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Configuring PeopleSoft Global Payroll for Optimal Performance Session 507

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Presentation on theme: "Configuring PeopleSoft Global Payroll for Optimal Performance Session 507"— Presentation transcript:

1 Configuring PeopleSoft Global Payroll for Optimal Performance Session 507 david.kurtz@go-faster.co.uk www.go-faster.co.uk gene.pirogovsky@omniasolutions.com www.omniasolutions.com

2 2 Who are we?  David Kurtz Independent Consultant working for UBS  Swiss GP project Systems Performance Tuning  Oracle Databases  Unix  PeopleSoft Applications  Gene Pirogovsky Independent Consultant working for UBS  Swiss GP project Global Payroll Interfaces Customizations

3 3 Configuring Global Payroll  Physical Database Considerations Oracle specific Reducing I/O CPU overhead  GP Changes Reduce CPU consumption of rules Engine Data Migration

4 4 Initial Impressions  Payroll is calculated by a Cobol program GPPDPRUN Single non-threaded process Four Stages  Cancel  Identify  (Re-)Calculate  Finalise

5 5 Two stages with different behaviours  Identify Populating temporary work tables Opening cursors Database Intensive ~20 minutes  Calculation Evaluation of rules  (Cobol only) Batch insert of results into database Cobol (CPU) Intensive ~5000 segments / hour / stream (was 1200)

6 6 What is Streaming?  Employees are split into groups defined by ranges of employee ID  Each group/range can be processed by a different instance/stream of GPPDPRUN  The streams can then be run in parallel.  Vanilla PeopleSoft functionality.

7 7 Why is Streaming Necessary?  GPPDPRUN is a standard Cobol program.  It is a single threaded process  One Cobol process can only run on one CPU at any one time  33000 employees at 5000 employees/hour/stream 6.6hrs if run in one stream 27.5 hours at 1200/hr  On a multi-processor server streaming enables consumption of extra CPU.

8 8 Calculation of Stream Definitions  Objective is roughly equal processing time for all stream PS_GP_PYE_PRC_STAT indicates work to be done by payroll. Calculate ranges of roughly equal numbers of rows for this table Script using Oracle’s Analytic functions that directly populates PS_GP_STRM  This does NOT lead to equally sized GP_RSLT* tables.

9 9 Partition Boundary Creep  As new employees hired EMPLIDs allocated into the same stream. That stream starts to run longer. Effective execution time is maximum execution time for all streams. Need to periodically recalculate stream ranges Need to reflect this is physical changes.

10 10 Database Contention  Rollback Contention  Snapshot Too Old  Insert Contention  I/O Volume Datafile I/O Redo/Archive Log Activity

11 11 Rollback Contention  Working Storage Tables Shared by all streams Rows inserted/deleted during run Different Streams never create locks that block each other, Do update different rows in same block during processing 1 interested transaction per stream in many blocks. There is a additional rollback overhead of 16 bytes per row if two rows in same block -v- different blocks  updates of ~<100 bytes / row

12 12 Read Consistency  Oracle guarantees that data is consistent throughout life of a query If a block has been updated by another transaction since a long running query started, it must be possible to reconstruct the state of that block at the time the query started using the rollback segment. If that information cannot be found in the rollback segment the long running query fails with ORA-01555.

13 13 ORA-01555 Snapshot Too Old  Rollback segments are not extended for read consistency.  Additional rollback overhead can cause rollback segments to spin and wrap.  Error message also described a ‘rollback segments too small.’  In this case, to simply extend the segments is the wrong response.  CPU overhead to navigate rollback segment header chain

14 14 Insert Contention  During the calculation phase results are written to the result tables.  A number of stream can simultaneously insert into the same result tables.  Increases chance that one block will contain rows relating to more than one stream.  This in turn causes rollback problems during the cancel in the next calculation.

