6 repositorios
Tools for aggregating and querying metrics across multiple distributed Prometheus clusters.
Distinct from Prometheus-Compatible Backends: None of the candidates cover the specific act of federating queries across multiple Prometheus clusters.
Explore 6 awesome GitHub repositories matching system administration & monitoring · Global Prometheus Querying. Refine with filters or upvote what's useful.
Thanos is a distributed metrics query engine and monitoring scalability suite designed to provide a unified interface for aggregating data from multiple Prometheus servers and clusters. It functions as a high availability monitoring backend that eliminates single points of failure by deduplicating data from replicated instances. The system enables long-term retention by persisting time-series data to cloud-native object storage, allowing for unlimited historical archiving beyond the limits of local disks. It further optimizes this storage through a downsampling and retention manager that comp
Provides a unified interface for aggregating and querying metrics across multiple distributed Prometheus clusters.
Thanos is a CNCF cloud native monitoring tool that provides a highly available and scalable extension to the Prometheus ecosystem. It functions as a global query engine, a long-term storage system, and a metric downsampler. The project enables a unified interface to aggregate and query metrics across multiple distributed clusters from a single view. It maintains historical data beyond local retention limits by persisting time-series metrics in object storage and eliminates data gaps by merging metrics from redundant server pairs. The system includes capabilities for reducing the resolution o
Provides a unified view by merging metrics from multiple distributed Prometheus clusters for centralized monitoring.
The Prometheus Operator is a Kubernetes monitoring orchestrator and controller that manages Prometheus clusters and observability components through declarative custom resources. It functions as a custom resource controller that translates high-level Kubernetes resource definitions into the configuration files required by the underlying monitoring software. The project automates the deployment, scaling, and lifecycle of an observability stack, including the integration of components like Thanos and Alertmanager. It distinguishes itself by syncing monitoring targets, alerting rules, and scrape
Deploys Thanos sidecars and rulers to enable global querying and long-term storage of Prometheus metrics.
GreptimeDB is a distributed, open-source time-series database built for unified observability. It stores and queries metrics, logs, and traces together in a single columnar engine, supporting both SQL and PromQL for analysis. The database is designed as a Kubernetes-native operator with a decoupled compute and storage architecture, enabling horizontal scaling and multi-region deployment. What distinguishes GreptimeDB is its role as a multi-protocol ingestion gateway, accepting data through OpenTelemetry, Prometheus Remote Write, InfluxDB, Loki, Elasticsearch, Kafka, and MQTT protocols without
Provides standard Prometheus HTTP endpoints for querying and exploring metric data.
Cortex is an open-source, horizontally scalable metrics platform that ingests, stores, and queries Prometheus-compatible time-series data with multi-tenant isolation. It accepts metrics via Prometheus remote write and OpenTelemetry, executes PromQL queries against both recent and historical data, and provides a Prometheus-compatible alerting and recording rule engine with an integrated Alertmanager. The system is built as a set of independently scalable microservices that use hash-ring-based sharding, gossip-based cluster membership, and tenant-aware object storage to distribute workloads acro
Exposes Prometheus-compatible query endpoints under a dedicated path prefix for standard PromQL queries.
Grafana Tempo is a high-scale distributed tracing backend and columnar trace database. It serves as an observability data store that persists and queries spans and traces using OpenTelemetry standards, allowing for the analysis of request flows across microservices. The system distinguishes itself by using an object-store based backend with columnar Parquet storage. This architecture enables efficient attribute searching and large-scale data retrieval through dedicated attribute columnization and block-based data partitioning. It includes a specialized TraceQL query engine for filtering trace
Identifies specific traces that contributed to a calculated metric value to provide context for performance spikes.