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Foglight for Siebel 5.2.1 - User and Reference Guide

Introducing Foglight for Siebel Systems Foglight for Siebel Systems Summary Dashboard About the Foglight SiebelServer Agent Foglight for Siebel Systems Reference

Sieb_Comp_Detail graph view

Purpose
When a Siebel task completes its operation, task level statistics that are gathered dynamically during the operation of a task roll up to the Siebel component level. These statistics can be used to monitor the progress and performance of each enabled Siebel component. Siebel stores component statistics as cumulative statistics, that is, since the Siebel server started.
The Foglight SiebelServer Agent calculates Siebel component statistic deltas by determining the change in the individual cumulative statistic since the last collection. The delta values are stored in the Foglight repository. This allows the Siebel administrator to analyze how the enabled Siebel components performed over time and identify peak usage time periods.
When the Siebel administrator identifies a peak usage time period for the Siebel server, use this graph to identify which Siebel components contribute the most to server utilization by identifying the Siebel components with the highest CPU and SQL run times during the peak time period.
When the Siebel administrator identifies the Siebel components that consume the most resources or produce the most throughput since start up with the Sieb_Comp_Statistics graph view, use the Sieb_Comp_Detail graph view to determine if Siebel component contribution occurs at one time period or as a recurring trend. Siebel components that contribute to the server’s resource utilization on a reoccurring basis should be examined more closely to determine the root cause. Corrective measures should be taken so that the Siebel component does not continue to contribute to the Siebel server’s poor performance.
Figure 2. Siebel Component Statistics Since Last Collection
If a batch component is found to be running simultaneously with a high number of interactive tasks such as object managers, the Siebel administrator should consider running the batch component during a time period when a smaller number of interactive tasks are running. Another alternative is to move the batch component to a Siebel server dedicated to batch components, or to a Siebel server with more available resources.
Use the Sieb_Comp_Detail graph to correlate each enabled Siebel component’s utilization and throughput for each refresh interval.
Description of the view
Table 3. Sieb_Comp_Detail graph view
Data Displayed
CPU Time (sec). Total CPU time, in seconds, for all the component’s tasks since the last collection.
 
SQL Execution Time (sec). Total time, in seconds, that the Siebel component spent performing SQL execute operations since the last collection. For SELECT statements, this is the amount of time spent identifying the rows if the statement requires a sort or uses FOR UPDATE. For INSERT, UPDATE, and DELETE statements, this is the amount of time spent modifying the data.
 
Tasks (count). Total number of Siebel tasks started since the last collection.
Where to go next
Drill down on:
 
•
CPU Time (sec) bar or Tasks (count) bar. For more information, see Sieb_Comp_SQLTime graph view. This display is used to analyze the SQL execute, SQL parse, and SQL fetch times for a particular Siebel component.
 
•
SQL Execution Time (sec) bar. For more information, see Sieb_Task_Detail Table view. This display is used to identify the Siebel tasks that are currently running for batch and background Siebel components.

Sieb_Comp_Group_Availability graph view

Purpose
The Foglight SiebelServer Agent calculates the availability of the Siebel component group as follows:
•
100 percent if the ComponentGroupState = Online or Running.
•
Zero percent for all other values.
Figure 3. Siebel Component Group Availability
If the Siebel administrator determines a Siebel component group is experiencing an unscheduled outage, the Siebel Server Manager should be used to start the component group.
Description of the view
 
Table 4. Sieb_Comp_Group_Availability graph information
Data Displayed
Availability %. The availability percentage of each enabled Siebel component group assigned to the Siebel server.
Where to go next
Drill down on Availability % bar. For more information, see Sieb_Comp_Availability graph view. This display is used to identify the individual Siebel component availability for the particular Siebel component group.

