prometheus-operator/kube-prometheus

Features

• Operator-Based Lifecycles - Automates the deployment, scaling, and upgrading of monitoring stacks using the Kubernetes Operator pattern.
• Observability Stack Deployments - Automates the installation and management of a full monitoring stack, including Prometheus, Alertmanager, and associated dashboards.
• Push-Based Controller Topologies - Distributes scrape targets based on network topology or zones to reduce cross-zone traffic.
• Topology Aware Sharding - Pins shards to specific zones to restrict scraping to local targets and reduce network traffic.
• Automated Target Configuration - Generates target configurations automatically using label selectors instead of manual configuration files.
• Dynamic Scrape Target Discovery - Identifies monitoring targets for scraping using label selectors and intermediary services.
• Scrape Target Sharding - Splits scrape targets into groups assigned to different instances to scale collection capacity.
• Target Distribution - Splits scrape targets across multiple instances using hash-based distribution to scale data collection.
• Automatic Monitoring Shard Scaling - Adjusts the number of monitoring instances automatically to distribute targets across shards based on workload.
• Custom Resource Mappings - Translates high-level Kubernetes custom resources into Prometheus and Alertmanager configuration files using the operator pattern.
• Monitoring Stack Automation - Automates the installation and lifecycle of Prometheus and Alertmanager using custom resource definitions.
• Monitoring Shard Scaling - Implements hash-based target sharding to distribute scrape workloads across multiple Prometheus instances for scalability.
• Alertmanager Deployment - Orchestrates the deployment of high-availability Alertmanager clusters integrated with custom configuration objects.
• Lightweight Metrics Agents - Runs lightweight instances focused on scraping and forwarding metrics without utilizing local storage.
• Alerting Integrations - Connects monitoring instances to an alerting cluster to automatically discover and forward triggered alerts.
• Service Discovery Integrations - Integrates with cloud providers and DNS to identify monitoring targets across diverse environments.
• Hash-Based Data Distribution - Distributes scrape targets across multiple Prometheus instances using consistent hashing to scale data collection.
• Alert Notification Management - Deduplicates alerts and routes grouped notifications to external integrations such as email or chat.
• Alert Routing - Configures how alerts are grouped, inhibited, and routed to specific receivers based on matching metadata rules.
• Label-Based Targeting - Automatically discovers monitoring targets by matching Kubernetes resource labels against predefined selector patterns.
• Pull-Based Metric Scraping - Implements a pull-based mechanism to collect telemetry from Kubernetes pods using service discovery.
• Monitoring Rule Definitions - Creates rules for triggering alerts or pre-calculating metrics based on discovered system data.
• Scrape Configuration Automators - Automates the generation of scrape configurations for various targets using custom resource definitions.
• Monitoring Instance Provisioning - Sets up required permissions and service accounts to discover and scrape targets within a cluster.
• Pod Discovery - Finds and configures monitoring for specific pods using Kubernetes label selectors.
• Prometheus Cluster Management - Automates the deployment and scaling of Prometheus instances using cluster-wide stateful sets or node-local scopes.
• Service Discovery - Identifies active Kubernetes pods and services as monitoring endpoints using label selectors.
• Data Forwarders - Streams collected metrics to remote long-term storage without requiring local evaluation.
• Data Persistence - Ensures metric data survives pod restarts and rescheduling by coupling pods with persistent volume claims.
• Shard Data Preservation - Retains pods from scaled-down shards so historical metric data remains queryable until the retention period expires.
• Custom Scrape Targets - Configures external monitoring targets or complex scrape patterns that exceed standard service monitor capabilities.
• Sharding Override Controls - Determines which shard handles a target by overriding the default sharding hash with specific labels.
• Dynamic Rule Management - Supports the dynamic loading of alert and recording rules without requiring a process restart.
• High-Availability Instance Redundancy - Runs multiple identical monitoring instances with unique labels to ensure high availability and eliminate single points of failure.
• Remote Write Configuration Delegation - Allows users to define their own remote write settings via scoped resources without requiring administrative access.
• Storage Provisioning - Integrates with storage classes to automatically create and mount high-performance volumes across cloud providers.
• Health Status APIs - Exposes operational status, replica counts, and health conditions via a dedicated API subresource.
• Zonal Target Deployment - Generates node selectors to deploy instances within the same zone as the targets they monitor.
• Alert Notification Routing - Prometheus Operator configures routing, inhibition rules, and custom receivers for alert notifications.
• Object Storage Backups - Streams time-series data to external object storage targets to enable global querying and long-term retention.
• Configuration Hot-Reloading - Uses a sidecar container to monitor configuration changes and trigger API reloads without restarting the main process.
• External Service Monitoring - Probes ingresses and static endpoints using a prober service to monitor external availability.
• Metric Data Preservation - Preserves metric samples during network outages or pod evictions by utilizing persistent storage.
• Alerting Rule Validators - Implements validation checks to ensure monitoring rules are syntactically and semantically correct before application.
• PromQL-Based Alerting and Recording Rule Evaluation - Executes PromQL alerting and recording rules against a distributed query API to generate alerts.
• Distributed Rule Processing - Executes recording and alerting rules across multiple instances using a centralized ruler for scalability.
• Rule Evaluators - Deploys specialized instances to evaluate monitoring rules via compatible API endpoints.
• Reusable Security Profiles - Defines reusable TLS and authorization profiles to avoid redundant configuration across monitoring resources.
• Node-Local Monitoring Agents - Runs monitoring agents on every node to optimize metric collection from local targets and reduce network traffic.
• Scrape Job Aggregation - Aggregates scrape configurations using service discovery mechanisms like DNS, cloud providers, and cluster APIs.
• Agent-Mode Metric Forwarding - Provides a lightweight agent deployment that collects metrics and forwards them to remote storage without utilizing local long-term storage.
• Remote Write Resource Constraints - Imposes constraints on queue capacity and metadata transmission to maintain cluster stability during remote writes.
• Node-Local Target Filtering - Ensures agents only target pods on the local node using field selectors to reduce API load.

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