![]() _sourceCategory=Apache/Error | parse regex "\. First, we need to form a query that extracts the information we’re after: Using the exact same process, we can create another panel that displays the most common error reasons. Knowing how many errors are occurring is a great first step towards making sense of our error logs, but it’s also useful to know what kinds of errors are occurring. You can then roll back the update and fix those issues before they affect too many of your visitors. If an update to your web application causes serious problems, this panel will let you know immediately. This kind of real-time window into your Apache servers is the perfect complement to continuous integration environments. Visualizing the number of serious errors in Apache system logs. Sumo Logic periodically re-executes the underlying query and updates the panel automatically. We can then save this chart as a panel by adding it to a dashboard. Visualizing the results as an area chart gives us a clear picture of how many errors our Apache system is generating. _sourceCategory=Apache/Error | parse regex "\ )\]" | where log_level in ("emerg", "alert", "crit") | timeslice 5m | count by _timeslice This lets us count the total logs in each group with the count operator: First, we need to group logs into 5-minute intervals with the timesliceoperator. To stay on top of system-critical errors, we can set up a live panel that displays the number of errors in real-time. Monitoring System-Critical Errors in Apache The real power of Apache log analytics is the ability to aggregate and visualize these error logs. This gives you a lot of debugging info, but it’s nothing you couldn’t find with a text editor. Running this query will list all of the matching error log entries in the Messages tab, as shown above. Also note the regular expression that parses the log_level assumes the default Apache 2.4 error log format. Sumo Logic is designed to record all of your log data, which is why we need to select Apache error logs with the _sourceCategory metadata field. _sourceCategory=Apache/Error | parse regex "\ )\]" | where log_level in ("emerg", "alert", "crit") In Sumo Logic, you can extract emergency-, alert-, and critical-level error messages with the following query: A good place to start your error log analysis is to strip away this noise by isolating serious errors. Isolating Apache System-Critical Error Logsĭepending on your LogLevel directive, Apache error logs can contain verbose details about the inner workings of your servers. Your server’s error logs contain all the information you need to do these things, but extracting useful insights from millions of log entries can be tricky without a dedicated tool. ![]() Monitoring system-critical errors in Sumo Logic.Īpache System-Critical Error Log AnalysisĪpache error log analysis makes it easier to monitor problems in real time and troubleshoot critical issues when they occur. Extracting specific Apache log messages with a custom query.Īpache error log analysis makes it easier to monitor problems in real time and troubleshoot critical issues when they occur. Together, the monitoring and troubleshooting features of an Apache log analyzer results in faster root cause analysis, increased uptime, and fewer headaches. ![]() Then, when these dashboards indicate that something has gone wrong, you need a powerful query language to dig deeper into relevant log messages. The goal of Apache log analytics is to efficiently extract these insights so you can respond to problems before they impact your users.Īpache log analysis revolves around two activities: monitoring and troubleshooting.įirst, you need to track key performance indicators in real-time dashboards so you can identify abnormal behavior as it’s happening. The problem is, this information is hidden within millions of log messages. Your Apache access and error logs contain a wealth of actionable insights about potential server configuration and web application issues. Gain Deep Insight into Your Apache Server Environment
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