1RRDCREATE(1)                        rrdtool                       RRDCREATE(1)
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3
4

NAME

6       rrdcreate - Set up a new Round Robin Database
7

SYNOPSIS

9       rrdtool create filename [--start|-b start time] [--step|-s step]
10       [DS:ds-name:DST:dst arguments] [RRA:CF:cf arguments]
11

DESCRIPTION

13       The create function of RRDtool lets you set up new Round Robin Database
14       (RRD) files.  The file is created at its final, full size and filled
15       with *UNKNOWN* data.
16
17   filename
18       The name of the RRD you want to create. RRD files should end with the
19       extension .rrd. However, RRDtool will accept any filename.
20
21   --start|-b start time (default: now - 10s)
22       Specifies the time in seconds since 1970-01-01 UTC when the first value
23       should be added to the RRD. RRDtool will not accept any data timed
24       before or at the time specified.
25
26       See also AT-STYLE TIME SPECIFICATION section in the rrdfetch
27       documentation for other ways to specify time.
28
29   --step|-s step (default: 300 seconds)
30       Specifies the base interval in seconds with which data will be fed into
31       the RRD.
32
33   DS:ds-name:DST:dst arguments
34       A single RRD can accept input from several data sources (DS), for
35       example incoming and outgoing traffic on a specific communication line.
36       With the DS configuration option you must define some basic properties
37       of each data source you want to store in the RRD.
38
39       ds-name is the name you will use to reference this particular data
40       source from an RRD. A ds-name must be 1 to 19 characters long in the
41       characters [a-zA-Z0-9_].
42
43       DST defines the Data Source Type. The remaining arguments of a data
44       source entry depend on the data source type. For GAUGE, COUNTER,
45       DERIVE, and ABSOLUTE the format for a data source entry is:
46
47       DS:ds-name:GAUGE | COUNTER | DERIVE | ABSOLUTE:heartbeat:min:max
48
49       For COMPUTE data sources, the format is:
50
51       DS:ds-name:COMPUTE:rpn-expression
52
53       In order to decide which data source type to use, review the
54       definitions that follow. Also consult the section on "HOW TO MEASURE"
55       for further insight.
56
57       GAUGE
58           is for things like temperatures or number of people in a room or
59           the value of a RedHat share.
60
61       COUNTER
62           is for continuous incrementing counters like the ifInOctets counter
63           in a router. The COUNTER data source assumes that the counter never
64           decreases, except when a counter overflows.  The update function
65           takes the overflow into account.  The counter is stored as a per-
66           second rate. When the counter overflows, RRDtool checks if the
67           overflow happened at the 32bit or 64bit border and acts accordingly
68           by adding an appropriate value to the result.
69
70       DERIVE
71           will store the derivative of the line going from the last to the
72           current value of the data source. This can be useful for gauges,
73           for example, to measure the rate of people entering or leaving a
74           room. Internally, derive works exactly like COUNTER but without
75           overflow checks. So if your counter does not reset at 32 or 64 bit
76           you might want to use DERIVE and combine it with a MIN value of 0.
77
78           NOTE on COUNTER vs DERIVE
79
80           by Don Baarda <don.baarda@baesystems.com>
81
82           If you cannot tolerate ever mistaking the occasional counter reset
83           for a legitimate counter wrap, and would prefer "Unknowns" for all
84           legitimate counter wraps and resets, always use DERIVE with min=0.
85           Otherwise, using COUNTER with a suitable max will return correct
86           values for all legitimate counter wraps, mark some counter resets
87           as "Unknown", but can mistake some counter resets for a legitimate
88           counter wrap.
89
90           For a 5 minute step and 32-bit counter, the probability of
91           mistaking a counter reset for a legitimate wrap is arguably about
92           0.8% per 1Mbps of maximum bandwidth. Note that this equates to 80%
93           for 100Mbps interfaces, so for high bandwidth interfaces and a
94           32bit counter, DERIVE with min=0 is probably preferable. If you are
95           using a 64bit counter, just about any max setting will eliminate
96           the possibility of mistaking a reset for a counter wrap.
97
98       ABSOLUTE
