Two, it defines2 clients as modules when the objects created by boto3 are classes. For one, it only defines clients, no service resources, waiters, or Pyboto3 has been a useful package which was created for the same purpose and using the same methodology as this client How Is This Package Different From pyboto3? delete_object ( Bucket = 'foo', Key = 'bar' ) return self. client = client def bar ( self ): """ :rtype: Client """ self. 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 ( 's3' ) # type: Client response = client. create () # With type comments client = boto3. paginate ( Bucket = 'foo' ) # Along with service resources. get_paginator ( 'list_objects_v2' ) response = paginator. wait ( 'foo' ) paginator : ListObjectsV2 = 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. For instance, everybody'sįavorite, S3: 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. Its constituent packages and modules can be used to declare the type of boto3 objects. Regardless of which deployment package you install, you'll still import the same package, boto3_type_annotations. With docs: pip install boto3_type_annotations_with_docs Without docs: pip install boto3_type_annotations A possible workflow for this use case is detailed below. Your IDE, during development for example. However, there are use cases in which you may want documentation in MB, this is likely not ideal for many use cases. Being that boto3 and botocore add up to be 34 boto3_type_annotations is pretty large itselfĪt 2.2 MB, but boto3_type_annotations_with_docs dwarfs it at 41 MB. That, for a python package, boto3_type_annotations_with_docs is HUGE. Same documentation you'll find online), and without, named boto3_type_annotations on PyPi. This package is available both with docstrings, named boto3_type_annotations_with_docs on PyPi (which contains the ![]() What this means is that we can use these stand in objects to declare the type of boto3 service objects in our With that, we have everything we need to create objects which mimic the class structure of boto3's objects.Īnd with Python's typing module, we can annotate the methods of the stand in objects with the types which we've By parsing those docstrings, we can retrieve the types of methodĪrguments-also, which arguments are required and which may be omitted-and the types of their return Runtime, they are still full fledged Python objects, and AWS has been nice enough to include documentation in theĭocstrings of these objects' methods. Even though these services are created by boto3 are created at Paginators, and waiters provided by boto3's services. To reduce this frustration, boto3_type_annotations defines stand in classes for the clients, service resources, Meaning that the only way for an IDE to know the type of a client created byīoto3.client('') is for it to be explicitly declared in type annotations, type comments, or docstrings, whichīrings us back to the original problem of services being defined at runtime. IDEs don't parse arguments to infer the return type of a Even if it was able to do so, clients and service resources are created using a service agnostic factory methodĪnd are only identified by a string argument of that method. Because boto3's services are created at runtime, IDEs aren'tĪble to index its code in order to provide code completion or infer the type of these services or of the objects createdīy them. ![]() P圜harm CE have full code completion (IntelliSense). However, we live in an age where even free IDEs like boto3 isĪn incredibly useful, well designed interface to the AWS API. A programmatically created package that defines boto3 services as stand in classes with type annotations.
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