> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tensor9.com/llms.txt
> Use this file to discover all available pages before exploring further.

# AWS Python SDK Guide

> Configure boto3 and Botocore to call a Cloud Adapter endpoint and preserve diagnostic context.

With boto3, your application continues to use the normal AWS service model and request dictionaries. Configure the endpoint for the process or for one client.

## Start a local adapter

This guide's bounded example uses S3 backed by Google Cloud Storage:

**1. Install or update the Tensor9 CLI**

Install the customer CLI with Homebrew, then confirm the binary is available:

```bash theme={null}
brew tap tensor9ine/tensor9
brew trust tensor9ine/tensor9
brew install tensor9
tensor9 --version
```

If `tensor9` is already installed, run `brew upgrade tensor9` instead of the install command.

**2. Authenticate with Tensor9**

To create a Tensor9 account and download an API key yourself, follow [Get a Tensor9 API key](https://docs.tensor9.com/byoc/getting-started/api-key.md).

A Tensor9 API key is required. Use the key issued during onboarding, or email [hello@tensor9.com](mailto:hello@tensor9.com) to request one for your organization. Do not share your account or key with another customer or organization.

If your secret manager has already supplied `T9_API_KEY`, skip the prompt. Otherwise, enter your key at a hidden prompt in the terminal where you will run `tensor9`:

> [tab] Bash
>
> ```bash theme={null}
> set +x
> IFS= read -r -s -p 'Tensor9 API key: ' T9_API_KEY
> printf '\n'
> export T9_API_KEY
> ```

> [tab] Zsh
>
> ```zsh theme={null}
> set +x
> IFS= read -r -s 'T9_API_KEY?Tensor9 API key: '
> printf '\n'
> export T9_API_KEY
> ```

Check that the variable is set without printing it, then verify your Tensor9 identity:

```bash theme={null}
: "${T9_API_KEY:?Set T9_API_KEY before continuing}"
tensor9 whoami
```

Confirm that the reported identity belongs to the intended customer account. Do not continue if authentication fails or the account is wrong. While `T9_API_KEY` is set, `tensor9` commands in this terminal use the key instead of any controller profile, so use a separate terminal for this work and run `unset T9_API_KEY` when you finish. Keep shell tracing disabled while using the key. Do not paste the key into commands, shell profiles, source files, screenshots or support messages.

See [Install and Authenticate](https://docs.tensor9.com/cloud-adapter-early-access/getting-started/install-and-authenticate.md) for the planned license lifecycle, entitlements and credential boundaries.

**3. Inspect the S3 to Google Cloud Storage mapping**

Ask Tensor9 to describe the exact directed service mapping used by this guide:

```bash theme={null}
tensor9 explain \
  -origin aws::1.0.0::s3 \
  -target google::1.0.0::gcs \
  -fmt Human
```

Read the supported S3 operations and Google Cloud Storage limitations before starting the process. The mapping is directional: this command describes S3 requests adapted to Google Cloud Storage, not the reverse direction.

**4. Prepare Google Cloud access**

Authenticate with Google Cloud using its standard Application Default Credentials chain. The adapter uses that identity when it calls Google Cloud Storage.

Use a dedicated test project and narrowly scoped credentials. Do not put credentials in the command line, source code, or screenshots.

**5. Start the S3 service adapter**

Run the S3 to Google Cloud Storage adapter as a foreground process:

```bash theme={null}
tensor9 adapt svc run \
  --origin aws::1.0.0::s3 \
  --backend google::1.0.0::gcs
```

Leave this terminal open. This is the current foreground launcher, not the pending Docker launcher for [local development mode](https://docs.tensor9.com/cloud-adapter-early-access/deployment/single-binary.md). It calls your actual target-cloud services; it is separate from the in-memory emulator. Acquisition and license renewal require Tensor9 authentication. Keep the endpoint local to your test.

**6. Copy the listening endpoint**

When the command reports its listening endpoint, copy that exact value into a second terminal:

```bash theme={null}
export T9_CLOUD_ADAPTER_ENDPOINT="<endpoint printed by tensor9>"
```

Keep the endpoint local to the test unless you have deliberately configured network access and authentication for another machine.

