[mrtg] Re: Greater granularity in past data

[Luke Dudney]

> -----Original Message-----
> From: Alex van den Bogaerdt [mailto:alex at slot.hollandcasino.nl]
> Sent: Wednesday, September 20, 2000 12:11 PM
> To: luke.dudney at wn.com.au
> Cc: mrtg at list.ee.ethz.ch
> Subject: [mrtg] Re: Greater granularity in past data
>  
> I assume you've used mrtg to create the database.  I have no 
> experience
> with this myself but I don't *think* there's a problem with 
> my suggestion:
> 
> YMMV
> 
> Try to expand the size of the "daily" part in the RRD.
> (or, at your opinion, the "weekly" part)
> 
> For "your.rrd" you fill in path+name to your rrd file, for x and y you
> need to specify a number.
> 
> - Run "rrdtool info your.rrd".  Most likely the RRA to alter will have
>   number 0 but it doesn't hurt to check.
> - Run "rrdtool resize your.rrd x GROW y" on your RRD.
> - Check the resulting file and if it's okay, rename the old file to
>   another name + rename the new file to "your.rrd".
> 
> Be aware that RRDtool will select the RRA with the best granularity
> by default when doing its job, so this may break some things.
> 
> The number of rows will probably be 600.  This is 600 times 5 minutes
> for a total of 2 days + 2 hours.  If you'd like to keep a year of the
> data, just grow the RRA with the missing number of rows.
> 
> 365 days * 24 hours * 60 minutes / 5 minutes_per_step = 105120 rows.
> You'd need to grow with 105120-600 = 104520 rows for this.
> 

I migrated the old mrtg .log files to .rrd using log2rrd.pl provided with
rrdtool.
Thank you for your help, this seems to easy, except for one thing.
I grew RRA 2&3 by 17520 rows (365days * 24hours * 2 entries / hr), now all
that remains is to fill the rows with data, which will take a while.
Is there an easy way around the problem you mentioned about rrd using the
best granularity rra for that time?
My mileage has indeed varied.
Now when I graph data, there is a gaping hole in the graph (obviously
because these new rows have been filled with NaNs, and are being chosen as
more specific data points).

Cheers
Luke.

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