Performance tuning is crucial for maintaining optimal CICS system performance and ensuring that applications meet response time and throughput requirements. Understanding performance tuning techniques helps maximize system efficiency and user satisfaction.
CICS performance tuning is the systematic process of optimizing CICS system parameters, application code, and resource utilization to achieve the best possible performance. It involves continuous monitoring, analysis, and adjustment to maintain optimal system efficiency.
Think of CICS performance tuning like tuning a high-performance car engine. You need to monitor various gauges (performance metrics), identify areas that aren't performing optimally (bottlenecks), and make adjustments (parameter changes) to achieve the best balance of power, efficiency, and reliability.
Effective performance tuning requires comprehensive monitoring tools that provide real-time insights into system behavior and performance metrics.
CICS PA is a comprehensive tool that collects, analyzes, and reports on CICS performance data. It provides detailed information about transaction performance, resource usage, and system bottlenecks.
12345678910111213141516171819202122232425WORKING-STORAGE SECTION. 01 MONITOR-COMMAND PIC X(80). 01 MONITOR-RESPONSE PIC X(256). PROCEDURE DIVISION. MONITOR-SYSTEM-PERFORMANCE. MOVE 'CEMT INQUIRE SYSTEM' TO MONITOR-COMMAND EXEC CICS LINK PROGRAM('CEMT') COMMAREA(MONITOR-COMMAND) RESP(EIBRESP) END-EXEC IF EIBRESP = 0 PERFORM ANALYZE-SYSTEM-STATUS ELSE PERFORM HANDLE-MONITOR-ERROR END-IF. ANALYZE-SYSTEM-STATUS. IF MONITOR-RESPONSE CONTAINS 'HIGH' PERFORM LOG-PERFORMANCE-WARNING END-IF.
System Management Facility (SMF) records provide detailed performance data that can be analyzed to identify trends, bottlenecks, and optimization opportunities.
This tool provides real-time monitoring capabilities, allowing system administrators to track performance metrics and make immediate adjustments as needed.
CICS system parameters directly impact performance and resource utilization. Understanding and optimizing these parameters is essential for achieving optimal system performance.
| Parameter | Purpose | Impact | Tuning Considerations |
|---|---|---|---|
| MAXTASK | Maximum concurrent tasks | Throughput, response time | Balance with available memory |
| MAXFILE | Maximum open files | I/O performance, resource usage | Match application requirements |
| MAXCURSOR | Maximum cursors | Database performance | Optimize for query patterns |
| MAXTABLES | Maximum tables | Memory usage, access speed | Balance memory and performance |
1234567891011121314151617181920212223WORKING-STORAGE SECTION. 01 TIMEOUT-VALUES. 05 TASK-TIMEOUT PIC S9(8) COMP VALUE 300. 05 FILE-TIMEOUT PIC S9(8) COMP VALUE 60. 05 PROGRAM-TIMEOUT PIC S9(8) COMP VALUE 120. PROCEDURE DIVISION. SET-TIMEOUT-PARAMETERS. EXEC CICS SET TASK(TASK-TIMEOUT) RESP(EIBRESP) END-EXEC IF EIBRESP = 0 PERFORM SET-FILE-TIMEOUTS END-IF. SET-FILE-TIMEOUTS. EXEC CICS SET FILE(FILE-TIMEOUT) RESP(EIBRESP) END-EXEC.
Optimizing application code is crucial for achieving optimal CICS performance. Efficient programming practices can significantly improve response times and reduce resource consumption.
123456789101112131415161718192021222324252627282930WORKING-STORAGE SECTION. 01 CUSTOMER-RECORD. 05 CUST-ID PIC X(10). 05 CUST-NAME PIC X(30). 05 CUST-BALANCE PIC 9(7)V99. 01 SEARCH-KEY PIC X(10). 01 FOUND-FLAG PIC X(1). PROCEDURE DIVISION. OPTIMIZED-CUSTOMER-LOOKUP. MOVE SEARCH-KEY TO CUST-ID EXEC CICS READ DATASET('CUSTFILE') INTO(CUSTOMER-RECORD) RIDFLD(CUST-ID) RESP(EIBRESP) END-EXEC IF EIBRESP = 0 MOVE 'Y' TO FOUND-FLAG PERFORM PROCESS-CUSTOMER ELSE IF EIBRESP = 13 MOVE 'N' TO FOUND-FLAG PERFORM HANDLE-NOT-FOUND ELSE PERFORM HANDLE-READ-ERROR END-IF.
This optimized approach uses direct key access instead of sequential searching, significantly reducing I/O operations and improving response times.
123456789101112131415161718192021222324WORKING-STORAGE SECTION. 01 BUFFER-POOL. 05 BUFFER-ENTRY OCCURS 100 TIMES. 10 BUFFER-KEY PIC X(10). 10 BUFFER-DATA PIC X(100). 01 BUFFER-INDEX PIC S9(4) COMP. 01 BUFFER-FOUND PIC X(1). PROCEDURE DIVISION. CHECK-BUFFER-CACHE. PERFORM VARYING BUFFER-INDEX FROM 1 BY 1 UNTIL BUFFER-INDEX > 100 IF BUFFER-KEY(BUFFER-INDEX) = SEARCH-KEY MOVE 'Y' TO BUFFER-FOUND MOVE BUFFER-DATA(BUFFER-INDEX) TO CUSTOMER-RECORD EXIT PERFORM END-IF END-PERFORM IF BUFFER-FOUND = 'N' PERFORM READ-FROM-FILE END-IF.
123456789101112131415161718192021222324PROCEDURE DIVISION. BATCH-PROCESSING. PERFORM OPEN-FILES PERFORM READ-INPUT-RECORDS PERFORM PROCESS-BATCH PERFORM CLOSE-FILES STOP RUN. READ-INPUT-RECORDS. PERFORM UNTIL END-OF-FILE READ INPUT-FILE AT END MOVE 'Y' TO END-OF-FILE NOT AT END PERFORM PROCESS-RECORD END-READ END-PERFORM. PROCESS-BATCH. PERFORM VARYING BATCH-INDEX FROM 1 BY 1 UNTIL BATCH-INDEX > BATCH-SIZE PERFORM PROCESS-BATCH-RECORD(BATCH-INDEX) END-PERFORM.
Following established performance best practices helps ensure optimal CICS system performance and provides a foundation for continuous improvement.
Continuously monitor system performance to identify trends and potential issues early.
Minimize resource consumption through efficient coding practices and proper resource management.
Optimize database access patterns and use appropriate indexing strategies.
Implement appropriate caching mechanisms to reduce redundant operations and improve response times.
| Issue | Symptoms | Solutions |
|---|---|---|
| High CPU Usage | Slow response times, system delays | Optimize algorithms, reduce loops, use efficient data structures |
| Excessive I/O | Long wait times, poor throughput | Implement caching, batch operations, optimize queries |
| Memory Leaks | Gradual performance degradation | Proper resource cleanup, memory management |
| Resource Contention | Deadlocks, timeouts | Optimize locking strategies, reduce contention |