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Sunday, 23 May 2010

Oracle Database: Say No Thanks! to a New Index

Posted on 11:02 by Unknown
(Original post is at blogs.sun.com at:
http://blogs.sun.com/mandalika/entry/oracle_database_say_i_no
)

.. unless you are working with a database that is largely read-only or if the new index is supposed to be fixing a critical performance issue with no side effect(s).

Two topics covered in this blog entry with plenty of simple examples:

  1. Why creating new indexes on a heavily modified table may not be a good idea? and
  2. How to identify unused indexes?
Read on.

Indexes are double-edged swords that may improve the performance of targeted queries, but in some cases they may accidentally degrade the performance of other queries that are not targeted. In any case, exercise caution while adding a new index to the database. After adding a new index, monitor the overall performance of the database, not just the targeted query.

If DML statements that modify data (INSERT, UPDATE, or DELETE) are being executed large number of times on a table, make sure that the addition of a new index on the same table does not negatively affect the performance of those DML operations. Usually this is not a problem if the SQLs being executed are simply retrieving but not adding or modifying the existing data. In all other cases, there is some performance overhead induced by the addition of each new index. For example, if there are 10 indexes created on a table DUMMY, adding a new row of data to the table DUMMY may require updating all 10 indexes behind the scenes by the database management system.

Here is an example demonstrating the performance overhead of a new index on a table.

SQL> CREATE TABLE VIDEO
2 (BARCODE VARCHAR(10) NOT NULL,
3 TITLE VARCHAR2(25) NOT NULL,
4 FORMAT VARCHAR2(10),
5 PRICE NUMBER,
6 DATA_OF_RELEASE DATE)
7 /

Table created.

SQL> insert into VIDEO values ('9301224321', 'AVATAR', 'BLU-RAY', 19.99, '22-APR-2010');

1 row created.

..

SQL> insert into VIDEO values ('3782460017', 'THE SIMPSONS - SEASON 20', 'BLU-RAY', 29.99, '04-JUL-2009');

1 row created.

SQL> select * from VIDEO;

BARCODE TITLE FORMAT PRICE DATA_OF_RELEASE
--------------- ----------------------------------- --------------- ---------- ---------------
9301224321 AVATAR BLU-RAY 19.99 22-APR-10
7619203043 BEN-HUR VHS 9.79 12-MAR-63
7305832093 THE MATRIX DVD 12.29 03-DEC-99
4810218795 MEMENTO DVD 8.49 02-FEB-02
3782460017 THE SIMPSONS - SEASON 20 BLU-RAY 29.99 04-JUL-09

SQL> select * from USER_INDEXES where TABLE_NAME = 'VIDEO';

no rows selected

SQL> alter session set events '10046 trace name context forever, level 8';

Session altered.

SQL> select * from VIDEO where FORMAT = 'BLU-RAY';

BARCODE TITLE FORMAT PRICE DATA_OF_RELEASE
--------------- ----------------------------------- --------------- ---------- ---------------
9301224321 AVATAR BLU-RAY 19.99 22-APR-10
3782460017 THE SIMPSONS - SEASON 20 BLU-RAY 29.99 04-JUL-09

SQL> alter session set events '10046 trace name context off';

Session altered.

SQL trace file has the following contents.

SQL ID: 0pu5s70nsdnzv
Plan Hash: 3846322456
SELECT *
FROM
VIDEO WHERE FORMAT = :"SYS_B_0"


call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 0.00 0.00 0 16 0 2
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 0.00 0.00 0 16 0 2

Misses in library cache during parse: 0
Optimizer mode: ALL_ROWS
Parsing user id: 28

Rows Row Source Operation
------- ---------------------------------------------------
2 TABLE ACCESS FULL VIDEO (cr=16 pr=0 pw=0 time=3 us cost=4 size=100 card=2)

Let's create an index and see what happens.

SQL> create index VIDEO_IDX1 on VIDEO (FORMAT);

Index created.

SQL> alter session set events '10046 trace name context forever, level 8';

Session altered.

SQL> select * from VIDEO where FORMAT = 'BLU-RAY';

BARCODE TITLE FORMAT PRICE DATA_OF_RELEASE
--------------- ----------------------------------- --------------- ---------- ---------------
9301224321 AVATAR BLU-RAY 19.99 22-APR-10
3782460017 THE SIMPSONS - SEASON 20 BLU-RAY 29.99 04-JUL-09

SQL> alter session set events '10046 trace name context off';

Session altered.

