KEDA (original) (raw)
Kubernetes Event-driven Autoscaling
Application autoscaling made simple
What is KEDA?
KEDA is a Kubernetes-based Event Driven Autoscaler. With KEDA, you can drive the scaling of any container in Kubernetes based on the number of events needing to be processed.
KEDA is a single-purpose and lightweight component that can be added into any Kubernetes cluster. KEDA works alongside standard Kubernetes components like the Horizontal Pod Autoscaler and can extend functionality without overwriting or duplication. With KEDA you can explicitly map the apps you want to use event-driven scale, with other apps continuing to function. This makes KEDA a flexible and safe option to run alongside any number of any other Kubernetes applications or frameworks.
Features
Autoscaling Made Simple
Bring rich scaling to every workload in your Kubernetes cluster
Event-driven
Intelligently scale your event-driven application
Built-in Scalers
Catalog of 50+ built-in scalers for various cloud platforms, databases, messaging systems, telemetry systems, CI/CD, and more
Multiple Workload Types
Support for variety of workload types such as deployments, jobs & custom resources with /scale sub-resource
Reduce environmental impact
Build sustainable platforms by optimizing workload scheduling and scale-to-zero
Extensible
Bring-your-own or use community-maintained scalers
Vendor-Agnostic
Support for triggers across variety of cloud providers & products
Azure Functions Support
Run and scale your Azure Functions on Kubernetes in production workloads
Scaling New Heights with KEDA: Performance, Extensions, and Beyond
From KubeCon Europe 2024
Exploring KEDA's Graduation and Advancements in Event-Driven Scaling
From KubeCon North America 2023
Scalers
Scalers represent event sources that KEDA can scale based on
Scale applications based on ActiveMQ Queue.
Scale applications based on an Apache Kafka topic or other services that support Kafka protocol.
Experimental scaler based on ‘segmentio/kafka-go’ library. Scale applications based on an Apache Kafka topic or other services that support Kafka protocol.
Scale applications based on an Apache Pulsar topic subscription.
Scale applications based on ArangoDB query result.
Scale applications based on the records count in AWS DynamoDB
Scale applications based on the count of blobs in a given Azure Blob Storage container.
Scale applications based on agent pool queues for Azure Pipelines.
Scale applications based on beanstalkd queues
Scale applications based on Cassandra query results.
Scale applications based on CouchDB query results.
Scale applications based on cpu metrics.
Scale applications based on a cron schedule.
Scale applications based on Datadog.
Scale applications based on Dynatrace metric data points
Scale applications based on ’elasticsearch search template query’ or ’elasticsearch query’ result.
Scale applications based on an etcd key-value pair. By watching an etcd key, a passively received push mode, the scaler can activate applications with lower load usage than frequent pull mode
Scale applications based on an external scaler.
Scale applications based on an external push scaler.
Scale applications based on metrics in Graphite.
Scale applications based on IBM MQ Queue
Scale applications based on InfluxDB queries
Scale applications based on the count of running pods that match the given selectors.
Scale applications based on Loki query result.
Scale applications based on memory metrics.
Scale applications based on a metric provided by an API
Scale applications based on MongoDB queries.
Scale applications based on Microsoft SQL Server (MSSQL) query results.
Scale applications based on MySQL query result.
Scale applications based on New Relic NRQL
Scale applications based on a threshold reached by a specific measure from OpenStack Metric API.
Scale applications based on the count of objects in a given OpenStack Swift container.
Scale applications based on a PostgreSQL query.
AI-based predictive scaling based on Prometheus metrics & PredictKube SaaS.
Scale applications based on Prometheus.
Scales Selenium browser nodes based on number of requests waiting in session queue
Scale applications based on Solr query results.
Scale applications based on Splunk saved search results.
Users
A variety of users are autoscaling applications with KEDA:
Partners
KEDA is supported by and built by our community, including the following companies:
Supported by
KEDA is supported by the following companies that provide their services for free:
