kedacore/keda

Features

• Event-Driven Autoscalers - Provides a Kubernetes event-driven autoscaling engine that scales workloads based on external event sources, including scaling to zero.
• Zero-Instance Scaling - Implements a core scale-to-zero workflow that reduces workloads to zero replicas during inactivity.
• Workload Scheduling and Scaling - Adjusts the number of pod instances based on metrics from external event sources.
• Event-Driven Autoscaling - Scales containerized workloads based on external events from message brokers, databases, or cloud metrics.
• Scale-to-Zero Activation - Implements a core workflow that reduces workloads to zero replicas during inactivity and activates them upon new events.
• External Event Scaling Triggers - Connects to third-party message brokers and monitoring services to trigger scaling actions based on specific external metrics.
• External Metric Translation - Acts as an intermediary that translates external event data into metrics the Kubernetes horizontal pod autoscaler understands.
• Event-Driven Job Scaling - Triggers and manages the scaling of jobs based on external event sources to process workloads dynamically.
• Cloud Event Orchestration - Provides an orchestration engine that monitors message brokers, databases, and cloud metrics to trigger real-time container scaling.
• Cloud-Metric-Based Auto Scaling - Adjusts pod counts based on AWS CloudWatch metrics using specific namespaces, dimensions, or queries.
• Composite Scaling Logic - Composes multiple metrics using mathematical formulas, conditional logic, and failover operators to determine target replica values.
• Custom Resource Scaling - Uses Kubernetes custom resources to define scaling triggers and target workloads for event-driven automation.
• Event-Driven Job Scaling - Triggers and scales the number of background jobs in response to specific external events.
• Message Queue Scaling - Monitors message counts across various queues via API to adjust workload replicas.
• Event-Driven - Triggers the creation and scaling of Kubernetes jobs in response to discrete external events.
• Kubernetes Job Orchestration - Orchestrates the creation and execution of isolated Kubernetes jobs in response to external events.
• Metric Aggregators - Consolidates metrics from multiple event sources using operations like maximum, minimum, average, or sum.
• Multi-Cloud Event Trigger Systems - Provides a framework for connecting Kubernetes workloads to diverse cloud event sources using integrated scalers and identity providers.
• Multi-Workload Scaling - Manages the scaling of various resource types including deployments, jobs, and custom resources.
• On-Demand Job Scaling - Triggers the creation of individual jobs based on external events rather than maintaining a steady pool of pods.
• Query-Based Auto Scaling - Adjusts pod counts based on the numeric result of an ArangoDB database query using custom thresholds.
• Workload Identity Authentication - Integrates with workload identities and secret managers to secure connections between the operator and external event sources.
• Event Trigger Extensibility - Allows developers to implement custom triggers or community adapters to expand the range of cloud providers that can initiate scaling.
• External Event Monitoring - Monitors message brokers, databases, and cloud metrics to trigger the scaling of containerized workloads.
• Metric Data Source Integrations - Connects Kubernetes autoscaling to third party data sources like Kafka, AWS SQS, or Prometheus to trigger scaling actions.
• External Metric Scaling Integrations - Connects to third-party queues and databases to use real-time data as triggers for automatic scaling.
• Scaling Metric Aggregation - Combines signals from multiple event triggers using mathematical formulas to calculate a single target replica value.
• Query-Based Scalers - Provides the ability to scale workloads based on the number of items returned by DynamoDB queries.
• Stream-Based Scalers - Adjusts pod counts based on the shard count of DynamoDB streams to match data demand.
• Replica Calculation Strategies - Adjusts how job replicas are calculated using strategies to handle queue lengths and locked messages.
• Blob Count Scaling - Adjusts the number of active pods based on the number of files in a storage container.
• Cron Scheduling - Adjusts the number of replicas for a workload during a specific time window based on a cron expression.
• External Metric Scalers - Adjusts pod counts based on metric data points or queries retrieved from Dynatrace.
• External Metric Scalers - Scales workloads based on metrics and queries retrieved via the Datadog API.
• Log-Based Scalers - Adjusts pod counts based on the result of queries executed against log analytics workspaces.
• Traffic-Based Scalers - Adjusts container replicas based on HTTP request rates monitored by a server.
• External Scaler Management - Connects to messaging queues and cloud monitoring services to derive scaling signals for containerized applications.
• Scale-Down Cooldowns - Defines cooldown and grace periods to prevent premature scaling down to zero after the last active trigger.
• Queue-Based Scaling Triggers - Adjusts the number of running pods based on the count of ready, reserved, or delayed jobs in a queue.
• HTTP Request-Based Scaling - Adjusts the number of active pods dynamically based on the volume of observed incoming HTTP requests.
• Storage Queue Scaling - Adjusts the number of pods based on the count of messages in a storage queue.
• Per-Event - Schedules a single job for each detected event to handle long-running executions.
• Namespace Isolation - Limits the controller to watch for events and manage workloads only within designated namespaces.
• Automated Partition Balancing - Ensures the number of scaled replicas is a divisor of the total partition count to maintain even work distribution.
• Capacity Scaling Configurations - Sets the minimum and maximum number of replicas a workload can scale between to protect system capacity.
• Scaling Suspensions - Allows suspending the scaling of workloads via annotations to prevent resource starvation or facilitate cluster maintenance.
• Query-Driven Autoscaling - Adjusts the number of running pods based on the numerical output of a query executed against a cluster.
• Resource-Metric-Based Auto Scaling - Adjusts pod counts based on average CPU utilization percentages or absolute values.
• Push Notification Listeners - Maintains a long-lived stream that accepts activity events from external services to trigger real-time scaling actions.
• Admission Webhooks - Intercepts resource requests to validate and mutate scaling configurations before they are persisted in the cluster.
• Scaler Plugin Interfaces - Allows new event sources to be added by implementing a common interface for metric retrieval and authentication.
• Operator Permission Restrictions - Limits RBAC permissions to prevent the operator from reading secrets or custom resources across the entire cluster.
• Token Injection - Pulls Kubernetes service account tokens into triggers to provide identity and permissions for scaling.
• Secret Retrieval - Pulls specific keys from Kubernetes secrets to authenticate with external scaling event sources.
• OAuth2 Providers - Retrieves and injects OAuth2 access tokens into triggers to authorize requests to external sources.
• Service Role Assignments - Injects cloud identity roles into pods via service account annotations to authorize external event source access.
• Secret Management Integrations - Integrates with GCP Secret Manager to retrieve authentication credentials for event triggers.
• Vault Integrations - Provides a connector to retrieve sensitive authentication data from HashiCorp Vault instances.
• Fallback Replica Counts - Sets a fallback replica count to maintain availability when a scaling trigger enters an error state.
• API Polling - Sets the frequency for checking trigger sources to balance responsiveness with API load.
• External Metrics Querying - Retrieves current metric values and lists of exposed external metrics via API to monitor scaling triggers.
• Polling-Based Health Monitors - Periodically queries external APIs and message brokers to determine the current load and required replica count.

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