Kedify | MongoDB

Use events from MongoDB to trigger autoscaling with Kedify and KEDA

MongoDB is a document-oriented NoSQL database designed for high performance, high availability, and automatic scaling.

Integrated with KEDA, it provides powerful event-driven autoscaling solutions for dynamic workload management based on real-time database queries.

Featured Use Cases

Scenario:

Scale a document management system dynamically as developers check-in or check-out code documents, ensuring optimal performance during peak development hours.

MongoDB Usage:

Scale based on the number of check-in and check-out operations logged in MongoDB.

KEDA Usage:

The version control system uses KEDA to monitor the transaction logs in MongoDB, dynamically adjusting the compute resources needed to handle peak loads.

Get Started

                    apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: version-control-scaler
  namespace: default
spec:
  scaleTargetRef:
    name: version-control-service
  minReplicaCount: 2
  maxReplicaCount: 10
  triggers:
  - type: mongodb
    metadata:
      dbName: "devops"
      collection: "transactions"
      query: '{"operation": {"$in": ["check-in", "check-out"]}}'
      queryValue: "10"
      activationQueryValue: "3"

Scenario:

Scale backend jobs that aggregate news content from multiple sources into MongoDB, triggered by the number of raw news items pending processing.

MongoDB Usage:

Scale based on the pending news items in the aggregation queue in MongoDB.

KEDA Usage:

News portals can use KEDA to scale aggregation jobs dynamically, ensuring timely processing of news content during high-news-flow events.

Get Started

                    apiVersion: keda.sh/v1alpha1
kind: ScaledJob
metadata:
  name: news-aggregator-job
  namespace: default
spec:
  jobTargetRef:
    template:
      spec:
        containers:
        - name: news-aggregator
          image: news-aggregation-service
          command:
          - 'python'
          - '-u'
          - 'aggregate.py'
  maxReplicaCount: 20
  triggers:
  - type: mongodb
    metadata:
      dbName: "media"
      collection: "news_queue"
      query: '{"status": "pending"}'
      queryValue: "100"
      activationQueryValue: "20"

Scenario:

Scale data analysis services that process experiment results from biotech research, based on the volume of new result entries in MongoDB.

MongoDB Usage:

Scale based on the count of new experiment results stored in MongoDB.

KEDA Usage:

Biotech firms can use KEDA to automatically scale computational resources for data analysis, enhancing the speed and efficiency of research outcomes.

Get Started

                    apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: biotech-data-analysis-scaler
  namespace: default
spec:
  scaleTargetRef:
    name: data-analysis-service
  minReplicaCount: 1
  maxReplicaCount: 15
  triggers:
  - type: mongodb
    metadata:
      dbName: "research"
      collection: "experiment_results"
      query: '{"status": "new"}'
      queryValue: "50"
      activationQueryValue: "10"

Scenario:

Scale analysis jobs to handle traffic data from smart city sensors, activated by the volume of new traffic data indicating congestion or unusual patterns.

MongoDB Usage:

Scale based on the real-time traffic data entries in MongoDB that indicate congestion.

KEDA Usage:

Smart city systems can dynamically scale traffic analysis jobs using KEDA, enabling rapid response to changing traffic conditions and optimizing city traffic flow.

Get Started

                    apiVersion: keda.sh/v1alpha1
kind: ScaledJob
metadata:
  name: traffic-flow-analysis-job
  namespace: default
spec:
  jobTargetRef:
    template:
      spec:
        containers:
        - name: traffic-data-analyzer
          image: traffic-analysis-tool
          command:
          - 'python'
          - '-u'
          - 'analyze_traffic.py'
  maxReplicaCount: 30
  triggers:
  - type: mongodb
    metadata:
      dbName: "city_management"
      collection: "traffic_sensors"
      query: '{"status": "congested"}'
      queryValue: "100"
      activationQueryValue: "10"