99           is for counters which get reset upon reading. This is used for fast
100           counters which tend to overflow. So instead of reading them
101           normally you reset them after every read to make sure you have a
102           maximum time available before the next overflow. Another usage is
103           for things you count like number of messages since the last update.
104
105       COMPUTE
106           is for storing the result of a formula applied to other data
107           sources in the RRD. This data source is not supplied a value on
108           update, but rather its Primary Data Points (PDPs) are computed from
109           the PDPs of the data sources according to the rpn-expression that
110           defines the formula. Consolidation functions are then applied
111           normally to the PDPs of the COMPUTE data source (that is the rpn-
112           expression is only applied to generate PDPs). In database software,
113           such data sets are referred to as "virtual" or "computed" columns.
114
115       heartbeat defines the maximum number of seconds that may pass between
116       two updates of this data source before the value of the data source is
117       assumed to be *UNKNOWN*.
118
119       min and max define the expected range values for data supplied by a
120       data source. If min and/or max any value outside the defined range will
121       be regarded as *UNKNOWN*. If you do not know or care about min and max,
122       set them to U for unknown. Note that min and max always refer to the
123       processed values of the DS. For a traffic-COUNTER type DS this would be
124       the maximum and minimum data-rate expected from the device.
125
126       If information on minimal/maximal expected values is available, always
127       set the min and/or max properties. This will help RRDtool in doing a
128       simple sanity check on the data supplied when running update.
129
130       rpn-expression defines the formula used to compute the PDPs of a
131       COMPUTE data source from other data sources in the same <RRD>. It is
132       similar to defining a CDEF argument for the graph command. Please refer
133       to that manual page for a list and description of RPN operations
134       supported. For COMPUTE data sources, the following RPN operations are
135       not supported: COUNT, PREV, TIME, and LTIME. In addition, in defining
136       the RPN expression, the COMPUTE data source may only refer to the names
137       of data source listed previously in the create command. This is similar
138       to the restriction that CDEFs must refer only to DEFs and CDEFs
139       previously defined in the same graph command.
140
141   RRA:CF:cf arguments
142       The purpose of an RRD is to store data in the round robin archives
143       (RRA). An archive consists of a number of data values or statistics for
144       each of the defined data-sources (DS) and is defined with an RRA line.
145
146       When data is entered into an RRD, it is first fit into time slots of
147       the length defined with the -s option, thus becoming a primary data
148       point.
149
150       The data is also processed with the consolidation function (CF) of the
151       archive. There are several consolidation functions that consolidate
152       primary data points via an aggregate function: AVERAGE, MIN, MAX, LAST.
153
154       AVERAGE
155           the average of the data points is stored.
156
157       MIN the smallest of the data points is stored.
158
159       MAX the largest of the data points is stored.
160
161       LAST
162           the last data points is used.
163
164       Note that data aggregation inevitably leads to loss of precision and
165       information. The trick is to pick the aggregate function such that the
166       interesting properties of your data is kept across the aggregation
167       process.
168
169       The format of RRA line for these consolidation functions is:
170
171       RRA:AVERAGE | MIN | MAX | LAST:xff:steps:rows
172
173       xff The xfiles factor defines what part of a consolidation interval may
174       be made up from *UNKNOWN* data while the consolidated value is still
175       regarded as known. It is given as the ratio of allowed *UNKNOWN* PDPs
176       to the number of PDPs in the interval. Thus, it ranges from 0 to 1
177       (exclusive).
178
179       steps defines how many of these primary data points are used to build a
180       consolidated data point which then goes into the archive.
181
182       rows defines how many generations of data values are kept in an RRA.
183       Obviously, this has to be greater than zero.
184

