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Aberrant Behavior Detection support. A brief overview added to rrdtoo…

…l.pod.

Major updates to rrd_update.c, rrd_create.c. Minor update to other core files.
This is backwards compatible! But new files using the Aberrant stuff are not readable
by old rrdtool versions. See http://cricket.sourceforge.net/aberrant/rrd_hw.htm
-- Jake Brutlag <jakeb@corp.webtv.net>


git-svn-id: svn://svn.oetiker.ch/rrdtool/trunk/program@26 a5681a0c-68f1-0310-ab6d-d61299d08faa
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oetiker committed Mar 4, 2001
1 parent e9670ed commit cafbbce69d9d1d4a1772299d97138b5a81d343f5
Showing with 4,003 additions and 291 deletions.
  1. +18 −0 NEWS
  2. +194 −10 doc/rrdcreate.pod
  3. +65 −1 doc/rrdgraph.pod
  4. +57 −0 doc/rrdtool.pod
  5. +67 −3 doc/rrdtune.pod
  6. +2 −0 src/Makefile.am
  7. +102 −0 src/fnv.h
  8. +152 −0 src/hash_32.c
  9. +341 −48 src/rrd_create.c
  10. +58 −16 src/rrd_dump.c
  11. +113 −16 src/rrd_format.h
  12. +90 −12 src/rrd_graph.c
  13. +714 −0 src/rrd_hw.c
  14. +58 −10 src/rrd_info.c
  15. +17 −1 src/rrd_open.c
  16. +69 −15 src/rrd_restore.c
  17. +36 −4 src/rrd_tool.h
  18. +178 −3 src/rrd_tune.c
  19. +488 −152 src/rrd_update.c
  20. +1,184 −0 src/rrdupdate.c
View
18 NEWS
@@ -0,0 +1,18 @@
RRDTOOL NEWS
============
In this file I am noting the Major changes to rrdtool
for details check the cvs ChangeLog
2001/03/21 Tobias Oetiker <oetiker@ee.ethz.ch>
Added Aberrant Patch from Jake Brutlag <jakeb@corp.webtv.net>
From now one, new rrd files use version tag 0002. They can
NOT be read by the old 1.0.x rrdtools
Jack:
Aberrant Behavior Detection support. A brief overview added to
rrdtool.pod. Major updates to rrd_update.c, rrd_create.c. Minor update to
other core files. Updated documentation: rrdcreate.pod, rrdgraph.pod,
rrdtune.pod. This is backwards compatible! See
http://cricket.sourceforge.net/aberrant/rrd_hw.htm
View
@@ -10,7 +10,7 @@ B<rrdtool> B<create> I<filename>
S<[B<--start>|B<-b> I<start time>]>
S<[B<--step>|B<-s> I<step>]>
S<[B<DS:>I<ds-name>B<:>I<DST>B<:>I<heartbeat>B<:>I<min>B<:>I<max>]>
S<[B<RRA:>I<CF>B<:>I<xff>B<:>I<steps>B<:>I<rows>]>
S<[B<RRA:>I<CF>B<:>I<cf arguments>]>
=head1 DESCRIPTION
@@ -106,30 +106,174 @@ I<If information on minimal/maximal expected values is available,
always set the min and/or max properties. This will help RRDtool in
doing a simple sanity check on the data supplied when running update.>
=item B<RRA:>I<CF>B<:>I<xff>B<:>I<steps>B<:>I<rows>
=item B<RRA:>I<CF>B<:>I<cf arguments>
The purpose of an B<RRD> is to store data in the round robin archives
(B<RRA>). An archive consists of a number of data values from all the
defined data-sources (B<DS>) and is defined with an B<RRA> line.
(B<RRA>). An archive consists of a number of data values or statistics for
each of the defined data-sources (B<DS>) and is defined with an B<RRA> line.
When data is entered into an B<RRD>, it is first fit into time slots of
the length defined with the B<-s> option becoming a I<primary data point>.
The data is also consolidated with the consolidation function (I<CF>)
of the archive. The following consolidation functions are defined:
B<AVERAGE>, B<MIN>, B<MAX>, B<LAST>.
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 consolidation function 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.
