rrdtool RDCREATE(1)
NAME
rrdcreate - Set up a new Round Robin Database
SYNOPSIS
rrdtool create filename [--start-b start time]
[--step-s step] [DS:ds-name:DST:dst arguments]
[RA:CF:cf arguments]
DESCRIPTION
The create function of RDtool lets you set up new Round
Robin Database (RD) files. The file is created at its
final, full size and filled with *UNKNOWN* data.
filename
The name of the RD you want to create. RD files should end
with the extension .rrd. However, RDtool 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 RD. RDtool 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.
--step-s step (default: 300 seconds)
Specifies the base interval in seconds with which data will
be fed into the RD.
DS:ds-name:DST:dst arguments
A single RD 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 RD.
ds-name is the name you will use to reference this
particular data source from an RD. 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, and ABSOLUTE the format for a data
source entry is:
DS:ds-name:GAUGE COUNTER DERIVE
ABSOLUTE:heartbeat:min:max
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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, RDtool checks
if the overflow happened at the 32bit or 64bit border
and acts accordingly by adding an appropriate value to
the result.
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.
NOTE on COUNTER vs DERIVE
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
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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 RD. 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 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 RDtool 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
. 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
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CDEFs must refer only to DEFs and CDEFs previously defined
in the same graph command.
RA:CF:cf arguments
The purpose of an RD is to store data in the round robin
archives (RA). An archive consists of a number of data
values or statistics for each of the defined data-sources
(DS) and is defined with an RA line.
When data is entered into an RD, 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 RA line for these consolidation functions is:
RA: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.
rows defines how many generations of data values are kept in
an RA. Obviously, this has to be greater than zero.
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Aberrant Behavior Detection with Holt-Winters Forecasting
In addition to the aggregate functions, there are a set of
specialized functions that enable RDtool to provide data
smoothing (via the Holt-Winters forecasting algorithm),
confidence bands, and the flagging aberrant behavior in the
data source time series:
]o RA:HWPREDICT:rows:alpha:beta:seasonal period[:rra-num]
]o RA:MHWPREDICT:rows:alpha:beta:seasonal period[:rra-num]
]o RA:SEASONAL:seasonal period:gamma:rra-
num[:smoothing-window=fraction]
]o RA:DEVSEASONAL:seasonal period:gamma:rra-
num[:smoothing-window=fraction]
]o RA:DEVPREDICT:rows:rra-num
]o RA:FAILURES:rows:threshold:window length:rra-num
These RAs differ from the true consolidation functions in
several ways. First, each of the RAs is updated once for
every primary data point. Second, these RAs 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 RA
and either an HWPREDICT or MHWPREDICT RA. Aberrant behavior
detection requires FAILURES, DEVSEASONAL, SEASONAL, and
either HWPREDICT or MHWPREDICT.
The predicted, or smoothed, values are stored in the
HWPREDICT or MHWPREDICT RA. 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 RA stores binary indicators. A 1 marks
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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 RAs to graph confidence bounds and
failures appears in rrdgraph.
The SEASONAL and DEVSEASONAL RAs 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 RAs, the
RDtool 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 RA prior to wrap around.
Remember that there is a one-to-one correspondence between
primary data points and entries in these RAs. For the
HWPREDICT CF, rows should be larger than the seasonal
period. If the DEVPREDICT RA is implicitly created, the
default number of rows is the same as the HWPREDICT rows
argument. If the FAILURES RA is implicitly created, rows
will be set to the seasonal period argument of the HWPREDICT
RA. Of course, the RDtool resize command is available if
these defaults are not sufficient and the creator wishes to
avoid explicit creations of the other specialized function
RAs.
seasonal period specifies the number of primary data points
in a seasonal cycle. If SEASONAL and DEVSEASONAL are
implicitly created, this argument for those RAs 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
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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 RAs 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 RAs are created explicitly,
gamma need not be the same for both. Note that gamma can
also be changed via the RDtool 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 RAs. If
HWPREDICT is specified alone and the other RAs are created
implicitly, then there is no need to worry about this
argument. If RAs are created explicitly, then carefully pay
attention to this argument. For each RA which includes this
argument, there is a dependency between that RA and another
RA. The rra-num argument is the 1-based index in the order
of RA creation (that is, the order they appear in the
create command). The dependent RA for each RA requiring
the rra-num argument is listed here:
]o HWPREDICT rra-num is the index of the SEASONAL RA.
