Type annotations for boto3. Adds code completion in IDEs such as PyCharm.
A programmatically created package that defines
boto3 services as dummy class with type annotations.
boto3 is 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 so that they can provide code completion or infer the type of these services or of the objects created by
them. This can be very frustrating, even more so when you're working with other dependencies which can be indexed.
To reduce this frustration,
boto3_type_annotations contains dummy objects of the clients, service resources,
paginators, and waiters provided by
boto3's services. Even though the client, service resources, paginators, and
waiters 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 object's methods. By parsing the docstrings of methods,
we can retrieve both the types of method arguments (We can also determine 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
boto3's objects. And with Python's
typing module, we can annotate the methods of the
dummy objects with the types which we've parsed. What this means is that we can use these dummy objects to declare the
boto3 service objects in our own code.
With or Without Docstrings
This package is available both with docstrings (which contain the same documentation you'll find online),
boto3_type_annotations_with_docs, and without,
boto3_type_annotations. The reason for this is that, for a python
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. With
botocore adding 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 defines it's 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
boto3 does something similar in that they have
requirements.txt for 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
boto3_type_annotations package, you won't have to change your code. You just need to install the
appropriate deployment package.
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 service that are essential to a 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
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