RRDCREATE(1) rrdtool RRDCREATE(1) NNAAMMEE rrdcreate - Set up a new Round Robin Database SSYYNNOOPPSSIISS rrrrddttooooll ccrreeaattee _f_i_l_e_n_a_m_e [----ssttaarrtt|--bb _s_t_a_r_t _t_i_m_e] [----sstteepp|--ss _s_t_e_p] [----nnoo--oovveerrwwrriittee] [DDSS::_d_s_-_n_a_m_e::_D_S_T::_d_s_t _a_r_g_u_m_e_n_t_s] [RRRRAA::_C_F::_c_f _a_r_g_u_m_e_n_t_s] DDEESSCCRRIIPPTTIIOONN The create function of RRDtool lets you set up new Round Robin Database (RRRRDD) 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 RRRRDD you want to create. RRRRDD files should end with the extension _._r_r_d. However, RRRRDDttooooll will accept any filename. ----ssttaarrtt||--bb _s_t_a_r_t _t_i_m_e ((ddeeffaauulltt:: nnooww -- 1100ss)) Specifies the time in seconds since 1970-01-01 UTC when the first value should be added to the RRRRDD. RRRRDDttooooll 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. ----sstteepp||--ss _s_t_e_p ((ddeeffaauulltt:: 330000 sseeccoonnddss)) Specifies the base interval in seconds with which data will be fed into the RRRRDD. ----nnoo--oovveerrwwrriittee Do not clobber an existing file of the same name. DDSS::_d_s_-_n_a_m_e::_D_S_T::_d_s_t _a_r_g_u_m_e_n_t_s A single RRRRDD can accept input from several data sources (DDSS), for example incoming and outgoing traffic on a specific communication line. With the DDSS configuration option you must define some basic properties of each data source you want to store in the RRRRDD. _d_s_-_n_a_m_e is the name you will use to reference this particular data source from an RRRRDD. 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: DDSS::_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: DDSS::_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. GGAAUUGGEE is for things like temperatures or number of people in a room or the value of a RedHat share. CCOOUUNNTTEERR is for continuous incrementing counters like the ifInOctets counter in a router. The CCOOUUNNTTEERR 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. DDEERRIIVVEE 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. NNOOTTEE oonn CCOOUUNNTTEERR vvss DDEERRIIVVEE 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. AABBSSOOLLUUTTEE 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. CCOOMMPPUUTTEE is for storing the result of a formula applied to other data sources in the RRRRDD. 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. _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 are specified 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-CCOOUUNNTTEERR 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 . It is similar to defining a CCDDEEFF 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 CCDDEEFFs must refer only to DDEEFFs and CCDDEEFFs previously defined in the same graph command. RRRRAA::_C_F::_c_f _a_r_g_u_m_e_n_t_s The purpose of an RRRRDD is to store data in the round robin archives (RRRRAA). An archive consists of a number of data values or statistics for each of the defined data-sources (DDSS) and is defined with an RRRRAA line. When data is entered into an RRRRDD, it is first fit into time slots of the length defined with the --ss 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: AAVVEERRAAGGEE, MMIINN, MMAAXX, LLAASSTT. AVERAGE the average of the data points is stored. MIN the smallest of the data points is stored. MAX the largest of the data points is stored. LAST the last data points is used. Note that data aggregation inevitably leads to loss of precision and information. The trick is to pick the aggregate function such that the _i_n_t_e_r_e_s_t_i_n_g properties of your data is kept across the aggregation process. The format of RRRRAA line for these consolidation functions is: RRRRAA::_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 RRRRAA. Obviously, this has to be greater than zero. AAbbeerrrraanntt BBeehhaavviioorr DDeetteeccttiioonn wwiitthh HHoolltt--WWiinntteerrss FFoorreeccaassttiinngg In addition to the aggregate functions, there are a set of specialized functions that enable RRRRDDttooooll to provide data smoothing (via the Holt- Winters forecasting algorithm), confidence bands, and the flagging aberrant behavior in the data source time series: · RRRRAA::_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] · RRRRAA::_M_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] · RRRRAA::_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[::ssmmooootthhiinngg--wwiinnddooww==_f_r_a_c_t_i_o_n] · RRRRAA::_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[::ssmmooootthhiinngg--wwiinnddooww==_f_r_a_c_t_i_o_n] · RRRRAA::_D_E_V_P_R_E_D_I_C_T::_r_o_w_s::_r_r_a_-_n_u_m · RRRRAA::_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 RRRRAAss differ from the true consolidation functions in several ways. First, each of the RRRRAAs is updated once for every primary data point. Second, these RRRRAAss 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 RRRRAA and either an HWPREDICT or MHWPREDICT RRRRAA. Aberrant behavior detection requires FAILURES, DEVSEASONAL, SEASONAL, and either HWPREDICT or MHWPREDICT. The predicted, or smoothed, values are stored in the HWPREDICT or MHWPREDICT RRRRAA. 