**7. Run one bounded S3 request**

Continue with this page's client or tool instructions. Point only S3 at `T9_CLOUD_ADAPTER_ENDPOINT`, then perform one operation whose Google Cloud Storage effect you can verify independently.

Check both sides: read the S3-shaped response and inspect the native object or bucket in Google Cloud Storage. A successful client response alone does not prove that the target state is correct.

**8. Stop the local adapter**

Return to the first terminal and press **Control-C**. Confirm the process exits before deleting the test object or bucket with Google Cloud's native client.

If the request failed, preserve the request ID, adapter endpoint, S3 operation name, Google Cloud request ID, and any `x-t9-explain-*` receipt headers before stopping the process.

## Set it for the process

```bash theme={null}
export AWS_ENDPOINT_URL_S3="$T9_CLOUD_ADAPTER_ENDPOINT"
```

Then create the client without `endpoint_url`:

```python theme={null}
import os
import boto3
from botocore.config import Config

session = boto3.Session(profile_name="adapter-test")
s3 = session.client(
    "s3",
    region_name=os.environ.get("AWS_REGION", "us-east-1"),
    config=Config(s3={"addressing_style": "path"}),
)
```

## Set it on one client

```bash theme={null}
: "${T9_CLOUD_ADAPTER_ENDPOINT:?start the local adapter and copy its endpoint first}"
```

```python theme={null}
import os
import boto3
from botocore.config import Config

session = boto3.Session(profile_name="adapter-test")
s3 = session.client(
    "s3",
    endpoint_url=os.environ["T9_CLOUD_ADAPTER_ENDPOINT"],
    region_name=os.environ.get("AWS_REGION", "us-east-1"),
    config=Config(
        s3={"addressing_style": "path"},
        retries={"mode": "standard", "max_attempts": 3},
        connect_timeout=5,
        read_timeout=30,
    ),
)
```

Use a separate `Session` or dependency-injected client for Cloud Adapter. Avoid changing a process-wide endpoint behind unrelated AWS clients.

An explicit `endpoint_url` takes precedence over `AWS_ENDPOINT_URL_S3`.

## Verify object behavior

```python theme={null}
import time

bucket = os.environ["TEST_BUCKET"]
key = f"python/{int(time.time())}-hello.txt"
expected = b"hello through Cloud Adapter\n"

s3.put_object(Bucket=bucket, Key=key, Body=expected, ContentType="text/plain")
head = s3.head_object(Bucket=bucket, Key=key)
downloaded = s3.get_object(Bucket=bucket, Key=key)["Body"].read()

assert downloaded == expected
assert head["ContentLength"] == len(expected)

s3.delete_object(Bucket=bucket, Key=key)
```

Inspect the corresponding native target object after `put_object`. If the application depends on metadata, ETags, version IDs, ranges, conditionals, or multipart uploads, assert those behaviors separately against the [S3 target profile](https://docs.tensor9.com/cloud-adapter-early-access/service-catalog/aws/databases-storage/s3.md).

## Distinguish service and transport failures

```python theme={null}
from botocore.exceptions import ClientError, EndpointConnectionError, ReadTimeoutError

try:
    s3.head_object(Bucket=bucket, Key="missing-key")
except ClientError as error:
    response = error.response
    print({
        "code": response.get("Error", {}).get("Code"),
        "status": response.get("ResponseMetadata", {}).get("HTTPStatusCode"),
        "request_id": response.get("ResponseMetadata", {}).get("RequestId"),
    })
except (EndpointConnectionError, ReadTimeoutError) as error:
    print({"transport_error": type(error).__name__, "message": str(error)})
```

A `ClientError` means an HTTP service response reached Botocore. A connection or read timeout may have no response metadata at all. After a timed-out write, check target state before deciding whether to retry.

## Production checklist

* Pin boto3 and Botocore versions together.
* Keep the endpoint, region, retry policy, and credentials explicit.
* Reuse clients across requests.
* Do not log `Authorization`, signed URLs, secret values, or object bodies.
* Capture `ResponseMetadata` and [Cloud Adapter diagnostic headers](https://docs.tensor9.com/cloud-adapter-early-access/debugging/response-headers.md).
* Add integration tests for paginators, waiters, retries, and error codes used by the application.

Backend credentials are configured on Cloud Adapter. They do not belong in the Python application's AWS session.


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