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This article explains how Nginx's master‑slave (primary‑backup) setup, combined with Keepalived and a virtual IP, provides high‑availability for web and API services by automatically detecting failures, shifting the VIP, and allowing the backup server to take over without service interruption.
This article explores how to embed load‑balancer concepts such as intelligent request distribution, health checks, automatic failover, and dynamic strategy adjustment directly into PHP applications using algorithms like weighted round‑robin, response‑time balancing, and circuit‑breaker patterns, providing code examples and practical deployment scenarios.
Redis Sentinel provides a robust high‑availability solution by monitoring master‑slave clusters, automatically detecting failures, electing leaders, and performing failover, while using quorum voting, Pub/Sub communication, and configuration provisioning to ensure seamless master promotion and client redirection without manual intervention.
The article explains how elastic architecture, through auto‑scaling and failover mechanisms, dynamically adjusts resources and ensures continuous service during traffic spikes and component failures, improving cost efficiency, reliability, and operational stability for modern cloud‑based applications.
This article investigates a Redis Sentinel failover anomaly in version 7.4.0 where the sentinel repeatedly elects a failed master, explains the underlying s_down/o_down states, examines network, configuration, and ACL settings, and resolves the issue by adjusting Pub/Sub permissions to allow proper failover.
This article presents a comprehensive design and implementation for achieving MySQL high availability by replacing the single‑master architecture with Orchestrator‑driven automatic failover, integrating DBProxy for transparent routing, and addressing topology changes and data compensation to ensure continuous, reliable service.
Redis Sentinel provides automatic monitoring, fault detection, and failover for Redis master‑slave clusters, enabling high availability by electing a new master when the original fails, using sdown/odown states, quorum voting, and pub/sub communication to keep services running with minimal downtime.
The article investigates why Go applications unexpectedly send AAAA DNS queries to a secondary nameserver, tracing the issue to the built‑in resolver’s handling of non‑recursive responses from a NetScaler proxy, and recommends using the cgo resolver, enabling recursion, or forcing IPv4 to eliminate the added latency.
This article introduces Redis Sentinel as the official high‑availability solution for Redis, explains its core functions, provides configuration examples, compares three ways to receive failover notifications (script, client subscription, and indirect service), and offers design recommendations for robust production deployments.
This article explains how to use Redis Sentinel for high‑availability deployments, covering its core features, configuration files, startup commands, monitoring behavior, three methods of receiving failover notifications, and recommended architectural patterns for robust backend systems.
An incident report details how a scheduled machine reboot on Alibaba Cloud triggered a Flink TaskManager failover, leading to excessive data replay, increased ES pressure, and significant business latency, and explains the root cause involving disabled checkpoints and timestamp‑based offset consumption.
This article explains how to design a high‑availability SMS system by identifying stability bottlenecks, defining reliability goals, implementing failover strategies for Redis, MySQL and external services, establishing a comprehensive observability framework, and measuring key quality metrics to ensure 99.99% uptime.
This article explains how Orchestrator selects a candidate replica during MySQL master failover, detailing the GetCandidateReplica and RegroupReplicasGTID functions, their sorting logic, promotion rules, GTID-based regrouping, and differences from MHA, while highlighting potential data loss issues and related bugs.
This article explains the complete DeadMaster recovery workflow of Orchestrator, detailing how the system selects the appropriate check‑and‑recover function, handles emergency grace periods, reads topology information, registers recovery attempts, validates promotion constraints, executes the actual failover, and runs post‑recovery hooks, with extensive Go code examples.
This article walks through a simulated MySQL 3307 cluster failure, examines Orchestrator's source code to explain the ContinuousDiscovery loop, discovery queues, health ticks, caretaking tasks, raft coordination, topology snapshots, and the logic distinguishing UnreachableMaster from DeadMaster states.
The article analyzes a MySQL MGR cluster failover incident caused by a VM pause and system time jump, explains the underlying detection mechanisms, presents detailed source code excerpts, and demonstrates through debugging that time changes alone do not trigger MGR failover.
This article demonstrates how to configure the open-source Orchestrator tool for automatic MySQL cluster failover, explains key parameters, and presents three test cases covering normal failover, lag‑induced prevention, and the effect of disabling global recoveries.
This article analyzes a real‑world Kafka outage caused by killing a broker process, explains why partitions with a replication factor of one lose their leader, and walks through the internal Zookeeper‑based failover mechanism and leader‑election logic that Kafka uses to recover from such failures.
The article explains how Redis Sentinel provides high availability by monitoring master‑slave instances, detecting failures through periodic pings, distinguishing subjective and objective down states, performing quorum arbitration, and automatically promoting a slave to master to ensure continuous service.
This guide walks through upgrading a single‑node Redis deployment to a high‑availability setup by building a master‑slave cluster, configuring Sentinel services, testing replication and failover, and enabling client auto‑detection of master changes, all using Docker containers and configuration files.