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 RRRRAA 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 RRRRAAss to graph confidence bounds and failures appears in rrdgraph. The SEASONAL and DEVSEASONAL RRRRAAss store the seasonal coefficients for the Holt-Winters forecasting algorithm and the seasonal deviations, respectively. There is one entry per observation time point in the seasonal cycle. For example, if primary data points are generated every five minutes and the seasonal cycle is 1 day, both SEASONAL and DEVSEASONAL will have 288 rows. In order to simplify the creation for the novice user, in addition to supporting explicit creation of the HWPREDICT, SEASONAL, DEVPREDICT, DEVSEASONAL, and FAILURES RRRRAAss, the RRRRDDttooooll 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 RRRRAA 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 RRRRAA is implicitly created, the default number of rows is the same as the HWPREDICT _r_o_w_s argument. If the FAILURES RRRRAA 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 RRRRAA. Of course, the RRRRDDttooooll _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 RRRRAAss. _s_e_a_s_o_n_a_l _p_e_r_i_o_d specifies the number of primary data points in a seasonal cycle. If SEASONAL and DEVSEASONAL are implicitly created, this argument for those RRRRAAss 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) coefficient 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 baseline component. _b_e_t_a is the adaption parameter of the slope (or linear trend) coefficient 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 parameter in the exponential smoothing update of the seasonal deviations. It must lie between 0 and 1. If the SEASONAL and DEVSEASONAL RRRRAAss are created 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 corresponding to that coefficient. If SEASONAL and DEVSEASONAL RRRRAAss 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 RRRRDDttooooll _t_u_n_e command. _s_m_o_o_t_h_i_n_g_-_w_i_n_d_o_w specifies the fraction of a season that should be averaged around each point. By default, the value of _s_m_o_o_t_h_i_n_g_-_w_i_n_d_o_w is 0.05, which means each value in SEASONAL and DEVSEASONAL will be occasionally replaced by averaging it with its (_s_e_a_s_o_n_a_l _p_e_r_i_o_d*0.05) nearest neighbors. Setting _s_m_o_o_t_h_i_n_g_-_w_i_n_d_o_w to zero will disable the running-average smoother altogether. _r_r_a_-_n_u_m provides the links between related RRRRAAss. If HWPREDICT is specified alone and the other RRRRAAss are created implicitly, then there is no need to worry about this argument. If RRRRAAss are created explicitly, then carefully pay attention to this argument. For each RRRRAA which includes this argument, there is a dependency between that RRRRAA and another RRRRAA. The _r_r_a_-_n_u_m argument is the 1-based index in the order of RRRRAA creation (that is, the order they appear in the _c_r_e_a_t_e command). The dependent RRRRAA for each RRRRAA 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 RRRRAA. · SEASONAL _r_r_a_-_n_u_m is the index of the HWPREDICT RRRRAA. · DEVPREDICT _r_r_a_-_n_u_m is the index of the DEVSEASONAL RRRRAA. · DEVSEASONAL _r_r_a_-_n_u_m is the index of the HWPREDICT RRRRAA. · FAILURES _r_r_a_-_n_u_m is the index of the DEVSEASONAL RRRRAA. _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 RRRRAA 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 interval between primary data points. If the FAILURES RRRRAA is implicitly created, the default value is 9. TThhee HHEEAARRTTBBEEAATT aanndd tthhee SSTTEEPP Here is an explanation by Don Baarda on the inner workings of RRDtool. It may help you to sort out why all this *UNKNOWN* data is popping up in your databases: RRDtool gets fed samples/updates at arbitrary times. From these it builds Primary Data Points (PDPs) on every "step" interval. The PDPs are then accumulated into the RRAs. The "heartbeat" defines the maximum acceptable interval between samples/updates. If the interval between samples is less than "heartbeat", then an average rate is calculated and applied for that interval. If the interval between samples is longer than "heartbeat", then that entire interval is considered "unknown". Note that there are other things that can make a sample interval "unknown", such as the rate exceeding limits, or a sample that was explicitly marked as unknown. The known rates during a PDP's "step" interval are used to calculate an average rate for that PDP. If the total "unknown" time accounts for more than hhaallff 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. _-_- _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. HHOOWW TTOO MMEEAASSUURREE Here are a few hints on how to measure: Temperature Usually you have some type of meter you can read to get the temperature. The temperature is not really connected with a time. The only connection is that the temperature reading happened at a certain time. You can use the GGAAUUGGEE data source type for this. RRDtool will then record your reading together with the time. Mail Messages Assume you have a method to count the number of messages transported by your mail server in a certain amount of time, giving you data like '5 messages in the last 65 seconds'. If you look at the count of 5 like an AABBSSOOLLUUTTEE 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). EEXXAAMMPPLLEE 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 RRRRDD called _t_e_m_p_e_r_a_t_u_r_e_._r_r_d which accepts one temperature 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, respectively. EEXXAAMMPPLLEE 22 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 RRRRAA tracks the traffic flow in octets; the second RRRRAA generates the specialized functions RRRRAAss for aberrant behavior detection. Note that the _r_r_a_-_n_u_m argument of HWPREDICT is missing, so the other RRRRAAss 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 RRRRAA. The same RRD file and RRRRAAss are created with the following command, which explicitly creates all specialized function RRRRAAss. rrdtool create monitor.rrd --step 300 \ DS:ifOutOctets:COUNTER:1800:0:4294967295 \ RRA:AVERAGE:0.5:1:2016 \ RRA:HWPREDICT:1440:0.1:0.0035:288:3 \ RRA:SEASONAL:288:0.1:2 \ RRA:DEVPREDICT:1440:5 \ RRA:DEVSEASONAL:288:0.1:2 \ RRA:FAILURES:288:7:9:5 Of course, explicit creation need not replicate implicit create, a number of arguments could be changed. EEXXAAMMPPLLEE 33 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 RRRRDD, 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. AAUUTTHHOORR Tobias Oetiker 1.4.8 2013-05-23 RRDCREATE(1)