Type Annotations for Boto3 DLM Service

1.42.84 · active · verified Sat Apr 11

mypy-boto3-dlm (version 1.42.84) provides static type annotations for the AWS Data Lifecycle Manager (DLM) service within the `boto3` library. It enables static analysis tools like MyPy and Pyright, and IDEs such as VSCode and PyCharm, to offer robust autocomplete, early error detection, and improved code quality for your `boto3` interactions. This package is automatically generated and kept in sync with `boto3` updates by the `mypy-boto3` project.

Warnings

breaking Starting with `mypy-boto3-builder` version 8.12.0 (which generates `mypy-boto3-dlm` versions like 1.42.84), support for Python 3.8 has been removed across all `mypy-boto3` packages. Projects must use Python 3.9 or newer.
Fix: Upgrade your project's Python version to 3.9 or higher.
breaking Type definition (TypeDef) naming conventions underwent breaking changes in `mypy-boto3-builder` version 8.9.0. If you were directly referencing generated TypeDefs by their full names (e.g., in custom code generation or by relying on old names), your code might break.
Fix: Review your code for direct references to TypeDef names and update them according to the new naming conventions (e.g., `CreateDistributionRequestRequestTypeDef` became `CreateDistributionRequestTypeDef`). Refer to the `mypy-boto3-builder` release notes for details.
gotcha The `mypy-boto3-dlm` package provides only type annotations. You must also install the `boto3` library itself for your code to function at runtime.
Fix: Ensure both `boto3` and `mypy-boto3-dlm` are installed: `pip install boto3 mypy-boto3-dlm`.
gotcha Users of PyCharm might experience slow performance or high CPU usage due to PyCharm's handling of `Literal` overloads in `mypy-boto3` packages. This is a known PyCharm issue (PY-40997).
Fix: Consider using `boto3-stubs-lite` (if available for DLM) which offers a more RAM-friendly version with fewer overloads, or disable PyCharm's internal type checker and rely on `mypy` or `pyright`.
gotcha Pylint can sometimes report `undefined-variable` warnings when `mypy-boto3` imports are only used within `if TYPE_CHECKING:` blocks. This is because Pylint may not correctly interpret these type-checking-only imports at runtime.
Fix: Use a pattern like `if TYPE_CHECKING: from mypy_boto3_dlm import DLMClient else: DLMClient = object` or add Pylint comments to suppress specific warnings (e.g., `# pylint: disable=unused-import`).

Install

pip install mypy-boto3-dlm boto3 Install `mypy-boto3-dlm` and `boto3`

Imports

DLMClient
```
from mypy_boto3_dlm import DLMClient
```
The client class is directly importable from the top-level package for convenience.

Quickstart

This example demonstrates how to import and use the `DLMClient` type for static analysis. The `if TYPE_CHECKING:` block ensures that the import is only active during type checking, avoiding potential runtime overhead or issues if stubs are not present in the production environment. Replace `policy-0123456789abcdef0` with an actual DLM policy ID for a successful response.

import boto3
from typing import TYPE_CHECKING

if TYPE_CHECKING:
    from mypy_boto3_dlm import DLMClient

# Instantiate the boto3 client with type hints
client: DLMClient = boto3.client("dlm", region_name="us-east-1")

# Example usage with type-checked methods and arguments
try:
    response = client.get_lifecycle_policies(
        PolicyIds=["policy-0123456789abcdef0"]
    )
    print("Successfully retrieved DLM policies:", response["Policies"])
except client.exceptions.ResourceNotFoundException as e:
    print(f"Policy not found: {e}")
except Exception as e:
    print(f"An error occurred: {e}")

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