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AWS SDK for Python (boto3)

boto3 is the high-level Python SDK for AWS. It wraps botocore (the low-level SDK) and provides two distinct interfaces: clients (low-level, 1:1 API mapping) and resources (high-level, object-oriented). Understanding which to use and when is essential.

Client vs Resource

Clients map directly to AWS service APIs. Every service has a client. Responses are plain dicts.

Resources provide an object-oriented interface with attributes and actions. Only some services have resources (S3, DynamoDB, EC2, IAM, SQS, SNS, CloudFormation, CloudWatch, Glacier). Resources auto-marshal types (especially useful for DynamoDB).

python

import boto3

# Client - low-level, all services
s3_client = boto3.client("s3")
response = s3_client.list_buckets()
buckets = response["Buckets"]  # plain dicts

# Resource - high-level, select services
s3_resource = boto3.resource("s3")
for bucket in s3_resource.buckets.all():
    print(bucket.name)  # attribute access, not dict keys

Use clients when you need full API coverage or the service has no resource interface. Use resources when they exist and simplify your code (especially DynamoDB and S3).

Session and Client Creation

python

import boto3

# Default session implicitly created
client = boto3.client("s3")
resource = boto3.resource("dynamodb")

# Explicit session use when you need to customize how
# clients are created, use an explicit profile, etc.
session = boto3.Session(
    profile_name="my-profile",
    region_name="us-west-2",
)
client = session.client("s3")

Do not create clients inside loops - reuse a single client instance. Clients are thread safe and can be shared across threads once they're instantiated.

Making API Calls

python

# Client - pass parameters as keyword arguments, get dicts back
response = client.get_object(Bucket="my-bucket", Key="my-key")
data = response["Body"].read()

# Resource - use object methods and attributes
obj = s3_resource.Object("my-bucket", "my-key")
response = obj.get()
data = response["Body"].read()

Parameter names match the exact casing of the AWS API, which is typically PascalCase, not snake_case.

Error Handling

Only catch exceptions when you have something actionable to do - return a fallback value, retry, take a different code path. Catching an exception just to print it and swallow it is wrong: it hides the real error and prevents callers from reacting. Let exceptions propagate by default.

When you do catch, prefer typed exceptions on the client over generic

ClientError

with string code matching through the

client.exceptions

attribute:

python

lambda_client = boto3.client("lambda")

def get_function_config(name: str) -> dict | None:
    """Return function configuration, or None if it doesn't exist."""
    try:
        return lambda_client.get_function_configuration(FunctionName=name)
    except lambda_client.exceptions.ResourceNotFoundException:
        return None  # actionable: convert missing function to None
    # Everything else propagates - caller or main() handles it

Use generic

ClientError

only as a catch-all in a top-level error handler, not in business logic functions. It lives in botocore, not boto3:

python

from botocore.exceptions import ClientError

def main() -> int:
    try:
        result = do_the_work()
        print(result)
        return 0
    except ClientError as e:
        print(f"Error: {e}", file=sys.stderr)
        return 1

For the full error hierarchy and botocore exceptions, see

references/error-handling.md

Script Structure

When asked to write a script that uses

boto3

botocore

, keep

if __name__ == "__main__"

to a single function call. Argument parsing, error presentation, and exit codes belong in

main()

, not scattered across business logic functions:

python

def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("bucket")
    args = parser.parse_args()

    try:
        do_the_work(args.bucket)
        return 0
    except ClientError as e:
        print(f"Error: {e}", file=sys.stderr)
        return 1

if __name__ == "__main__":
    sys.exit(main())

Never call

sys.exit()

from a business logic function -- it makes the function untestable and unusable as a library. Raise an exception or return an error value instead, and let

main()

decide how to present it.

Pagination

Never manually loop with

NextToken

-- use paginators. When you only need specific fields, use

.search()

with a JMESPath expression to extract and flatten across pages:

python

paginator = iam.get_paginator("list_users")
for name in paginator.paginate().search("Users[].UserName"):
    print(name)

# Filter and project
for arn in paginator.paginate().search("Users[?Path == '/admin/'][].Arn"):
    print(arn)

When you need the full response object per item, or need per-page control (e.g. counting pages, batching by page), iterate pages directly:

python

for page in paginator.paginate():
    for user in page.get("Users", []):
        process(user)

For more details on pagination, see:

references/pagination.md

Waiters

Wait for a resource to reach a desired state:

python

waiter = client.get_waiter("bucket_exists")
waiter.wait(
    Bucket="my-bucket",
    WaiterConfig={"Delay": 5, "MaxAttempts": 20},
)

For more details on waiters, see

references/waiters.md

Client Configuration

Use

botocore.config.Config

for retries, timeouts, and connection pool settings, etc.:

python

from botocore.config import Config

config = Config(
    retries={"total_max_attempts": 2, "mode": "adaptive"},
    connect_timeout=5,
    read_timeout=10,
    max_pool_connections=50,
)
client = boto3.client("s3", config=config)

When creating custom configuration for a client, see

references/configuration.md

Logging

Both boto3 and botocore use the standard library

logging

module. You can configure logging through the standard

logging

APIs, or you can use helpers provided by boto3 and botocore for convenience:

python

# Quick: log all botocore wire-level details to stderr
boto3.set_stream_logger("")  # root logger -- everything
boto3.set_stream_logger("botocore")  # just botocore

# Botocore, log all botocore details
import logging

from botocore.session import Session

session = Session()

session.set_stream_logger('botocore', logging.DEBUG)
# OR: Configure logging to a file.
session.set_file_logger(logging.DEBUG, '/tmp/botocore.log')

set_stream_logger(name, level=logging.DEBUG)

adds a

StreamHandler

to the named logger. This is the idiomatic way to get request/response debug output from the SDK.

Common Issues

Issue: ClientError import location

Wrong:

from boto3.exceptions import ClientError

Right:

from botocore.exceptions import ClientError

Service specific customizations

When writing any Python code that uses the following services, you MUST load these additional reference files for best practices and custom high level APIs:

S3 - you MUST load
```
references/s3.md
```
.
Dynamodb - you MUST load
```
references/dynamodb.md
```
.

References

Client configuration (retries, timeouts, endpoints):
```
references/configuration.md
```
Credentials and sessions:
```
references/credentials.md
```
Error handling patterns:
```
references/error-handling.md
```
Pagination:
```
references/pagination.md
```
Waiters:
```
references/waiters.md
```
S3 transfers and presigned URLs:
```
references/s3.md
```
DynamoDB operations:
```
references/dynamodb.md
```

aws-sdk-python-usage

NPX Install

Tags

SKILL.md Content

AWS SDK for Python (boto3)

Client vs Resource

Session and Client Creation

Making API Calls

Error Handling

Script Structure

Pagination

Waiters

Client Configuration

Logging

Common Issues

Issue: ClientError import location

Service specific customizations

References