[rrdtool.git] / doc / rrdcreate.pod

=head1 NAME

rrdtool create - Set up a new Round Robin Database

=for html <div align="right"><a href="rrdcreate.pdf">PDF</a> version.</div>

=head1 SYNOPSIS

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<cf arguments>]>

=head1 DESCRIPTION

The create function of the RRDtool lets you set up new
Round Robin Database (B<RRD>) files. 
The file is created at its final, full size and filled
with I<*UNKNOWN*> data.

=over 8

=item I<filename>

The name of the B<RRD> you want to create. B<RRD> files should end
with the extension F<.rrd>. However, B<rrdtool> will accept any
filename.

=item B<--start>|B<-b> I<start time> (default: now - 10s)

Specifies the time in seconds since 1970-01-01 UTC when the first
value should be added to the B<RRD>. B<rrdtool> will not accept
any data timed before or at the time specified.

See also AT-STYLE TIME SPECIFICATION section in the
I<rrdfetch> documentation for more ways to specify time.

=item B<--step>|B<-s> I<step> (default: 300 seconds)

Specifies the base interval in seconds with which data will be fed
into the B<RRD>.

=item B<DS:>I<ds-name>B<:>I<DST>B<:>I<heartbeat>B<:>I<min>B<:>I<max>

A single B<RRD> can accept input from several data sources (B<DS>).
(e.g. Incoming and Outgoing traffic on a specific communication
line). With the B<DS> configuration option you must define some basic
properties of each data source you want to use to feed the B<RRD>.

I<ds-name> is the name you will use to reference this particular data
source from an B<RRD>. A I<ds-name> must be 1 to 19 characters long in
the characters [a-zA-Z0-9_].

I<DST> defines the Data Source Type. See the section on "How to Measure" below for further insight.
The Datasource Type must be onw of the following:

=over 4

=item B<GAUGE> 

is for things like temperatures or number of people in a
room or value of a RedHat share.

=item B<COUNTER>

is for continuous incrementing counters like the
InOctets counter in a router. The B<COUNTER> data source assumes that
the counter never decreases, except when a counter overflows.  The update
function takes the overflow into account.  The counter is stored as a
per-second rate. When the counter overflows, RRDtool checks if the overflow happened at
the 32bit or 64bit border and acts accordingly by adding an appropriate value to the result.

=item B<DERIVE>

will store the derivative of the line going from the last to the
current value of the data source. This can be useful for gauges, for
example, to measure the rate of people entering or leaving a
room. Internally, derive works exaclty like COUNTER but without
overflow checks. So if your counter does not reset at 32 or 64 bit you
might want to use DERIVE and combine it with a MIN value of 0.

=over

=item NOTE on COUNTER vs DERIVE

by Don Baarda E<lt>don.baarda@baesystems.comE<gt>

If you cannot tolerate ever mistaking the occasional counter reset for a
legitimate counter wrap, and would prefer "Unknowns" for all legitimate
counter wraps and resets, always use DERIVE with min=0. Otherwise, using
COUNTER with a suitable max will return correct values for all legitimate
counter wraps, mark some counter resets as "Unknown", but can mistake some
counter resets for a legitimate counter wrap.

For a 5 minute step and 32-bit counter, the probability of mistaking a
counter reset for a legitimate wrap is arguably about 0.8% per 1Mbps of
maximum bandwidth. Note that this equates to 80% for 100Mbps interfaces, so
for high bandwidth interfaces and a 32bit counter, DERIVE with min=0 is
probably preferable. If you are using a 64bit counter, just about any max
setting will eliminate the possibility of mistaking a reset for a counter
wrap.

=back

=item B<ABSOLUTE> 

is for counters which get reset upon reading. This is used for fast counters
which tend to overflow. So instead of reading them normally you reset them
after every read to make sure you have a maximal time available before the
next overflow. Another usage is for things you count like number of messages
since the last update.

=back

I<heartbeat> defines the maximum number of seconds that may pass
between two updates of this data source before the value of the 
data source is assumed to be I<*UNKNOWN*>.

I<min> and I<max> are optional entries defining the expected range of
the data supplied by this data source. If I<min> and/or I<max> are
defined, any value outside the defined range will be regarded as
I<*UNKNOWN*>. If you do not know or care about min and max, set them
to U for unknown. Note that min and max always refer to the processed values
of the DS. For a traffic-B<COUNTER> type DS this would be the max and min
data-rate expected from the device.

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<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 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 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<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.
It may help you to sort out why all this *UNKNOWN* data is popping
up in your databases:

RRD gets fed samples at arbitrary times. From these it builds Primary
Data Points (PDPs) at exact times every "step" interval. The PDPs are
then accumulated into RRAs.

The "heartbeat" defines the maximum acceptable interval between
samples. 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.

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
as "unknown". This means that a mixture of known and "unknown" sample
time 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").

The "heartbeat" can be short (unusual) or long (typical) relative to
the "step" interval between PDPs. A short "heartbeat" means you
require multiple samples per PDP, and if you don't get them mark the
PDP unknown. A long heartbeat can span multiple "steps", which means
it is acceptable to have multiple PDPs calculated from a single
sample. An extreme example of this might be a "step" of 5mins and a
"heartbeat" of one day, in which case a single sample every day will
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>>


=head1 HOW TO MEASURE

Here are a few hints on how to measure:

=over


=item Temperature

Normally you have some type of meter you can read to get the temperature.
The temperature is not realy connected with a time. The only connection is
that the temperature reading happened at a certain time. You can use the
B<GAUGE> data source type for this. RRRtool will the record your reading
together with the time.

=item Mail Messages

Assume you have a methode to count the number of messages transported by
your mailserver in a certain amount of time, this give you data like '5
messages in the last 65 seconds'. If you look at the count of 5 like and
B<ABSOLUTE> datatype you can simply update the rrd with the number 5 and the
end time of your monitoring period. RRDtool will then record the number of
messages per second. If at some later stage you want to know the number of
messages transported in a day, you can get the average messages per second
from RRDtool for the day in question and multiply this number with the
number of seconds in a day. Because all math is run with Doubles, the
precision should be acceptable.

=item It's always a Rate

RRDtool stores rates in amount/second for COUNTER, DERIVE and ABSOLUTE data.
When you plot the data, you will get on the y axis amount/second which you
might be tempted to convert to absolute amount volume by multiplying by the
delta-time between the points. RRDtool plots continuous data, and as such is
not appropriate for plotting absolute volumes as for example "total bytes"
sent and received in a router. What you probably want is plot rates that you
can scale to for example bytes/hour or plot volumes with another tool that
draws bar-plots, where the delta-time is clear on the plot for each point
(such that when you read the graph you see for example GB on the y axis,
days on the x axis and one bar for each day).

=back


=head1 EXAMPLE

C<rrdtool create temperature.rrd --step 300 DS:temp:GAUGE:600:-273:5000
RRA:AVERAGE:0.5:1:1200 RRA:MIN:0.5:12:2400 RRA:MAX:0.5:12:2400
RRA:AVERAGE:0.5:12:2400>

This sets up an B<RRD> called F<temperature.rrd> which accepts one
temperature value every 300 seconds. If no new data is supplied for
more than 600 seconds, the temperature becomes I<*UNKNOWN*>.  The
minimum acceptable value is -273 and the maximum is 5000.

A few archives areas are also defined. The first stores the
temperatures supplied for 100 hours (1200 * 300 seconds = 100
hours). The second RRA stores the minimum temperature recorded over
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>