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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.

       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 commu
	       nication 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: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.

		       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
		       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 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
	       running update.

	       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.

       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. 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:

	  RRA:HWPREDICT:rows:alpha:beta:seasonal period[:rra-num]

	  RRA:SEASONAL:seasonal period:gamma:rra-num

	  RRA:DEVSEASONAL:seasonal period:gamma: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 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
       function RRAs.

       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
       line component.

       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
       linear trend.

       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.

       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
	      u03   /
	      u04  /
	      u05 /
	      u06/     "hbt" expired
	       08----* sample2, restart "hb"
	       09   /
	       10  /
	      u11----* sample3, restart "hb"
	      u12   /
	      u13  /
	step1_u14 /
	      u15/     "swt" expired
	       17----* sample4, restart "hb", create "pdp" for step1 =
	       18   /  = unknown due to 10 "u" labled secs > "hb"
	       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.

       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.

       Mail Messages
	   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 \
	 DS:temp:GAUGE:600:-273:5000 \
	 RRA:AVERAGE:0.5:1:1200 \
	 RRA:MIN:0.5:12:2400 \
	 RRA:MAX:0.5:12:2400 \

       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	     \
	  DS:ifOutOctets:COUNTER:1800:0:4294967295   \
	  RRA:AVERAGE:0.5:1:2016		     \

       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
       2000 paper.

       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 \
	  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 \

       Of course, explicit creation need not replicate implicit create, a num
       ber of arguments could be changed.

	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,/ \

       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.

       Tobias Oetiker 

1.2.15				  2006-07-14			  RRDCREATE(1)

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