User`s guide

Index variables are variables whose values you want to index at one or more level. The purpose of an index is to
speed up the generation of reports when you run a report definition that has one or more of the indexed variables.
d. Review the other variables in your table to check whether their Kept status and statistics to be calculated
are appropriate for your site.
Within your table(s), the variables that are generally most useful for performance analysis are the ones whose
default values for the Kept status are Yes. These are the variables that you logged in the first section of this
document.
If in the SAS log, you received messages similar to this:
NOTE: CSIIPDI not initialized
while running %CSPROCES on the exported data, then some of the variables defined (in the IT Service Vision
table definition) for this device are not being logged. To enable logging of these attributes, see Cabletron
Spectrum Appendix 5: Editing Model Types for SPECTRUM.
Note: If you decide on a Kept status of Yes, you can decide later to change it to No, in which case the values are
not processed into the detail level from that time forward. Similarly, if you decide on a Kept status of No, you can
decide later to change it to Yes, in which case values are processed into the detail level from that time forward.
e. Check the age limit of each table.
i. Check the Age Limit at
detail level
.
By default, IT Service Vision keeps ten days of data at the detail level, but the age limit in your tables
may be different. If you want your reports on detail level to be able to cover a different number of days
(for example, 14 days), overwrite the current value with the new value (in this case, 14).
Note: If you do not want to generate reports on detail-level data in this table but you do want to generate
reports on other levels, you can set the value to 0.
Note:
The process step deletes existing data in the detail level that are beyond the age limit for the detail
level, but the process step keeps incoming data (and adds it to existing data in the detail level) regardless
of the dates in the incoming data. When the age limit at detail level is 0, the process step behaves just as it
always does: it deletes existing detail level data whose age is greater than the age limit (thus, in this case,
it deletes all of the existing detail level data), and it keeps the incoming data in the detail level (thus, in
this case, what were the incoming data are now all the existing data).
The reduce step ages out data in non-detail levels that are beyond the age limits for those levels and uses
the existing data in detail level to update the non-detail levels. If and only if the age limit at detail level is
0, the reduce step also deletes the existing data (which, if age limit is 0, are the data that were incoming
data to the process step) in the detail level.
If the age limit is 0, the existing data in detail level would be cleared anyway (at the time of the next
process step) because the data by now are existing data and over the age limit for detail level. The
purpose of this exception is to remove the existing data in detail level many hours sooner. This is quite
useful when there is a tremendous volume of detail-level data and the data can be ’thrown away’ after the
day, week, month, and/or year levels have been updated. Performing this frees extra disk space between