Azul Scaler

Dynamically scales Azul components based on system throughput.

THIS IS EXPERIMENTAL AND IS A WORK IN PROGRESS.

Do not enable this unless you know what you are doing, and are willing to watch your HPAs constantly.

This matches Kafka consumer group names, provided by dispatcher in Redis and determines a metric similar to an ETA for a given plugin to guide system scaling decisions.

Architecture

The scaler utilises both a 'frontend' gRPC server to service requests from KEDA (the custom metrics server utilised by Azul to provide scaling information to Kubernetes HPAs), as well as a Tracker which periodically polls backend data sources (currently just Kafka) for information on how well system components are performing.

The lifecycle of a plugin being scaled through this looks like the following:

Installation

pip install azul-scaler

To build the Protobuf protocol, run the following:

pip install grpcio-tools
mkdir -p azul_scaler/grpc/
python -m grpc_tools.protoc -I protos --python_out=azul_scaler/grpc --pyi_out=azul_scaler/grpc --grpc_python_out=azul_scaler/grpc protos/externalscaler.proto

Usage

By default, launching the default entrypoint (defined in setup.py) will start up a unencrypted gRPC listener on port :8090. This can be configured via environmental variables listed in settings.py.

This can be used with KEDA directly - create a service for this scaler, and use KEDA's external scaler with the scalerAddress pointed at the service (including the namespace for cross-namespace connectivity).

Model

The scaler has a series of parameters that need to be configured in order to use them.

These parameters differ between the entity type used (currently only 'plugin').

KEY	VALUE
entityName	'plugin'
pluginName	The name of the plugin deployment.
targetSize	The maximum consumer lag before scaling (pass as string to KEDA).
includeHistoric	If the historical backlog should be included ('true' or 'false').