As IT systems become more sophisticated and the volume and velocity of data grow, it’s more important than ever to have a logging system that can provide valuable insights in a real-time, time-series fashion. It may be challenging to identify which log files provide the most value, so you may wonder which ones to ingest. For example, some of these logs might include operating system metrics that your applications run on or appliance data that secures your internal systems. They might not seem valuable as independent data points, but they can collectively tell a much bigger story when aggregated together.
A log file represents a record of an action taken on a system within the environment. These actions can include rebooting the server or consuming resources such as CPU and memory. Log files can range in size from kilobytes to gigabytes, and the events they record typically have a timestamp so that you can identify when they occurred.
As your system grows, these log files will become impossible to manage without a centralized logging system. Companies scale horizontally by adding more machines to the resource pool to get better throughput – a process that exponentially increases the number of log files that the logging system generates.
Log files come in all shapes and sizes, including XML, JSON, CSV, and simple key-value pairs. They can be operating system logs, application logs, performance monitoring logs, firewall logs, ticketing system logs, or audit trails logs. Since they vary in format, it’s challenging to identify timestamps and line breaks when onboarding them to your logging system. When aiming to decrease time to value (TTV), it’s essential to choose a logging system that is able to structure your data in a machine-parsable format.
As competition grows, companies have to keep innovating to stay relevant in the marketplace. Therefore, businesses often take on more projects and initiatives to capture more market share, increasing log volume and velocity.
Many companies struggle with forecasting the optimal way to account for this growth, so they try to determine which logs have the most significant value and then onboard only those logs. Since companies are often unaware of other options, it’s understandable to take this approach.
There are, however, ways to be clever, such as preprocessing data that are already in motion to reduce log size and collect more data that will provide deeper insight into what the environment is doing. Preprocessing, in this case, includes flattening XML files, removing whitespaces, and converting to clean key-value pairs.
We can define observability to measure how well we can infer a system’s internal state based on external outputs. Observability is different from monitoring in that it allows you to ask questions based on a hypothesis. In contrast, monitoring is more passive and will only alert when it sees a defined problem.
Most companies quantify their success according to service availability, and they usually compare current availability to past availability in order to understand how well they are doing. It can become exponentially more challenging to improve these availability numbers over time without innovative ideas. By utilizing an observability platform, you can ask questions from the outside and tune internal processes to get the desired result, resulting in reduced Mean Time to Resolution (MTTR) for critical business incidents.
When building an observability platform, the type of log data you’re currently ingesting affects the success of your project. It may seem like some types of logs are not crucial to digest because you don’t think they’re impactful. Still, when you tie them all together into a single solution, those supposedly low-value logs can paint a high-value picture that improves your overall monitoring posture. Having structure (schemas) gives your organization the building blocks it needs to build an observability platform. So, if you’re wondering which log files you should ingest, the answer is all of them.