter('bucket_exists') waiter.wait('foo') paginator: ListObjectsV2 = client.get_paginator('list_objects_v2') response = paginator.paginate(Bucket='foo') # Along with service resources. resource: ServiceResource = boto3.resource('s3') bucket = resource.Bucket('bar') bucket.create() # With type comments client = boto3.client('s3') # type: Client response = client.get_object(Bucket='foo', Key='bar') # In docstrings class Foo: def __init__(self, client): """ :param client: It's an S3 Client and the IDE is gonna know what it is! :type client: Client """ self.client = client def bar(self): """ :rtype: Client """ self.client.delete_object(Bucket='foo', Key='bar') return self.client
How Is This Package Different From
pyboto3 has been a useful package which was created for the same purpose and using the same methodology as this
package. It does have its shortcomings, though. For one, it only defines clients, no service resources, waiters, or
paginators. Two, it defines2 clients as modules when the objects created by
boto3 are classes. This seems
nitpicky until you realize that modules can't be used to declare type with type annotations. Even a variable in the
outermost scope of a module would require rst docstring to declare its type. Also, and this is actually is nitpicky,
the package structure doesn't mimic that of
boto3--which you can see in the documentation i.e.
ec2.waiter.InstanceExists. Though I don't want to purport that this is perfectly one to one with what is
in the docs. For instance, there's not much consistency in the docs as far as casing. You'll sometimes see
S3.Waiter.BucketExists and in other places
sqs.Bucket. I chose to go with the pep8 guidelines where module names are
in snake case and classes are in Pascal case.
Development Workflow With Docstring
As mentioned above, there may be scenarios in which you would want to have docstrings in development, but not want
to package a 41MB dependency with your production code. To accommodate this and similar scenarios, I decided to provide
two deployment packages, each containing a
boto3_type_annotations package. So, one workflow may be to have two
requirements files: requirements.txt and requirements-dev.txt (
boto3 does something similar in that they have
requirements.txt for the API resource and requirements-docs.txt for building documentation.). These two files would
look like this:
boto3_type_annotations # other dependencies
boto3_type_annotations_with_docs # other dependencies
You would then install
requirements.txt in production and
requirements-dev.txt in development. Because both
deployment packages define the
boto3_type_annotations package, you won't have to change your code. You just need to
install the appropriate deployment package.
In cases when you're only using a small number of
boto3 services, you may not want to depend on a package containing
every service available. To provide a bit more flexibility, this package provides a way to create a custom build of
boto3_type_annotations package. The
configs/ directory contains configurations for
boto3_type_annnotations_with_docs, along with a couple example configurations.
services: # A list of services. Use `boto3.session.Session.get_available_services()` to view services. - ec2 - rds - sqs - sns - lambda - s3 with_docs: true # Include docstrings. with_clients: true # Include client classes with_service_resources: true # Include service resources. with_paginators: true # Include paginators with_waiters: true # Include waiters package_name: boto3_type_annotations_essentials # The name of the package. module_name: boto3_type_annotations # The name of the module. version: 0.2.4 # Version of the package. readme: README.md # Path to readme file. license: LICENSE # Path to file containing license.
The preceding configuration is the contents of
build.py is run with this
config, it will parse and write the ec2, rds, sqs, sns, lambda, and s3 services with docstrings and including all
clients, service resources, paginators, and waiters.
$ python build_scripts/build.py ../configs/example.essentials.yaml
It will create a directory named
in the root directory of the repository. That directory will contain a python module named
a license file, and a
setup.py file. Now all you need to do is package everything up and install it.
$ python setup.py sdist bdist_wheel $ pip3 install dist/boto3_type_annotations_essentials-0.2.4-py3-none-any.whl --user
Create an "essentials" deployment package only containing often used services like Lambda, S3, SQS, and CloudFormation
Package related services into separate deployment packages, to create smaller packages containing only services which are essential to a certain use case, group EC2 and RDS for instance.
Create custom builds. If a project only uses S3's service resource, provide a way to build a deployment package containing just that package. This would require some sort of configuration and more mature build script.
Reduce the size of
boto3_type_annotations_with_docs. I'm already cutting out extraneous new lines and some whitespaces which reduced the size by 10 MB(!), but I'd like to see it closer to the 34 MB of