{
  "id": 17802267,
  "title": "Why Your Docker Builds Are Slow: Diagnosing Linux Disk I/O Bottlenecks",
  "url": "https://urgent.news/2026/11/02/why-your-docker-builds-are-slow-diagnosing-linux-disk-i-o-bottlenecks",
  "topic": "health",
  "section": "Health & Medicine",
  "published": "2026-11-02T13:44:11.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/roy31/why-your-docker-builds-are-slow-diagnosing-linux-disk-io-bottlenecks-gen"
  },
  "original_language": "en",
  "account": null,
  "summary": "Docker builds can experience significant slowdowns due to a variety of factors, with storage delays often being a primary culprit. These delays can arise from the host system's inability to efficiently process build operations, which encompass tasks such as reading source files, downloading dependencies, extracting packages, compiling code, and creating image layers. These diverse demands on CPU, memory, storage, and network resources can lead to prolonged build times, especially when compiling large applications or installing thousands of small dependencies. Moreover, network latency or suboptimal performance from remote registries can also contribute to slow builds. Before hastily attributing the issue to hardware limitations, it is crucial to pinpoint the specific stage of the build process that consumes the most time and identify which system resource is the bottleneck. Utilizing Docker's `--progress=plain` option during a build can provide valuable insights into the operations taking the longest, whether it be package downloads, source compilation, file copying, or layer extraction. Comparing multiple builds with the same Dockerfile and unchanged source files can also help determine if the issue lies within the build cache, image layers, or host resources. Tools like `iostat` can further aid in diagnosing Linux disk I/O performance issues by providing detailed disk statistics, such as average I/O request completion times, read and write latency, and utilization percentages. By methodically analyzing these metrics, developers can uncover the root cause of slow Docker builds and take targeted steps to optimize performance.",
  "key_points": [],
  "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."
}