svcadm(1M) 을 검색하려면 섹션에서 1M 을 선택하고, 맨페이지명에 svcadm을 입력하고 검색을 누른다.
rrdcreate
Name
rrdcreate - Set up a new Round Robin Database
Synopsis
rrdtool create filename [--start|-b start time]
[--step|-s step] [--template|-t template-file]
[--source|-r source-file] [--no-overwrite|-O]
[--daemon|-d address] [DS:ds-name[=mapped-ds-name[[source-
index]]]:DST:dst arguments] [RRA:CF:cf arguments]
Description
The create function of RRDtool lets you set up new Round
Robin Database (RRD) files. The file is created at its
final, full size and filled with *UNKNOWN* data, unless one
or more source RRD files have been specified and they hold
suitable data to "pre-fill" the new RRD file.
filename
The name of the RRD you want to create. RRD files should end
with the extension .rrd. However, RRDtool will accept any
filename.
--start|-b start time (default: now - 10s)
Specifies the time in seconds since 1970-01-01 UTC when the
first value should be added to the RRD. RRDtool will not
accept any data timed before or at the time specified.
See also AT-STYLE TIME SPECIFICATION section in the rrdfetch
documentation for other ways to specify time.
If one or more source files is used to pre-fill the new RRD,
the --start option may be omitted. In that case, the latest
update time among all source files will be used as the last
update time of the new RRD file, effectively setting the
start time.
--step|-s step (default: 300 seconds)
Specifies the base interval in seconds with which data will
be fed into the RRD. A scaling factor may be present as a
suffix to the integer; see "STEP, HEARTBEAT, and Rows As
Durations".
--no-overwrite|-O
Do not clobber an existing file of the same name.
--daemon|-d address
Address of the rrdcached daemon. For a list of accepted
formats, see the -l option in the rrdcached manual.
rrdtool create --daemon unix:/var/run/rrdcached.sock /var/lib/rrd/foo.rrd I<other options>
[--template|-t template-file]
Specifies a template RRD file to take step, DS and RRA
definitions from. This allows one to base the structure of a
new file on some existing file. The data of the template
file is NOT used for pre-filling, but it is possible to
specify the same file as a source file (see below).
Additional DS and RRA definitions are permitted, and will be
added to those taken from the template.
--source|-r source-file
One or more source RRD files may be named on the command
line. Data from these source files will be used to prefill
the created RRD file. The output file and one source file
may refer to the same file name. This will effectively
replace the source file with the new RRD file. While there
is the danger to loose the source file because it gets
replaced, there is no danger that the source and the new
file may be "garbled" together at any point in time, because
the new file will always be created as a temporary file
first and will only be moved to its final destination once
it has been written in its entirety.
Prefilling is done by matching up DS names, RRAs and
consolidation functions and choosing the best available data
resolution when doing so. Prefilling may not be
mathematically correct in all cases (eg. if resolutions have
to change due to changed stepping of the target RRD and old
and new resolutions do not match up with old/new bin
boundaries in RRAs).
In other words: A best effort is made to preserve data
during prefilling. Also, pre-filling of RRAs may only be
possible for certain kinds of DS types. Prefilling may also
have strange effects on Holt-Winters forecasting RRAs. In
other words: there is no guarantee for data-correctness.
When "pre-filling" a RRD file, the structure of the new file
must be specified as usual using DS and RRA specifications
as outlined below. Data will be taken from source files
based on DS names and types and in the order the source
files are specified in. Data sources with the same name from
different source files will be combined to form a new data
source. Generally, for any point in time the new RRD file
will cover after its creation, data from only one source
file will have been used for pre-filling. However, data from
multiple sources may be combined if it refers to different
times or an earlier named source file holds unknown data for
a time where a later one holds known data.
If this automatic data selection is not desired, the DS
syntax allows one to specify a mapping of target and source
data sources for prefilling. This syntax allows one to
rename data sources and to restrict prefilling for a DS to
only use data from a single source file.