The latest contents of the trace file are as follows. Notice the reduction in buffer gets from 16 to 4. That is, the new index improved the query performance by 75%.

SQL ID: 0pu5s70nsdnzv
Plan Hash: 2773508764
SELECT *
FROM
VIDEO WHERE FORMAT = :"SYS_B_0"


call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 0 0 0
Fetch 2 0.00 0.00 0 4 0 2
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 4 0.00 0.00 0 4 0 2

Misses in library cache during parse: 0
Optimizer mode: ALL_ROWS
Parsing user id: 28 (CS90)

Rows Row Source Operation
------- ---------------------------------------------------
2 TABLE ACCESS BY INDEX ROWID VIDEO (cr=4 pr=0 pw=0 time=12 us cost=2 size=100 card=2)
2 INDEX RANGE SCAN VIDEO_IDX1 (cr=2 pr=0 pw=0 time=10 us cost=1 size=0 card=2)(object id 76899)


Rows Execution Plan
------- ---------------------------------------------------
0 SELECT STATEMENT MODE: ALL_ROWS
2 TABLE ACCESS (BY INDEX ROWID) OF 'VIDEO' (TABLE)
2 INDEX MODE: ANALYZED (RANGE SCAN) OF 'VIDEO_IDX1' (INDEX)

So far so good. Let's add a new row of data and examine the trace file one more time. From hereafter, keep an eye on the "current" column (logical IOs performed due to an INSERT, UPDATE or DELETE) and notice how it changes with different actions -- adding and removing: indexes, new row(s) of data etc.,

SQL ID: dnb2d8cpdj56p
Plan Hash: 0
INSERT INTO VIDEO
VALUES
(:"SYS_B_0", :"SYS_B_1", :"SYS_B_2", :"SYS_B_3", :"SYS_B_4")


call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 1 7 1
Fetch 0 0.00 0.00 0 0 0 0
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 2 0.00 0.00 0 1 7 1

Misses in library cache during parse: 0
Optimizer mode: ALL_ROWS
Parsing user id: 28 (CS90)

Rows Row Source Operation
------- ---------------------------------------------------
0 LOAD TABLE CONVENTIONAL (cr=1 pr=0 pw=0 time=0 us)


Rows Execution Plan
------- ---------------------------------------------------
0 INSERT STATEMENT MODE: ALL_ROWS
0 LOAD TABLE CONVENTIONAL OF 'VIDEO'

Now drop the index, re-insert the last row and get the tracing data again.

SQL> drop index VIDEO_IDX1;

Index dropped.

SQL> delete from VIDEO where BARCODE ='4457332907';

1 row deleted.

SQL> commit;

Commit complete.

SQL> alter session set events '10046 trace name context forever, level 8';

Session altered.

SQL> insert into VIDEO values ('4457332907', 'KING OF THE HILL - ALL', 'DVD', 90.00, '01-JAN-2011');

1 row created.

SQL> alter session set events '10046 trace name context off';

Session altered.

The contents of the latest trace file are shown below.

call     count       cpu    elapsed       disk      query    current        rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.01 0.00 0 2 5 1
Fetch 0 0.00 0.00 0 0 0 0
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 2 0.01 0.00 0 2 5 1

Misses in library cache during parse: 1
Misses in library cache during execute: 1
Optimizer mode: ALL_ROWS
Parsing user id: 28 (CS90)

Rows Row Source Operation
------- ---------------------------------------------------
0 LOAD TABLE CONVENTIONAL (cr=1 pr=0 pw=0 time=0 us)


Rows Execution Plan
------- ---------------------------------------------------
0 INSERT STATEMENT MODE: ALL_ROWS
0 LOAD TABLE CONVENTIONAL OF 'VIDEO'

This time create two indexes and see what happens.

SQL> CREATE INDEX VIDEO_IDX1 ON VIDEO (FORMAT);

Index created.

SQL> CREATE INDEX VIDEO_IDX2 ON VIDEO (TITLE);

Index created.

