Provided by: rrdtool_1.7.2-4ubuntu1_amd64 bug


       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.

       graphv  Create a graph from data stored in one or several RRDs. Same as graph, but
               metadata are printed before the graph. 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 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.

               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.

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

       list    List the directories and rrd databases remotely. Check rrdlist.

       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 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 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 include:

               •   An algorithm for predicting the value of a time series one time step into the

               •   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 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: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

   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

        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 <>