>
> Well, I think that the HDF5 case is similar than the NetCDF for this
> scenario: if you need to efficiently retrieve measurements that are
> near in time, the best would be to save them in that order. However, in
> order to take advantage of this (disk-sorted) arrangement, you will
> need to
A Monday 12 May 2008, Nick Bower escrigué:
> > 2. Has anyone contended with managing schema differences between
> > sources? In other words, if I have say 500 loggers, each logging
> > slightly different schemas (ie 100 different columns and so
> > different table definitions), the suggested Pytabl
A Monday 12 May 2008, Nick Bower escrigué:
> Hi - just investigating pytables from storing data from many
> distributed remote logging stations, each logging about 100 channels
> at 1 second frequency (a fair bit).
>
> My questions;
>
> 1. How does one handle ordering timeseries data within a table
> 2. Has anyone contended with managing schema differences between sources?
> In other words, if I have say 500 loggers, each logging slightly different
> schemas (ie 100 different columns and so different table definitions), the
> suggested Pytables way of binding static data definitions for each
Hi - just investigating pytables from storing data from many distributed
remote logging stations, each logging about 100 channels at 1 second
frequency (a fair bit).
My questions;
1. How does one handle ordering timeseries data within a table? *Does* one
actually order on the way in (eg re-sh