Prefilling currently only works reliably for RRAs using one
of the classic consolidation functions, that is one of:
AVERAGE, MIN, MAX, LAST. It might also currently have
problems with COMPUTE data sources.
Note that the act of prefilling during create is similar to
a lot of the operations available via the tune command, but
using create syntax.
DS:ds-name[=mapped-ds-name[[source-index]]]:DST:dst arguments
A single RRD can accept input from several data sources
(DS), for example incoming and outgoing traffic on a
specific communication line. With the DS configuration
option you must define some basic properties of each data
source you want to store in the RRD.
ds-name is the name you will use to reference this
particular data source from an RRD. A ds-name must be 1 to
19 characters long in the characters [a-zA-Z0-9_].
DST defines the Data Source Type. The remaining arguments of
a data source entry depend on the data source type. For
GAUGE, COUNTER, DERIVE, DCOUNTER, DDERIVE and ABSOLUTE the
format for a data source entry is:
DS:ds-name:{GAUGE | COUNTER | DERIVE | DCOUNTER | DDERIVE
ABSOLUTE}:heartbeat:min:max
For COMPUTE data sources, the format is:
DS:ds-name:COMPUTE:rpn-expression
In order to decide which data source type to use, review the
definitions that follow. Also consult the section on "HOW TO
MEASURE" for further insight.
GAUGE
is for things like temperatures or number of people in a
room or the value of a RedHat share.
COUNTER
is for continuous incrementing counters like the
ifInOctets counter in a router. The COUNTER data source
assumes that the counter never decreases, except when a
counter overflows. The update function takes the
overflow into account. The counter is stored as a per-
second rate. When the counter overflows, RRDtool checks
if the overflow happened at the 32bit or 64bit border
and acts accordingly by adding an appropriate value to
the result.
DCOUNTER
the same as COUNTER, but for quantities expressed as
double-precision floating point number. Could be used
to track quantities that increment by non-integer
numbers, i.e. number of seconds that some routine has
taken to run, total weight processed by some technology
equipment etc. The only substantial difference is that
DCOUNTER can either be upward counting or downward
counting, but not both at the same time. The current
direction is detected automatically on the second non-
undefined counter update and any further change in the
direction is considered a reset. The new direction is
determined and locked in by the second update after
reset and its difference to the value at reset.
DERIVE
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.
DDERIVE
the same as DERIVE, but for quantities expressed as
double-precision floating point number.
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.
ABSOLUTE
is for counters which get reset upon reading. This is
used for fast counters which tend to overflow. So
instead of reading them normally you reset them after
every read to make sure you have a maximum time
available before the next overflow. Another usage is for
things you count like number of messages since the last
update.
COMPUTE
is for storing the result of a formula applied to other
data sources in the RRD. This data source is not
supplied a value on update, but rather its Primary Data
Points (PDPs) are computed from the PDPs of the data
sources according to the rpn-expression that defines the
formula. Consolidation functions are then applied
normally to the PDPs of the COMPUTE data source (that is
the rpn-expression is only applied to generate PDPs). In
database software, such data sets are referred to as
"virtual" or "computed" columns.
heartbeat defines the maximum number of seconds that may
pass between two updates of this data source before the
value of the data source is assumed to be *UNKNOWN*.
min and max define the expected range values for data
supplied by a data source. If min and/or max are specified
any value outside the defined range will be regarded as
*UNKNOWN*. If you do not know or care about min and max, set
them to U for unknown. Note that min and max always refer to
the processed values of the DS. For a traffic-COUNTER type
DS this would be the maximum and minimum data-rate expected
from the device.
If information on minimal/maximal expected values is
available, always set the min and/or max properties. This
will help RRDtool in doing a simple sanity check on the data
supplied when running update.
rpn-expression defines the formula used to compute the PDPs
of a COMPUTE data source from other data sources in the same
<RRD>. It is similar to defining a CDEF argument for the
graph command. Please refer to that manual page for a list
and description of RPN operations supported. For COMPUTE
data sources, the following RPN operations are not
supported: COUNT, PREV, TIME, and LTIME. In addition, in
defining the RPN expression, the COMPUTE data source may
only refer to the names of data source listed previously in
the create command. This is similar to the restriction that
CDEFs must refer only to DEFs and CDEFs previously defined
in the same graph command.
