man > rrdtool(1)

RRDTOOL(1)                                     rrdtool                                    RRDTOOL(1)

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.

       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.

       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 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 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 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.

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 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 AT oetiker.ch>

1.7.2                                        2024-03-31                                   RRDTOOL(1)