This guide walks through the complete Prometheus monitoring lifecycle—from binary, Docker, and Kubernetes deployments to Ansible‑driven node_exporter rollout, SNMP switch and router monitoring, alert routing via WeChat, SMS and email, production‑grade tuning, high‑availability designs, and systematic troubleshooting.
This article examines the storage, query performance, high‑availability, and high‑cardinality challenges of running Prometheus on a thousand‑node Kubernetes cluster and presents a complete, step‑by‑step Thanos‑based architecture, capacity‑planning models, configuration examples, and operational best practices for reliable horizontal scaling.
This guide walks you through constructing a production‑grade, highly available Prometheus monitoring stack, covering architecture choices, sharding strategies, common pitfalls such as memory bloat, query latency and storage growth, and provides concrete tuning steps, Kubernetes deployment examples, and advanced optimisation techniques.
This article details the end‑to‑end migration from Thanos to VictoriaMetrics, covering background analysis, architectural comparison, a phased migration plan, encountered configuration and performance issues, resolution strategies, and the resulting performance, cost, and scalability improvements for the monitoring system.
This article compares Thanos and VictoriaMetrics as long‑term storage solutions for Prometheus, evaluating their architecture, write and read paths, reliability, consistency, performance, scalability, high‑availability, and hosting costs to help you choose the most suitable option for your monitoring stack.
This article examines the migration from Thanos to VictoriaMetrics for large‑scale metric storage, detailing background challenges, VictoriaMetrics architecture and storage engine, data write and read processes, and a comparative analysis of performance, scalability, and operational costs between the two systems.
This article compares Thanos and VictoriaMetrics as Prometheus long‑term storage solutions, evaluating their architectures, write and read paths, reliability, data consistency, performance, scalability, high‑availability, and cost to help you choose the best fit for your monitoring stack.
This guide explains how to move from a self‑built open‑source Prometheus + Thanos monitoring stack to Alibaba Cloud's fully managed Prometheus service, covering typical deployment scenarios, migration requirements, step‑by‑step procedures for metric collection, visualization, and alerting, and key considerations for each environment.
Prometheus, a leading monitoring system, can achieve high availability through several common architectures—including dual-node with external storage, federated mode with external storage, and multi-node clusters combined with Thanos and object storage—each offering data persistence and load distribution to enhance system stability and performance.
This article explains why a global view is needed when Prometheus metrics are scattered across many instances, compares community approaches such as Federation, Thanos, and Remote Write, and details Alibaba Cloud's Global Aggregation Instance and Remote Write solutions with configuration examples and a real‑world case study.
This multi‑chapter guide provides in‑depth, hands‑on instruction for configuring and optimizing all Prometheus components, exploring Kubernetes monitoring, source‑code analysis, custom exporter development, high‑availability setups, service discovery, resource‑efficient scraping, and integrating Thanos for long‑term storage.
The author reflects on the shortcomings of current logging, metrics, and tracing practices, explains why they become costly and unscalable, and offers concrete recommendations—including log level discipline, structured logging, metric aggregation, and the use of tools like Prometheus, Cortex, and Thanos—to build a more efficient observability stack.
This article explores the limitations of a Prometheus‑based monitoring stack and demonstrates how adopting a Thanos‑based architecture improves metric retention, enables multi‑cluster querying, and reduces overall infrastructure costs while providing a scalable, cloud‑native solution.
This article explains why native Prometheus HA solutions fall short for large, multi‑region clusters and shows how to use Thanos components—including sidecar, query, store gateway, and compactor—to achieve long‑term storage, unlimited scaling, a global view, and non‑intrusive integration with existing Prometheus deployments.
This article explores the challenges of building a fault‑tolerant monitoring platform, compares open‑source solutions, details why Prometheus is preferred, and shows how to achieve high availability and horizontal scaling using Thanos, remote‑write, hash‑ring sharding, and Kubernetes integration.
This article explains the limitations of a standard Prometheus monitoring stack on Kubernetes and demonstrates how to migrate to a Thanos‑based solution for long‑term metric retention, reduced infrastructure cost, and scalable multi‑cluster observability using Terraform and cloud‑native components.
This article introduces four CNCF‑graduated open‑source projects—Prometheus, Jaeger, OpenTelemetry, and Thanos—that together provide metrics, alerts, tracing, and long‑term storage to improve observability, reduce downtime, and streamline troubleshooting for workloads running on Kubernetes.
