I'm trying to implement rrdtool. I've read the various tutorials and got my first database up and running. However, there is something that I don't understand.
What eludes me is why so many of the examples I come across instruct me to create multiple RRAs?
Allow me to explain: Let's say I have a sensor that I wish to monitor. I will want to ultimately see graphs of the sensor data on an hourly, daily, weekly and monthly basis and one that spans (I'm still on the fence on this one) about 1.5 yrs (for visualising seasonal influences).
Now, why would I want to create an RRA for each of these views? Why not just create a database like this (stepsize=300 seconds):
DS:sensor:GAUGE:600:U:U \
RRA:AVERAGE:0.5:1:160000
If I understand correctly, I can then create any graph I desire, for any given period with whatever resolution I need. What would be the use of all the other RRAs people tell me I need to define?
BTW: I can imagine that in the past this would have been helpful when computing power was more rare. Nowadays, with fast disks, high-speed interfaces and powerful CPUs I guess you don't need the kind of pre-processing that RRAs seem to be designed for.
EDIT:
I'm aware of this page. Although it explains about consolidation very clearly, it is my understanding that rrdtool graph can do this consolidation aswell at the moment the data is graphed. There still appears (to me) no added value in "harvest-time consolidation".
Each RRA is a pre-consolidated set of data points at a specific resolution. This performs two important functions.
Firstly, it saves on disk space. So, if you are interested in high-detail graphs for the last 24h, but only low-detail graphs for the last year, then you do not need to keep the high-detail data for a whole year -- consolidated data will be sufficient. In this way, you can minimise the amount of storage required to hold the data for graph generation (although of course you lose the detail so cant access it if you should want to). Yes, disk is cheap, but if you have a lot of metrics and are keeping low-resolution data for a long time, this can be a surprisingly large amount of space (in our case, it would be in the hundreds of GB)
Secondly, it means that the consolidation work is moved from graphing time to update time. RRDTool generates graphs very quickly, because most of the calculation work is already done in the RRAs at update time, if there is an RRA of the required configuration. If there is no RRA available at the correct resolution, then RRDtool will perform the consolidation on the fly from a high-granularity RRA, but this takes time and CPU. RRDTool graphs are usually generated on the fly by CGI scripts, so this is important, particularly if you expect to have a large number of queries coming in. In your example, using a single 5min RRA to make a 1.5yr graph (where 1pixel would be about 1 day) you would need to read and process 288 times more data in order to generate the graph than if you had a 1-day granularity RRA available!
In short, yes, you could have a single RRA and let the graphing work harder. If your particular implementation needs faster updates and doesnt care about slower graph generation, and you need to keep the detailed data for the entire time, then maybe this is a solution for you, and RRDTool can be used in this way. However, usually, people will optimise for graph generation and disk space, meaning using tiered sets of RRAs with decreasing granularity.