I am using your method (except for formatting differences as per what GD::Graph wants) for daily graphs, but the graphs I am referring to here is for a long term trend per managed system.

Each managed system could easily contain 20 logical partitions, and for a 3 month trend it could be about 3500 - 5000 values per LPAR.

Using the "select just the 100% common times" method takes about 30 odd seconds to do such a graph for nearly 20 members of the managed system, but to get this result took quite a bit of SQLite3 tuning.

The really big problem with just inserting undefs would be the number of samples. If even just one server was set to gather stats at a short interval, each of the other servers involved running at a different interval would now have to include extra empty values.

I would therefore be inclined to discard the times for which less than x % of hosts have values, but is this the best solution? Since I have the number of samples per data-series, is there no way to fit each data-series between a start and end time using some sort of approximation or mathematical transform?

Niel

In reply to Re^4: Time series normalization by 0xbeef
in thread Time series normalization by 0xbeef

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