https://github.com/fluxninja/aperture-py

Aperture Python SDK
https://github.com/fluxninja/aperture-py

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Aperture Python SDK

• Host: GitHub
• URL: https://github.com/fluxninja/aperture-py
• Owner: fluxninja
• Created: 2023-03-14T14:51:11.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-01-30T11:24:21.000Z (over 2 years ago)
• Last Synced: 2025-11-09T13:16:37.115Z (9 months ago)
• Language: Python
• Size: 723 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          


🚀 Just launched v1 of managed rate limiting service

  
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# Rate Limiting for Python Applications

The `aperture-py` SDK provides an easy way to integrate your Python applications

with [FluxNinja Aperture](https://github.com/fluxninja/aperture). It allows flow

control functionality on fine-grained features inside service code.

Refer to [documentation](https://docs.fluxninja.com/sdk/python/) for more

details.

## Usage

### Install SDK

Run the command below to install the SDK:

```bash

pip install aperture-py

```

### Create Aperture Client

The next step is to create an Aperture Client instance, for which, the address

of the organization created in Aperture Cloud and API key are needed. You can

locate both these details by clicking on the Aperture tab in the sidebar menu of

Aperture Cloud.

```python

from aperture_sdk.client import ApertureClient, FlowParams

agent_address = os.getenv("APERTURE_AGENT_ADDRESS", default_agent_address)

api_key = os.getenv("APERTURE_API_KEY", "")

insecure = os.getenv("APERTURE_AGENT_INSECURE", "true").lower() == "true"

aperture_client = ApertureClient.new_client(

    address=agent_address, insecure=insecure, api_key=api_key

)

```

### Flow Functionality

The created instance can then be used to start a flow:

```python

# business logic produces labels

    labels = {

        "user_id": "some_user_id",

        "user_tier": "gold",

        "priority": "100",

    }

    flow_params = FlowParams(

        check_timeout=timedelta(seconds=200),

        explicit_labels=labels,

    )

    # start_flow performs a flowcontrol.v1.Check call to Aperture Agent.

    # It returns a Flow or raises an error if any.

    flow = await aperture_client.start_flow(

        control_point="AwesomeFeature",

        params=flow_params,

    )

    # Check if flow check was successful.

    if not flow.success:

        logger.info("Flow check failed - will fail-open")

    # See whether flow was accepted by Aperture Agent.

    if flow.should_run():

        # do actual work

        pass

    else:

        # handle flow rejection by Aperture Agent

        flow.set_status(FlowStatus.Error)

    res = await flow.end()

    if res.get_error():

        logger.error("Error: {}".format(res.get_error()))

    elif res.get_flow_end_response():

        logger.info("Flow End Response: {}".format(res.get_flow_end_response()))

    # Simulate work being done

    await asyncio.sleep(2)

    return "", 202

```

The above code snippet is making `start_flow` calls to Aperture. For this call,

it is important to specify the control point (`AwesomeFeature` in the example)

and `FlowParams` that will be aligned with the policy created in Aperture Cloud.

For request prioritization use cases, it's important to set a higher gRPC

deadline. This parameter specifies the maximum duration a request can remain in

the queue. For each flow that is started, a `should_run` decision is made,

determining whether to allow the request into the system or to rate limit it. It

is important to make the `end` call made after processing each request, to send

telemetry data that would provide granular visibility for each flow.