Provided by: rrdtool_1.4.7-1_i386
rrdcreate - Set up a new Round Robin Database
rrdtool create filename [--start|-b start time] [--step|-s step]
[--no-overwrite] [DS:ds-name:DST:dst arguments] [RRA:CF:cf arguments]
The create function of RRDtool lets you set up new Round Robin Database
(RRD) files. The file is created at its final, full size and filled
with *UNKNOWN* data.
The name of the RRD you want to create. RRD files should end with the
extension .rrd. However, RRDtool will accept any filename.
--start|-b start time (default: now - 10s)
Specifies the time in seconds since 1970-01-01 UTC when the first value
should be added to the RRD. RRDtool will not accept any data timed
before or at the time specified.
See also AT-STYLE TIME SPECIFICATION section in the rrdfetch
documentation for other ways to specify time.
--step|-s step (default: 300 seconds)
Specifies the base interval in seconds with which data will be fed into
Do not clobber an existing file of the same name.
A single RRD can accept input from several data sources (DS), for
example incoming and outgoing traffic on a specific communication line.
With the DS configuration option you must define some basic properties
of each data source you want to store in the RRD.
ds-name is the name you will use to reference this particular data
source from an RRD. A ds-name must be 1 to 19 characters long in the
DST defines the Data Source Type. The remaining arguments of a data
source entry depend on the data source type. For GAUGE, COUNTER,
DERIVE, and ABSOLUTE the format for a data source entry is:
DS:ds-name:GAUGE | COUNTER | DERIVE | ABSOLUTE:heartbeat:min:max
For COMPUTE data sources, the format is:
In order to decide which data source type to use, review the
definitions that follow. Also consult the section on "HOW TO MEASURE"
for further insight.
is for things like temperatures or number of people in a room or
the value of a RedHat share.
is for continuous incrementing counters like the ifInOctets counter
in a router. The 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.
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 exactly 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.
NOTE on COUNTER vs DERIVE
by Don Baarda <firstname.lastname@example.org>
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
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.
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
maximum time available before the next overflow. Another usage is
for things you count like number of messages since the last update.
is for storing the result of a formula applied to other data
sources in the RRD. This data source is not supplied a value on
update, but rather its Primary Data Points (PDPs) are computed from
the PDPs of the data sources according to the rpn-expression that
defines the formula. Consolidation functions are then applied
normally to the PDPs of the COMPUTE data source (that is the rpn-
expression is only applied to generate PDPs). In database software,
such data sets are referred to as "virtual" or "computed" columns.
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 *UNKNOWN*.
min and max define the expected range values for data supplied by a
data source. If min and/or max are specified any value outside the
defined range will be regarded as *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-COUNTER
type DS this would be the maximum and minimum data-rate expected from
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.
rpn-expression defines the formula used to compute the PDPs of a
COMPUTE data source from other data sources in the same <RRD>. It is
similar to defining a CDEF argument for the graph command. Please refer
to that manual page for a list and description of RPN operations
supported. For COMPUTE data sources, the following RPN operations are
not supported: COUNT, PREV, TIME, and LTIME. In addition, in defining
the RPN expression, the COMPUTE data source may only refer to the names
of data source listed previously in the create command. This is similar
to the restriction that CDEFs must refer only to DEFs and CDEFs
previously defined in the same graph command.
The purpose of an RRD is to store data in the round robin archives
(RRA). An archive consists of a number of data values or statistics for
each of the defined data-sources (DS) and is defined with an RRA line.
When data is entered into an RRD, it is first fit into time slots of
the length defined with the -s option, thus becoming a primary data
The data is also processed with the consolidation function (CF) of the
archive. There are several consolidation functions that consolidate
primary data points via an aggregate function: AVERAGE, MIN, MAX, LAST.
the average of the data points is stored.
MIN the smallest of the data points is stored.
MAX the largest of the data points is stored.
the last data points is used.