]o SEASONAL rra-num is the index of the HWPREDICT RA.
]o DEVPREDICT rra-num is the index of the DEVSEASONAL RA.
]o DEVSEASONAL rra-num is the index of the HWPREDICT RA.
]o FAILURES rra-num is the index of the DEVSEASONAL RA.
threshold is the minimum number of violations (observed
values outside the confidence bounds) within a window that
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constitutes a failure. If the FAILURES RA 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 RA is implicitly created, the
default value is 9.
The HEARTBEAT and the STEP
Here is an explanation by Don Baarda on the inner workings
of RDtool. It may help you to sort out why all this
*UNKNOWN* data is popping up in your databases:
RDtool 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 RAs.
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 warrent for
a known PDP.
The "heartbeat" can be short (unusual) or long (typical)
relative to the "step" interval between PDPs. A short
"heartbeat" means you require multiple samples per PDP, and
if you don't get them mark the PDP unknown. A long heartbeat
can span multiple "steps", which means it is acceptable to
have multiple PDPs calculated from a single sample. An
extreme example of this might be a "step" of 5 minutes and a
"heartbeat" of one day, in which case a single sample every
day will result in all the PDPs for that entire day period
being set to the same average rate. -- Don Baarda
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time
axis
begin00
01
u02----* sample1, restart "hb"-timer
u03 /
u04 /
u05 /
u06/ "hbt" expired
u07
08----* sample2, restart "hb"
09 /
10 /
u11----* sample3, restart "hb"
u12 /
u13 /
step1u14 /
u15/ "swt" expired
u16
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"
step228 /
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. RDtool 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 mailserver in a certain amount of
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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 RD with the number
5 and the end time of your monitoring period. RDtool
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 RDtool 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
RDtool 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. RDtool 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 \
RA:AVERAGE:0.5:1:1200 \
RA:MIN:0.5:12:2400 \
RA:MAX:0.5:12:2400 \
RA:AVERAGE:0.5:12:2400
This sets up an RD 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 RA stores the minimum temperature
recorded over every hour (12 * 300 seconds = 1 hour), for
100 days (2'400 hours). The third and the fourth RA's do
the same for the maximum and average temperature,
respectively.
EXAMPLE 2
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rrdtool create monitor.rrd --step 300 \
DS:ifOutOctets:COUNTER:1800:0:4294967295 \
RA:AVERAGE:0.5:1:2016 \
RA:HWPREDICT:1440:0.1:0.0035:288
This example is a monitor of a router interface. The first
RA tracks the traffic flow in octets; the second RA
generates the specialized functions RAs for aberrant
behavior detection. Note that the rra-num argument of
HWPREDICT is missing, so the other RAs 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 RD 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 RA.
The same RD file and RAs are created with the following
command, which explicitly creates all specialized function
RAs.
rrdtool create monitor.rrd --step 300 \
DS:ifOutOctets:COUNTER:1800:0:4294967295 \
RA:AVERAGE:0.5:1:2016 \
RA:HWPREDICT:1440:0.1:0.0035:288:3 \
RA:SEASONAL:288:0.1:2 \
RA:DEVPREDICT:1440:5 \
RA:DEVSEASONAL:288:0.1:2 \
RA:FAILURES:288:7:9:5
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:Total:DERIVE:1800:0:U \
DS:Duration:DERIVE:1800:0:U \
DS:AvgReqDur:COMPUTE:Duration,Requests,0,EQ,1,Requests,IF,/ \
RA:AVERAGE:0.5:1:2016
This example is monitoring the average request duration
during each 300 sec interval for requests processed by a web
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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 RD, 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.
AUTHOR
Tobias Oetiker
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