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Global Payroll Performance Optimisation - II David Kurtz Go-Faster Consultancy Ltd.

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Presentation on theme: "Global Payroll Performance Optimisation - II David Kurtz Go-Faster Consultancy Ltd."— Presentation transcript:

1 Global Payroll Performance Optimisation - II David Kurtz Go-Faster Consultancy Ltd.

2 Global Payroll Performance Optimisation ©2011 2 Oracle Database Specialist –Independent consultant Performance tuning –PeopleSoft ERP –Oracle RDBMS Book – UKOUG Director Server Tech & PeopleSoft Oak Table Who Am I?

3 Global Payroll Performance Optimisation ©2011 3 Agenda ‘Streaming’ –Parallel processing Data Volume Read Consistency Partitioning Reporting Archiving

4 Global Payroll Performance Optimisation ©2011 4 Warning This is an unashamedly technical session. I am going to talk about database internals.

5 Global Payroll Performance Optimisation ©2011 5 Size matters!

6 Global Payroll Performance Optimisation ©2011 6 Parallel processing All modern machines have multiple processors, –most of the processors have multiple cores. –Even the CPU in my 4 year old laptop has a 2 core CPU.

7 Global Payroll Performance Optimisation ©2011 7 Database Parallelism All objects in the PeopleSoft schema are explicitly set NOPARALLEL –Indexes are built parallel, but later reset. –Can invoke parallel query with PARALLEL hint –Parallel insert in direct path model –Parallel DML only works on partitioned objects 1 PQ slave per partition

8 Global Payroll Performance Optimisation ©2011 8 PeopleSoft Batch Programs Only run on one CPU at any one time. Client Server processes –Program (COBOL or Application Engine) –Database (eg. Oracle) Either busy executing COBOL or waiting for the database. –If your payroll calculation is a single process you are not getting value for money!

9 Global Payroll Performance Optimisation ©2011 9 Payroll ‘Streaming’ Several GP processes can be split up. –Each piece processes a distinct set of employees Range of EMPLID –The pieces can be run concurrently. –Maximum number of streams determined by hardware.

10 Global Payroll Performance Optimisation ©2011 10 Streamable Processes COBOL –Payroll Calculation Application Engine –Banking Preparation –GL Preparation –EDI Preparation –Payslip Preparation Database Intensive

11 Global Payroll Performance Optimisation ©2011 11 Payroll ‘Streaming’ Challenges Payroll isn’t over until the last stream completes. –Streams need to be evenly balanced. –Employee churn? One global definition of streams –Balance for largest payroll? Inter-stream contention –Shared working storage tables in COBOL

12 Global Payroll Performance Optimisation ©2011 12 Payroll Calculation Process Phases Identify –Populate working storage and some result tables Database Intensive Calculation –COBOL Intensive Cancellation –Delete results Database Intensive

13 Global Payroll Performance Optimisation ©2011 13 How Many Streams? In a well tuned systems, the payroll calculation phase spends about –2/3 of its time in COBOL –1/3 on the database. Number of streams should not exceed –3 * CPU on database server –1.5 * CPU on Process Scheduler server Payroll identification process is database intensive.

14 Global Payroll Performance Optimisation ©2011 14 How Many Streams? First GP I ever worked on –20 CPUs on Application/Batch server –20 CPUs on Database server Maximum number of streams? –20 / 1/3 = 60 on database server –20 / 2/3 = 30 on Application server So we used 30 streams –Application server fully utilised during payroll calc –Database about 50% during calc, –Probably overloaded during identification.

15 Global Payroll Performance Optimisation ©2011 15 How Many Streams? Optimise number of streams for calculation phase. Restrict concurrency of database intensive process on process scheduler. –To limit CPU consumption, and possibly also I/O contention. Consider use of Oracle Resource Manager –Mainly for Payroll identification –I’ve never had to do this myself. –Cancellation will be restricted by I/O

16 Global Payroll Performance Optimisation ©2011 16 Balancing Streams Balance employees across streams on basis of –80% number of payroll segments per stream –20% number of JOB history rows Longer serving employees in earlier streams likely to have more payroll segment and job history. –Make allowance for employee churn. You will need to periodically rebalance the streams. –Balance for the largest payroll.

17 Global Payroll Performance Optimisation ©2011 17 Employee Churn EMPLID is allocated as an accession number. Streams are a range of EMPLIDs –New employees are hired into the last stream –Employees are terminated across all streams Over time the streams will go out of balance –Last stream will take longest Periodically rebalance the streams

18 Global Payroll Performance Optimisation ©2011 18 Bulk Churn Effects Migration –If migrated to GP in tranches then order of migration could affect stream balance Company merger/divestment history can affect balance of payroll.

19 Global Payroll Performance Optimisation ©2011 19 Rebalancing the streams? Calculate new stream range values –Allow space for estimated future growth Rebuild all range partitioned tables –Half the I/O of partition merge/split –About 42 tables in UK tables. –Need working storage space to do this

20 Global Payroll Performance Optimisation ©2011 20 Reversing the EMPLID Reverse the EMPLID –Instead of EMPLID 0000012345 –Use EMPLID 543210000 Streams stay balanced because new employees hired across range Improved search performance across HCM BUT you must do this before you go live!

