RRDTOOL(1) 1.9.0 RRDTOOL(1)
rrdtool rrdtool
2024-07-29
NAME
rrdtool - Round Robin Database Tool
SYNOPSIS
rrdtool - [workdir]| function
DESCRIPTION
OVERVIEW
It is pretty easy to gather status information from all sorts of
things, ranging from the temperature in your office to the number of
octets which have passed through the FDDI interface of your router.
But it is not so trivial to store this data in an efficient and
systematic manner. This is where RRDtool comes in handy. It lets you
log and analyze the data you gather from all kinds of data-sources
(DS). The data analysis part of RRDtool is based on the ability to
quickly generate graphical representations of the data values
collected over a definable time period.
In this man page you will find general information on the design and
functionality of the Round Robin Database Tool (RRDtool). For a more
detailed description of how to use the individual functions of RRDtool
check the corresponding man page.
For an introduction to the usage of RRDtool make sure you consult the
rrdtutorial.
FUNCTIONS
While the man pages talk of command line switches you have to set in
order to make RRDtool work it is important to note that RRDtool can be
remotely controlled through a set of pipes. This saves a considerable
amount of startup time when you plan to make RRDtool do a lot of
things quickly. Check the section on "REMOTE CONTROL" further down.
There is also a number of language bindings for RRDtool which allow
you to use it directly from Perl, python, Tcl, PHP, etc.
create Set up a new Round Robin Database (RRD). Check rrdcreate.
update Store new data values into an RRD. Check rrdupdate.
updatev Operationally equivalent to update except for output. Check
rrdupdate.
graph Create a graph from data stored in one or several RRDs. Apart
from generating graphs, data can also be extracted to stdout.
Check rrdgraph.
graphv Create a graph from data stored in one or several RRDs. Same
as graph, but metadata are printed before the graph. Check
rrdgraph.
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dump Dump the contents of an RRD in plain ASCII. In connection with
restore you can use this to move an RRD from one computer
architecture to another. Check rrddump.
restore Restore an RRD in XML format to a binary RRD. Check rrdrestore
fetch Get data for a certain time period from a RRD. The graph
function uses fetch to retrieve its data from an RRD. Check
rrdfetch.
tune Alter setup and structure of an RRD. Check rrdtune.
first Find the first update time of an RRD. Check rrdfirst.
last Find the last update time of an RRD. Check rrdlast.
lastupdate
Find the last update time of an RRD. It also returns the value
stored for each datum in the most recent update. Check
rrdlastupdate.
info Get information about an RRD. Check rrdinfo.
resize Change the size of individual RRAs. This is dangerous! Check
rrdresize.
xport Export data retrieved from one or several RRDs. Check
rrdxport.
flushcached
Flush the values for a specific RRD file from memory. Check
rrdflushcached.
list List the directories and rrd databases remotely. Check
rrdlist.
HOW DOES RRDTOOL WORK?
Data Acquisition
When monitoring the state of a system, it is convenient to
have the data available at a constant time interval.
Unfortunately, you may not always be able to fetch data at
exactly the time you want to. Therefore RRDtool lets you
update the log file at any time you want. It will
automatically interpolate the value of the data-source (DS) at
the latest official time-slot (interval) and write this
interpolated value to the log. The original value you have
supplied is stored as well and is also taken into account when
interpolating the next log entry.
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Consolidation
You may log data at a 1 minute interval, but you might also be
interested to know the development of the data over the last
year. You could do this by simply storing the data in 1 minute
intervals for the whole year. While this would take
considerable disk space it would also take a lot of time to
analyze the data when you wanted to create a graph covering
the whole year. RRDtool offers a solution to this problem
through its data consolidation feature. When setting up a
Round Robin Database (RRD), you can define at which interval
this consolidation should occur, and what consolidation
function (CF) (average, minimum, maximum, last) should be used
to build the consolidated values (see rrdcreate). You can
define any number of different consolidation setups within one
RRD. They will all be maintained on the fly when new data is
loaded into the RRD.
Round Robin Archives
Data values of the same consolidation setup are stored into
Round Robin Archives (RRA). This is a very efficient manner to
store data for a certain amount of time, while using a known
and constant amount of storage space.
It works like this: If you want to store 1'000 values in 5
minute interval, RRDtool will allocate space for 1'000 data
values and a header area. In the header it will store a
pointer telling which slots (value) in the storage area was
last written to. New values are written to the Round Robin
Archive in, you guessed it, a round robin manner. This
automatically limits the history to the last 1'000 values (in
our example). Because you can define several RRAs within a
single RRD, you can setup another one, for storing 750 data
values at a 2 hour interval, for example, and thus keep a log
for the last two months at a lower resolution.
The use of RRAs guarantees that the RRD does not grow over
time and that old data is automatically eliminated. By using
the consolidation feature, you can still keep data for a very
long time, while gradually reducing the resolution of the data
along the time axis.
Using different consolidation functions (CF) allows you to
store exactly the type of information that actually interests
you: the maximum one minute traffic on the LAN, the minimum
temperature of your wine cellar, ... etc.