When pre-filling the new RRD file using one or more source
RRDs, the DS specification may hold an optional mapping
after the DS name. This takes the form of an equal sign
followed by a mapped-to DS name and an optional source index
enclosed in square brackets.
For example, the DS
DS:a=b[2]:GAUGE:120:0:U
specifies that the DS named a should be pre-filled from the
DS named b in the second listed source file (source indices
are 1-based).
RRA:CF:cf arguments
The purpose of an RRD is to store data in the round robin
archives (RRA). An archive consists of a number of data
values or statistics for each of the defined data-sources
(DS) and is defined with an RRA line.
When data is entered into an RRD, it is first fit into time
slots of the length defined with the -s option, thus
becoming a primary data point.
The data is also processed with the consolidation function
(CF) of the archive. There are several consolidation
functions that consolidate primary data points via an
aggregate function: AVERAGE, MIN, MAX, LAST.
AVERAGE
the 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 interesting properties of
your data is kept across the aggregation process.
The format of RRA line for these consolidation functions is:
RRA:{AVERAGE | MIN | MAX | LAST}:xff:steps:rows
xff The xfiles factor defines what part of a consolidation
interval may be made up from *UNKNOWN* data while the
consolidated value is still regarded as known. It is given
as the ratio of allowed *UNKNOWN* PDPs to the number of PDPs
in the interval. Thus, it ranges from 0 to 1 (exclusive).
steps defines how many of these primary data points are used
to build a consolidated data point which then goes into the
archive. See also "STEP, HEARTBEAT, and Rows As Durations".
rows defines how many generations of data values are kept in
an RRA. Obviously, this has to be greater than zero. See
also "STEP, HEARTBEAT, and Rows As Durations".
Aberrant Behavior Detection with Holt-Winters Forecasting
In addition to the aggregate functions, there are a set of
specialized functions that enable RRDtool to provide data
smoothing (via the Holt-Winters forecasting algorithm),
confidence bands, and the flagging aberrant behavior in the
data source time series:
* RRA:HWPREDICT:rows:alpha:beta:seasonal period[:rra-num]
* RRA:MHWPREDICT:rows:alpha:beta:seasonal period[:rra-num]
* RRA:SEASONAL:seasonal period:gamma:rra-
num[:smoothing-window=fraction]
* RRA:DEVSEASONAL:seasonal period:gamma:rra-
num[:smoothing-window=fraction]
* RRA:DEVPREDICT:rows:rra-num
* RRA:FAILURES:rows:threshold:window length:rra-num
These RRAs differ from the true consolidation functions in
several ways. First, each of the RRAs is updated once for
every primary data point. Second, these RRAs are
interdependent. To generate real-time confidence bounds, a
matched set of SEASONAL, DEVSEASONAL, DEVPREDICT, and either
HWPREDICT or MHWPREDICT must exist. Generating smoothed
values of the primary data points requires a SEASONAL RRA
and either an HWPREDICT or MHWPREDICT RRA. Aberrant behavior
detection requires FAILURES, DEVSEASONAL, SEASONAL, and
either HWPREDICT or MHWPREDICT.
The predicted, or smoothed, values are stored in the
HWPREDICT or MHWPREDICT RRA. HWPREDICT and MHWPREDICT are
actually two variations on the Holt-Winters method. They are
interchangeable. Both attempt to decompose data into three
components: a baseline, a trend, and a seasonal coefficient.
HWPREDICT adds its seasonal coefficient to the baseline to
form a prediction, whereas MHWPREDICT multiplies its
seasonal coefficient by the baseline to form a prediction.