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<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>.
=back
=head1 Aberrant Behaviour detection with Holt-Winters forecasting
by Jake Brutlag E<lt>jakeb@corp.webtv.netE<gt>
In addition to the aggregate functions, there are a set of specialized
functions that enable B<RRDtool> to provide data smoothing (via the
Holt-Winters forecasting algorithm), confidence bands, and the flagging
aberrant behavior in the data source time series:
=over 4
=item 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>
=item B<RRA:>I<DEVSEASONAL>B<:>I<seasonal period>B<:>I<gamma>B<:>I<rra num>
=item B<RRA:>I<DEVPREDICT>B<:>I<rows>B<:>I<rra num>
=item B<RRA:>I<FAILURES>B<:>I<rows>B<:>I<threshold>B<:>I<window length>B<:>I<rra num>
=back
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, then 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 store 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 a
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.
There is one entry per observation time point in the seasonal cycle. For
example, if primary data points are generated every five minutes, and the
seasonal cycle is 1 day, both SEASONAL and DEVSEASONAL with have 288 rows.
In order to simplify the creation for the novice user, in addition to
supporting explicit creation the HWPREDICT, SEASONAL, DEVPREDICT,
DEVSEASONAL, and FAILURES B<RRAs>, the B<rrdtool> create command supports
implicit creation of the other four when HWPREDICT is specified alone and
the final argument I<rra num> is omitted.
I<rows> specifies the length of the B<RRA> prior to wrap around. Remember
that there is a one-to-one correspondence between primary data points and
entries in these RRAs. For the HWPREDICT CF, I<rows> should be larger than
the I<seasonal period>. If the DEVPREDICT B<RRA> is implicity created, the
default number of rows is the same as the HWPREDICT I<rows> argument. If the
FAILURES B<RRA> is implicitly created, I<rows> will be set to the I<seasonal
period> argument of the HWPREDICT B<RRA>. Of course, the B<rrdtool>
I<resize> command is available if these defaults are not sufficient and the
create wishes to avoid explicit creations of the other specialized function
B<RRAs>.
I<seasonal period> specifies the number of primary data points in a seasonal
cycle. If SEASONAL and DEVSEASONAL are implicitly created, this argument for
those B<RRAs> is set automatically to the value specified by HWPREDICT. If
they are explicity created, the creator should verify that all three
I<seasonal period> arguments agree.
I<alpha> is the adaptation parameter of the intercept (or baseline)
coefficient in the Holt-Winters Forecasting algorithm. See L<rrdtool> for a
description of this algorithm. I<alpha> must lie between 0 and 1. A value
closer to 1 means that more recent observations carry greater weight in
predicting the baseline component of the forecast. A value closer to 0 mean
that past history carries greater weight in predicted the baseline
component.
I<beta> is the adaption parameter of the slope (or linear trend) coefficient
in the Holt-Winters Forecating algorihtm. I<beta> must lie between 0 and 1
and plays the same role as I<alpha> with respect to the predicted linear
trend.
I<gamma> is the adaption parameter of the seasonal coefficients in the
Holt-Winters Forecasting algorithm (HWPREDICT) or the adaption parameter in
the exponential smoothing update of the seasonal deviations. It must lie
between 0 and 1. If the SEASONAL and DEVSEASONAL B<RRAs> are created
implicitly, they will both have the same value for I<gamma>: the value
specified for the HWPREDICT I<alpha> argument. Note that because there is
one seasonal coefficient (or deviation) for each time point during the
seasonal cycle, the adaption rate is much slower than the baseline. Each
seasonal coefficient is only updated (or adapts) when the observed value
occurs at the offset in the seasonal cycle corresponding to that
coefficient.
If SEASONAL and DEVSEASONAL B<RRAs> are created explicity, I<gamma> need not
be the same for both. Note that I<gamma> can also be changed via the
B<rrdtool> I<tune> command.