Aberrant Behavior Detection with Holt-Winters Forecasting

186       In addition to the aggregate functions, there are a set of specialized
187       functions that enable RRDtool to provide data smoothing (via the Holt-
188       Winters forecasting algorithm), confidence bands, and the flagging
189       aberrant behavior in the data source time series:
190
191       ·   RRA:HWPREDICT:rows:alpha:beta:seasonal period[:rra-num]
192
193       ·   RRA:MHWPREDICT:rows:alpha:beta:seasonal period[:rra-num]
194
195       ·   RRA:SEASONAL:seasonal period:gamma:rra-
196           num[:smoothing-window=fraction]
197
198       ·   RRA:DEVSEASONAL:seasonal period:gamma:rra-
199           num[:smoothing-window=fraction]
200
201       ·   RRA:DEVPREDICT:rows:rra-num
202
203       ·   RRA:FAILURES:rows:threshold:window length:rra-num
204
205       These RRAs differ from the true consolidation functions in several
206       ways.  First, each of the RRAs is updated once for every primary data
207       point.  Second, these RRAs are interdependent. To generate real-time
208       confidence bounds, a matched set of SEASONAL, DEVSEASONAL, DEVPREDICT,
209       and either HWPREDICT or MHWPREDICT must exist. Generating smoothed
210       values of the primary data points requires a SEASONAL RRA and either an
211       HWPREDICT or MHWPREDICT RRA. Aberrant behavior detection requires
212       FAILURES, DEVSEASONAL, SEASONAL, and either HWPREDICT or MHWPREDICT.
213
214       The predicted, or smoothed, values are stored in the HWPREDICT or
215       MHWPREDICT RRA. HWPREDICT and MHWPREDICT are actually two variations on
216       the Holt-Winters method. They are interchangeable. Both attempt to
217       decompose data into three components: a baseline, a trend, and a
218       seasonal coefficient.  HWPREDICT adds its seasonal coefficient to the
219       baseline to form a prediction, whereas MHWPREDICT multiplies its
220       seasonal coefficient by the baseline to form a prediction. The
221       difference is noticeable when the baseline changes significantly in the
222       course of a season; HWPREDICT will predict the seasonality to stay
223       constant as the baseline changes, but MHWPREDICT will predict the
224       seasonality to grow or shrink in proportion to the baseline. The proper
225       choice of method depends on the thing being modeled. For simplicity,
226       the rest of this discussion will refer to HWPREDICT, but MHWPREDICT may
227       be substituted in its place.
228
229       The predicted deviations are stored in DEVPREDICT (think a standard
230       deviation which can be scaled to yield a confidence band). The FAILURES
231       RRA stores binary indicators. A 1 marks the indexed observation as
232       failure; that is, the number of confidence bounds violations in the
233       preceding window of observations met or exceeded a specified threshold.
234       An example of using these RRAs to graph confidence bounds and failures
235       appears in rrdgraph.
236
237       The SEASONAL and DEVSEASONAL RRAs store the seasonal coefficients for
238       the Holt-Winters forecasting algorithm and the seasonal deviations,
239       respectively.  There is one entry per observation time point in the
240       seasonal cycle. For example, if primary data points are generated every
241       five minutes and the seasonal cycle is 1 day, both SEASONAL and
242       DEVSEASONAL will have 288 rows.
243
244       In order to simplify the creation for the novice user, in addition to
245       supporting explicit creation of the HWPREDICT, SEASONAL, DEVPREDICT,
246       DEVSEASONAL, and FAILURES RRAs, the RRDtool create command supports
247       implicit creation of the other four when HWPREDICT is specified alone
248       and the final argument rra-num is omitted.
249
250       rows specifies the length of the RRA prior to wrap around. Remember
251       that there is a one-to-one correspondence between primary data points
252       and entries in these RRAs. For the HWPREDICT CF, rows should be larger
253       than the seasonal period. If the DEVPREDICT RRA is implicitly created,
254       the default number of rows is the same as the HWPREDICT rows argument.
255       If the FAILURES RRA is implicitly created, rows will be set to the
256       seasonal period argument of the HWPREDICT RRA. Of course, the RRDtool
