Mike, One workaround I've seen for the huge volume of metric data is to break your large RRD files with multiple DS's into multiple RRD files with a single (or small number of) DS. If you must avoid a spike in your traffic, catching up with all the data at once impossible (i.e., you must throw something away or delay more recent data in order to catch up with historical data). You could discard historical information for some DS's for which historical information is less important and gaps are acceptable, while catching up w/ historical and current data for the more important DS's. With some more complex queuing logic, you may not have to discard any data.
Another solution is to perform some data consolidation on the queued data in order to reduce the number of data points you need to update in your RRDs. Unfortunately, if you have different RRAs using different consolidation functions, consolidating the data before passing it to rrdtool may (probably will) result in a loss of information. If it would be better to show only average numbers (without min and max) instead of a gap in the graph, this may be an reasonable trade-off. -Sam Umbach _______________________________________________ rrd-users mailing list [email protected] https://lists.oetiker.ch/cgi-bin/listinfo/rrd-users
