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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_SQLParse_Detail graph view

Purpose
When a Siebel task completes its operation, task level statistics gathered dynamically during the operation of a task roll up to the Siebel component level. These statistics include the total time that the Siebel component spends parsing SQL, the average time it spends parsing and the number of parse operations.
The Foglight SiebelServer Agent collects the delta SQL parse time, the average SQL parse time and the number of SQL parses 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 parse time periods for each Siebel component.
When the Siebel administrator identifies a peak SQL parse time period, use the Sieb_Comp_SQLParse_Detail graph view to determine if a small number of operations contributed to the total SQL parse time. If the number of operations is low compared to the total amount of time spent parsing versus a high number of operations contributing to the same parsing time, this indicates that the database is not reusing SQL statements in the database cache but re-parsing them, thus increasing the total elapsed time for the SQL statement.
Since Siebel dynamically generates SQL statements and uses bind variables, the Siebel administrator is limited in being able to positively affect the SQL parse time. However, if the Siebel database is Oracle, there are two initialization parameters that can be adjusted to decrease the SQL parse time:
•
SESSION_CACHED_CURSORS. Set this Oracle parameter to a value between 20 and 50. This causes Oracle to remember previously parsed SQL and, if the user session requests a parse of a SQL statement in the cache, then Oracle avoids the parse.
•
CURSOR_SHARING. Set this Oracle parameter to FORCE. This causes Oracle to substitute literals in SQL statements with bind variables, allowing otherwise unique SQL to be reused.
Description of the view
 
Table 7. Sieb_Comp_SQLParse Detail graph information
Data Displayed
SQL Parse Time (sec). The number of seconds since the last collection spent translating the SQL statements into an execution plan. This time includes checking for proper security authorization and checks for the existence of tables, columns, and other referenced objects.
 
Avg SQL Parse Time (sec). Average number of seconds since the last collection spent translating the SQL statements into an execution plan. This time includes checking for proper security authorization and checks for the existence of tables, columns, and other referenced objects.
 
SQL Parses (count). Number of parse operations since the last collection.
Where to go next
n/a.

Sieb_Comp_SQLTime graph view

Purpose
When a Siebel task completes its operation, task level statistics gathered dynamically during the operation of a task roll up to the component level. Among the many statistics gathered and rolled up to the Siebel component level are statistics on the time that the Siebel component spent parsing, executing, and fetching SQL statements. Siebel server statistics are cumulative, i.e., since the Siebel server started.
The Foglight SiebelServer Agent collects delta SQL parse, execute, and fetch statistics 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 usage time periods for each Siebel component.
SQL parse time should be low, indicating a well-designed component that is parsing the SQL statement once but executing it multiple times.
High SQL execute time in relation to a low SQL fetch time indicates that the component is spending more time performing INSERT, UPDATE, and DELETE statements than SELECT statements.
Low SQL execute time in relation to a high SQL fetch time indicates that the component is spending more time performing SELECT statements than INSERT, UPDATE and DELETE statements.
Is the Siebel component’s contribution to the Siebel server’s SQL Time happening once or as a reoccurring trend? Siebel components that contribute to the server’s SQL Time on a reoccurring basis should be examined closely to ensure that the component is optimally configured.
Description of the view
 
Table 8. Sieb_Comp_SQLTime graph information
Data Displayed
SQL Parse Time (sec). The number of seconds since the last collection spent translating the SQL statements into an execution plan. This time includes checking for proper security authorization and checks for the existence of tables, columns, and other referenced objects.
 
Avg SQL Parse Time (sec). Average number of seconds since the last collection spent translating the SQL statements into an execution plan. This time includes checking for proper security authorization and checks for the existence of tables, columns, and other referenced objects.
 
SQL Parses (count). Number of parse operations since the last collection.
Where to go next
Drill down on:
 
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SQL Parse Time (sec) bar. For more information, see Sieb_Comp_SQLParse_Detail graph view. This display is used to analyze the SQL parse time, average SQL parse time, and number of SQL parses for a particular Siebel component during the same time range.
 
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SQL Execute Time (sec) bar. For more information, see Sieb_Comp_SQLExecute_Detail graph view. This display is used to analyze the SQL execute time, average SQL execute time and the number of SQL executes for a particular Siebel component during the same time range.
 
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SQL Fetch Time (sec) bar. For more information, see Sieb_Comp_SQLFetch_Detail graph view. This display is used to analyze the SQL fetch time, average SQL fetch time and the number of SQL fetches for a particular Siebel component during the same time range.

Sieb_Comp_Statistics 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.
Figure 4. Siebel Component Statistics Since Startup
The Foglight SiebelServer Agent collects both cumulative and delta Siebel component statistics. Foglight calculates Siebel component statistic deltas by determining the change in the individual statistic since the last collection. The delta values are stored in the Foglight repository along with the cumulative value for selected statistics.
Use the Sieb_Comp_Statistics graph to analyze the amount of throughput and resource consumption that each enabled Siebel component contributes to the Siebel server’s total throughput and resource consumption since start up.
Description of the view
 
Table 9. Sieb_Comp_Statistics graph information
Data Displayed
CPU Time (min). Total CPU time (minutes) for all the component’s tasks since the Siebel server started.
 
SQL Execution Time (min). Total time (minutes) the Siebel component spent performing SQL execute operations since the Siebel server started. For SELECT statements, the amount of time spent identifying the rows if the statement requires a sort or uses FOR UPDATE. For INSERT, UPDATE, and DELETE statements, the amount of time spent modifying the data.
 
Tasks (count). Total number of Siebel tasks started since the Siebel server started.
Where to go next
Drill down on:
 
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CPU Time (min) or Tasks bar. For more information, see Sieb_Comp_Detail graph view. This display is used to analyze the Siebel component throughput and resource utilization since the last collection.
 
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SQL Execution Time (min) bar. For more information, see Sieb_Comp_SQLTime graph view. This display is used to identify the time that Siebel components spent parsing, executing, and fetching SQL statements.

Sieb_Comp_Tasks graph view

Purpose
Each Siebel component has a maximum task parameter that represents the maximum number of tasks and users that can run concurrently.
Figure 5. Siebel Component Tasks Snapshot
The Siebel server supports both multiprocessor and multithreaded components, and can operate components in background, batch, and interactive modes. Multiprocess components have one operating system process for each of the Siebel component’s corresponding running tasks. By default, all background mode Siebel components are multiprocessor. Multithreaded components, such as Siebel object managers (SOM) have multithreaded operating system processes where tasks and client sessions run as threads within the individual operating system process. Each multithreaded operating system process is called an MTServer.
For batch and interactive mode Siebel components that have multithreading enabled, the Maximum MT Servers parameter represents the maximum number of multithreaded server processes that can run concurrently. The number of tasks that can run in each server process is determined by the value of the Maximum Tasks parameter divided by Max MTServers.
The Foglight SiebelServer Agent collects the task and MTServer statistics for each enabled Siebel component:
Description of the view
 
Table 10. Sieb_Comp_Tasks graph information
Data Displayed
Running Tasks. Current number of running tasks.
 
Max Tasks. Maximum number of tasks defined for the component.
 
Running MTS. Current number of MTServers.
 
Max MTS. Maximum number of MTServers defined for the component.
Where to go next
Drill down on:
 
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Running Tasks 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.
 
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Max Tasks, Running MTS, or Max MTS bar to isolate the analysis to an individual Siebel component.
Siebel Support Web provides many resources detailing how to optimally set these parameters.
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