21 Global Payroll Performance Optimisation ©2011 21 Reversing the EMPLID 0000012345 0000012346 0000012347 0000012348 0000012349 0000012350 0000012351 0000012352 5432100000 6432100000 7432100000 8432100000 9432100000 0532100000 1532100000 2532100000

22 Global Payroll Performance Optimisation ©2011 22 Inter-stream Contention Streams are just ranges of EMPLIDs. Oracle inserts data into the first available block (roughly speaking) Multiple streams insert data simultaneously into the same data blocks in result tables. Payroll cancel/recalculation deletes from result tables. Multiple transactions concurrently update different rows in the same block. –On Oracle/SQL Server >=2005: No locking, streams continue to run, but read consistency processing is expensive –Other database can experience page level locking

23 Global Payroll Performance Optimisation ©2011 23 Working Storage Tables COBOL –One shared instance of each working storage table Shared SQL –Candidate for Global – Temporary Table so one instance per session Application Engine –PeopleSoft Temporary Record –One instance of record per process Different SQL Still consider GTT to reduce redo

24 Global Payroll Performance Optimisation ©2011 24 Read Consistency The data set that you query remains the same throughout the life of your query. –If somebody else updates data that you are reading (and commits), after your query starts, then you see the original value. Thus, readers do not block writers or vice versa. Oracle has always done this, like this since 1990. SQL Server 2005 has ‘read committed snapshot’ option Other databases either block or can permit ‘dirty read’.

25 Global Payroll Performance Optimisation ©2011 25 Read Consistency Oracle achieves this by storing ‘undo’ information for every change –Recovers ‘read-consistent’ in-memory copy of data block to point in time when query started. –A good reason for buying Oracle –Resource intensive process –Performance problem if abused. Global Payroll is the perfect storm!

26 Global Payroll Performance Optimisation ©2011 26 Read Consistency Query @ 10023 Update @ 10024

27 Global Payroll Performance Optimisation ©2011 27 Avoiding Inter-stream Contention Prevent different streams accessing the same data blocks –Range Partition result tables to match stream ranges –Use Global Temporary Tables (Oracle) for working storage tables –Partition these also on other platforms. Now different streams access different partitions. No code change, a job for the DBA –licensed option on most platforms

28 Global Payroll Performance Optimisation ©2011 28 Partitioning Partitioned Table –Different physical components Value of data determines physical location –Logically still one table –Transparent to application –Rather like a multi-part encyclopaedia. Partition Elimination

29 Global Payroll Performance Optimisation ©2011 29 What is Partitioning? Typically used in DSS But can also be effective in OLTP –(From Oracle documentation)

30 Global Payroll Performance Optimisation ©2011 30 Partitioning Keep similar things together –Employees for one stream in on partition Keep different things apart –Only one transaction in each block of each segment –No need for read consistency

31 Global Payroll Performance Optimisation ©2011 31 Partitioning GP Recommendation Range Partitioning –EMPLID – to match streams List Sub-partition –CAL_RUN_ID – calendar group ID.

32 Global Payroll Performance Optimisation ©2011 32 Secondary Benefits CAL_RUN_ID list sub-partition Easier to archive later –Historical partitions –Different Tablespaces –Different Data Files Old data on slower disk Read Only Less frequent back-up of read-only tables Faster Backup

33 Global Payroll Performance Optimisation ©2011 33 Global Temporary Tables Global because the data is private Temporary because the definition is permanent Global because everyone can see the definition Temporary because physical existance of the table is temporary so it does not need to be recovered.

34 Global Payroll Performance Optimisation ©2011 34 Global Temporary Tables A temporary object –No redo generation But there is undo, and there is redo on the undo! –Each session gets its own physical copy. Again no read consistency problems No high water mark issues Lower high water marks – less I/O

35 Global Payroll Performance Optimisation ©2011 35 Building the DDL Demonstrate GFCBUILD utility.

36 Global Payroll Performance Optimisation ©2011 36 Group Lists Specify a list of individual EMPLIDs for whom to run pay calc or another process. Some customers have experienced problems when run groups shortly before or during larger batch payroll calculations. Why?

37 Global Payroll Performance Optimisation ©2011 37 Cost Based Optimizer SQL Execution Plan Caching Bind Variable Peeking during Parse Different Plan for Group List –Because different bind variables But plan cached and gets used for main pay calculation which then runs longer than usual!

38 Global Payroll Performance Optimisation ©2011 38 Plan Stability Remember the good plan used by large payroll. Force it to be used for all payrolls including group list. –Data Volumes small so poor plan won’t really matter. Oracle Stored Outline –No code change, DBA can implement.

