RRDCREATE(1)                        rrdtool                       RRDCREATE(1)



NNAAMMEE
       rrdcreate - Set up a new Round Robin Database

SSYYNNOOPPSSIISS
       rrrrddttooooll ccrreeaattee _f_i_l_e_n_a_m_e [----ssttaarrtt|--bb _s_t_a_r_t _t_i_m_e] [----sstteepp|--ss _s_t_e_p]
       [DDSS::_d_s_-_n_a_m_e::_D_S_T::_d_s_t _a_r_g_u_m_e_n_t_s] [RRRRAA::_C_F::_c_f _a_r_g_u_m_e_n_t_s]

DDEESSCCRRIIPPTTIIOONN
       The create function of RRDtool lets you set up new Round Robin Database
       (RRRRDD) files.  The file is created at its final, full size and filled
       with _*_U_N_K_N_O_W_N_* data.

       _f_i_l_e_n_a_m_e
           The name of the RRRRDD you want to create. RRRRDD files should end with
           the extension _._r_r_d. However, RRRRDDttooooll will accept any filename.

       ----ssttaarrtt|--bb _s_t_a_r_t _t_i_m_e (default: now - 10s)
           Specifies the time in seconds since 1970-01-01 UTC when the first
           value should be added to the RRRRDD. RRRRDDttooooll will not accept any data
           timed before or at the time specified.

           See also AT-STYLE TIME SPECIFICATION section in the _r_r_d_f_e_t_c_h docu-
           mentation for other ways to specify time.

       ----sstteepp|--ss _s_t_e_p (default: 300 seconds)
           Specifies the base interval in seconds with which data will be fed
           into the RRRRDD.

       DDSS::_d_s_-_n_a_m_e::_D_S_T::_d_s_t _a_r_g_u_m_e_n_t_s
           A single RRRRDD can accept input from several data sources (DDSS), for
           example incoming and outgoing traffic on a specific communication
           line. With the DDSS configuration option you must define some basic
           properties of each data source you want to store in the RRRRDD.

           _d_s_-_n_a_m_e is the name you will use to reference this particular data
           source from an RRRRDD. A _d_s_-_n_a_m_e must be 1 to 19 characters long in
           the characters [a-zA-Z0-9_].

           _D_S_T 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:

           DDSS::_d_s_-_n_a_m_e::_G_A_U_G_E _| _C_O_U_N_T_E_R _| _D_E_R_I_V_E _| _A_B_S_O_L_U_T_E::_h_e_a_r_t_b_e_a_t::_m_i_n::_m_a_x

           For COMPUTE data sources, the format is:

           DDSS::_d_s_-_n_a_m_e::_C_O_M_P_U_T_E::_r_p_n_-_e_x_p_r_e_s_s_i_o_n

           In order to decide which data source type to use, review the defi-
           nitions that follow. Also consult the section on "HOW TO MEASURE"
           for further insight.

           GGAAUUGGEE
               is for things like temperatures or number of people in a room
               or the value of a RedHat share.

           CCOOUUNNTTEERR
               is for continuous incrementing counters like the ifInOctets
               counter in a router. The CCOOUUNNTTEERR 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.

           DDEERRIIVVEE
               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 mis-
               taking 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.

           AABBSSOOLLUUTTEE
               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.

           CCOOMMPPUUTTEE
               is for storing the result of a formula applied to other data
               sources in the RRRRDD. 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-expres-
               sion 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.

           _h_e_a_r_t_b_e_a_t 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 _*_U_N_K_N_O_W_N_*.

           _m_i_n and _m_a_x define the expected range values for data supplied by a
           data source. If _m_i_n and/or _m_a_x any value outside the defined range
           will be regarded as _*_U_N_K_N_O_W_N_*. 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-CCOOUUNNTTEERR type
           DS this would be the maximum and minimum data-rate expected from
           the device.

           _I_f _i_n_f_o_r_m_a_t_i_o_n _o_n _m_i_n_i_m_a_l_/_m_a_x_i_m_a_l _e_x_p_e_c_t_e_d _v_a_l_u_e_s _i_s _a_v_a_i_l_a_b_l_e_,
           _a_l_w_a_y_s _s_e_t _t_h_e _m_i_n _a_n_d_/_o_r _m_a_x _p_r_o_p_e_r_t_i_e_s_. _T_h_i_s _w_i_l_l _h_e_l_p _R_R_D_t_o_o_l _i_n
           _d_o_i_n_g _a _s_i_m_p_l_e _s_a_n_i_t_y _c_h_e_c_k _o_n _t_h_e _d_a_t_a _s_u_p_p_l_i_e_d _w_h_e_n _r_u_n_n_i_n_g
           _u_p_d_a_t_e_.

