boto3

boto3 Best Practices

Boto3 is the definitive Python SDK for AWS. Mastering it means writing code that is performant, maintainable, and resilient. This guide outlines the essential patterns and anti-patterns for modern boto3 development.

1. Client/Resource Management & Reuse

Never instantiate boto3 clients or resources inside performance-critical loops or frequently called functions. Reusing client objects reduces credential churn, improves connection pooling, and significantly boosts performance.

✅ GOOD: Module-level or Dependency Injected Clients

Instantiate clients/resources once per module or inject them via a wrapper class.

# my_aws_service.py
import boto3
from typing import TYPE_CHECKING

if TYPE_CHECKING:
    from mypy_boto3_s3.client import S3Client
    from mypy_boto3_s3.service_resource import S3ServiceResource

# Module-level client/resource for simple cases
S3_CLIENT: S3Client = boto3.client("s3", region_name="us-east-1")
S3_RESOURCE: S3ServiceResource = boto3.resource("s3", region_name="us-east-1")

class S3Manager:
    """Manages S3 operations with a reusable client."""
    def __init__(self, s3_client: S3Client = S3_CLIENT):
        self._s3_client = s3_client

    def list_my_buckets(self) -> list[str]:
        response = self._s3_client.list_buckets()
        return [b["Name"] for b in response.get("Buckets", [])]

# In another part of your application:
manager = S3Manager()
buckets = manager.list_my_buckets()

❌ BAD: Repeated Client Instantiation

Avoid creating new clients/resources for every operation.

# my_aws_service_bad.py
import boto3

def get_bucket_names_bad() -> list[str]:
    # This creates a new client every time the function is called
    s3_client = boto3.client("s3", region_name="us-east-1")
    response = s3_client.list_buckets()
    return [b["Name"] for b in response.get("Buckets", [])]

# This is inefficient if called frequently
for _ in range(100):
    get_bucket_names_bad()

2. Choose Clients vs. Resources Wisely