Provided by: rrdtool_1.3.1-4ubuntu1_i386 bug

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.

       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 of an RRD. Check rrdtune.

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

       info    Get information about an RRD. Check rrdinfo.

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

       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.

   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 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.

       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 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 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, the total minutes of down
               time, etc.

       Unknown Data
               As mentioned earlier, the RRD stores data at a constant
               interval. 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
               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 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
       quit, cd, mkdir and ls. For detailed help on the server commands type:

          rrdtool help cd|mkdir|pwd|ls|quit

       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
       something 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,
       RRDtool 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 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.

SEE ALSO

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

BUGS

       Bugs? Features!

AUTHOR

       Tobias Oetiker <tobi@oetiker.ch>