Back to main site | Back to man page index

RRDCREATE(1)                                           rrdtool                                           RRDCREATE(1)



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.
           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 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.

       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.

       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 <[email protected]>

              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 [email protected].

           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.

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 <[email protected]>



1.4.8                                                 2013-05-23                                         RRDCREATE(1)