Provided by: rrdtool_1.4.7-2ubuntu5_amd64 
NAME
rrdcreate - Set up a new Round Robin Database
SYNOPSIS
rrdtool create filename [--start|-b start time] [--step|-s step] [--no-overwrite] [DS:ds-
name:DST:dst arguments] [RRA:CF:cf arguments]
DESCRIPTION
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.
filename
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.
--no-overwrite
Do not clobber an existing file of the same name.
DS:ds-name:DST:dst arguments
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:
DS:ds-name:COMPUTE:rpn-expression
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.
GAUGE
is for things like temperatures or number of people in a room or the value of a RedHat share.
COUNTER
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.
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 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 <don.baarda@baesystems.com>
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.
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 maximum
time available before the next overflow. Another usage is for things you count like number of
messages since the last update.
COMPUTE
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
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.
RRA:CF:cf arguments
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.
AVERAGE
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.
LAST
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:
• RRA:HWPREDICT:rows:alpha:beta:seasonal period[:rra-num]
• RRA:MHWPREDICT:rows:alpha:beta:seasonal period[:rra-num]
• RRA:SEASONAL:seasonal period:gamma:rra-num[:smoothing-window=fraction]
• RRA:DEVSEASONAL:seasonal period:gamma:rra-num[:smoothing-window=fraction]
• RRA:DEVPREDICT:rows:rra-num
• 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:
• 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 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 PDP.
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 <don.baarda@baesystems.com>
time|
axis|
begin__|00|
|01|
u|02|----* sample1, restart "hb"-timer
u|03| /
u|04| /
u|05| /
u|06|/ "hbt" expired
u|07|
|08|----* sample2, restart "hb"
|09| /
|10| /
u|11|----* sample3, restart "hb"
u|12| /
u|13| /
step1_u|14| /
u|15|/ "swt" expired
u|16|
|17|----* sample4, restart "hb", create "pdp" for step1 =
|18| / = unknown due to 10 "u" labled secs > 0.5 * step
|19| /
|20| /
|21|----* sample5, restart "hb"
|22| /
|23| /
|24|----* sample6, restart "hb"
|25| /
|26| /
|27|----* sample7, restart "hb"
step2__|28| /
|22| /
|23|----* sample8, restart "hb", create "pdp" for step1, create "cdp"
|24| /
|25| /
graphics by vladimir.lavrov@desy.de.
HOW TO MEASURE
Here are a few hints on how to measure:
Temperature
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.
Mail Messages
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 day).
EXAMPLE
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 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, respectively.
EXAMPLE 2
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 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 \
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.
EXAMPLE 3
rrdtool create proxy.rrd --step 300 \
DS:Total:DERIVE:1800:0:U \
DS:Duration:DERIVE:1800:0:U \
DS:AvgReqDur:COMPUTE:Duration,Requests,0,EQ,1,Requests,IF,/ \
RRA:AVERAGE:0.5:1:2016
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.
AUTHOR
Tobias Oetiker <tobi@oetiker.ch>
1.4.7 2011-01-06 RRDCREATE(1)