This centralization of log data not only solves the storage challenges, but also allows you to troubleshoot issues at an increased pace. Loggly has a simple setup and an agentless architecture which makes sending logs to Loggly easier. You can use a simple script loggly-jslogger for this purpose, or apply other methods as described here
Instead, you can click and browse through your logs to extract useful information. The tool can significantly reduce the time spent on troubleshooting. Additionally, you can also view your logs in real time with the live tail feature. Loggly also offers near-instant results to all your log searches, which is a huge advantage as most tools might slow down when the log volume increases.
Loggly is built to improve interactivity and collaboration. You can use integrated charts to visualize your search results. The charts can be grouped together and resized using the drag and drop feature to create unified dashboards.
You can share the visualized data with your team members, and project the dashboard over a larger screen for a command-center view. Loggly also integrates with notification services like Hipchat and Slack for alerting. Its integration with JIRA and GitHub improves collaboration between team members and helps them resolve issues faster.
Loggly allows you to turn your saved searches into alerts so you no longer have to monitor your app and infrastructure with manual searches. With alerts, you can schedule them to run at a predetermined time and control how often the alert condition should be checked. Alerts are a great way to implement monitoring for response times and to be notified whenever they rise above your SLA limits.
Get notifications whenever an alert triggers by using one of the many alert endpoints Loggly supports, including Slack, PagerDuty, and GitHub. And of course, you can always receive notifications over email or send them to custom HTTP endpoints using POST and GET requests. With support for alert endpoints, you can be sure the right team will know about issues before your customers do.
This is quite handy for finding all log messages that include the full spectrum of HTTP response error codes or finding all transactions with a response time above a certain threshold. If you’re interested in finding all log messages from a specific time period, you can use time ranges in your search to efficiently narrow down the results.
Discovering anomalies can help you identify new, unexpected problems with your app, but it can also confirm that the fix you’ve implemented for a previous issue is now working. The Loggly anomaly detection feature makes it simple to get early indications of major changes in your app’s performance and behavior.
This feature allows you to specify a field name and show if the event frequency has increased or decreased by comparing the expected frequency value with the observed frequency. The anomaly search compares the current time period with a background time range you can control. Once you’ve got log messages for your query, you can sort the output by value significance, value difference, or actual and expected frequency counts to instantaneously see the most important results.
Tracing errors and exceptions across services and correlating them with requests is normally a complex and time-consuming task. While you can use logging session IDs and API tokens to try and establish which log messages belong to which request, Loggly offers a better solution.
Loggly integrates seamlessly with SolarWinds AppOptics? to provide additional trace context for your logs by automatically inserting a shared trace ID. This ID can be used to effortlessly correlate log messages with requests, so you can filter out the noise in your log data and find the root cause of issues sooner. The integration uses a shared agent design to automatically instrument your app and propagates the trace ID using HTTP headers, which means you don’t need to modify your code to benefit from the additional trace context.