Note that data aggregation inevitably leads to loss of precision and
information. The trick is to pick the aggregate function such that the
interesting properties of your data is kept across the aggregation
The format of RRA line for these consolidation functions is:
RRA:AVERAGE | MIN | MAX | LAST:xff:steps:rows
xff The xfiles factor defines what part of a consolidation interval may
be made up from *UNKNOWN* data while the consolidated value is still
regarded as known. It is given as the ratio of allowed *UNKNOWN* PDPs
to the number of PDPs in the interval. Thus, it ranges from 0 to 1
steps defines how many of these primary data points are used to build a
consolidated data point which then goes into the archive.
rows defines how many generations of data values are kept in an RRA.
Obviously, this has to be greater than zero.
Aberrant Behavior Detection with Holt-Winters Forecasting
In addition to the aggregate functions, there are a set of specialized
functions that enable RRDtool to provide data smoothing (via the Holt-
Winters forecasting algorithm), confidence bands, and the flagging
aberrant behavior in the data source time series:
o RRA:HWPREDICT:rows:alpha:beta:seasonal period[:rra-num]
o RRA:MHWPREDICT:rows:alpha:beta:seasonal period[:rra-num]
o RRA:SEASONAL:seasonal period:gamma:rra-
o RRA:DEVSEASONAL:seasonal period:gamma:rra-
o RRA:FAILURES:rows:threshold:window length:rra-num
These RRAs differ from the true consolidation functions in several
ways. First, each of the RRAs is updated once for every primary data
point. Second, these RRAs are interdependent. To generate real-time
confidence 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 RRA and either an
HWPREDICT or MHWPREDICT 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 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
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 RRAs to graph confidence bounds and failures
appears in rrdgraph.
The SEASONAL and DEVSEASONAL 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 will have 288 rows.
In order to simplify the creation for the novice user, in addition to
supporting explicit creation of the HWPREDICT, SEASONAL, DEVPREDICT,
DEVSEASONAL, and FAILURES RRAs, the RRDtool create command supports
implicit creation of the other four when HWPREDICT is specified alone
and the final argument rra-num is omitted.
rows specifies the length of the 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, rows should be larger
than the seasonal period. If the DEVPREDICT RRA is implicitly created,
the default number of rows is the same as the HWPREDICT rows argument.
If the FAILURES RRA is implicitly created, rows will be set to the
seasonal period argument of the HWPREDICT RRA. Of course, the RRDtool
resize command is available if these defaults are not sufficient and
the creator wishes to avoid explicit creations of the other specialized
seasonal period specifies the number of primary data points in a
seasonal cycle. If SEASONAL and DEVSEASONAL are implicitly created,
this argument for those RRAs is set automatically to the value
specified by HWPREDICT. If they are explicitly created, the creator
should verify that all three seasonal period arguments agree.
alpha is the adaption parameter of the intercept (or baseline)
coefficient in the Holt-Winters forecasting algorithm. See rrdtool for
a description of this algorithm. 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 means that past history carries greater weight in
predicting the baseline component.
beta is the adaption parameter of the slope (or linear trend)
coefficient in the Holt-Winters forecasting algorithm. beta must lie
between 0 and 1 and plays the same role as alpha with respect to the
predicted linear trend.
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 RRAs are created implicitly, they will both have the same
value for gamma: the value specified for the HWPREDICT alpha argument.
Note that because there is one seasonal coefficient (or deviation) for
each time point during the seasonal cycle, the adaptation 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 RRAs are created explicitly, gamma need not
be the same for both. Note that gamma can also be changed via the
RRDtool tune command.
smoothing-window specifies the fraction of a season that should be
averaged around each point. By default, the value of smoothing-window
is 0.05, which means each value in SEASONAL and DEVSEASONAL will be
occasionally replaced by averaging it with its (seasonal period*0.05)
nearest neighbors. Setting smoothing-window to zero will disable the
running-average smoother altogether.
rra-num provides the links between related RRAs. If HWPREDICT is
specified alone and the other RRAs are created implicitly, then there
is no need to worry about this argument. If RRAs are created
explicitly, then carefully pay attention to this argument. For each RRA
which includes this argument, there is a dependency between that RRA
and another RRA. The rra-num argument is the 1-based index in the order
of RRA creation (that is, the order they appear in the create command).
