Kedify | Redis

Use events from Redis to trigger autoscaling with Kedify and KEDA

Redis is an in-memory data structure store, used as a database, cache, and message broker, with support for complex queries

It’s ideal for fast data access and real-time processing. It’s perfect to use KEDA to automatically scale applications based on data volume in different Redis data structures like Lists, Streams, and using Sentinel for high availability setups.

Featured Use Cases

Scenario:

High-performance message queuing system required for real-time data processing applications.

Redis Usage:

Messages are queued into a Redis List, providing fast access and write speeds essential for high-demand environments like financial transactions or real-time analytics.

KEDA Usage:

KEDA automatically scales processing backends based on the Redis List length, ensuring low latency and high throughput without manual intervention.

Get Started

                    apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: message-queue-scaledobject
  namespace: default
spec:
  scaleTargetRef:
    name: message-processor-deployment
  triggers:
  - type: redis
    metadata:
      address: redis:6379
      listName: messages
      listLength: '10'

Scenario:

Need to process continuous data streams for applications such as event monitoring or live data feeds.

Redis Usage:

Data elements are continuously pushed to Redis Streams, which are designed to handle high-volume writes and provide a persistent log of data records.

KEDA Usage:

Utilizing KEDA, the system scales data processing jobs based on consumer group lag, maintaining the ability to handle incoming data spikes without data loss.

Get Started

                    apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: stream-data-processor
  namespace: default
spec:
  scaleTargetRef:
    name: stream-processor-deployment
  triggers:
  - type: redis-streams
    metadata:
      address: redis:6379
      stream: data-stream
      consumerGroup: data-group
      lagCount: '5'
      activationLagCount: '2'

Scenario:

Migrating large datasets with high availability requirements between distributed databases.

Redis Usage:

Redis Sentinel enhances data migration tasks with its high availability features, ensuring data integrity during the migration process.

KEDA Usage:

KEDA initiates scalable data migration jobs based on predefined thresholds of pending data entries, leveraging Redis Sentinel for reliable execution.

Get Started

                    apiVersion: keda.sh/v1alpha1
kind: ScaledJob
metadata:
  name: data-migration-job
  namespace: default
spec:
  jobTargetRef:
    template:
      spec:
        containers:
        - name: data-migrator
          image: data-migrator:latest
        restartPolicy: Never
  triggers:
  - type: redis-sentinel-streams
    metadata:
      addresses: sentinel1:26379,sentinel2:26379
      sentinelMaster: mymaster
      stream: migration-stream
      pendingEntriesCount: '100'