Francois Mikus | 15 Jun 03:29 2011

Re: Getting frequent "NaN" gaps in graphs, yet data is being retrieved ok

Hello,

You can also try out genDevConfig just to see what kind of target files 
it will create, as a strating point to create new defaults. Or just 
peruse the code to see what logic is used to determine what counters to use.

Glad to hear you got things fixed up.

Cheers

Francois Mikus

On 01/04/2011 10:24 AM, Andy Dills wrote:
> On Thu, 31 Mar 2011, Andy Dills wrote:
>
>> Thanks, and thank you to the people who replied off list as well.
>>
>> The solution was as simple as adding "snmp-version = 3" to the interfaces
>> file for the routers and switches that have gigabit interfaces.
> ...or I just didn't see the problem for a few hours and was completely
> mistaken :)
>
> Turns out you can change the version to 3, and it will silently just keep
> using 1. I spent a bit of time trying to get version 3 to work (easy on
> the router end, difficult on the cricket end...router debug says I got the
> username sending with the alternat snmpUtil.pm, but was getting a strange
> "Unknown Report" when running the collector), and so I decided to just use
> snmp 2c. But that still didn't fix it...because you need to query
> different counters. They don't just magically turn into 64 bit counters
> (duh).
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Rodney McDuff | 15 Jun 05:37 2011
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Re: Getting frequent "NaN" gaps in graphs, yet data is being retrieved ok

http://www.its.uq.edu.au/public/cricket/rrdoutlier.pl

RRDOUTLIER(1)         User Contributed Perl Documentation       
RRDOUTLIER(1)

NAME
       rrdoutliers.pl: A program to remove outliers from a RRD file

USAGE
       rrdoutliers.pl -in <infile> [-out <outfile>]
                      [-alpha <alpha>] [-passes <n>] [-printdata]
                      [-rra <rra:ds[[:attr=val]...]>]
                      [-rra <rra:ds[[:attr=val]...]>]

DESCRIPTION
       This programs removes outliers from a RRD file using Grubbs test to
       detect them and remove or modify them.  The Grubb test calculates G,
       the maximum absolute deviation between a data point and the mean
of the
       data set normalized by the standard deviation of the data set. 
If this
       value is greater that a critical value of G, the point is
considered an
       outlier and removed or modified. This process is repeated until no
       outliers remain or some arbitrary limit is reached. The critical
value
       of G is calculated using the student t distribution and a specified
       significance level.

       The options are:
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Gmane