Man Pages

rrdtool(1) - phpMan rrdtool(1) - phpMan

Command: man perldoc info search(apropos)  

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 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.

       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.

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

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

               Flush the values for a specific RRD file from memory. Check rrdflushcached.

       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 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.

               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.

               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

               ?   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.

       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 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.

   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

        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.

       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 you lessen
       the stress on your disks.

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

       Bugs? Features!

       Tobias Oetiker <>

1.4.7                             2009-10-14                        RRDTOOL(1)