By: Anindya Neogi
Originally Published On: June 25th, 2016
Application developers rely on logs to debug code. As the application goes to production, the same logs prove to be very useful for troubleshooting the software. No wonder when bad things happen, the support guys always ask for logs. With the shift to Cloud and DevOps, logs have become even more important because the developers and the operations team need the same data to debug and fix the software asap. This is where “log analytics” becomes extremely useful in the hands of Dev and Ops as they leverage production logs in real-time to keep a complex IT system running.
However, what we’ve all realised is that it is not just about the logs. Useful information in an IT environment is buried in logs, metrics, events, transaction traces, stacktraces, heap dumps, tickets etc. etc. When we are able to bring all this data together, only then it is useful for detecting, diagnosing and often proactively warn about an issue in production. Also, IT Ops and Application SMEs have been collecting and using almost all of this data through various tools all along to keep the application infrastructure running. For e.g. tickets are created in a Service Desk, metrics are collected, processed for alerts, and visualised in a Monitoring / Performance Management tool, events are processed by an Event Management system. Unless we integrate all this data and the way users work with these tools, Log analysis will not deliver to its potential for detecting and diagnosing problems.
Interestingly, as we get all of this data together, many other use cases are enabled beyond just problem diagnosis of the application. For e.g. looking at events and tickets data, we are able to tell a CIO or LOB exec what are the problem hotspots and trends, where should there be more SMEs allocated, who are the experts that solve certain types of problems, and how much time to they typically take, are certain types of problems not being sent to the right SME initially impacting customer satisfaction, and many many more.
IBM Operations Analytics – Log Analysis, especially with the latest v1.3.1 release, is addressing specifically these areas to be more valuable – increasing the breadth of data from logs, events, support documents to now tickets. It is getting more into Analytics on the breadth of data to deliver insights for a business executive, besides making life simpler for IT Ops and Application SMEs.
In the coming days, I’ll talk more about
(a) how IBM provides value on top of an open big data platform for Log Analysis and
(b) our focus on “analytics” to provide insight to ITSM users beyond just searching and visualising logs.
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