Viewing Performance Metrics Using the Spy Servlet

4-10 Oracle Fusion Middleware Performance and Tuning Guide Key features available from Grid Control that are applicable to and relevant in monitoring Fusion Middleware performance include the following: ■ Monitor multiple WebLogic Server Domains from a single console. ■ Out-of-the-box availability and performance monitoring ■ Monitor availability and performance in real-time as well as from an historical perspective ■ Specify warning versus critical thresholds for key performance metrics ■ Receive email andor page notifications when metric thresholds are reached ■ Perform trend analysis on collected performance information ■ Application Diagnostics for Java AD4J which provides production diagnostics with no application instrumentation. The AD4J screen is shown below. Key features of AD4J include the following: – Full method, stack and thread state visibility – Quick ranking of high-cost code being executed for bottleneck identification – Line-of-code granularity – Cross-tier database and EJBRMI correlation – Java thread lock and synchronization detection Note: Grid Control is not provided out-of-box with the Fusion Middleware installation; it requires a separate installation. Refer to the Oracle Enterprise Manager 11g Grid Control Installation and Advanced Configuration Guide for further details on how to install Grid Control. Monitoring Oracle Fusion Middleware 4-11 – Thread activity tracing – Heap snapshot and analysis – Differential heap analysis to quickly isolate memory leaks – Threshold based alerting – Alert actions through SNMP traps, SMTP, or HTTP request ■ Composite Application Monitor and Modeler CAMM provides application service management for complex composite services such as Portal, BPEL, ESB, OSB, and Web Services. The CAMM screen is shown below. Key features of CAMM include the following: – Automated discovery of complex services Portal, BPEL, ESB, OSB, Web Services – Automatic metadata analysis and monitoring instrumentation configuration – run time dependency analysis between SOA services and endpoints – Metadata model presents monitored targets using native terminology – Invocation metrics based on both arrival and completion of inbound request – Response time mean, max, and min metrics – Tiered data aggregation for long term storage and trending – Historical views into arbitrary datetime ranges – Comparative views to compare any two arbitrary datetime ranges with simultaneous scrolling – Custom views to combine arbitrary graphs, tables, and functional views – Customizable hierarchy of custom views