Trace file contents:

call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 1 9 1
Fetch 0 0.00 0.00 0 0 0 0
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 2 0.00 0.00 0 1 9 1

Notice the two additional logical IOs (look under "current" column). Those additional logical input/output operations are the result of the new indexes. The number goes up as we add more indexes and data to the table VIDEO.

SQL> delete from VIDEO where BARCODE ='4457332907';

1 row deleted.

SQL> commit;

Commit complete.

SQL> create index VIDEO_IDX3 on VIDEO (PRICE, DATA_OF_RELEASE);

Index created.

SQL> alter session set events '10046 trace name context forever, level 8';

Session altered.

SQL> insert into VIDEO values ('4457332907', 'KING OF THE HILL - ALL', 'DVD', 90.00, '01-JAN-2011');

1 row created.

SQL> alter session set events '10046 trace name context off';

Session altered.


SQL trace:

call count cpu elapsed disk query current rows
------- ------ -------- ---------- ---------- ---------- ---------- ----------
Parse 1 0.00 0.00 0 0 0 0
Execute 1 0.00 0.00 0 1 11 1
Fetch 0 0.00 0.00 0 0 0 0
------- ------ -------- ---------- ---------- ---------- ---------- ----------
total 2 0.00 0.00 0 1 11 1

You can try other operations such as UPDATE, DELETE on your own.

Since there are only few rows of data in the table VIDEO, it is hard to notice the real performance impact in these examples. If you really want to see the negative performance impact due to the large number of indexes on a heavily updated table, try adding thousands or millions of rows of data and few more indexes.

Moral of the story: Indexes aren't always cheap - they may have some overhead associated with them. Be aware of those overheads and ensure that the index maintenance overhead do not offset the performance gains resulting from the indexes created on a particular table.


Monitoring Index Usage

Now we know the possible disadvantage of having too many indexes on a heavily updated table. One way to reduce the index maintenance overhead is to instrument the indexes so we can monitor their usage from time to time and remove the unused indexes. To start monitoring the index usage, alter the index by specifying the keywords MONITORING USAGE.

SQL> select index_name from user_indexes where table_name = 'VIDEO';

INDEX_NAME
--------------------------------------------------------------------------------
VIDEO_IDX3
VIDEO_IDX1
VIDEO_IDX2

SQL> alter index VIDEO_IDX1 MONITORING USAGE;

Index altered.

SQL> alter index VIDEO_IDX2 MONITORING USAGE;

Index altered.

SQL> alter index VIDEO_IDX3 MONITORING USAGE;

Index altered.

Once the indexes are instrumented, query the V$OBJECT_USAGE view occasionally to see if the instrumented indexes are being used in executing SQL queries.

SQL> select * from VIDEO where BARCODE LIKE '%22%';

BARCODE TITLE FORMAT PRICE DATA_OF_RELEASE
--------------- ----------------------------------- --------------- ---------- ---------------
9301224321 AVATAR BLU-RAY 19.99 22-APR-10

SQL> select * from VIDEO where FORMAT = 'VHS';

BARCODE TITLE FORMAT PRICE DATA_OF_RELEASE
--------------- ----------------------------------- --------------- ---------- ---------------
7619203043 BEN-HUR VHS 9.79 12-MAR-63

SQL> select * from VIDEO where PRICE < 20;

BARCODE TITLE FORMAT PRICE DATA_OF_RELEASE
--------------- ----------------------------------- --------------- ---------- ---------------
4810218795 MEMENTO DVD 8.49 02-FEB-02
7619203043 BEN-HUR VHS 9.79 12-MAR-63
7305832093 THE MATRIX DVD 12.29 03-DEC-99
9301224321 AVATAR BLU-RAY 19.99 22-APR-10

SQL> select * from VIDEO where FORMAT = 'BLU-RAY' AND DATA_OF_RELEASE < '01-JAN-2010';

BARCODE TITLE FORMAT PRICE DATA_OF_RELEASE
--------------- ----------------------------------- --------------- ---------- ---------------
3782460017 THE SIMPSONS - SEASON 20 BLU-RAY 29.99 04-JUL-09


SQL> column INDEX_NAME format A25
SQL> column START_MONITORING format A30

SQL> select INDEX_NAME, USED, START_MONITORING
2 from V$OBJECT_USAGE
3 where INDEX_NAME LIKE 'VIDEO_IDX%'
4 /

INDEX_NAME USED START_MONITORING
------------------------- --------- ------------------------------
VIDEO_IDX1 YES 04/27/2010 01:10:20
VIDEO_IDX2 NO 04/27/2010 01:10:25
VIDEO_IDX3 YES 04/27/2010 01:10:31

In the above example, the index VIDEO_IDX2 was not in use during the period of index monitoring. If we are convinced that the queries that will be executed are similar to the ones that were executed during the index monitoring period, we can go ahead and remove the index VIDEO_IDX2 to reduce the performance overhead during updates on table VIDEO.