The difference is noticeable when the baseline changes
significantly in the course of a season; HWPREDICT will
predict the seasonality to stay constant as the baseline
changes, but MHWPREDICT will predict the seasonality to grow
or shrink in proportion to the baseline. The proper choice
of method depends on the thing being modeled. For
simplicity, the rest of this discussion will refer to
HWPREDICT, but MHWPREDICT may be substituted in its place.
The predicted deviations are stored in DEVPREDICT (think a
standard deviation which can be scaled to yield a confidence
band). The FAILURES RRA stores binary indicators. A 1 marks
the indexed observation as failure; that is, the number of
confidence bounds violations in the preceding window of
observations met or exceeded a specified threshold. An
example of using these RRAs to graph confidence bounds and
failures appears in rrdgraph.
The SEASONAL and DEVSEASONAL RRAs 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 RRAs, the
RRDtool create command supports implicit creation of the
other four when HWPREDICT is specified alone and the final
argument rra-num is omitted.
rows specifies the length of the RRA 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, rows should be larger than the seasonal
period. If the DEVPREDICT RRA is implicitly created, the
default number of rows is the same as the HWPREDICT rows
argument. If the FAILURES RRA is implicitly created, rows
will be set to the seasonal period argument of the HWPREDICT
RRA. Of course, the RRDtool resize command is available if
these defaults are not sufficient and the creator wishes to
avoid explicit creations of the other specialized function
RRAs.
seasonal period specifies the number of primary data points
in a seasonal cycle. If SEASONAL and DEVSEASONAL are
implicitly created, this argument for those RRAs is set
automatically to the value specified by HWPREDICT. If they
are explicitly created, the creator should verify that all
three seasonal period arguments agree.
alpha is the adaption parameter of the intercept (or
baseline) coefficient in the Holt-Winters forecasting
algorithm. See rrdtool for a description of this algorithm.
alpha must lie between 0 and 1. A value closer to 1 means
that more recent observations carry greater weight in
predicting the baseline component of the forecast. A value
closer to 0 means that past history carries greater weight
in predicting the baseline component.
beta is the adaption parameter of the slope (or linear
trend) coefficient in the Holt-Winters forecasting
algorithm. beta must lie between 0 and 1 and plays the same
role as alpha with respect to the predicted linear trend.
gamma is the adaption parameter of the seasonal coefficients
in the Holt-Winters forecasting algorithm (HWPREDICT) or the
adaption parameter in the exponential smoothing update of
the seasonal deviations. It must lie between 0 and 1. If the
SEASONAL and DEVSEASONAL RRAs are created implicitly, they
will both have the same value for gamma: the value specified
for the HWPREDICT alpha argument. Note that because there is
one seasonal coefficient (or deviation) for each time point
during the seasonal cycle, the adaptation rate is much
slower than the baseline. Each seasonal coefficient is only
updated (or adapts) when the observed value occurs at the
offset in the seasonal cycle corresponding to that
coefficient.
If SEASONAL and DEVSEASONAL RRAs are created explicitly,
gamma need not be the same for both. Note that gamma can
also be changed via the RRDtool tune command.
smoothing-window specifies the fraction of a season that
should be averaged around each point. By default, the value
of smoothing-window is 0.05, which means each value in
SEASONAL and DEVSEASONAL will be occasionally replaced by
averaging it with its (seasonal period*0.05) nearest
neighbors. Setting smoothing-window to zero will disable
the running-average smoother altogether.
rra-num provides the links between related RRAs. If
HWPREDICT is specified alone and the other RRAs are created
implicitly, then there is no need to worry about this
argument. If RRAs are created explicitly, then carefully pay
attention to this argument. For each RRA which includes this
argument, there is a dependency between that RRA and another
RRA. The rra-num argument is the 1-based index in the order
of RRA creation (that is, the order they appear in the
create command). The dependent RRA for each RRA requiring
the rra-num argument is listed here:
* HWPREDICT rra-num is the index of the SEASONAL RRA.
* SEASONAL rra-num is the index of the HWPREDICT RRA.
