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RRDTOOL(1)			    rrdtool			    RRDTOOL(1)

       rrdtool - Round Robin Database Tool

       rrdtool - [workdir] function


       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 ana
       lyze 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


       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

       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.

       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 func
	       tion uses fetch to retrieve its data from an RRD. Check rrd

       tune    Alter setup of an RRD. Check rrdtune.

       last    Find the last update time of an RRD. Check rrdlast.

       info    Get information about an RRD. Check rrdinfo.

	       Change the size of individual RRAs. This is dangerous! Check

       xport   Export data retrieved from one or several RRDs. Check rrdxport

       rrdcgi  This is a standalone tool for producing RRD graphs on the fly.
	       Check rrdcgi.


       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 logfile at
	       any time you want. It will automatically interpolate the value
	       of the data-source (DS) at the latest official time-slot
	       (intervall) 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.

	       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 consider
	       able 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 con
	       solidation feature. When setting up an Round Robin Database
	       (RRD), you can define at which interval this consolidation
	       should occur, and what consolidation function (CF) (average,
	       minimum, maximum, total, last) should be used to build the con
	       solidated 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 1000 values in 5
	       minute interval, RRDtool will allocate space for 1000 data
	       values and a header area. In the header it will store a pointer
	       telling which slots (value) in the storage area was last writ
	       ten 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 1000 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, the total minutes of down
	       time, etc.

       Unknown Data
	       As mentioned earlier, the RRD stores data at a constant inter
	       val. Sometimes it may happen that no new data is 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 han
	       dle 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.

	       RRDtool allows you to generate reports in numerical and graphi
	       cal form based on the data stored in one or several RRDs. The
	       graphing feature is fully configurable. Size, color and con
	       tents 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 aber
	       rant 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 val

	       *   A mechanism to decide if and when an observed value or
		   sequence of observed values is too deviant from the pre
		   dicted 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 gov
	       erned by 3 parameters, alpha (intercept), beta (slope), and
	       gamma (seasonal). The prediction can also be viewed as a
	       smoothed value for the time series.

	       The measure of deviation is a seasonal weighted absolute devia
	       tion. 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

	       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 rrdcre


       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 exam
       ple) through a set of pipes. Over these pipes RRDtool accepts the same
       arguments as on the command line and some special commands like quit,
       cd, mkdir and ls. For detailed help on the server commands type:

	  rrdtool help cdmkdirpwdlsquit

       When a command is completed, RRDtool will print the string  "OK",
       followed by timing information of the form u:usertime s:systemtime.
       Both values are the running totals of seconds since RRDtool was
       started. If an error occurs, a line of the form "ERROR:" Description
       of error will be printed instead. RRDtool will not abort, unless some
       thing realy 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, RRD
       tool only changes the current directory to workdir.

       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 isnt 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

       Dont forget to create the database directory /var/rrd and reinitialize
       your inetd.

       If all was setup correctly, you can access the server with perl sock
       ets, 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.

       rrdcreate, rrdupdate, rrdgraph, rrddump, rrdfetch, rrdtune, rrdlast,

       Bugs? Features!

       Tobias Oetiker 

1.2.15				  2006-07-14			    RRDTOOL(1)

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