Billing metrics¶

With the metering feature, detailed metrics are made available for function invocations. These can be used to implement your own billing system.

Use-cases:

Billing SaaS customers - when you are hosting functions, bill customers based upon usage
Chargeback for internal teams - charge internal teams for a proportion of their use of an internal functions service

After each function has completed an invocation, a summary is published to NATS JetStream, this is then collected by a resilient and scalable event-worker. The event-worker will collect the events and send them in groups to a webhook endpoint for efficiency.

Events are batched within each HTTP call to your webhook endpoint, and will also contain a HMAC header that can be used to verify the origin of the event.

OpenFaaS Enterprise feature

This feature is included for OpenFaaS Enterprise customers.

Installation¶

To start receiving events with detailed usage metrics you need to enable metering and configure a webhook endpoint OpenFaaS can deliver events to. This can be done by editing the values.yaml file of the OpenFaaS chart.

You will need to create an endpointSecret which will be shared with the HTTP receiver, and used to sign the webhook payload.

# If openssl is not available on your system, use the following:
head -c 32 /dev/urandom | base64 | cut -d "-" -f1 > billing-endpoint-secret.txt

# openssl is preferred to generate a random secret:
openssl rand -base64 32 > billing-endpoint-secret.txt

kubectl create secret generic \
    -n openfaas \
    webhook-secret \
    --from-file webhook-secret=./billing-endpoint-secret.txt

Next, update your copy of the values.yaml file for the main OpenFaaS chart:

eventSubscription:
  endpoint: "https://example.com/openfaas-events"
  endpointSecret: webhook-secret

  metering:
    enabled: true
    defaultRAM: 512Mi

The eventSubscription section is also used to configure auditing.
Set the endpoint to your HTTP endpoint that will receive the events, including any Path you want to include.
The endpointSecret is used to sign the webhook payload with a symmetric secret using HMAC and a 256-bit digest.
The defaultRAM under the metering secret is expressed in the same notation as Kubernetes Pods, i.e. 128Mi or 1Gi. This value is used when there is no memory limit configured on a function.

When you're ready, apply the changes to your cluster using helm upgrade --install and your values.YAML file.

The webhook receiver endpoint¶

You can modify an existing part of your platform to expose a new HTTP path, or deploy a new HTTP microservice to collect the webhooks.

How to use a function to receive webhooks¶

We recommend recording billing information by a service outside of the OpenFaaS cluster.

However, with additional configuration, a function can be used so long as it is in a separate namespace, and is configured in the Helm chart to be excluded from metering.

metering:
  excludeNamespaces: "openfaas-system"

Without this configuration, an infinite loop would occur, as the function receiving the metering events, would itself also be metered, and therefore trigger itself.

How to validate the webhook is genuine¶

The event-worker will use the endpointSecret to create a hash signature of the webhook's payload. The hash signature will be in the X-Openfaas-Signature-256 header for each webhook delivery. Your handler will need to verify the signature matches the payload before processing any events.

Webhook delivery headers¶

X-Openfaas-Event: The name of the event that triggered the delivery.
X-Openfaas-Signature-256: This header is sent if the endpointSecret is configured in the Helm chart. This is the HMAC hex digest of the request body, and is generated using the SHA-256 hash function and the webhook secret as the HMAC key. It can be used to verify the origin of the webhook payload.
X-Openfaas-Delivery: A unique ID identifying the webhook delivery.

Example metering webhook delivery¶

> POST https://example.com/openfaas-events HTTP/2

> X-Openfaas-Event: function_usage
> X-Openfaas-Signature-256: sha256=d57c68ca6f92289e6987922ff26938930f6e66a2d161ef06abdf1859230aa23c
> X-Openfaas-Delivery: fe0f677c-c431-498e-8ace-9ba857434334
> Content-Type: application/json

> [
>   {
>     "event": "function_usage",
>     "namespace": "openfaas-fn",
>     "function_name": "env",
>     "started": "2023-11-14T15:01:20.349527036Z",
>     "duration": 3798742,
>     "memory_bytes": 20971520
>   }
> ]

The request body contains a list of function usage events:

event - the type of event
namespace - the namespace of the function that triggered the event
function_name - the function name that triggered the event
started - the timestamp the function invocation was started
duration - the duration of the function invocation
memory_bytes - the memory limit configured for the function in bytes (See: Memory/CPU limits). If no memory limit is configured for a function a default value will be used. This default values can be configured in the Helm chart by setting the parameter eventSubscription.metering.defaultRAM.

Example: Persisting event data to PostgreSQL¶

Once the event data is saved to a persistent data store it can be used to calculate billing metrics e.g the total number of invocations, total invocation duration and Gigabyte seconds (GB/s) used by a function over a certain time period.

This example uses a PostgreSQL database to persist event data. In this case the data was inserted into a table openfaas_metering.function_usage.

Assuming the following table schema:

CREATE TABLE IF NOT EXISTS
    openfaas_metering.function_usage (
        id SERIAL PRIMARY KEY,
        namespace TEXT NOT NULL,
        function_name TEXT NOT NULL,
        duration interval NOT NULL,
        started_at TIMESTAMP NOT NULL,
        ram_bytes BIGINT NOT NULL,
        created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
    );

Example insert statement:

    insert, err := tx.Prepare(`INSERT INTO
    openfaas_metering.function_usage (namespace, function_name, duration, started_at, ram_bytes)
    VALUES
    ($1, $2, make_interval(secs => $3::NUMERIC / 1000000000), $4, $5)`)

It is possible to get detailed usage information per function over a certain time period. For example:

  namespace  | function_name | total_invocations | total_duration  |  total_gb_seconds  
-------------+---------------+-------------------+-----------------+--------------------
 openfaas-fn | sleep         |                43 | 00:01:26.742422 | 3.3883758593749995
 openfaas-fn | env           |             74724 | 00:29:09.566917 |  68.34245769531262
 openfaas-fn | figlet        |                 4 | 00:00:00.008232 |       0.0009953125
(3 rows)

An example of the PostgreSQL query that was used to get this data:

select
    u.namespace,
    u.function_name,
    count(u) as total_invocations,
    sum(u.duration) as total_duration,
    sum(extract(epoch from u.duration) * u.memory_bytes / power(1024,3)) as total_gb_seconds
from openfaas_metering.function_usage u
where u.started > now() - interval '30 days'
group by namespace,function_name;

Q&A¶

Where is the metering data collected from?

The OpenFaaS gateway is responsible for collecting the metering data, then when a buffer fills up or a timer goes off, the data will be flushed from memory to NATS JetStream.
Will metering add overhead to my function invocations?

Events are published asynchronously to NATS JetStream, so should not add any overhead to the function invocation itself.
How many events will be sent within a webhook?

The amount of events are 1 to many, depending on how many were available when they were published to NATS JetStream from the OpenFaaS gateway.
How much load will the event-worker generate on the cluster?

This depends on the rate of invocations on your cluster, it's unlikely to be noticeable with moderate traffic.
Can the events be written to our database?

You can write a webhook receiver and use it to perform inserts into the database. If your chosen datastore supports batch inserts, we would recommend using that instead of inserting each event individually. We have provided an example schema and query for calculating billing metrics from PostgreSQL.

Do you have questions, comments, or suggestions?¶

Feel free to reach out to the team for a call.