![]() ![]() Instead, you’ll want to archive or compress them, or in some cases, delete them. Not using log rotation policies: You shouldn’t need to and probably won’t want to keep logs on a long term basis.For example, your app might use hundreds of microservices that interact with each other, generating massive amounts of log data. All these interactions mean there will be more events and your customers may encounter more errors. The growing complexity of your application architecture: As your architecture becomes more complex, interactions between all of the components in it increases.There’s a variety of reasons, but some common reasons are: Factors contributing to high log volumeįirst, let’s understand why you might be running into a high volume of logs. In this post, I’ll discuss tricks and tips for managing and scaling your log management. But, how can you best sort through this mountain of data to get the crucial insights you need when you encounter an issue? Monitoring can help you to navigate this constant inflow so you spend less time looking and more time problem-solving. Using a log management solution can provide visibility into the performance data you need to reduce the mean time to resolution (MTTR) and mean time to detection (MTTD). But as your infrastructure grows or your application becomes more complex, at some point you may feel like you’re buried in a mountain of never-ending information. These individual messages about events or errors provide you with more information and context when things go wrong, which sounds incredibly helpful. Logs, or information captured at a point in time about an action that took place, are constantly being generated by applications, servers, and network devices. ![]() Managing logs in the modern world is a serious challenge. ![]()
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