Log files are far from perfect. They’re outdated and notoriously difficult to manage, which helped contribute to the success of log aggregators like Splunk.
Log files help identify when or where something has gone wrong, but to troubleshoot an issue, there is still a lot of work to get to what actually happened and why. There are 4 main challenges that prevent us from seeing the whole picture:
1. Log statements are manual and shallow
Developers manually choose where log statements are used and decide what information will be sent to the log file. This information is typically shallow, providing only a few variables that are often just representations of values or shorthand of what needs to be communicated.
2. Searching through log files and classification of entries is tricky
Log files lack structure, and in order to sift through them you need to use grep, grok, regex, etc. or other more advanced tools. Regardless of how you process them, classification and de-duplication of log file entries is more than a simple challenge. Further, it is difficult to correlate a log statement with a specific version of code.
3. Log files provide limited visibility
Related to the manual nature of a log file, they are not comprehensive across every error and exception. You only get visibility into what you choose to log. Log files give you no visibility into uncaught or swallowed exceptions.
4. Tracking down and tracing errors in microservices is impossible
The transition to microservices introduces further complexity. We are now facing an enormous amount of application exhaust across multiple, disconnected distributed services that need to be aggregated and then searched through to gain insight into where our systems are failing. Some frameworks have been introduced to trace events across services, but they are good for performance tracking only.
Logs have been with us for ages and are of huge value, especially with the use of data-focused tools that help manage them. What we want to do, now, is add additional value to these tools by supplementing the data we can pull from the logs with comprehensive code analysis for every known and unknown error or exception.
Register for our webinar on September 12th to learn how to integrate new machine data from OverOps into your Splunk instance to overcome these shortcomings of log files.
