Kedify | Apache ActiveMQ

Use events from Apache ActiveMQ to trigger autoscaling with Kedify and KEDA

ActiveMQ is an open-source messaging server that allows applications in various languages and on many platforms to communicate with each other using queues and topics

With KEDA, ActiveMQ setups can achieve dynamic scaling, which allows applications to maintain high performance and responsiveness under various load conditions. This integration facilitates enhanced operational agility and efficient resource utilization, critical in high-demand and real-time processing environments.

Featured Use Cases

Scenario:

During high-traffic sales events, an online retail platform must efficiently process a large volume of customer orders placed on its e-commerce site.

Apache ActiveMQ Usage:

ActiveMQ queues customer orders which need to be processed to complete transactions, ensuring order details are maintained and communicated effectively between services.

KEDA Usage:

KEDA scales the order processing services based on the length of the ActiveMQ queue, ensuring that resources are allocated dynamically to meet demand peaks without manual scaling.

Get Started

                    apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: order-processing-scaledobject
  namespace: default
spec:
  scaleTargetRef:
    name: order-processing-deployment
  triggers:
  - type: activemq
    metadata:
      managementEndpoint: "activemq.activemq-test:8161"
      destinationName: "orderQueue"
      brokerName: "activemq_broker"
      targetQueueSize: "50"
      activationTargetQueueSize: "10"

Scenario:

A global media distribution platform needs to efficiently manage the dissemination of digital content to various geographical locations.

Apache ActiveMQ Usage:

ActiveMQ queues media items that are ready for distribution, serving as a central hub for managing the flow of content to various consumer endpoints.

KEDA Usage:

KEDA automatically scales content delivery network nodes based on the ActiveMQ queue length, optimizing resource use and ensuring content is delivered promptly during high demand.

Get Started

                    apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: content-distribution-scaledobject
  namespace: default
spec:
  scaleTargetRef:
    name: cdn-node-deployment
  triggers:
  - type: activemq
    metadata:
      managementEndpoint: "activemq.activemq-test:8161"
      destinationName: "mediaQueue"
      brokerName: "activemq_broker"
      targetQueueSize: "100"
      activationTargetQueueSize: "20"

Scenario:

A marketing platform needs to send out millions of personalized email campaigns based on user activities and preferences.

Apache ActiveMQ Usage:

ActiveMQ holds queued messages for each email campaign, which includes personalized content for each recipient.

KEDA Usage:

KEDA scales the number of workers processing and sending out emails based on the queue size in ActiveMQ, ensuring timely delivery of marketing campaigns during peak periods.

Get Started

                    apiVersion: keda.sh/v1alpha1
kind: ScaledJob
metadata:
  name: email-dispatcher-job
  namespace: default
spec:
  jobTargetRef:
    template:
      spec:
        containers:
        - name: email-sender
          image: campaign-sender:latest
        restartPolicy: OnFailure
  triggers:
  - type: activemq
    metadata:
      managementEndpoint: "activemq.activemq-test:8161"
      destinationName: "emailQueue"
      brokerName: "activemq_broker"
      targetQueueSize: "500"