15 15 Another cause of ORA-1555 If not processing calendar for the first time, previous results cancelled  Result table are deleted  Monolithic deletes from each table. If Streams start together tend to delete same table at same time in each stream. A long running delete is also a query for the purposes of read consistency.  It is necessary to reconstruct a block as at the time the long running delete started in order to delete a row from it.  Reconstruction occurs during ‘consistent read’.  Deletes by primary key columns, thus Oracle tends to look each row up row by index. Thus index reads also ‘consistent’.

16 16 Datafile and Log Physical I/O Activity  During the identify phase data is shuffled from table to table This generates datafile and redo log I/O  Rollback activity is also written to disk, undo information is also written to the redo log.  All the data placed in the temporary working tables by a stream is of no use to any other instance of the calculation process.  It will be deleted by a future process.

17 17 High Water Marks  The working storage tables tend to be used to drive processing.  Thus, the SQL tends to use full table scans.  In Oracle, High Water Mark is the highest block that has ever contained data.  Full Scans scan the table up to the high water mark.  Temporary tables contain data for ALL streams.  All streams can have to scan data for all streams.

18 18 How to avoid inter-stream contention?  Keep rows from different streams in different blocks  Each block should contain rows for one and only one stream.  Two Oracle Features

19 19 What is Partitioning?  Logically, a partitioned table is a still a single table  Physically, each partition is a separate table. in a range partitioned table, the partition in which a row is placed is determined by the value of one or more columns.  Local Index is partitioned on the same logical basis as the table.

20 20 How should Partitioning used in GP?  Largest Result tables range each partitioned on EMPLID to match GP streaming 1 stream : 1 partition Thus each stream references one partition in each result table. Only 1 interested transaction per block Indexes ‘locally’ partitioned  Partitioning really designed for DSS systems. Only efficient for large tables. GP_RSLT_ACUM, GP_RSLT_ERN_DED, GP_RSLT_PIN, GP_RSLT_PI_DATA GP_PYE_PRC_STAT, GP_PYE_SEG_STAT

21 21 Global Temporary Tables  Definition is permanently defined in database catalogue.  Physically created on demand by database in temporary tablespace for duration of session/transaction. Then dropped.  Each session has its own copy of each referenced GT table.  Each physical instance of each GT table only contains data for one stream.  Working Storage Tables PS_GP_%_WRK converted to GT tables.

22 22 Global Temporary Tables  Advantages Not recoverable, therefore no Redo/Archive Logging  some undo information  improved performance  reduce rollback No High Water Mark problems Smaller object to scan. No permanent tablespace overhead.  Disadvantages Does consume temporary tablespace but only during payroll No CBO Statistics Can hamper debugging New in Oracle 8.1, some bugs.

23 23 How many streams should be run?  Cobol run on database server Either Cobol is active or database is active No more than one stream per CPU Perhaps CPUs -1  be careful not to starve database of CPU  run process scheduler at lower OS priority  Cobol and database on different servers Cobol active for 2/3 of execution time. Up to 1.5 streams per CPU on Cobol server Up to 3 streams per CPU on database server

24 24 UBS Production Payroll Configuration  2 nodes Database Node Application Server/Process Scheduler Node  20 CPUs each  30 Streams 2/3 of 30 is 20, so all 20 application server node CPUS active during calculate phase  ‘nice’ the Cobol processes 1/3 of 30 is 10, so 10 of 16 CPUs active  important to leave some free CPU for database else spins escalated to sleeps generating latch contention

25 25 QA Payroll Configuration  2 nodes Database Node Application Server/Process Scheduler Node  10 CPUs each  15 Streams Full production volume payroll < 1 hour

26 26 Goals  How to create and test efficient rules that work without adversely effecting performance  How best to identify problems particularly in the area of system setup/data versus a problem in a rule or underlying program  How to use GP payroll debugging tools

27 27 Efficient Rules  Responsible for two thirds of the execution time, and so could produce the greatest saving, it will also require the greatest effort.  Detailed functional and technical analysis of the definition of the payroll rules.