This article explains the limitations of a traditional Prometheus monitoring stack, demonstrates how Thanos provides unlimited long‑term storage and lower infrastructure costs, and walks through a complete multi‑cluster deployment on Kubernetes using Terraform and AWS.
Learn how to design a resilient, scalable monitoring solution for multi‑cluster Kubernetes environments using Thanos, Prometheus, and Grafana, covering architecture, data ingestion, querying, long‑term storage on S3, cost savings, and practical deployment tips.
This article examines the limitations of a standard Prometheus‑based monitoring stack on Kubernetes, explains how adopting Thanos improves metric retention and reduces infrastructure costs, and provides a detailed multi‑cluster deployment guide with Terraform, TLS configuration, and Grafana visualization.
This article explains how to design and deploy a robust monitoring solution using Prometheus, Thanos, Pushgateway, and Alertmanager on Kubernetes, covering metric collection, naming conventions, query language, high‑availability strategies, and practical YAML configurations for a production‑grade observability platform.
This article explains why monitoring is essential for production stability, compares white‑box and black‑box approaches, and provides a step‑by‑step guide to deploying Prometheus, configuring scrape targets, using Pushgateway and Alertmanager, and scaling the solution with Thanos in a Kubernetes environment.
This article explains the limitations of a traditional Prometheus‑based monitoring stack for Kubernetes, demonstrates how integrating Thanos improves metric retention, scalability, and storage cost, and provides a complete multi‑cluster deployment example with Terraform and Helm configurations.
This article examines the limitations of a traditional Prometheus monitoring stack on Kubernetes, explains how adopting a Thanos‑based architecture improves metric retention and reduces infrastructure costs, and provides a detailed multi‑cluster deployment guide with Terraform, code snippets, and visualizations.
This article explains why monitoring is essential for production stability, compares white‑box and black‑box approaches, details the advantages of Prometheus, walks through its architecture, metric types, query language, high‑availability strategies with Thanos, and provides practical Kubernetes deployment manifests and configuration tips.
This article explores the challenges of building a reliable monitoring platform, compares open‑source solutions such as Elasticsearch, Nagios, Zabbix and Prometheus, and details how to achieve high availability and horizontal scaling using Prometheus, Thanos, sharding, remote‑write, and Kubernetes orchestration.
This article explains why monitoring is essential for production stability, introduces Prometheus fundamentals, metric naming conventions, query types, and high‑availability solutions such as Thanos federation, then walks through a complete Kubernetes deployment including StatefulSets, RBAC, Pushgateway, Alertmanager, and Ingress configuration.
This article explains why standard Prometheus HA solutions fall short for large, multi‑region deployments, and walks through using Thanos—its components, configuration, and best‑practice tips—to achieve long‑term storage, unlimited scaling, a global view, and non‑intrusive monitoring across 300+ clusters.
This article explains why monitoring is essential, compares white‑box and black‑box approaches, details Prometheus features, metric naming, query language, high‑availability challenges, and shows how to extend Prometheus with Thanos, Pushgateway, Alertmanager, and Kubernetes deployments for a robust observability stack.
The article analyzes a persistent active alert observed via Thanos Ruler's HTTP interface, identifies the buffering queue bottleneck as the root cause, and proposes adjusting the maxBatchSize parameter to prevent alert delay and automatic resolution failures.
This article explains the Thanos architecture, compares Sidecar and Receiver deployment modes, walks through object‑storage configuration, and provides complete Kubernetes YAML examples for Prometheus, Thanos Sidecar, Query, Store Gateway, Ruler, Compact, and Receiver to build a large‑scale cloud‑native monitoring system.
This article provides a detailed analysis of Thanos' architecture, explaining each core component—Query, Sidecar, Store Gateway, Ruler, Compact, and the upcoming Receiver—how they enable global view, high availability, and long‑term storage for distributed Prometheus deployments, and discusses design trade‑offs and optimization strategies.
This guide explains the background, key features, architecture, and step‑by‑step deployment of Thanos—including Sidecar, Store, Query, Compact, Bucket, Rule, and Check components—to provide a unified, high‑availability Prometheus monitoring view with unlimited historical data storage using object storage.
The article explains how Thanos combined with Prometheus provides a cloud‑native, highly available solution for long‑term metric storage and fast querying to address the exponential growth of monitoring data in large OpenStack and Ceph deployments.
This article explains how the Thanos and Prometheus combination provides long‑term, highly available monitoring for massive OpenStack and Ceph clusters, detailing its features, architecture, key components, practical deployment issues, and the operational problems it resolves.