I<rra num> provides the links between related B<RRAs>. If HWPREDICT is
specified alone and the other B<RRAs> created implicitly, then there is no
need to worry about this argument. If B<RRAs> are created explicitly, then
pay careful attention to this argument. For each B<RRA> which includes this
argument, there is a dependency between that B<RRA> and another B<RRA>. The
I<rra num> argument is the 1-based index in the order of B<RRA> creation
(that is, the order they appear in the I<create> command). The dependent
B<RRA> for each B<RRA> requiring the I<rra num> argument is listed here:
=over 4
=item *
HWPREDICT I<rra num> is the index of the SEASONAL B<RRA>.
=item *
SEASONAL I<rra num> is the index of the HWPREDICT B<RRA>.
=item *
DEVPREDICT I<rra num> is the index of the DEVSEASONAL B<RRA>.
=item *
DEVSEASONAL I<rra num> is the index of the HWPREDICT B<RRA>.
=item *
FAILURES I<rra num> is the index of the DEVSEASONAL B<RRA>.
=back
I<threshold> is the minimum number of violations (observed values outside
the confidence bounds) within a window that constitutes a failure. If the
FAILURES B<RRA> is implicitly created, the default value is 7.
I<window length> is the number of time points in the window. Specify an
integer greater than or equal to the threshold and less than or equal to 28.
The time interval this window represents depends on the interval between
primary data points. If the FAILURES B<RRA> is implicity created, the
default value is 9.
=head1 The HEARTBEAT and the STEP
Here is an explanation by Don Baarda on the inner workings of rrdtool.
@@ -232,6 +376,46 @@ every hour (12 * 300 seconds = 1 hour), for 100 days (2400 hours). The
third and the fourth RRA's do the same with the for the maximum and
average temperature, respectively.
=head1 EXAMPLE 2
C<rrdtool create monitor.rrd --step 300
DS:ifOutOctets:COUNTER:1800:0:4294967295
RRA:AVERAGE:0.5:1:2016
RRA:HWPREDICT:1440:0.1:0.0035:288>
This example is a monitor of a router interface. The first B<RRA> tracks the
traffic flow in octects; the second B<RRA> generates the specialized
functions B<RRAs> for aberrant behavior detection. Note that the I<rra num>
argument of HWPREDICT is missing, so the other B<RRAs> will be implicitly be
created with default parameter values. In this example, the forecasting
algorithm baseline adapts quickly; in fact the most recent one hour of
observations (each at 5 minute intervals) account for 75% of the baseline
prediction. The linear trend forecast adapts much more slowly. Observations
made in during the last day (at 288 observations per day) account for only
65% of the predicted linear trend. Note: these computations rely on an
exponential smoothing formula described in a forthcoming LISA 2000 paper.
The seasonal cycle is one day (288 data points at 300 second intervals), and
the seasonal adaption paramter will be set to 0.1. The RRD file will store 5
days (1440 data points) of forecasts and deviation predictions before wrap
around. The file will store 1 day (a seasonal cycle) of 0-1 indicators in
the FAILURES B<RRA>.
The same RRD file and B<RRAs> are created with the following command, which explicitly
creates all specialized function B<RRAs>.
C<rrdtool create monitor.rrd --step 300
DS:ifOutOctets:COUNTER:1800:0:4294967295
RRA:AVERAGE:0.5:1:2016
RRA:HWPREDICT:1440:0.1:0.0035:288:3
RRA:SEASONAL:288:0.1:2
RRA:DEVPREDICT:1440:5
RRA:DEVSEASONAL:288:0.1:2
RRA:FAILURES:288:7:9:5>
Of course, explicit creation need not replicate implicit create, a number of arguments
could be changed.
=head1 AUTHOR
Tobias Oetiker E<lt>oetiker@ee.ethz.chE<gt>
View
@@ -41,6 +41,7 @@ S<[B<VRULE:>I<time>B<#>I<rrggbb>[B<:>I<legend>]]>
S<[B<LINE>{B<1>|B<2>|B<3>}B<:>I<vname>[B<#>I<rrggbb>[B<:>I<legend>]]]>
S<[B<AREA:>I<vname>[B<#>I<rrggbb>[B<:>I<legend>]]]>
S<[B<STACK:>I<vname>[B<#>I<rrggbb>[B<:>I<legend>]]]>
S<[B<TICK:>I<vname>B<#>I<rrggbb>[B<:>I<axis-fraction>[B<:>I<legend>]]]>
=head1 DESCRIPTION
@@ -472,10 +473,18 @@ B<AREA> or B<LINE?> -- you need something to stack something onto in
the first place ;)
Note, that when you STACK onto *UNKNOWN* data, rrdtool will not draw
any graphics ... *UNKNOWN* is not zero ... if you want it to zero
any graphics ... *UNKNOWN* is not zero ... if you want it to be zero
then you might want to use a CDEF argument with IF and UN functions to
turn *UNKNOWN* into zero ...