To stop monitoring the index usage, alter the index with the keywords NOMONITORING USAGE.

SQL> alter index VIDEO_IDX1 NOMONITORING USAGE;

Index altered.

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Monday, 10 May 2010

Music : Few Mixed Tunes with GarageBand

Posted on 02:07 by Unknown
(Originally posted on 12/09/2009. This blog posted will be updated as new compositions come along.)

For the past couple of weeks I have had fun playing with Apple's GarageBand. It is a nice piece of software with intuitive user interface and tons of free & ready-to-use music loops. It only took a couple of tries and about 6 hours to produce my first ever mixed tune with software of any kind. The second one just took two hours as my main focus was on only two loops. I'm happy with the output and decided to share it with my family and friends. Hence I uploaded those two tracks to iCompositions, an internet community web site that facilitates sharing each individuals' creative work with the rest of the community at free of cost. Click on the following music player images to listen to those instrumental tracks.
[12/09/09] Track #1 Hokum 
 
[12/09/09] Track #2 Thrum 
 
[12/18/09] Track #3 Kabuki Dance 
 
[01/05/10] Track #4 Phantasm 
 
[05/10/10] Track #5 Transgression 


Now if only I knew how to play a real instrument ..
________________
Technorati Tags:
 Music |  GarageBand
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Friday, 7 May 2010

Oracle Database 11g – Underground Advice for Database Administrators

Posted on 22:24 by Unknown


.. review coming soon ..

Meanwhile feel free to explore the Table of Contents and check the freely download-able chapter Chapter 2: Maintaining Oracle Standards
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Monday, 3 May 2010

Oracle 11g R1: Poor Data Pump Performance when Exporting a Partitioned Table

Posted on 00:12 by Unknown
(Originally posted on blogs.sun.com at http://blogs.sun.com/mandalika/entry/oracle_11g_r1_poor_data)

Symptom(s)

Data Pump Export utility, expdp, performs well with non-partitioned tables, but exhibits extreme poor performance when exporting objects from a partitioned table of similar size. In some cases the degradation can be as high as 3X or worse.

SQL traces may show that much of the time is being spent in a SQL statement that is similar to:

UPDATE "schema"."TABLE" mtu 
SET mtu.base_process_order = NVL((SELECT mts1.process_order FROM "schema"."TABLE" mts1
WHERE ..

Here is an example data export session:

Export: Release 11.1.0.7.0 - 64bit Production on Wednesday, 31 March, 2010 6:56:50

Copyright (c) 2003, 2007, Oracle. All rights reserved.
;;;
Connected to: Oracle Database 11g Enterprise Edition Release 11.1.0.7.0 - 64bit Production
With the Partitioning, OLAP, Data Mining and Real Application Testing options
Starting "SCHMA"."SYS_EXPORT_TABLE_01": SCHMA/******** DIRECTORY=exp_dir DUMPFILE=SOME_DUMMY_PART_FULL.DMP TABLES=SOME_DUMMY_PART
Estimate in progress using BLOCKS method...
Processing object type TABLE_EXPORT/TABLE/TABLE_DATA
Total estimation using BLOCKS method: 20.56 GB
Processing object type TABLE_EXPORT/TABLE/TABLE
Processing object type TABLE_EXPORT/TABLE/INDEX/INDEX
Processing object type TABLE_EXPORT/TABLE/INDEX/STATISTICS/INDEX_STATISTICS
Processing object type TABLE_EXPORT/TABLE/STATISTICS/TABLE_STATISTICS
. . exported "SCHMA"."SOME_DUMMY_PART":"DUMMY_PART_P01" 1.143 GB 13788224 rows
. . exported "SCHMA"."SOME_DUMMY_PART":"DUMMY_PART_P02" 1.143 GB 13788224 rows
. . exported "SCHMA"."SOME_DUMMY_PART":"DUMMY_PART_P03" 1.143 GB 13788224 rows
...
. . exported "SCHMA"."SOME_DUMMY_PART":"DUMMY_PART_P32" 151.1 MB 1789216 rows
. . exported "SCHMA"."SOME_DUMMY_PART":"DUMMY_PART_P33" 11.37 MB 136046 rows
. . exported "SCHMA"."SOME_DUMMY_PART":"DUMMY_PART_P00" 0 KB 0 rows
Master table "SCHMA"."SYS_EXPORT_TABLE_01" successfully loaded/unloaded
******************************************************************************
Dump file set for SCHMA.SYS_EXPORT_TABLE_01 is:
/DBDUMP/SOME_DUMMY_PART_FULL.DMP
Job "SCHMA"."SYS_EXPORT_TABLE_01" successfully completed at 11:22:36

