aws-sdk-python-usage

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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` or `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`