=item B<TICK:>I<vname>B<#>I<rrggbb>[B<:>I<axis-fraction>[B<:>I<legend>]]
Plot a tick mark (a vertical line) for each value of I<vname> that is
non-zero and not *UNKNOWN*. The I<axis-fraction> argument specifies the
length of the tick mark as a fraction of the y-axis; the default value
is 0.1 (10% of the axis). Note that the color specification is not
optional.
=back
=head1 NOTES on legend arguments
@@ -587,6 +596,61 @@ Note that this example assumes that your data is in the positive half of the y-a
otherwhise you would would have to add NEGINF in order to extend the coverage
of the rea to whole graph.
=head1 EXAMPLE 4
If the specialized function B<RRAs> exist for aberrant behavior detection, they
can be used to generate the graph of a time series with confidence bands and
failures.
rrdtool graph example.gif \
DEF:obs=monitor.rrd:ifOutOctets:AVERAGE \
DEF:pred=monitor.rrd:ifOutOctets:HWPREDICT \
DEF:dev=monitor.rrd:ifOutOctets:DEVPREDICT \
DEF:fail=monitor.rrd:ifOutOctets:FAILURES \
TICK:fail#ffffa0:1.0:"Failures\: Average bits out" \
CDEF:scaledobs=obs,8,* \
CDEF:upper=pred,dev,2,*,+ \
CDEF:lower=pred,dev,2,*,- \
CDEF:scaledupper=upper,8,* \
CDEF:scaledlower=lower,8,* \
LINE2:scaledobs#0000ff:"Average bits out" \
LINE1:scaledupper#ff0000:"Upper Confidence Bound: Average bits out" \
LINE1:scaledlower#ff0000:"Lower Confidence Bound: Average bits out"
This example generates a graph of the data series in blue (LINE2 with the scaledobs
virtual data source), confidence bounds in red (scaledupper and scaledlower virtual
data sources), and potential failures (i.e. potential aberrant aberrant behavior)
marked by vertical yellow lines (the fail data source).
The raw data comes from an AVERAGE B<RRA>, the finest resolution of the observed
time series (one consolidated data point per primary data point). The predicted
(or smoothed) values are stored in the HWPREDICT B<RRA>. The predicted deviations
(think standard deviation) values are stored in the DEVPREDICT B<RRA>. Finally,
the FAILURES B<RRA> contains indicators, with 1 denoting a potential failure.
All of the data is rescaled to bits (instead of Octets) by multiplying by 8.
The confidence bounds are computed by an offset of 2 deviations both above
and below the predicted values (the CDEFs upper and lower). Vertical lines
indicated potential failures are graphed via the TICK graph element, which
converts non-zero values in an B<RRA> into tick marks. Here an axis-fraction
argument of 1.0 means the tick marks span the entire y-axis, and hence become
vertical lines on the graph.
The choice of 2 deviations (a scaling factor) matches the default used internally
by the FAILURES B<RRA>. If the internal value is changed (see L<rrdtune>), this
graphing command should be changed to be consistent.
=head2 A note on data reduction:
The B<rrdtool> I<graph> command is designed to plot data at a specified temporal
resolution, regardless of the actually resolution of the data in the RRD file.
This can present a problem for the specialized consolidation functions which
maintain a one-to-one mapping between primary data points and consolidated
data points. If a graph insists on viewing the contents of these B<RRAs> on a
coarser temporal scale, the I<graph> command tries to do something intelligent,
but the confidence bands and failures no longer have the same meaning and may
be misleading.
=head1 AUTHOR
Tobias Oetiker E<lt>oetiker@ee.ethz.chE<gt>
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