* DEVPREDICT rra-num is the index of the DEVSEASONAL RRA.
* DEVSEASONAL rra-num is the index of the HWPREDICT RRA.
* FAILURES rra-num is the index of the DEVSEASONAL RRA.
threshold is the minimum number of violations (observed
values outside the confidence bounds) within a window that
constitutes a failure. If the FAILURES RRA is implicitly
created, the default value is 7.
window length 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 RRA is implicitly created, the
default value is 9.
STEP, HEARTBEAT, and Rows As Durations
Traditionally RRDtool specified PDP intervals in seconds,
and most other values as either seconds or PDP counts. This
made the specification for databases rather opaque; for
example
rrdtool create power.rrd \
--start now-2h --step 1 \
DS:watts:GAUGE:300:0:24000 \
RRA:AVERAGE:0.5:1:864000 \
RRA:AVERAGE:0.5:60:129600 \
RRA:AVERAGE:0.5:3600:13392 \
RRA:AVERAGE:0.5:86400:3660
creates a database of power values collected once per
second, with a five minute (300 second) heartbeat, and four
RRAs: ten days of one second, 90 days of one minute, 18
months of one hour, and ten years of one day averages.
Step, heartbeat, and PDP counts and rows may also be
specified as durations, which are positive integers with a
single-character suffix that specifies a scaling factor.
See "rrd_scaled_duration" in librrd for scale factors of the
supported suffixes: "s" (seconds), "m" (minutes), "h"
(hours), "d" (days), "w" (weeks), "M" (months), and "y"
(years).
Scaled step and heartbeat values (which are natively
durations in seconds) are used directly, while consolidation
function row arguments are divided by their step to produce
the number of rows.
With this feature the same specification as above can be
written as:
rrdtool create power.rrd \
--start now-2h --step 1s \
DS:watts:GAUGE:5m:0:24000 \
RRA:AVERAGE:0.5:1s:10d \
RRA:AVERAGE:0.5:1m:90d \
RRA:AVERAGE:0.5:1h:18M \
RRA:AVERAGE:0.5:1d:10y
The HEARTBEAT and the STEP
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 half the "step", the
entire PDP is marked as "unknown". This means that a mixture
of known and "unknown" sample times in a single PDP "step"
may or may not add up to enough "known" time to warrant a
known PDP.
The "heartbeat" can be short (unusual) or long (typical)
relative to the "step" interval between PDPs. A short
"heartbeat" means you require multiple samples per PDP, and
if you don't get them mark the PDP unknown. A long heartbeat
can span multiple "steps", which means it is acceptable to
have multiple PDPs calculated from a single sample. An
extreme example of this might be a "step" of 5 minutes and a
"heartbeat" of one day, in which case a single sample every
day will result in all the PDPs for that entire day period
being set to the same average rate. -- Don Baarda
<don.baarda@baesystems.com>
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 vladimir.lavrov@desy.de.
How to Measure
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 GAUGE 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 ABSOLUTE
data type you can simply update the RRD with the number
5 and the end time of your monitoring period. RRDtool
will then record the number of messages per second. If
at some later stage you want to know the number of
messages transported in a day, you can get the average
messages per second from RRDtool for the day in question
and multiply this number with the number of seconds in a
day. Because all math is run with Doubles, the precision
should be acceptable.
It's always a Rate
RRDtool stores rates in amount/second for COUNTER,
DERIVE, DCOUNTER, DDERIVE and ABSOLUTE data. When you
plot the data, you will get on the y axis amount/second
which you might be tempted to convert to an absolute
amount by multiplying by the delta-time between the
points. RRDtool plots continuous data, and as such is
not appropriate for plotting absolute amounts as for
example "total bytes" sent and received in a router.
What you probably want is plot rates that you can scale
to bytes/hour, for example, or plot absolute amounts
with another tool that draws bar-plots, where the
delta-time is clear on the plot for each point (such
that when you read the graph you see for example GB on
the y axis, days on the x axis and one bar for each
day).