257       resize command is available if these defaults are not sufficient and
258       the creator wishes to avoid explicit creations of the other specialized
259       function RRAs.
260
261       seasonal period specifies the number of primary data points in a
262       seasonal cycle. If SEASONAL and DEVSEASONAL are implicitly created,
263       this argument for those RRAs is set automatically to the value
264       specified by HWPREDICT. If they are explicitly created, the creator
265       should verify that all three seasonal period arguments agree.
266
267       alpha is the adaption parameter of the intercept (or baseline)
268       coefficient in the Holt-Winters forecasting algorithm. See rrdtool for
269       a description of this algorithm. alpha must lie between 0 and 1. A
270       value closer to 1 means that more recent observations carry greater
271       weight in predicting the baseline component of the forecast. A value
272       closer to 0 means that past history carries greater weight in
273       predicting the baseline component.
274
275       beta is the adaption parameter of the slope (or linear trend)
276       coefficient in the Holt-Winters forecasting algorithm. beta must lie
277       between 0 and 1 and plays the same role as alpha with respect to the
278       predicted linear trend.
279
280       gamma is the adaption parameter of the seasonal coefficients in the
281       Holt-Winters forecasting algorithm (HWPREDICT) or the adaption
282       parameter in the exponential smoothing update of the seasonal
283       deviations. It must lie between 0 and 1. If the SEASONAL and
284       DEVSEASONAL RRAs are created implicitly, they will both have the same
285       value for gamma: the value specified for the HWPREDICT alpha argument.
286       Note that because there is one seasonal coefficient (or deviation) for
287       each time point during the seasonal cycle, the adaptation rate is much
288       slower than the baseline. Each seasonal coefficient is only updated (or
289       adapts) when the observed value occurs at the offset in the seasonal
290       cycle corresponding to that coefficient.
291
292       If SEASONAL and DEVSEASONAL RRAs are created explicitly, gamma need not
293       be the same for both. Note that gamma can also be changed via the
294       RRDtool tune command.
295
296       smoothing-window specifies the fraction of a season that should be
297       averaged around each point. By default, the value of smoothing-window
298       is 0.05, which means each value in SEASONAL and DEVSEASONAL will be
299       occasionally replaced by averaging it with its (seasonal period*0.05)
300       nearest neighbors.  Setting smoothing-window to zero will disable the
301       running-average smoother altogether.
302
303       rra-num provides the links between related RRAs. If HWPREDICT is
304       specified alone and the other RRAs are created implicitly, then there
305       is no need to worry about this argument. If RRAs are created
306       explicitly, then carefully pay attention to this argument. For each RRA
307       which includes this argument, there is a dependency between that RRA
308       and another RRA. The rra-num argument is the 1-based index in the order
309       of RRA creation (that is, the order they appear in the create command).
310       The dependent RRA for each RRA requiring the rra-num argument is listed
311       here:
312
313       ·   HWPREDICT rra-num is the index of the SEASONAL RRA.
314
315       ·   SEASONAL rra-num is the index of the HWPREDICT RRA.
316
317       ·   DEVPREDICT rra-num is the index of the DEVSEASONAL RRA.
318
319       ·   DEVSEASONAL rra-num is the index of the HWPREDICT RRA.
320
321       ·   FAILURES rra-num is the index of the DEVSEASONAL RRA.
322
323       threshold is the minimum number of violations (observed values outside
324       the confidence bounds) within a window that constitutes a failure. If
325       the FAILURES RRA is implicitly created, the default value is 7.
326
327       window length is the number of time points in the window. Specify an
328       integer greater than or equal to the threshold and less than or equal
329       to 28.  The time interval this window represents depends on the
330       interval between primary data points. If the FAILURES RRA is implicitly
331       created, the default value is 9.
332