28 28 Efficient Rules  The process ideally starts during the design stage when various implementation schemes are analysed, intermediate tests are performed and the most efficient scheme is chosen.  All aspects of Global Payroll must be considered since creating rules to simplify calculation can adversely affect reporting or other online and batch areas and vice versa.

29 29 Efficient Rules  Arrays  Re-calculate?  Store / Don’t store  Formulas  Proration and Count  Historical rules  Generation control versus conditional section

30 30 Efficient Rules  Keyed by Employee - 1 select, multiple fetches, small result set to search  User Defined - 1 select, multiple fetches, all searches in memory.  User Defined with the Reload Option - multiple selects, multiple fetches, small result set to search.

31 31 Efficient Rules

32 32 Efficient Rules

33 33 Efficient Rules

34 34 Efficient Rules

35 35 Efficient Rules

36 36 Efficient Rules

37 37 Efficient Rules

38 38 Efficient Rules

39 39 Efficient Rules

40 40 Migration/Customization  PI v. Array PI can be used during identification. PI has special considerations during eligibility checking. PI allows easy override of components on element definition such as Unit, Rate, Percent or Base. The Array cannot handle multiple instances of earning/deduction. t126297:

41 41 Debugging Tools  Audit Trace Trace All Trace Errors Large number of records, potential rollback segment size problems View on-line Query with SQL t126297:

42 42 Debugging Tools t126297:

43 43 Debugging Tools t126297:

44 44 Debugging Tools select * from sysadm.ps_gp_audit_tbl where emplid = '884324' and cal_run_id = 'ErrMigr' and pin_num = 40811 order by audit_sort_key, audit_seq_num; select * from sysadm.ps_gp_audit_tbl where emplid = '884324' and cal_run_id = 'ErrMigr' and audit_sort_key = 229 order by audit_seq_num; t126297:

45 45 Debugging Tools select emplid, audit_sort_key as key,audit_seq_num as seq, pin_chain_rslt_num as rslt_num,b.pin_nm, a.pin_num,pin_status_ind as status, c.pin_nm,a.pin_parent_num as parent, a.fld_fmt as fmt,calc_rslt_val as num, date_pin_val as dateval,chr_pin_val as chr, pin_val_num as pin from ps_gp_audit_tbl a, ps_gp_pin b, ps_gp_pin c where cal_run_id = 'U_22_CI0101' and (emplid, audit_sort_key) in (select emplid, audit_sort_key from ps_gp_audit_tbl where cal_run_id = 'U_22_CI0101' and pin_num = (select pin_num from ps_gp_pin where pin_nm = 'CH_EP_CHK_1002FF')) and a.pin_num = b.pin_num and a.pin_parent_num = c.pin_num order by emplid, audit_sort_key, audit_seq_num t126297:

46 46 Debugging Tools t126297:

47 47 Debugging Tools AUDIT_SORT_KEY AUDIT_SEQ_NUM INSTANCE_NUM SLICE_BGN_DT SLICE_END_DT PIN_CHAIN_LVL_NUM PIN_CHAIN_RSLT_NUM PIN_NUM PIN_STATUS_IND PIN_PARENT_NUM FLD_FMT OLD_VALUE_IND CALC_RSLT_VAL CALC_ADJ_VAL CALC_RAW_VAL DATE_PIN_VAL CHR_PIN_VAL PIN_VAL_NUM DIFF_SECONDS BAD_TRACE_IND SUM_INSTANCE_IND

48 48 Conclusion  Use of Partitioning and Global Temporary Tables reduce (almost eliminate) inter- stream contention.  This permits use of streaming to utilise all available CPUs.  GP will always be a CPU bound process Rule Tuning will reduce CPU overhead It is an on-going process

49 49 And there’s more  This has been a very concentrated session  Round Table Discussion session 626 15.30-16.30 Discuss some areas in more detail.  However, we do have time for some questions now...

50 Configuring PeopleSoft Global Payroll for Optimal Performance Session 507 david.kurtz@go-faster.co.uk www.go-faster.co.uk gene.pirogovsky@omniasolutions.com www.omniasolutions.com


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