Example
rrdtool create temperature.rrd --step 300 \
DS:temp:GAUGE:600:-273:5000 \
RRA:AVERAGE:0.5:1:1200 \
RRA:MIN:0.5:12:2400 \
RRA:MAX:0.5:12:2400 \
RRA:AVERAGE:0.5:12:2400
This sets up an RRD called temperature.rrd which accepts one
temperature value every 300 seconds. If no new data is
supplied for more than 600 seconds, the temperature becomes
*UNKNOWN*. The minimum acceptable value is -273 and the
maximum is 5'000.
A few archive areas are also defined. The first stores the
temperatures supplied for 100 hours (1'200 * 300 seconds =
100 hours). The second RRA stores the minimum temperature
recorded over every hour (12 * 300 seconds = 1 hour), for
100 days (2'400 hours). The third and the fourth RRA's do
the same for the maximum and average temperature,
respectively.
Example 2
rrdtool create monitor.rrd --step 300 \
DS:ifOutOctets:COUNTER:1800:0:4294967295 \
RRA:AVERAGE:0.5:1:2016 \
RRA:HWPREDICT:1440:0.1:0.0035:288
This example is a monitor of a router interface. The first
RRA tracks the traffic flow in octets; the second RRA
generates the specialized functions RRAs for aberrant
behavior detection. Note that the rra-num argument of
HWPREDICT is missing, so the other RRAs will implicitly be
created with default parameter values. In this example, the
forecasting algorithm baseline adapts quickly; in fact the
most recent one hour of observations (each at 5 minute
intervals) accounts for 75% of the baseline prediction. The
linear trend forecast adapts much more slowly. Observations
made during the last day (at 288 observations per day)
account for only 65% of the predicted linear trend. Note:
these computations rely on an exponential smoothing formula
described in the LISA 2000 paper.
The seasonal cycle is one day (288 data points at 300 second
intervals), and the seasonal adaption parameter will be set
to 0.1. The RRD file will store 5 days (1'440 data points)
of forecasts and deviation predictions before wrap around.
The file will store 1 day (a seasonal cycle) of 0-1
indicators in the FAILURES RRA.
The same RRD file and RRAs are created with the following
command, which explicitly creates all specialized function
RRAs using "STEP, HEARTBEAT, and Rows As Durations".
rrdtool create monitor.rrd --step 5m \
DS:ifOutOctets:COUNTER:30m:0:4294967295 \
RRA:AVERAGE:0.5:1:2016 \
RRA:HWPREDICT:5d:0.1:0.0035:1d:3 \
RRA:SEASONAL:1d:0.1:2 \
RRA:DEVSEASONAL:1d:0.1:2 \
RRA:DEVPREDICT:5d:5 \
RRA:FAILURES:1d:7:9:5
Of course, explicit creation need not replicate implicit
create, a number of arguments could be changed.
Example 3
rrdtool create proxy.rrd --step 300 \
DS:Requests:DERIVE:1800:0:U \
DS:Duration:DERIVE:1800:0:U \
DS:AvgReqDur:COMPUTE:Duration,Requests,0,EQ,1,Requests,IF,/ \
RRA:AVERAGE:0.5:1:2016
This example is monitoring the average request duration
during each 300 sec interval for requests processed by a web
proxy during the interval. In this case, the proxy exposes
two counters, the number of requests processed since boot
and the total cumulative duration of all processed requests.
Clearly these counters both have some rollover point, but
using the DERIVE data source also handles the reset that
occurs when the web proxy is stopped and restarted.
In the RRD, the first data source stores the requests per
second rate during the interval. The second data source
stores the total duration of all requests processed during
the interval divided by 300. The COMPUTE data source divides
each PDP of the AccumDuration by the corresponding PDP of
TotalRequests and stores the average request duration. The
remainder of the RPN expression handles the divide by zero
case.
Authors
Tobias Oetiker <tobi@oetiker.ch>, Peter Stamfest
<peter@stamfest.at>