The HEARTBEAT and the STEP

334       Here is an explanation by Don Baarda on the inner workings of RRDtool.
335       It may help you to sort out why all this *UNKNOWN* data is popping up
336       in your databases:
337
338       RRDtool gets fed samples/updates at arbitrary times. From these it
339       builds Primary Data Points (PDPs) on every "step" interval. The PDPs
340       are then accumulated into the RRAs.
341
342       The "heartbeat" defines the maximum acceptable interval between
343       samples/updates. If the interval between samples is less than
344       "heartbeat", then an average rate is calculated and applied for that
345       interval. If the interval between samples is longer than "heartbeat",
346       then that entire interval is considered "unknown". Note that there are
347       other things that can make a sample interval "unknown", such as the
348       rate exceeding limits, or a sample that was explicitly marked as
349       unknown.
350
351       The known rates during a PDP's "step" interval are used to calculate an
352       average rate for that PDP. If the total "unknown" time accounts for
353       more than half the "step", the entire PDP is marked as "unknown". This
354       means that a mixture of known and "unknown" sample times in a single
355       PDP "step" may or may not add up to enough "known" time to warrent for
356       a known PDP.
357
358       The "heartbeat" can be short (unusual) or long (typical) relative to
359       the "step" interval between PDPs. A short "heartbeat" means you require
360       multiple samples per PDP, and if you don't get them mark the PDP
361       unknown. A long heartbeat can span multiple "steps", which means it is
362       acceptable to have multiple PDPs calculated from a single sample. An
363       extreme example of this might be a "step" of 5 minutes and a
364       "heartbeat" of one day, in which case a single sample every day will
365       result in all the PDPs for that entire day period being set to the same
366       average rate. -- Don Baarda <don.baarda@baesystems.com>
367
368              time|
369              axis|
370        begin__|00|
371               |01|
372              u|02|----* sample1, restart "hb"-timer
373              u|03|   /
374              u|04|  /
375              u|05| /
376              u|06|/     "hbt" expired
377              u|07|
378               |08|----* sample2, restart "hb"
379               |09|   /
380               |10|  /
381              u|11|----* sample3, restart "hb"
382              u|12|   /
383              u|13|  /
384        step1_u|14| /
385              u|15|/     "swt" expired
386              u|16|
387               |17|----* sample4, restart "hb", create "pdp" for step1 =
388               |18|   /  = unknown due to 10 "u" labled secs > 0.5 * step
389               |19|  /
390               |20| /
391               |21|----* sample5, restart "hb"
392               |22|   /
393               |23|  /
394               |24|----* sample6, restart "hb"
395               |25|   /
396               |26|  /
397               |27|----* sample7, restart "hb"
398        step2__|28|   /
399               |22|  /
400               |23|----* sample8, restart "hb", create "pdp" for step1, create "cdp"
401               |24|   /
402               |25|  /
403
404       graphics by vladimir.lavrov@desy.de.
405

HOW TO MEASURE

407       Here are a few hints on how to measure:
408
409       Temperature
410           Usually you have some type of meter you can read to get the
411           temperature.  The temperature is not really connected with a time.
412           The only connection is that the temperature reading happened at a
413           certain time. You can use the GAUGE data source type for this.
414           RRDtool will then record your reading together with the time.
415
416       Mail Messages
417           Assume you have a method to count the number of messages
418           transported by your mailserver in a certain amount of time, giving
419           you data like '5 messages in the last 65 seconds'. If you look at
420           the count of 5 like an ABSOLUTE data type you can simply update the
421           RRD with the number 5 and the end time of your monitoring period.
422           RRDtool will then record the number of messages per second. If at
423           some later stage you want to know the number of messages
424           transported in a day, you can get the average messages per second
425           from RRDtool for the day in question and multiply this number with
426           the number of seconds in a day. Because all math is run with
427           Doubles, the precision should be acceptable.
428
429       It's always a Rate
430           RRDtool stores rates in amount/second for COUNTER, DERIVE and
431           ABSOLUTE data.  When you plot the data, you will get on the y axis
432           amount/second which you might be tempted to convert to an absolute
433           amount by multiplying by the delta-time between the points. RRDtool
434           plots continuous data, and as such is not appropriate for plotting
435           absolute amounts as for example "total bytes" sent and received in
436           a router. What you probably want is plot rates that you can scale
437           to bytes/hour, for example, or plot absolute amounts with another
438           tool that draws bar-plots, where the delta-time is clear on the
439           plot for each point (such that when you read the graph you see for
440           example GB on the y axis, days on the x axis and one bar for each
441           day).
442