Solution(s) / Workaround

This is a known issue (that is, a bug) and a solution is readily available. Try any of the following to resolve the issue:

  • Apply the 11g database patch 8845859
  • Upgrade to 11.2.0.2 patchset when it is available, or
  • Specify "VERSION=10.2.0.3" expdp option as a workaround

I ran into this issue and I chose the workaround to make some quick progress. With the string "VERSION=10.2.0.3" appended, export time went down from 265 minutes to 60+ minutes.
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Wednesday, 21 April 2010

2004-2010 : A Look Back at Sun Published Oracle Benchmarks

Posted on 01:25 by Unknown
(Originally published on blogs.sun.com at:
http://blogs.sun.com/mandalika/entry/2004_2010_a_look_back
)

Since Sun Microsystems became a legacy, I got this idea of a reminiscent [farewell] blog post for the company that gave me the much needed break when I was a graduate student back in 2002. As I spend more than 50% of my time benchmarking different Oracle products on Sun hardware, it'd be fitting to fill this blog entry with a recollection of the benchmarks I was actively involved in over the past 6+ years. Without further ado, the list follows.

2004

 1.  10,000 user Siebel 7.5.2 PSPP benchmark on a combination of SunFire v440, v890 and E2900 servers. Database: Oracle 9i
  • Benchmark Report
  • Blog: Sun achieves winning Siebel benchmark
  • Blog: Sun and Siebel Kick Some Benchmark Butt
  • Blog: When Good Benchmarks Go Bad

2005

 2.  8,000 user Siebel 7.7 PSPP benchmark on a combination of SunFire v490, v890, T2000 and E2900 servers. Database: Oracle 9i

  • Benchmark Report

 3.  12,500 user Siebel 7.7 PSPP benchmark on a combination of SunFire v490, v890, T2000 and E2900 servers. Database: Oracle 9i

  • Benchmark Report

2006

 4.  10,000 user Siebel Analytics 7.8.4 benchmark on multiple SunFire T2000 servers. Database: Oracle 10g

  • Benchmark Report

2007

 5.  10,000 user Siebel 8.0 PSPP benchmark on two T5220 servers. Database: Oracle 10g R2

  • Benchmark Report
  • Blog: Sun publishes 10,000 user Siebel 8.0 PSPP benchmark on Niagara 2 systems

2008

 6.  Oracle E-Business Suite 11i Payroll benchmark for 5,000 employees. Database: Oracle 10g R1

  • White Paper (didn't qualify as a benchmark since we configured more than 4 payroll threads)
  • Blog: Running Batch Workloads on Sun's CMT Servers

 7.  14,000 user Siebel 8.0 PSPP benchmark on a single T5440 server. Database: Oracle 10g R2

  • Benchmark Report
  • Blog: Siebel 8.0 on Sun SPARC Enterprise T5440 - More Bang for the Buck!!
  • Blog: Siebel on Sun CMT hardware : Best Practices
  • Blueprint: Consolidating Oracle Siebel CRM 8 on a Single Sun SPARC Enterprise Server

 8.  10,000 user Siebel 8.0 PSPP benchmark on a single T5240 server. Database: Oracle 10g R2

  • Benchmark Report
  • Blog: Yet Another Siebel 8.0 PSPP Benchmark on Sun CMT Hardware ..

