RRDTOOL for Graphing

RRDtool is the OpenSource industry standard, high performance data logging and graphing system for time series data. RRDtool can be easily integrated in shell scripts, perl, python, ruby, lua or tcl applications.

It has the similar features as Graphite graphing. This is very use full in for industrial level approaches .

Also,

• RRDtool stores data; that makes it a back-end tool. The RRDtool command set allows the creation of graphs; that makes it a front-end tool as well. Other databases just store data and can not create graphs.

• In case of linear databases, new data gets appended at the bottom of the database table. Thus its size keeps on increasing, whereas the size of an RRDtool database is determined at creation time. Imagine an RRDtool database as the perimeter of a circle. Data is added along the perimeter. When new data reaches the starting point, it overwrites existing data. This way, the size of an RRDtool database always remains constant. The name "Round Robin" stems from this behaviour.

• Other databases store the values as supplied. RRDtool can be configured to calculate the rate of change from the previous to the current value and store this information instead.

• Other databases get updated when values are supplied. The RRDtool database is structured in such a way that it needs data at predefined time intervals. If it does not get a new value during the interval, it stores an UNKNOWN value for that interval. So, when using the RRDtool database, it is imperative to use scripts that run at regular intervals to ensure a constant data flow to update the RRDtool database.

RRDtool assumes time-variable data in intervals of a certain length. This interval, usually named step, is specified upon creation of an RRD file and cannot be changed afterwards. Because data may not always be available at just the right time, RRDtool will automatically interpolate any submitted data to fit its internal time-steps.

The value for a specific step, that has been interpolated, is named a primary data point (PDP). Multiple PDPs may be consolidated according to a consolidation function (CF) to form a consolidated data point (CDP). Typical consolidation functions are average, minimum, maximum.

After the data have been consolidated, the resulting CDP is stored in a round-robin archive (RRA). A round-robin archive stores a fixed number of CDPs and specifies how many PDPs should be consolidated into one CDP and which CF to use.

Reference :

http://en.wikipedia.org/wiki/RRDtool

http://oss.oetiker.ch/rrdtool/index.en.html