X-Git-Url: https://git.octo.it/?a=blobdiff_plain;f=doc%2Frrdcreate.pod;h=49db70ec4ca86c33c424cefb90b3ab65b03fc1c6;hb=c1f4d92a5b64793471b6a4ca78ec7fa71db18df9;hp=3d4b8dd8eadcd982e7a62a3cfa98f211b1d661ae;hpb=cb092381724e4ba8a1f18712fefc541a3d370f99;p=rrdtool.git diff --git a/doc/rrdcreate.pod b/doc/rrdcreate.pod index 3d4b8dd..49db70e 100644 --- a/doc/rrdcreate.pod +++ b/doc/rrdcreate.pod @@ -173,14 +173,44 @@ data point>. The data is also processed with the consolidation function (I) of the archive. There are several consolidation functions that consolidate primary data points via an aggregate function: B, -B, B, B. The format of B line for these +B, B, B. + +=over + +=item AVERAGE + +the average of the data points is stored. + +=item MIN + +the smallest of the data points is stored. + +=item MAX + +the largest of the data points is stored. + +=item LAST + +the last data points is used. + +=back + +Note that data aggregation inevitably leads to loss of precision and +information. The trick is to pick the aggregate function such that the +I properties of your data is kept across the aggregation +process. + + +The format of B line for these consolidation functions is: BIB<:>IB<:>IB<:>I I The xfiles factor defines what part of a consolidation interval may be made up from I<*UNKNOWN*> data while the consolidated value is still -regarded as known. +regarded as known. It is given as the ratio of allowed I<*UNKNOWN*> PDPs +to the number of PDPs in the interval. Thus, it ranges from 0 to 1 (exclusive). + I defines how many of these I are used to build a I which then goes into the archive. @@ -200,7 +230,11 @@ flagging aberrant behavior in the data source time series: =item * -BIB<:>IB<:>IB<:>IB<:>IB<:>I +BIB<:>IB<:>IB<:>IB<:>I[B<:>I] + +=item * + +BIB<:>IB<:>IB<:>IB<:>I[B<:>I] =item * @@ -223,19 +257,32 @@ BIB<:>IB<:>IB<:>IB<:>I These B differ from the true consolidation functions in several ways. First, each of the Bs is updated once for every primary data point. Second, these B are interdependent. To generate real-time confidence -bounds, a matched set of HWPREDICT, SEASONAL, DEVSEASONAL, and -DEVPREDICT must exist. Generating smoothed values of the primary data points -requires both a HWPREDICT B and SEASONAL B. Aberrant behavior -detection requires FAILURES, HWPREDICT, DEVSEASONAL, and SEASONAL. - -The actual predicted, or smoothed, values are stored in the HWPREDICT -B. The predicted deviations are stored in DEVPREDICT (think a standard -deviation which can be scaled to yield a confidence band). The FAILURES -B stores binary indicators. A 1 marks the indexed observation as -failure; that is, the number of confidence bounds violations in the -preceding window of observations met or exceeded a specified threshold. An -example of using these B to graph confidence bounds and failures -appears in L. +bounds, a matched set of SEASONAL, DEVSEASONAL, DEVPREDICT, and either +HWPREDICT or MHWPREDICT must exist. Generating smoothed values of the primary +data points requires a SEASONAL B and either an HWPREDICT or MHWPREDICT +B. Aberrant behavior detection requires FAILURES, DEVSEASONAL, SEASONAL, +and either HWPREDICT or MHWPREDICT. + +The predicted, or smoothed, values are stored in the HWPREDICT or MHWPREDICT +B. HWPREDICT and MHWPREDICT are actually two variations on the +Holt-Winters method. They are interchangeable. Both attempt to decompose data +into three components: a baseline, a trend, and a seasonal coefficient. +HWPREDICT adds its seasonal coefficient to the baseline to form a prediction, whereas +MHWPREDICT multiplies its seasonal coefficient by the baseline to form a +prediction. The difference is noticeable when the baseline changes +significantly in the course of a season; HWPREDICT will predict the seasonality +to stay constant as the baseline changes, but MHWPREDICT will predict the +seasonality to grow or shrink in proportion to the baseline. The proper choice +of method depends on the thing being modeled. For simplicity, the rest of this +discussion will refer to HWPREDICT, but MHWPREDICT may be substituted in its +place. + +The predicted deviations are stored in DEVPREDICT (think a standard deviation +which can be scaled to yield a confidence band). The FAILURES B stores +binary indicators. A 1 marks the indexed observation as failure; that is, the +number of confidence bounds violations in the preceding window of observations +met or exceeded a specified threshold. An example of using these B to graph +confidence bounds and failures appears in L. The SEASONAL and DEVSEASONAL B store the seasonal coefficients for the Holt-Winters forecasting algorithm and the seasonal deviations, respectively. @@ -378,6 +425,44 @@ sample. An extreme example of this might be a "step" of 5 minutes and a result in all the PDPs for that entire day period being set to the same average rate. I<-- Don Baarda Edon.baarda@baesystems.comE> + time| + axis| + begin__|00| + |01| + u|02|----* sample1, restart "hb"-timer + u|03| / + u|04| / + u|05| / + u|06|/ "hbt" expired + u|07| + |08|----* sample2, restart "hb" + |09| / + |10| / + u|11|----* sample3, restart "hb" + u|12| / + u|13| / + step1_u|14| / + u|15|/ "swt" expired + u|16| + |17|----* sample4, restart "hb", create "pdp" for step1 = + |18| / = unknown due to 10 "u" labled secs > "hb" + |19| / + |20| / + |21|----* sample5, restart "hb" + |22| / + |23| / + |24|----* sample6, restart "hb" + |25| / + |26| / + |27|----* sample7, restart "hb" + step2__|28| / + |22| / + |23|----* sample8, restart "hb", create "pdp" for step1, create "cdp" + |24| / + |25| / + +graphics by I. + =head1 HOW TO MEASURE @@ -510,4 +595,4 @@ RPN expression handles the divide by zero case. =head1 AUTHOR -Tobias Oetiker Eoetiker@ee.ethz.chE +Tobias Oetiker Etobi@oetiker.chE