{
  "id": 5510854,
  "title": "How to Design Highly Available IT Infrastructure",
  "url": "https://urgent.news/2026/09/04/how-to-design-highly-available-it-infrastructure",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-09-04T07:02:33.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/ronak_sharma_913570f6e215/how-to-design-highly-available-it-infrastructure-3pa2"
  },
  "original_language": "en",
  "account": "Achieving high availability in IT infrastructure requires a precise approach, rather than merely describing systems as \"highly available\" or \"enterprise-grade.\" Genuine high availability means designing the infrastructure so that the failure of any single component does not bring down the entire service. Many systems that claim high availability have not been engineered to specific availability targets, but rather assembled from components without verifying the combined system's actual availability or functionality of the redundancy.\n\nDefining an actual availability target, such as 99.95% availability, translates to a specific amount of acceptable downtime per year, like roughly 4.4 hours. This target should be defined per system based on its business impact—critical revenue-generating systems need higher targets than internal reporting tools.\n\nOne of the foundational principles is to eliminate single points of failure, which requires tracing end-to-end dependencies rather than assuming redundancy from diagrams. Redundancy comes in different levels—component-level (dual power supplies, RAID storage, multiple network interfaces), system-level (multiple servers, load balancers), and site-level (distributed infrastructure across multiple physical locations). Design choices should match each system's actual availability target, ensuring the chosen redundancy level matches the expected failure scenario.\n\nData consistency across redundant systems is crucial; failover to a redundant system running on stale or inconsistent data does not count as a successful failover. Design decisions about data replication should be made based on the system's tolerance for data loss during a failover, balancing the benefits of redundancy against potential consistency gaps.\n\nLoad balancing is essential to make system-level redundancy useful; it detects failures and redirects traffic to healthy components quickly, minimizing user impact. Health checks need to verify genuine application health, not just network connectivity, to ensure that a server serving broken functionality is not kept online. Finally, testing failover scenarios is vital; it verifies that the system can indeed recover and serve traffic without user notice, turning high availability from an aspiration into an achievable engineering target.",
  "summary": "\"High availability\" gets used loosely enough in vendor material that it's genuinely lost some of its meaning everything gets described as highly available, the same way everything gets described as enterprise-grade. The actual concept is precise and worth reclaiming: high availability means designing infrastructure so that the failure of any single component doesn't take down the service that…",
  "key_points": [
    "Define an actual availability target (e.g., 99.95%) to determine acceptable downtime per year.",
    "Ensure data consistency across redundant systems during failover to maintain service integrity."
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}