2009

 9.  4,000 user PeopleSoft HR Self-Service 8.9 benchmark on a combination of M3000 and T5120 servers. Database: Oracle 10g R2

  • Benchmark Report
  • Blog: PeopleSoft HRMS 8.9 Self-Service Benchmark on M3000 & T5120 Servers

 10.  28,000 user Oracle Business Intelligence Enterprise Edition (OBIEE) 10.1.3.4 benchmark on a single T5440 server. Database: Oracle 11g R1

  • Benchmark Report
  • Blog: T5440 Rocks [again] with Oracle Business Intelligence Enterprise Edition Workload
  • Blog: Oracle Business Intelligence on Sun : Few Best Practices

 11.  50,000 user Oracle Business Intelligence Enterprise Edition (OBIEE) 10.1.3.4 benchmark on two T5440 servers. Database: Oracle 11g R1

  • Benchmark Report
  • Blog: Sun achieves the Magic Number 50,000 on T5440 with Oracle Business Intelligence EE 10.1.3.4
  • Blueprint: Deploying Oracle Business Intelligence Enterprise Edition

 12.  PeopleSoft North American Payroll 9.0 240K EE 8-stream benchmark on a single M4000 server with F5100 Flash Array storage. Database: Oracle 11g R1

  • Benchmark Report
  • Blog: PeopleSoft North American Payroll on Sun Solaris with F5100 Flash Array : A blog Reprise
  • Blog: Oracle PeopleSoft Payroll (NA) Sun SPARC Enterprise M4000 and Sun Storage F5100 World Record Performance
  • Blog: App benchmarks, incorrect conclusions and the Sun Storage F5100
  • Blueprint: Best Practices for Oracle PeopleSoft Enterprise Payroll for North America using the Sun Storage F5100 Flash Array or Sun Flash Accelerator F20 PCIe Card

2010

 13.  PeopleSoft North American Payroll 9.0 240K EE 16-stream benchmark on a single M4000 server with F5100 Flash Array storage. Database: Oracle 11g R1

  • Benchmark Report
  • Blog: PeopleSoft NA Payroll 240K EE Benchmark with 16 Job Streams : Another Home Run for Sun

 14.  6,000 user PeopleSoft Campus Solutions 9.0 benchmark on a combination of X6270 blades and M4000 server. Database: Oracle 11g R1

  • Benchmark Report
  • Blog: PeopleSoft Campus Solutions 9.0 benchmark on Sun SPARC Enterprise M4000 and X6270 blades

Although challenging and exhilarating, benchmarks aren't always pleasant to work on, and really not for people with weak hearts. While running most of these benchmarks, my blood pressure shot up several times leaving me wonder why do I keep working on time sensitive and/or politically, strategically incorrect benchmarks (apparently not every benchmark finds a home somewhere on the public network). Nevertheless in the best interest of my employer, the showdown must go on.
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Monday, 1 March 2010

PeopleSoft Campus Solutions 9.0 benchmark on Sun SPARC Enterprise M4000 and X6270 blades

Posted on 00:54 by Unknown

Oracle|Sun published PeopleSoft Campus Solutions 9.0 benchmark results on February 18, 2010. Here is the direct URL to the benchmark results white paper:

      PeopleSoft Enterprise Campus Solutions 9.0 using Oracle 11g on a Sun SPARC Enterprise M4000 & Sun Blade X6270 Modules

Sun published three PeopleSoft benchmarks on SPARC platform over the last 12 month period -- one OLTP and two batch benchmarks[1]. The latest benchmark is somewhat special for at least couple of reasons:
  • Campus Solutions 9.0 workload has both online transactions and batch processes, and
  • This is the very first time ever Sun published a PeopleSoft benchmark on x64 hardware running Oracle Enterprise Linux

The summary of the benchmark test results is shown below. These numbers were extracted from the very first page of the benchmark results white papers where Oracle|PeopleSoft highlights the significance of the test results and the actual numbers that are of interest to the customers. Test results are sorted by the hourly throughput (invoices & transcripts per hour) in the descending order. Click on the link that is underneath the vendor name to open corresponding benchmark result.

While analyzing these test results, remember that the higher the throughput, the better. In the case of online transactions, it is desirable to keep the response times as low as possible.


