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 file-
               name.

       ----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
               documentation 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 commu-
               nication 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
               definitions 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 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.

               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 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.

               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-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.

               _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 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 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
               consolidate 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 con-
               solidation 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 consoli-
               dated 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 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 PDP's "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
       "step").

       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 "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. _-_- _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 > "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 _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>



1.2.19                            2007-02-01                      RRDCREATE(1)