The dependent RRA for each RRA requiring the rra-num argument is listed
o HWPREDICT rra-num is the index of the SEASONAL RRA.
o SEASONAL rra-num is the index of the HWPREDICT RRA.
o DEVPREDICT rra-num is the index of the DEVSEASONAL RRA.
o DEVSEASONAL rra-num is the index of the HWPREDICT RRA.
o FAILURES rra-num is the index of the DEVSEASONAL RRA.
threshold is the minimum number of violations (observed values outside
the confidence bounds) within a window that constitutes a failure. If
the FAILURES RRA is implicitly created, the default value is 7.
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 RRA is implicitly
created, the default value is 9.
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:
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/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 a sample that was explicitly marked as
The known rates during a PDP's "step" interval are used to calculate an
average rate for that PDP. If the total "unknown" time accounts for
more than 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 "known" time to warrant a
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 5 minutes 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. -- Don Baarda <email@example.com>
u|02|----* sample1, restart "hb"-timer
u|06|/ "hbt" expired
|08|----* sample2, restart "hb"
u|11|----* sample3, restart "hb"
u|15|/ "swt" expired
|17|----* sample4, restart "hb", create "pdp" for step1 =
|18| / = unknown due to 10 "u" labled secs > 0.5 * step
|21|----* sample5, restart "hb"
|24|----* sample6, restart "hb"
|27|----* sample7, restart "hb"
|23|----* sample8, restart "hb", create "pdp" for step1, create "cdp"
graphics by firstname.lastname@example.org.
HOW TO MEASURE
Here are a few hints on how to measure:
Usually you have some type of meter you can read to get the
temperature. The temperature is not really connected with a time.
The only connection is that the temperature reading happened at a
certain time. You can use the GAUGE data source type for this.
RRDtool will then record your reading together with the time.
Assume you have a method to count the number of messages
transported by your mail server in a certain amount of time, giving
you data like '5 messages in the last 65 seconds'. If you look at
the count of 5 like an ABSOLUTE data type 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.
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 an absolute
amount by multiplying by the delta-time between the points. RRDtool
plots continuous data, and as such is not appropriate for plotting
absolute amounts as for example "total bytes" sent and received in
a router. What you probably want is plot rates that you can scale
to bytes/hour, for example, or plot absolute amounts 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
rrdtool create temperature.rrd --step 300 \
This sets up an RRD called temperature.rrd which accepts one
temperature value every 300 seconds. If no new data is supplied for
more than 600 seconds, the temperature becomes *UNKNOWN*. The minimum
acceptable value is -273 and the maximum is 5'000.
A few archive areas are also defined. The first stores the temperatures
supplied for 100 hours (1'200 * 300 seconds = 100 hours). The second
RRA stores the minimum temperature recorded over every hour (12 * 300
seconds = 1 hour), for 100 days (2'400 hours). The third and the fourth
RRA's do the same for the maximum and average temperature,
rrdtool create monitor.rrd --step 300 \
This example is a monitor of a router interface. The first RRA tracks
the traffic flow in octets; the second RRA generates the specialized
functions RRAs for aberrant behavior detection. Note that the rra-num
argument of HWPREDICT is missing, so the other RRAs will 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) accounts for 75% of the
baseline prediction. The linear trend forecast adapts much more slowly.
Observations made 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 the
LISA 2000 paper.
The seasonal cycle is one day (288 data points at 300 second
intervals), and the seasonal adaption parameter will be set to 0.1. The
RRD file will store 5 days (1'440 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 RRA.
The same RRD file and RRAs are created with the following command,
which explicitly creates all specialized function RRAs.
rrdtool create monitor.rrd --step 300 \
Of course, explicit creation need not replicate implicit create, a
number of arguments could be changed.
rrdtool create proxy.rrd --step 300 \
This example is monitoring the average request duration during each 300
sec interval for requests processed by a web proxy during the interval.
In this case, the proxy exposes two counters, the number of requests
processed since boot and the total cumulative duration of all processed
requests. Clearly these counters both have some rollover point, but
using the DERIVE data source also handles the reset that occurs when
the web proxy is stopped and restarted.
In the RRD, the first data source stores the requests per second rate
during the interval. The second data source stores the total duration
of all requests processed during the interval divided by 300. The
COMPUTE data source divides each PDP of the AccumDuration by the
corresponding PDP of TotalRequests and stores the average request
duration. The remainder of the RPN expression handles the divide by
Tobias Oetiker <email@example.com>