RRDCREATE(1) rrdtool RRDCREATE(1)
rrdcreate - Set up a new Round Robin Database
rrdtool create filename [--start-b start time] [--step-s step]
[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 file
--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.
A single RRD can accept input from several data sources (DS),
for example incoming and outgoing traffic on a specific commu
nication line. With the DS configuration option you must define
some basic properties of each data source you want to store in
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
DS:ds-name:GAUGE COUNTER DERIVE ABSOLUTE:heart
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 over
flows. 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 hap
pened 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
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
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 COM
PUTE 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 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 RRD
tool in doing a simple sanity check on the data supplied when
rpn-expression defines the formula used to compute the PDPs of
a COMPUTE data source from other data sources in the same
. 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 expres
sion, 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 format of RRA line for these con
solidation 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 consoli
dated 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
rows defines how many generations of data values are kept in an
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:
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 HWPREDICT, SEASONAL, DEVSEASONAL,
and DEVPREDICT must exist. Generating smoothed values of the primary
data points requires both a HWPREDICT RRA and SEASONAL RRA. Aberrant
behavior detection requires FAILURES, HWPREDICT, DEVSEASONAL, and SEA
The actual predicted, or smoothed, values are stored in the HWPREDICT
RRA. The predicted deviations are stored in DEVPREDICT (think a stan
dard deviation which can be scaled to yield a confidence band). The
FAILURES RRA stores binary indicators. A 1 marks the indexed observa
tion 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 DEVSEA
SONAL 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 sea
sonal 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 sea
sonal 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) coeffi
cient 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 base
beta is the adaption parameter of the slope (or linear trend) coeffi
cient 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
gamma is the adaption parameter of the seasonal coefficients in the
Holt-Winters forecasting algorithm (HWPREDICT) or the adaption parame
ter in the exponential smoothing update of the seasonal deviations. It
must lie between 0 and 1. If the SEASONAL and DEVSEASONAL RRAs are cre
ated 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 corre
sponding 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 RRD
tool tune command.
rra-num provides the links between related RRAs. If HWPREDICT is speci
fied 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:
HWPREDICT rra-num is the index of the SEASONAL RRA.
SEASONAL rra-num is the index of the HWPREDICT RRA.
DEVPREDICT rra-num is the index of the DEVSEASONAL RRA.
DEVSEASONAL rra-num is the index of the HWPREDICT RRA.
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 inter
val between primary data points. If the FAILURES RRA is implicitly cre
ated, 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 at arbitrary times. From these it builds Pri
mary Data Points (PDPs) at exact times on every "step" interval. The
PDPs are then accumulated into RRAs.
The "heartbeat" defines the maximum acceptable interval between sam
ples. If the interval between samples is less than "heartbeat", then an
average rate is calculated and applied for that interval. If the inter
val between samples is longer than "heartbeat", then that entire inter
val 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 PDPs "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
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 sam
ples, but also the maximum acceptable amount of "unknown" time per PDP
(obviously this is only significant if you have "heartbeat" less than
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 dont 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 "heart
beat" 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 aver
age rate. -- Don Baarda
u02----* sample1, restart "hb"-timer
u06/ "hbt" expired
08----* sample2, restart "hb"
u11----* sample3, restart "hb"
u15/ "swt" expired
17----* sample4, restart "hb", create "pdp" for step1 =
18 / = unknown due to 10 "u" labled secs > "hb"
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 temper
ature. The temperature is not really connected with a time. The
only connection is that the temperature reading happened at a cer
tain 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 trans
ported by your mailserver 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. RRD
tool 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 preci
sion should be acceptable.
Its always a Rate
RRDtool stores rates in amount/second for COUNTER, DERIVE and ABSO
LUTE 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 tempera
ture 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 5000.
A few archive 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
RRAs do the same for the maximum and average temperature, respec
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 base
line 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 compu
tations rely on an exponential smoothing formula described in the LISA
The seasonal cycle is one day (288 data points at 300 second inter
vals), and the seasonal adaption parameter 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 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 num
ber 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 COM
PUTE data source divides each PDP of the AccumDuration by the corre
sponding PDP of TotalRequests and stores the average request duration.
The remainder of the RPN expression handles the divide by zero case.
1.2.15 2006-07-14 RRDCREATE(1)