grafana/mimir

Features

• Data Tenant Isolators - Separates metrics and queries from different tenants within a single cluster to ensure strict data isolation.
• Multi-Tenant Observability - Provides isolated monitoring and observability environments for different teams within a shared cluster.
• Telemetry Query Engines - Provides a distributed engine that executes complex telemetry queries using parallelization and caching.
• Block Storage - Implements a block-based storage architecture that transforms samples into immutable compressed blocks.
• Time-Range Query Splitting and Caching - Splits large time-range queries into smaller sub-queries for parallel execution and efficient caching.
• Time Series Data Storage - Provides scalable storage optimized for maintaining historical records of numerical performance telemetry data over time.
• Distributed Ingestion - Validates, shards, and replicates incoming metrics across a pool of nodes to ensure high availability.
• Data Replication - Replicates metric data across multiple machines to ensure high availability and prevent data gaps during failures.
• Distributed Metrics Stores - Implements a sharded and replicated system for time-series data to ensure durability and availability.
• Distributed Query Processing - Executes and parallelizes data queries across multiple nodes, fetching from both memory and object storage.
• Multi-Tenant Resource Isolation - Enforces resource constraints on series and sample counts per tenant to ensure fair resource distribution and cluster stability.
• Shuffle Shard Isolation - Utilizes shuffle sharding to distribute tenant data across subsets of instances, limiting the blast radius and preventing tenant interference.
• Object-Storage Persistence - Persists massive volumes of time-series data and indexes directly to cloud object storage for scaling.
• Metric Query Languages - Provides specialized query capabilities to retrieve and analyze multi-dimensional time-series telemetry metrics.
• Long-Term Metric Retentions - Implements mechanisms for retaining metric data over extended historical periods while optimizing storage efficiency.
• Cross-Tier Querying - Routes queries to in-memory ingestors for recent data or object storage for historical data based on age.
• Multi-Tenant Databases - Provides a distributed time-series database that isolates telemetry data and resource limits for multiple organizations.
• Query Throughput Scaling - Distributes metric data across a cluster of multiple nodes to handle massive ingestion and query throughput.
• Time-Series Ownership Rings - Uses a consistent hash ring to distribute ownership of time series and blocks across cluster instances.
• Cross-Region Metric Querying - Combines telemetry data from distributed servers into a single global view for cross-region analysis.
• External Data Source Integrations - Retrieves telemetry and metrics from external time-series databases and search engines to centralize observability.
• Global Metric Aggregation - Aggregates metric data from multiple distributed sources through a unified service for system-wide visibility.
• Prometheus-Compatible Backends - Serves as a long-term storage backend for Prometheus using compatible remote write and read protocols.
• Distributed Rule Processing - Distributes the processing of recording rules across multiple nodes to scale the pre-aggregation of high-volume telemetry.
• Scalable Metrics Infrastructure - Manages massive volumes of time-series data across a distributed cluster to handle high ingestion and query loads.
• AI-Powered Observability Analysis - Employs large language models to analyze system telemetry and generate operational insights for troubleshooting.
• Observability Automation - Uses AI assistants to automatically generate prebuilt dashboards, intelligent filters, and customized alerts.
• Troubleshooting Workflows - Provides self-directed AI workflows to discover and summarize investigations for system failures.
• Query Result Caching - Stores index and chunk data in external memory to reduce redundant computation and accelerate repeat requests.
• Query Time Alignments - Aligns query start and end times to a fixed step to improve cache hit rates and ensure consistent results.
• Ingestion Deduplication - Removes duplicate entries from redundant data pairs during ingestion to ensure total data integrity.
• Distributed Key-Value Stores - Maintains consistent distributed state by sharing and merging timestamped binary blobs between nodes.
• High-Availability Metric Deduplications - Deduplicates samples from multiple Prometheus replicas to eliminate data gaps during server failures.
• Federated Alerting - Implements alerting rules to monitor metrics and trigger notifications across multiple tenants via a federated architecture.
• Object Storage Archiving - Archives Prometheus metrics in object storage to maintain historical data beyond short-term limits.
• Real-Time Data Caching - Stores recent time-series data in memory to accelerate frequent queries and reduce load on object storage.
• TSDB Block Index Caches - Caches time-series block indexes in memory to accelerate repeat queries by avoiding redundant lookups.
• Derived Data Generation - Generates new time-series datasets by applying recording rules to primary metrics.
• Configurable Data Retentions - Assigns configurable expiration timelines to specific metrics to balance data priority and storage costs.
• Per-Tenant Limit Override APIs - Allows administrators to adjust resource constraints and limits for specific tenants via API overrides.
• Metric Aggregation & Downsampling - Reduces the resolution of historical metrics via downsampling to save storage space while maintaining visibility.
• Gossip-Based Cluster Membership - Synchronizes cluster state and ring membership using a decentralized gossip protocol.
• Cluster State Synchronization - Propagates ring data and membership changes across nodes to maintain synchronized cluster state.
• Denial of Service Prevention - Prevents resource exhaustion and maintains availability by queuing requests and scheduling them to stop single users from monopolizing the system.
• Age-Based Request Routing - Implements routing that directs requests for recent data to ingestors and historical requests to long-term storage.
• AI-Powered Incident Analysis - Uses AI to automatically summarize and correlate telemetry data during the incident response process.
• Incident Response Workflows - Provides automated systems for detecting and resolving infrastructure outages and performance degradations.
• Unified Observability Ingestion - Aggregates diverse telemetry signals including metrics, logs, and traces into a single unified backend.
• Recording Rules - Precomputes expensive metric expressions using recording rules to optimize dashboard performance.
• Notification Noise Reduction - Filters false positives and reduces alert volume through grouping and inhibition to prevent operator distraction.
• Synthetic Monitoring - Executes simulated user interactions and tests to proactively identify availability and performance issues.
• Telemetry Visualization - Creates interactive dashboards and visual representations of metrics, logs, and traces to monitor system health.

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