The data is also processed with the consolidation function (I<CF>) of
the archive. There are several consolidation functions that
consolidate primary data points via an aggregate function: B<AVERAGE>,
-B<MIN>, B<MAX>, B<LAST>. The format of B<RRA> line for these
+B<MIN>, B<MAX>, B<LAST>.
+
+=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<interesting> properties of your data is kept across the aggregation
+process.
+
+
+The format of B<RRA> line for these
consolidation functions is:
B<RRA:>I<AVERAGE | MIN | MAX | LAST>B<:>I<xff>B<:>I<steps>B<:>I<rows>
I<xff> 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<steps> defines how many of these I<primary data points> are used to build
a I<consolidated data point> which then goes into the archive.
I<rows> defines how many generations of data values are kept in an B<RRA>.
+Obviously, this has to be greater than zero.
=back
=item *
-B<RRA:>I<HWPREDICT>B<:>I<rows>B<:>I<alpha>B<:>I<beta>B<:>I<seasonal period>B<:>I<rra-num>
+B<RRA:>I<HWPREDICT>B<:>I<rows>B<:>I<alpha>B<:>I<beta>B<:>I<seasonal period>[B<:>I<rra-num>]
=item *
-B<RRA:>I<SEASONAL>B<:>I<seasonal period>B<:>I<gamma>B<:>I<rra-num>
+B<RRA:>I<MHWPREDICT>B<:>I<rows>B<:>I<alpha>B<:>I<beta>B<:>I<seasonal period>[B<:>I<rra-num>]
=item *
-B<RRA:>I<DEVSEASONAL>B<:>I<seasonal period>B<:>I<gamma>B<:>I<rra-num>
+B<RRA:>I<SEASONAL>B<:>I<seasonal period>B<:>I<gamma>B<:>I<rra-num>[B<:smoothing-window=>I<fraction>]
+
+=item *
+
+B<RRA:>I<DEVSEASONAL>B<:>I<seasonal period>B<:>I<gamma>B<:>I<rra-num>[B<:smoothing-window=>I<fraction>]
=item *
These B<RRAs> differ from the true consolidation functions in several ways.
First, each of the B<RRA>s is updated once for every primary data point.
Second, these B<RRAs> 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<RRA> and SEASONAL B<RRA>. Aberrant behavior
-detection requires FAILURES, HWPREDICT, DEVSEASONAL, and SEASONAL.
-
-The actual predicted, or smoothed, values are stored in the HWPREDICT
-B<RRA>. The predicted deviations are stored in DEVPREDICT (think a standard
-deviation which can be scaled to yield a confidence band). The FAILURES
-B<RRA> 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<RRAs> to graph confidence bounds and failures
-appears in L<rrdgraph>.
+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<RRA> and either an HWPREDICT or MHWPREDICT
+B<RRA>. 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<RRA>. 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<RRA> 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<RRAs> to graph
+confidence bounds and failures appears in L<rrdgraph>.
The SEASONAL and DEVSEASONAL B<RRAs> store the seasonal coefficients for the
Holt-Winters forecasting algorithm and the seasonal deviations, respectively.
be the same for both. Note that I<gamma> can also be changed via the
B<RRDtool> I<tune> command.
+I<smoothing-window> specifies the fraction of a season that should be
+averaged around each point. By default, the value of I<smoothing-window> is
+0.05, which means each value in SEASONAL and DEVSEASONAL will be occasionally
+replaced by averaging it with its (I<seasonal period>*0.05) nearest neighbors.
+Setting I<smoothing-window> to zero will disable the running-average smoother
+altogether.
+
I<rra-num> provides the links between related B<RRAs>. If HWPREDICT is
specified alone and the other B<RRAs> are created implicitly, then
there is no need to worry about this argument. If B<RRAs> are created
It may help you to sort out why all this *UNKNOWN* data is popping
up in your databases:
-RRDtool gets fed samples at arbitrary times. From these it builds Primary
-Data Points (PDPs) at exact times on every "step" interval. The PDPs are
-then accumulated into RRAs.
+RRDtool gets fed samples/updates at arbitrary times. From these it builds Primary
+Data Points (PDPs) on every "step" interval. The PDPs are
+then accumulated into the RRAs.
The "heartbeat" defines the maximum acceptable interval between
-samples. If the interval between samples is less than "heartbeat",
+samples/updates. If the interval between samples is less than "heartbeat",
then an average rate is calculated and applied for that interval. If
the interval between samples is longer than "heartbeat", then that
entire interval is considered "unknown". Note that there are other
things that can make a sample interval "unknown", such as the rate
-exceeding limits, or even an "unknown" input sample.
+exceeding limits, or a sample that was explicitly marked as unknown.
The known rates during a PDP's "step" interval are used to calculate
-an average rate for that PDP. Also, if the total "unknown" time during
-the "step" interval exceeds the "heartbeat", the entire PDP is marked
+an average rate for that PDP. If the total "unknown" time accounts for
+more than B<half> the "step", the entire PDP is marked
as "unknown". This means that a mixture of known and "unknown" sample
-times in a single PDP "step" may or may not add up to enough "unknown"
-time to exceed "heartbeat" and hence mark the whole PDP "unknown". So
-"heartbeat" is not only the maximum acceptable interval between
-samples, but also the maximum acceptable amount of "unknown" time per
-PDP (obviously this is only significant if you have "heartbeat" less
-than "step").
+times in a single PDP "step" may or may not add up to enough "known"
+time to warrent for a known PDP.
The "heartbeat" can be short (unusual) or long (typical) relative to
the "step" interval between PDPs. A short "heartbeat" means you
result in all the PDPs for that entire day period being set to the
same average rate. I<-- Don Baarda E<lt>don.baarda@baesystems.comE<gt>>
+ 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 > 0.5 * step
+ |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<vladimir.lavrov@desy.de>.
+
=head1 HOW TO MEASURE
=head1 AUTHOR
-Tobias Oetiker E<lt>oetiker@ee.ethz.chE<gt>
+Tobias Oetiker E<lt>tobi@oetiker.chE<gt>
projects / rrdtool.git / blobdiff