Advance Configurations

Service Runtime

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package: dl.Package
service = package.deploy(
    service_name='my-service',
    runtime=dl.KubernetesRuntime(
        pod_type=dl.InstanceCatalog.REGULAR_S,
        concurrency=10,
        runner_image='python:3.9',  # optional - any custom docker image,
        autoscaler=dl.KubernetesRabbitmqAutoscaler(
            min_replicas=0,
            max_replicas=1,
            queue_length=10,
            cooldown_period=300,
            polling_interval=10
        )))

Autoscaler

When we have changing loads of work, we want the number of replicas of the service to scale up when there are many executions coming in, and scale down otherwise.
To do so, we need to create an autoscaler:

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autoscaler = dl.KubernetesRabbitmqAutoscaler(
    min_replicas=0,
    max_replicas=1,
    queue_length=10,
    # cooldown_period: define how long to wait before scaling down (reducing the number of replicas) in case the queue is below the queue_length.
    cooldown_period=300,
    # polling_interval (in seconds): this parameter defines how often the queue is being sampled to perform any action.
    polling_interval=10
)

Using Secrets in a Function

You can use organization integration secrets (key-value) as environment variable inside the function's env.
First you'll need to add the integrations (UI only), then simply add the integrations' ids whe deploying the service:

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package: dl.Package
package.services.deploy(name='with-integrations',
                        secrets=['integrationId'])

Inside the service you can access the values using os package:

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import os
print(os.environ['INTEGRATION_NAME'])

Progress and Context

In any function you can add two more inputs: progress and context.

For example:

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class ServiceRunner(dl.BaseServiceRunner):  
def detect(self, item: dl.Item, progress: dl.Progress, context: dl.Context):  
    progress.update(status='inProgress', progress=0,  
                    message='execution started')

Context is a class that saves all the relevant information for this run, e.g. context.execution_id, context.service_id and more.

You can also use the context.logger to get a logger instance. Using this logger will help us save the logs in the correct way (log level) and will make it easier to filter them in the logs page.

Use the python auto complete to check all the dl.Context options.

Progress can be used to update the progress percent and change the status of each step:

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progress.update(progress=0,  message='execution started')  
progress.update(progress=80,  message='almost done')  
progress.update(progress=99,  message='one more sec...')  
progress.update(progress=100,  message='done')