EXAMPLE

444        rrdtool create temperature.rrd --step 300 \
445         DS:temp:GAUGE:600:-273:5000 \
446         RRA:AVERAGE:0.5:1:1200 \
447         RRA:MIN:0.5:12:2400 \
448         RRA:MAX:0.5:12:2400 \
449         RRA:AVERAGE:0.5:12:2400
450
451       This sets up an RRD called temperature.rrd which accepts one
452       temperature value every 300 seconds. If no new data is supplied for
453       more than 600 seconds, the temperature becomes *UNKNOWN*.  The minimum
454       acceptable value is -273 and the maximum is 5'000.
455
456       A few archive areas are also defined. The first stores the temperatures
457       supplied for 100 hours (1'200 * 300 seconds = 100 hours). The second
458       RRA stores the minimum temperature recorded over every hour (12 * 300
459       seconds = 1 hour), for 100 days (2'400 hours). The third and the fourth
460       RRA's do the same for the maximum and average temperature,
461       respectively.
462

EXAMPLE 2

464        rrdtool create monitor.rrd --step 300        \
465          DS:ifOutOctets:COUNTER:1800:0:4294967295   \
466          RRA:AVERAGE:0.5:1:2016                     \
467          RRA:HWPREDICT:1440:0.1:0.0035:288
468
469       This example is a monitor of a router interface. The first RRA tracks
470       the traffic flow in octets; the second RRA generates the specialized
471       functions RRAs for aberrant behavior detection. Note that the rra-num
472       argument of HWPREDICT is missing, so the other RRAs will implicitly be
473       created with default parameter values. In this example, the forecasting
474       algorithm baseline adapts quickly; in fact the most recent one hour of
475       observations (each at 5 minute intervals) accounts for 75% of the
476       baseline prediction. The linear trend forecast adapts much more slowly.
477       Observations made during the last day (at 288 observations per day)
478       account for only 65% of the predicted linear trend. Note: these
479       computations rely on an exponential smoothing formula described in the
480       LISA 2000 paper.
481
482       The seasonal cycle is one day (288 data points at 300 second
483       intervals), and the seasonal adaption parameter will be set to 0.1. The
484       RRD file will store 5 days (1'440 data points) of forecasts and
485       deviation predictions before wrap around. The file will store 1 day (a
486       seasonal cycle) of 0-1 indicators in the FAILURES RRA.
487
488       The same RRD file and RRAs are created with the following command,
489       which explicitly creates all specialized function RRAs.
490
491        rrdtool create monitor.rrd --step 300 \
492          DS:ifOutOctets:COUNTER:1800:0:4294967295 \
493          RRA:AVERAGE:0.5:1:2016 \
494          RRA:HWPREDICT:1440:0.1:0.0035:288:3 \
495          RRA:SEASONAL:288:0.1:2 \
496          RRA:DEVPREDICT:1440:5 \
497          RRA:DEVSEASONAL:288:0.1:2 \
498          RRA:FAILURES:288:7:9:5
499
500       Of course, explicit creation need not replicate implicit create, a
501       number of arguments could be changed.
502

EXAMPLE 3

504        rrdtool create proxy.rrd --step 300 \
505          DS:Total:DERIVE:1800:0:U  \
506          DS:Duration:DERIVE:1800:0:U  \
507          DS:AvgReqDur:COMPUTE:Duration,Requests,0,EQ,1,Requests,IF,/ \
508          RRA:AVERAGE:0.5:1:2016
509
510       This example is monitoring the average request duration during each 300
511       sec interval for requests processed by a web proxy during the interval.
512       In this case, the proxy exposes two counters, the number of requests
513       processed since boot and the total cumulative duration of all processed
514       requests. Clearly these counters both have some rollover point, but
515       using the DERIVE data source also handles the reset that occurs when
516       the web proxy is stopped and restarted.
517
518       In the RRD, the first data source stores the requests per second rate
519       during the interval. The second data source stores the total duration
520       of all requests processed during the interval divided by 300. The
521       COMPUTE data source divides each PDP of the AccumDuration by the
522       corresponding PDP of TotalRequests and stores the average request
523       duration. The remainder of the RPN expression handles the divide by
524       zero case.
525

AUTHOR

527       Tobias Oetiker <tobi@oetiker.ch>
528
529
530
5311.3.8                             2008-06-11                      RRDCREATE(1)
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