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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. It allows flow control functionality on fine-grained features inside service code.

Refer to documentation for more details.

Usage

Install SDK

Run the command below to install the SDK:

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.

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:

# 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.