RRDCREATE(1) rrdtool RRDCREATE(1)
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 the RRD.
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 characters [a-zA-Z0-9_].
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
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 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.
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
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 the device.
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 point.
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 process.
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 (exclusive).
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-num[:smoothing-window=fraction]
o RRA:DEVSEASONAL:seasonal period:gamma:rra-num[:smoothing-window=fraction]
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 function RRAs.
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 here:
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 unknown.
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 known
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
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 day).
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 zero case.
Tobias Oetiker <email@example.com>
1.4.8 2013-05-23 RRDCREATE(1)