In the last 3 blogs on the topic of “Troubleshooting troubles”, we looked at how troubleshooting steps can be generalized and how a holistic approach can be arrived at building tools/systems/processes that can significantly enhance the productivity of a support group.
Search as the starting point is a great way to start any analytics with Machine data. As a user, initially you don’t know what you are searching for and hence searching for “needle in a hay stack” is easy, because all you need to do is type needle! Yes, you will get a lot of results back which then needs to be filtered/ranked and presented in a meaningful way, but open source search engines, that allow full text search of any document like SOLR/Lucene, provide a good starting point for search implementation.
In the previous blog, we looked at possible systems/processes that can help L1/L2 support teams be more optimized. In this blog, we can continue that thought process and look at how L3/L4 troubleshooting processes can be optimized.
Next week many of our customers will see a power new upgrade to our existing Glassbeam Search product. We now fully leverage the power of Apache Solr with the advanced technology behind Glassbeam’s SPL™ and text processing capabilities to enable our users to drill deep into their product and application logs. Any section and any attribute captured in the log can be graphed and charted with an instant view into that section of the raw log. More importantly our users now can instantly see what changed since last time for any of their devices.
Vishwanath works as a front desk/security supervisor at our Glassbeam India office. For those of us who work in India would know that the salary he earns is barely enough to meet basic living needs. He has a daughter who finished her 10th standard and got her results a couple of days back. She cleared the exams with flying colours, scoring 88.5% overall with – 98 in math, 98 in Social science, 94 in English, 100 in Kannada (out of 125), 86 in Hindi and 77 in General science. While it is an amazing achievement on its own, the following factors make it even better.
Working with our customers on analyzing their machine log data, we found various common usage patterns of the log data. First, the users in a support organization are troubleshooting and hence are most interested in searching, viewing logs and looking at specific sections or exploring various logs in a log bundle. This is a basic use case that requires correlating various logs in a bundle, whether they have a time stamp or not and allowing users to do full text and attribute based search on the logs( attributes are the output of parsed logs).
Last week was an eventful week for Glassbeam. We were recognized by the industry with two distinct awards. First was the nomination to CRN BIG DATA 100 LIST. Second was the selection at TieCon 2013 to be one of the winners of the BIG DATA LIGHTNING ROUND. Srikanth Desikan, our fearless VP Products & Marketing, presented the Glassbeam story on machine data analytics in a truly lightning style in less than 3 minutes!
There are many ways to analyze machine logs and machine data. Most companies start out with simple manual/semi-automated ways to understand logs. The least sophisticated way is for individual users who are responsible for support or system administration to use standard windows or Unix tools to search for strings, find interesting sections withing logs etc. Tools and applications that enable sophisticated search on logs have empowered sysadmins and dramatically increased their productivity.