Sieb_Comp_SQLExecute_Detail graph view

Purpose
When a Siebel task completes its operation, task-level statistics that are gathered dynamically during the operation of a task roll up to the Siebel component level. Among the many statistics that are gathered and rolled up to the Siebel component level are statistics on the total time that the Siebel component spends executing SQL, the average time it spends executing and the number of execute operations.
This graph correlates SQL Execute statistics for each refresh interval.
The Foglight SiebelServer Agent collects delta SQL execute time, average SQL execute time and the number of SQL executions for each enabled Siebel component. Foglight calculates the deltas by determining the change in the individual cumulative statistic since the last collection. The delta value is stored in the Foglight repository. This allows the Siebel administrator to identify peak SQL execute time periods for each Siebel component.
When the Siebel administrator identifies a peak SQL execute time period, use this graph to determine if a small number of operations contributed to the total SQL execute time. If the number of operations is low compared to the total amount of time spent executing versus a high number of operations contributing to the same execute time, this indicates that the database is spending more time identifying the rows for SELECT statements that require a sort or use FOR UPDATE or performing INSERT, UPDATE, and DELETEs.
The amount of time it takes to update the Siebel database is impacted by the number of indexes that must be updated every time a record is inserted, updated, or deleted because all the indexes for the table must also be updated. Siebel Systems does not support the removal of indexes that are created during installation without guidance from Siebel Support or Siebel Expert Services. However, if the Siebel administrator creates additional indexes to increase SELECT performance, the SQL execute time may be negatively impacted. Monitoring the SQL execute statistics after the addition of any indexes helps the Siebel administrator determine if the additional indexes are having an unacceptable negative impact on the SQL execute times, that is, time to perform modifications to the data.
Description of the view
 
Table 5. Sieb_Comp_SQLExecute_Detail graph view information
Data Displayed
SQL Execute Time (sec). The number of seconds since the last collection spent identifying the rows if the statement requires a sort or uses FOR UPDATE. For INSERT, UPDATE, and DELETE statements, this is the amount of time spent modifying the data since the last collection.
Avg SQL Execute Time (sec). Average number of seconds since the last collection spent identifying the rows if the statement requires a sort or uses FOR UPDATE. For INSERT, UPDATE, and DELETE statements, this is the amount of time spent modifying the data since the last collection.
SQL Executes (count). Number of executes since the last collection.
Where to go next
n/a.

Sieb_Comp_SQLFetch_Detail graph view

Purpose
When a Siebel task completes its operation, task-level statistics that are gathered dynamically during the operation of a task roll up to the Siebel component level. Among the many statistics that are gathered and rolled up to the Siebel component level are statistics on the total time that the Siebel component spends fetching SQL, the average time it spends fetching and the number of fetch operations.
The Siebel Component SQL Fetch Detail graph correlates SQL Fetch statistics for each refresh interval.
The Foglight SiebelServer Agent collects delta SQL fetch time, average SQL fetch time and the number of SQL fetches for each enabled Siebel component. Foglight calculates the deltas by determining the change in the individual cumulative statistic since the last collection. The delta value is stored in the Foglight repository. This allows the Siebel administrator to identify peak SQL fetch time periods for each Siebel component.
When the Siebel administrator identifies a peak SQL fetch time period, use this graph to determine if a small number of operations contributed to the total SQL fetch time. If the number of operations is low compared to the total amount of time spent fetching, this indicates that the Siebel component is issuing a number of large queries that return a large number of rows such as in list applets. If the Siebel component is an object manager, the Siebel administrator should verify that the object manager's applets are configured with the optimal join specifications and indexes.
If the Siebel administrator identifies a high number of operations contributing to the fetch time, this indicates that the Siebel component is performing a number of small queries that return a small number of rows such as in detail applets. Since there are a larger number of SQL statements being issued, a low SQL parse time for the same time period indicates that the statements are being reused in the database cache.
Description of the View
 
Table 6. Sieb_Comp_SQLFetch Detail graph information
Data Displayed
SQL Fetch Time (sec). For SELECT statements only, the number of seconds since the last collection spent retrieving the rows returned by the query.
 
Avg SQL Fetch Time (sec). For SELECT statements only, the average number of seconds since the last collection spent retrieving the rows returned by the query.
 
SQL Fetches (count). Number of fetch operations since the last collection.
Where to go next
n/a.
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