Drilldown Analysis

Analytics has been designed around an effective performance analysis technique called drill-down analysis. This involves checking high level statistics first, and to focus on finer details based on findings so far. This allows you to quickly narrow the focus to the most likely areas.

For example, a performance issue may be experienced and the following high level statistics are checked first:

• Network bytes/sec

• NFSv3 operations/sec

• Disk operations/sec

• CPU utilization

Network bytes/sec is found to be at normal levels, and the same for disk operations and CPU utilization. NFSv3 operations/sec is somewhat high, and the type of NFS operation is then checked and found to be of type "read". So far we have drilled down to a statistic which could be named "NFS operations/sec of type read", which we know is higher than usual.

Some systems may have exhausted available statistics at this point, however Analytics can drill down much further. "NFSv3 operations/sec of type read" can then be viewed by client - which means, rather than examining a single graph - we can now see separate graphs for each NFS client. (These separate graphs sum to the original statistic that we had.)

Let's say we find that the host "kiowa" is responsible for a majority of the NFS reads. We can use Analytics to drill down further, to see what files this client is reading. Our statistic becomes "NFSv3 operations/sec of type read for client kiowa broken down by filename". From this, we can see that kiowa is reading through every file on the NFS server. Armed with this information, we can ask the owner of kiowa to explain.

The above example is possible in Analytics, which can keep drilling down further if needed. To summarize, the statistics we examined were:

• "NFSv3 operations/sec"

• "NFSv3 operations/sec by type"

• "NFSv3 operations/sec of type read by client"

• "NFSv3 operations/sec of type read for client kiowa broken down by filename"

These match the statistic names as created and viewed in Analytics.