39 Global Payroll Performance Optimisation ©2011 39 Plan Stability Collect and applied stored outline with database trigger –http://www.go- Use Active Session History to demonstrate the problem and solution

40 Global Payroll Performance Optimisation ©2011 40 Capture Stored Outline CREATE OR REPLACE TRIGGER sysadm.gfc_create_stored_outlines BEFORE UPDATE OF runstatus ON sysadm.psprcsrqst FOR EACH ROW WHEN (new.prcsname = 'GPPDPRUN' AND (new.runstatus = 7 OR old.runstatus = 7)) DECLARE l_sql VARCHAR2(100); BEGIN l_sql := 'ALTER SESSION SET create_stored_outlines = '; IF :new.runstatus = 7 THEN EXECUTE IMMEDIATE l_sql||:new.prcsname; ELSIF :old.runstatus = 7 THEN EXECUTE IMMEDIATE l_sql||'FALSE'; END IF; --because I dont want to crash the process scheduler EXCEPTION WHEN OTHERS THEN NULL; END; /

41 Global Payroll Performance Optimisation ©2011 41 Apply Stored Outline CREATE OR REPLACE TRIGGER sysadm.gfc_use_stored_outlines BEFORE UPDATE OF runstatus ON sysadm.psprcsrqst FOR EACH ROW WHEN (new.prcsname = 'GPPDPRUN' AND (new.runstatus = 7 OR old.runstatus = 7)) DECLARE l_sql VARCHAR2(100); BEGIN l_sql := 'ALTER SESSION SET use_stored_outlines = '; IF :new.runstatus = 7 THEN EXECUTE IMMEDIATE l_sql||:new.prcsname; ELSIF :old.runstatus = 7 THEN EXECUTE IMMEDIATE l_sql||'FALSE'; END IF; --because I dont want to crash the process scheduler EXCEPTION WHEN OTHERS THEN NULL; END; /

42 Global Payroll Performance Optimisation ©2011 42 Three Scenarios Compared Large / Small / Plan Stable Small SQL_ID SCENARIO 1 ASH_SECS SCENARIO 2 ASH_SECS SCENARIO 3 ASH_SECS ------------- ------------------- ---------- ------------ ---------- ------------ ---------- 4uzmzh74rdrnz 2514155560 280 3829487612 28750 **SAME** 5023 4n482cm7r9qyn 1595742310 680 869376931 140 **SAME** 889 2f66y2u54ru1v 1145975676 630 **SAME** 531 1n2dfvb3jrn2m 1293172177 150 **SAME** 150 652y9682bqqvp 3325291917 30 **SAME** 110 d8gxmqp2zydta 1716202706 10 678016679 10 **SAME** 32 2np47twhd5nga 3496258537 10 **SAME** 27 4ru0618dswz3y 2621940820 10 539127764 22 4ru0618dswz3y 539127764 100 **SAME** 22 4ru0618dswz3y 3325291917 10 539127764 22 4ru0618dswz3y 1403673054 110 539127764 22 gnnu2hfkjm2yd 1559321680 80 **SAME** 19 fxz4z38pybu3x 1478656524 30 4036143672 18 2xkjjwvmyf99c 1393004311 20 **SAME** 18 a05wrd51zy3kj 2641254321 10 **SAME** 15

43 Global Payroll Performance Optimisation ©2011 43 Data Volume Payroll generates a lot of data. Every pay period it generates more data. Partitioning can offer ways of accessing the data you want quickly –Without having to trawl through data you don’t want. Need to consider how long you need data –Do you still need data from last tax year?

44 Global Payroll Performance Optimisation ©2011 44 Archiving Put the data you do need to keep into a reporting table –Remove data from the live result tables –Partitioning can help you move/delete this data efficiently –May need to rebuild tables where you have to use DELETE Reduced data volumes should improve performance of reports.

45 Global Payroll Performance Optimisation ©2011 45 Reporting Payroll result tables delivered with single index –Not suitably indexed for all reporting requirements Particularly single PIN queries –Adding more indexes would degrade calculation performance –Consider generating reporting table Subset of data, and indexed as necessary.

46 Global Payroll Performance Optimisation ©2011 46 GFC_GPRPTGEN Reporting Table for Single Pin Queries –List Partitioned by Pin –One Partition for each Pin Incremental Maintenance by Application Engine –Uses Parallel DML to maintain reporting table. –Sub-Paritioned GP Result Tables may still be faster for single employee, single calendar group ID queries!

47 Global Payroll Performance Optimisation ©2011 47 Further Reading Configuring and Operating Streamed Processing in PeopleSoft Global PayrollConfiguring and Operating Streamed Processing in PeopleSoft Global Payroll –www.go- l Managing Oracle Table Partitioning in PeopleSoft Applications with GFC_PSPART PackageManaging Oracle Table Partitioning in PeopleSoft Applications with GFC_PSPART Package –www.go- Use of Oracle Plan Stability (Stored Outlines) in PeopleSoft Global PayrollUse of Oracle Plan Stability (Stored Outlines) in PeopleSoft Global Payroll –

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