Portal Sizing
The average size adjusts for variations in sizes of RDs. A collection of long, complex RDs with many indexed terms and a list of short RDs with a few indexed terms require different search times, even if the complex RDs have the same number of RDs.
RDs are stored in a hierarchical database format, where the intrinsic size of the database must be accounted for, even when no RD is stored.
•The number of concurrent users who perform
number of concurrent users / average time between search hits
Use the number of concurrent users value calculated in “Average Time Between Page Requests” on page 140.
•The type of search operators used
Types of search functions include basic, combining, proximity, passage and field operator, and wildcard scans. Each function uses different search algorithms and data structures. Because differences in search algorithms and data structures increase as the number of search and indexed terms increase, the type of search function affects times for search result return trips.
TIP | You can now give the above figures to your technical representative |
| and ask that the sizing tool be run to identify your estimated number |
| of CPUs. |
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Portal Desktop Configuration
Portal Desktop configuration explicitly determines the amount of data held in memory on a
The more channels on the Portal Desktop, the bigger data session size, and the lesser the throughput of Portal Server.
Another factor is how much interactivity the Portal Desktop offers. For example, channel clicks can generate load on Portal Server or on some other external server. If channel selections generate load on Portal Server, a higher user activity profile and higher CPU overhead occur on the node that hosts the Portal Desktop than on a node that hosts some other external server.
Chapter 4
