thanos-io/thanos

Features

• Global Prometheus Querying - Provides a unified interface for aggregating and querying metrics across multiple distributed Prometheus clusters.
• Unified Live and Historical Metric Queries - Provides a query interface that seamlessly combines real-time data with archived object-storage blocks.
• Distributed Query Engines - Acts as a distributed query engine that aggregates metric data from multiple Prometheus servers into a single interface.
• Distributed Query Processing - Implements parallel execution of data queries across multiple distributed nodes to retrieve a unified result set.
• High-Availability Metric Deduplications - Deduplicates metric samples from redundant Prometheus replicas to ensure query results are accurate.
• Object-Storage Persistence - Persists immutable blocks of time-series data to cloud object storage for durable and unlimited scaling.
• Object Storage Persistence - Persists time-series data to cloud-native object stores to provide an unlimited historical metric archive.
• Long-Term Metric Retentions - Implements policies and mechanisms for long-term metric retention in object storage without scaling storage nodes.
• Cross-Cluster Federation - Connects disparate monitoring clusters to enable unified querying and analysis across different environment boundaries.
• High Availability Observability - Ensures continuous telemetry collection and visibility by running redundant monitoring instances and deduplicating data.
• Cross-Cluster Metric Federation - Provides the ability to connect disparate clusters for unified querying and data visibility across environment boundaries.
• Global Metric Aggregation - Aggregates metric data from multiple distributed sources through a unified service to create a global view of system performance.
• High-Availability Metric Deduplication - Eliminates single points of failure by identifying and removing duplicate metric samples from redundant Prometheus replicas.
• Global Query Engines - Provides a global query engine that aggregates and unifies results from distributed Prometheus instances.
• Block Consolidation - Merges small time-series data blocks into larger ones within object storage to reduce storage footprint and improve query performance.
• Data Downsampling Strategies - Employs data downsampling strategies to reduce the storage cost of old metrics and improve retrieval speed.
• Distributed Block Compaction - Merges and deduplicates metric blocks in object storage through parallel compaction across multiple instances.
• Data Retention Policies - Applies down-sampling and retention policies to stored metrics to control storage growth.
• Query Fan-out - Splits a single global request into multiple parallel queries across distributed data sources to aggregate a unified result.
• Stateless Query Execution - Runs queries across stateless instances that discover available data sources to minimize request fanout.
• Object Storage Retrieval - Implements optimized data retrieval from object stores using block metadata and index caching to accelerate queries.
• Metric Store Discovery - Locates available data sources using address lists or DNS lookups to build a dynamic cluster for querying.
• TSDB Block Index Caches - Caches TSDB block indexes in memory to translate data requests into optimized lookups within object storage.
• Metric Aggregation & Downsampling - Reduces the resolution of historical time-series data to lower storage costs and accelerate long-term trend queries.
• Fault Tolerance Mechanisms - Implements fault-tolerant query routing to distribute requests across available components, ensuring resilience against node failures.
• High Availability Systems - Distributes metric data and query processing across multiple nodes to ensure fault tolerance and continuous system availability.
• Sidecar Data Uploaders - Runs as a companion process to Prometheus to ship local time-series blocks to remote object storage.
• Cluster Discovery Services - Provides automated services for identifying and registering nodes in a distributed monitoring system.
• Gossip-Based Discovery - Uses gossip protocols to exchange network addresses and discover available nodes for query routing.
• Stateless Serving Layers - Decouples stateless request handling for queries from stateful data storage to enable independent scaling.
• Retention Management - Compresses historical metric data and applies retention policies to optimize storage costs and query speeds.
• PromQL Rule Evaluation Engines - Evaluates PromQL-based alerting and recording rules on a schedule to trigger notifications and pre-compute metrics.
• Prometheus Remote Write Ingestion - Receives, validates, and batches Prometheus remote-write samples for long-term persistence in cloud storage.
• Database Tools - Highly available Prometheus setup.
• Monitoring and Logging - A system for high-availability and distributed storage for Prometheus.

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