           _r_p_n_-_e_x_p_r_e_s_s_i_o_n 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 CCDDEEFF 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 CCDDEEFFs must
           refer only to DDEEFFs and CCDDEEFFs previously defined in the same graph
           command.

       RRRRAA::_C_F::_c_f _a_r_g_u_m_e_n_t_s
           The purpose of an RRRRDD is to store data in the round robin archives
           (RRRRAA). An archive consists of a number of data values or statistics
           for each of the defined data-sources (DDSS) and is defined with an
           RRRRAA line.

           When data is entered into an RRRRDD, it is first fit into time slots
           of the length defined with the --ss option, thus becoming a _p_r_i_m_a_r_y
           _d_a_t_a _p_o_i_n_t.

           The data is also processed with the consolidation function (_C_F) of
           the archive. There are several consolidation functions that consol-
           idate primary data points via an aggregate function: AAVVEERRAAGGEE, MMIINN,
           MMAAXX, LLAASSTT. The format of RRRRAA line for these consolidation functions
           is:

           RRRRAA::_A_V_E_R_A_G_E _| _M_I_N _| _M_A_X _| _L_A_S_T::_x_f_f::_s_t_e_p_s::_r_o_w_s

           _x_f_f The xfiles factor defines what part of a consolidation interval
           may be made up from _*_U_N_K_N_O_W_N_* data while the consolidated value is
           still regarded as known. It is given as the ratio of allowed
           _*_U_N_K_N_O_W_N_* PDPs to the number of PDPs in the interval. Thus, it
           ranges from 0 to 1 (exclusive).

           _s_t_e_p_s defines how many of these _p_r_i_m_a_r_y _d_a_t_a _p_o_i_n_t_s are used to
           build a _c_o_n_s_o_l_i_d_a_t_e_d _d_a_t_a _p_o_i_n_t which then goes into the archive.

           _r_o_w_s defines how many generations of data values are kept in an
           RRRRAA.

AAbbeerrrraanntt BBeehhaavviioorr DDeetteeccttiioonn wwiitthh HHoolltt--WWiinntteerrss FFoorreeccaassttiinngg
       In addition to the aggregate functions, there are a set of specialized
       functions that enable RRRRDDttooooll to provide data smoothing (via the Holt-
       Winters forecasting algorithm), confidence bands, and the flagging
       aberrant behavior in the data source time series:

          RRRRAA::_H_W_P_R_E_D_I_C_T::_r_o_w_s::_a_l_p_h_a::_b_e_t_a::_s_e_a_s_o_n_a_l _p_e_r_i_o_d[::_r_r_a_-_n_u_m]

          RRRRAA::_S_E_A_S_O_N_A_L::_s_e_a_s_o_n_a_l _p_e_r_i_o_d::_g_a_m_m_a::_r_r_a_-_n_u_m

          RRRRAA::_D_E_V_S_E_A_S_O_N_A_L::_s_e_a_s_o_n_a_l _p_e_r_i_o_d::_g_a_m_m_a::_r_r_a_-_n_u_m

          RRRRAA::_D_E_V_P_R_E_D_I_C_T::_r_o_w_s::_r_r_a_-_n_u_m

          RRRRAA::_F_A_I_L_U_R_E_S::_r_o_w_s::_t_h_r_e_s_h_o_l_d::_w_i_n_d_o_w _l_e_n_g_t_h::_r_r_a_-_n_u_m

       These RRRRAAss differ from the true consolidation functions in several
       ways.  First, each of the RRRRAAs is updated once for every primary data
       point.  Second, these RRRRAAss 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 RRRRAA and SEASONAL RRRRAA. Aberrant
       behavior detection requires FAILURES, HWPREDICT, DEVSEASONAL, and SEA-
       SONAL.

       The actual predicted, or smoothed, values are stored in the HWPREDICT
       RRRRAA. The predicted deviations are stored in DEVPREDICT (think a stan-
       dard deviation which can be scaled to yield a confidence band). The
       FAILURES RRRRAA 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 RRRRAAss to graph confidence bounds
       and failures appears in rrdgraph.

       The SEASONAL and DEVSEASONAL RRRRAAss 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 RRRRAAss, the RRRRDDttooooll create command supports
       implicit creation of the other four when HWPREDICT is specified alone
       and the final argument _r_r_a_-_n_u_m is omitted.

       _r_o_w_s specifies the length of the RRRRAA 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, _r_o_w_s should be larger
       than the _s_e_a_s_o_n_a_l _p_e_r_i_o_d. If the DEVPREDICT RRRRAA is implicitly created,
       the default number of rows is the same as the HWPREDICT _r_o_w_s argument.
       If the FAILURES RRRRAA is implicitly created, _r_o_w_s will be set to the _s_e_a_-
       _s_o_n_a_l _p_e_r_i_o_d argument of the HWPREDICT RRRRAA. Of course, the RRRRDDttooooll
       _r_e_s_i_z_e command is available if these defaults are not sufficient and
       the creator wishes to avoid explicit creations of the other specialized
       function RRRRAAss.

