NAME¶
rrdcreate - Set up a new Round Robin Database
SYNOPSIS¶
rrdtool create filename [
--start|
-b start time] [
--step|
-s step] [
--no-overwrite] [
DS:
ds-name: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.
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.
--step|-s step (default: 300 seconds)¶
Specifies the base interval in seconds with which data will be fed into the
RRD.
--no-overwrite¶
Do not clobber an existing file of the same name.
DS:ds-name: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, and
ABSOLUTE the format for a data source entry is:
DS:ds-name:GAUGE | COUNTER | DERIVE |
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.
- 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 <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.
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.
rows defines how many generations of data values are kept in an
RRA. Obviously, this has to be greater than zero.
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.
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 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.
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.
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,/ \
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.
AUTHOR¶
Tobias Oetiker <tobi@oetiker.ch>