Unknown Data
As mentioned earlier, the RRD stores data at a constant
interval. Sometimes it may happen that no new data is
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available when a value has to be written to the RRD. Data
acquisition may not be possible for one reason or other. With
RRDtool you can handle these situations by storing an
*UNKNOWN* value into the database. The value '*UNKNOWN*' is
supported through all the functions of the tool. When
consolidating a data set, the amount of *UNKNOWN* data values
is accounted for and when a new consolidated value is ready to
be written to its Round Robin Archive (RRA), a validity check
is performed to make sure that the percentage of unknown
values in the data point is above a configurable level. If
not, an *UNKNOWN* value will be written to the RRA.
Graphing
RRDtool allows you to generate reports in numerical and
graphical form based on the data stored in one or several
RRDs. The graphing feature is fully configurable. Size, color
and contents of the graph can be defined freely. Check
rrdgraph for more information on this.
Aberrant Behavior Detection
by Jake Brutlag
RRDtool provides the building blocks for near real-time
aberrant behavior detection. These components include:
+ An algorithm for predicting the value of a time series one
time step into the future.
+ A measure of deviation between predicted and observed
values.
+ A mechanism to decide if and when an observed value or
sequence of observed values is too deviant from the
predicted value(s).
Here is a brief explanation of these components:
The Holt-Winters time series forecasting algorithm is an on-
line (or incremental) algorithm that adaptively predicts
future observations in a time series. Its forecast is the sum
of three components: a baseline (or intercept), a linear trend
over time (or slope), and a seasonal coefficient (a periodic
effect, such as a daily cycle). There is one seasonal
coefficient for each time point in the period (cycle). After a
value is observed, each of these components is updated via
exponential smoothing. This means that the algorithm "learns"
from past values and uses them to predict the future. The rate
of adaptation is governed by 3 parameters, alpha (intercept),
beta (slope), and gamma (seasonal). The prediction can also be
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viewed as a smoothed value for the time series.
The measure of deviation is a seasonal weighted absolute
deviation. The term seasonal means deviation is measured
separately for each time point in the seasonal cycle. As with
Holt-Winters forecasting, deviation is predicted using the
measure computed from past values (but only at that point in
the seasonal cycle). After the value is observed, the
algorithm learns from the observed value via exponential
smoothing. Confidence bands for the observed time series are
generated by scaling the sequence of predicted deviation
values (we usually think of the sequence as a continuous line
rather than a set of discrete points).
Aberrant behavior (a potential failure) is reported whenever
the number of times the observed value violates the confidence
bands meets or exceeds a specified threshold within a
specified temporal window (e.g. 5 violations during the past
45 minutes with a value observed every 5 minutes).
This functionality is embedded in a set of related RRAs. In
particular, a FAILURES RRA logs potential failures. With these
data you could, for example, use a front-end application to
RRDtool to initiate real-time alerts.
For a detailed description on how to set this up, see
rrdcreate.
REMOTE CONTROL
When you start RRDtool with the command line option '-' it waits for
input via standard input (STDIN). With this feature you can improve
performance by attaching RRDtool to another process (MRTG is one
example) through a set of pipes. Over these pipes RRDtool accepts the
same arguments as on the command line and some special commands like
cd, mkdir, pwd, ls and quit. For detailed help on the server commands
type:
rrdtool help cd
When a command is completed, RRDtool will print the string '"OK"',
followed by timing information of the form u:usertime
s:systemtimer:runtime. The usertime and systemtime values are the
running totals of seconds since RRDtool was started. runtime is the
total amount of seconds that RRDtool has been running. If an error
occurs, a line of the form '"ERROR:" Description of error' will be
printed instead. RRDtool will not abort, unless something really
serious happens. If a workdir is specified and the UID is 0, RRDtool
will do a chroot to that workdir. If the UID is not 0, RRDtool only
changes the current directory to workdir.
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RRD Server
If you want to create a RRD-Server, you must choose a TCP/IP Service
number and add them to /etc/services like this:
rrdsrv 13900/tcp # RRD server
Attention: the TCP port 13900 isn't officially registered for rrdsrv.
You can use any unused port in your services file, but the server and
the client system must use the same port, of course.
With this configuration you can add RRDtool as meta-server to
/etc/inetd.conf. For example:
rrdsrv stream tcp nowait root /opt/rrd/bin/rrdtool rrdtool - /var/rrd
Don't forget to create the database directory /var/rrd and
reinitialize your inetd.
If all was setup correctly, you can access the server with Perl
sockets, tools like netcat, or in a quick interactive test by using
'telnet localhost rrdsrv'.
NOTE: that there is no authentication with this feature! Do not setup
such a port unless you are sure what you are doing.
ENVIRONMENT VARIABLES
The following environment variables may be used to change the behavior
of most of the utilities.
RRD_LOCKING
If this environment variable is set, the RRD file is locked in the
given mode:
- try fails, when the file is locked by another process. This
is the default.
- block waits until the lock is released.
- none skips locking at all. Caller has to ensure a proper
locking which should be compatible with "fcntl(fd, F_SETLK,
...)".
Some utilities have command line options (--locking) which
override this variable.
RRDCACHED, THE CACHING DAEMON
For very big setups, updating thousands of RRD files often becomes a
serious IO problem. If you run into such problems, you might want to
take a look at rrdcached, a caching daemon for RRDtool which may help
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you lessen the stress on your disks.
SEE ALSO
rrdcreate, rrdupdate, rrdgraph, rrddump, rrdfetch, rrdtune, rrdlast,
rrdxport, rrdflushcached, rrdcached
BUGS
Bugs? Features!
AUTHOR
Tobias Oetiker <tobi@oetiker.ch>
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