{
  "id": 7222773,
  "title": "The Logging Dilemma",
  "url": "https://urgent.news/2026/09/14/the-logging-dilemma",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-09-14T02:04:13.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/arhuman/the-logging-dilemma-58po"
  },
  "original_language": "en",
  "account": "The logging dilemma faced by developers is an all-too-common experience, marked by a surge of adrenaline upon receiving news of a production incident. In a recent instance, the reporter found themselves frantically searching through logs, only to discover basic information and an unhelpful error message: “Unable to load cache.” This frustration quickly evolved into anger, as the team had recently reduced the log level in an attempt to reduce the flood of debug logs that were occurring daily.\n\nThe issue at hand was the persistent struggle between having too much or too little information. By lowering the log level, the team aimed to avoid being inundated with unnecessary details, yet this left them blind when a true incident struck. The logging tool promised powerful filters that would enable them to access the necessary information once the log level was switched to debug, but raising the log level after the fact often proved ineffective, particularly when the error was tied to a temporal context such as a load spike or database backup.\n\nMoreover, operational friction added to the challenge. Identifying the right pod to raise the log level, handling the security of toggle endpoints, and ensuring clear semantics for the toggle commands all contributed to slowed reproduction, understanding, and ultimately, the resolution of the incident. The reporter concluded that while the dynamic toggle offered an improvement, it was not the ultimate solution for their needs.\n\nThe true revelation came from the realization that the problem lay in deciding beforehand which logs should be retained, rather than trying to retroactively collect information after the fact. This realization birthed the idea of a \"dynamic\" per-operation log level, which was implemented using dllog. When everything was operating smoothly, the log level remained at a standard setting (e.g., Info), but in the event of an error, debug logs from before and after the incident were displayed retroactively.\n\nThis dynamic approach allowed developers to maintain the benefits of a streamlined logging system when things were functioning normally, while still having the capability to access crucial context when errors occurred. By plugging into existing loggers like slog or zap, developers could easily implement this solution without disrupting their current workflows. The middleware opened a scope per request, triggering a replay of buffered logs on 5xx status codes or panics, ensuring that the relevant context was available when needed most.\n\nIn summary, the logging dilemma was not an insurmountable curse, but rather an opportunity for innovation and improvement. By implementing a dynamic per-operation log level, developers can strike a balance between having too much information and not enough, ultimately leading to more efficient incident resolution and a reduced burden of managing excessive log data.",
  "summary": "Every developer has lived through this scene: the adrenaline spike when a production incident is announced. That mix of dread about what you are going to find and frenzy to collect any piece of information that will let you understand and then fix the problem. It happened to me again a few days ago. I can still picture myself rushing to the logs, and I still remember the frustration of finding…",
  "key_points": [
    "Logging dilemma causes frustration for developers during production incidents.",
    "Team reduced log level to avoid debug log flood, but lost crucial context.",
    "Dynamic per-operation log level implemented to balance information retention."
  ],
  "editors_take": "Developers can now balance streamlined logging with access to crucial context during errors by implementing a dynamic per-operation log level, allowing for more efficient incident resolution and reduced log data management burden.",
  "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."
}