       _s_e_a_s_o_n_a_l _p_e_r_i_o_d specifies the number of primary data points in a sea-
       sonal cycle. If SEASONAL and DEVSEASONAL are implicitly created, this
       argument for those RRRRAAss is set automatically to the value specified by
       HWPREDICT. If they are explicitly created, the creator should verify
       that all three _s_e_a_s_o_n_a_l _p_e_r_i_o_d arguments agree.

       _a_l_p_h_a 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. _a_l_p_h_a 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.

       _b_e_t_a is the adaption parameter of the slope (or linear trend) coeffi-
       cient in the Holt-Winters forecasting algorithm. _b_e_t_a must lie between
       0 and 1 and plays the same role as _a_l_p_h_a with respect to the predicted
       linear trend.

       _g_a_m_m_a 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 RRRRAAss are cre-
       ated implicitly, they will both have the same value for _g_a_m_m_a: the
       value specified for the HWPREDICT _a_l_p_h_a 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 RRRRAAss are created explicitly, _g_a_m_m_a need not
       be the same for both. Note that _g_a_m_m_a can also be changed via the RRRRDD--
       ttooooll _t_u_n_e command.

       _r_r_a_-_n_u_m provides the links between related RRRRAAss. If HWPREDICT is speci-
       fied alone and the other RRRRAAss are created implicitly, then there is no
       need to worry about this argument. If RRRRAAss are created explicitly, then
       carefully pay attention to this argument. For each RRRRAA which includes
       this argument, there is a dependency between that RRRRAA and another RRRRAA.
       The _r_r_a_-_n_u_m argument is the 1-based index in the order of RRRRAA creation
       (that is, the order they appear in the _c_r_e_a_t_e command). The dependent
       RRRRAA for each RRRRAA requiring the _r_r_a_-_n_u_m argument is listed here:

          HWPREDICT _r_r_a_-_n_u_m is the index of the SEASONAL RRRRAA.

          SEASONAL _r_r_a_-_n_u_m is the index of the HWPREDICT RRRRAA.

          DEVPREDICT _r_r_a_-_n_u_m is the index of the DEVSEASONAL RRRRAA.

          DEVSEASONAL _r_r_a_-_n_u_m is the index of the HWPREDICT RRRRAA.

          FAILURES _r_r_a_-_n_u_m is the index of the DEVSEASONAL RRRRAA.

       _t_h_r_e_s_h_o_l_d is the minimum number of violations (observed values outside
       the confidence bounds) within a window that constitutes a failure. If
       the FAILURES RRRRAA is implicitly created, the default value is 7.

       _w_i_n_d_o_w _l_e_n_g_t_h 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 RRRRAA is implicitly cre-
       ated, the default value is 9.

TThhee HHEEAARRTTBBEEAATT aanndd tthhee SSTTEEPP
       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 sam-
       ples/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 hhaallff 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 warrent for
       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. _-_- _D_o_n _B_a_a_r_d_a _<_d_o_n_._b_a_a_r_d_a_@_b_a_e_s_y_s_t_e_m_s_._c_o_m_>

              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 _v_l_a_d_i_m_i_r_._l_a_v_r_o_v_@_d_e_s_y_._d_e.

HHOOWW TTOO MMEEAASSUURREE
       Here are a few hints on how to measure:

       Temperature
           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 GGAAUUGGEE 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 AABBSSOOLLUUTTEE 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.

       It's 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
           day).

EEXXAAMMPPLLEE
        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 RRRRDD called _t_e_m_p_e_r_a_t_u_r_e_._r_r_d which accepts one tempera-
       ture value every 300 seconds. If no new data is supplied for more than
       600 seconds, the temperature becomes _*_U_N_K_N_O_W_N_*.  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, respec-
       tively.

EEXXAAMMPPLLEE 2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 RRRRAA tracks
       the traffic flow in octets; the second RRRRAA generates the specialized
       functions RRRRAAss for aberrant behavior detection. Note that the _r_r_a_-_n_u_m
       argument of HWPREDICT is missing, so the other RRRRAAss 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 (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 RRRRAA.

       The same RRD file and RRRRAAss are created with the following command,
       which explicitly creates all specialized function RRRRAAss.

        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 num-
       ber of arguments could be changed.

EEXXAAMMPPLLEE 3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 RRRRDD, 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.

AAUUTTHHOORR
       Tobias Oetiker <tobi@oetiker.ch>



1.2.30                            2009-01-19                      RRDCREATE(1)