Oracle PeopleSoft Campus Solutions 9.0 Benchmark Test Results

VendorHardware ConfigurationOSResource UtilizationResponse/Elapsed Times at Peak Load (6,000 users)
Online Transactions: Avg Response Times (sec)Batch Throughput/hr
CPU%Mem (GB)LogonLSCPage LoadPage SaveInvoiceTranscripts
SunDB1 x M4000 with 2 x 2.53GHz SPARC64 VII QC processors, 32GB RAM
1 x Sun Storage Flash Accelerator F20 with 4 x 24GB FMODs
1 x ST2540 array with 11 × 136.7GB SAS 15K RPM drives
Solaris 1037.2920.940.640.780.821.5731,79736,652
APP2 x X6270 blades with 2 x 2.93GHz Xeon 5570 QC processors, 24GB RAMOEL4 U841.69*4.99*
WEB+PS1 x X6270 blade with 2 x 2.8GHz Xeon 5560 QC processors, 24GB RAMOEL4 U833.086.03
HPDB1 x Integrity rx6600 with 4 x 1.6GHz Itanium 9050 DC procs, 32G RAM
1 x HP StorageWorks EVA8100 array with 58 x 146GB drives
HP-UX 11iv361300.710.910.831.6322,75330,257
APP2 x BL460c blade with 2 x 3.16GHz Xeon 5460 QC procs, 16GB RAMRHEL4U561.813.6
WEB1 x BL460c blade with 2 x 3GHz Xeon 5160 DC procs, 8GB RAMRHEL4U544.363.77
PS1 x BL460c blade with 2 x 3GHz Xeon 5160 DC procs, 8GB RAMRHEL4U521.901.48
HPDB1 x ProLiant DL580 G4 w/ 4 x 3.4GHz Xeon 7140M DC procs, 32G RAM
1 x HP StorageWorks XP128 array with 28 x 73GB drives
Win2003R270.3721.260.721.170.941.8017,62125,423
APP4 x BL480c G1 blades with 2 x 3GHz Xeon 5160 DC procs, 12GB RAMWin2003R265.612.17
WEB1 x BL460c G1 blades with 2 x 3GHz Xeon 5160 DC procs, 12GB RAMWin2003R254.113.13
PS1 x BL460c G1 blades with 2 x 3GHz Xeon 5160 DC procs, 12GB RAMWin2003R232.441.40


This is all public information. Feel free to compare the hardware configurations & the data presented in the table and draw your own conclusions. Since both Sun and HP used the same benchmark workload, toolkit and ran the benchmark with the same number of concurrent users and job streams for the batch processes, comparison should be pretty straight forward.

Hopefully the following paragraphs will provide relevant insights into the benchmark and the application.

Caution in interpreting the Online Transaction Response Times

Average response times for the online transactions were measured using HP's QuickTest Pro (QTP) tool. This is a benchmark requirement. QTP test scripts have a dependency on the web browser (IE in particular) -- hence it is extremely sensitive to the web browser latencies, remote desktop/VNC latencies and other latencies induced by the operating system. Be aware that all these latencies will be factored into the transaction response times and due to this, the final average transaction response times might be skewed a little. In other words, the reported average transaction response times may not necessarily be very accurate. In most of the cases we might be looking at the approximate values and the actual values might be far better than the ones reported in the benchmark report. (I really wish Oracle|PeopleSoft would throw away some of the skewed samples to make the data more accurate and reliable.). Please keep this in mind when looking at the response times of the online transactions.

Quick note about Consolidation

In our benchmark environment, we had the PeopleSoft Process Scheduler (batch server) set up on the same node as that of the web server node. In general, Oracle recommends setting up the process scheduler either on the database server node or on a dedicated system. However in the benchmark environment, we chose not to run the process scheduler on the database server node as it would hurt the performance of the online transactions. At the same time, we noticed plenty of idle CPU cycles on the web server node even at the peak load of 6,000 concurrent users, so we decided to run the PS on the web server node. In case if customers are not comfortable with this kind of setup, they can use any supported virtualization technology (eg., Logical Domains, Containers on Solaris, Oracle VM on OEL) to separate the process scheduler from the web server by allocating the system resources as they like. It is just a matter of choice.

PeopleSoft Load Balancing

PeopleSoft has load balancing mechanism built into the web server to forward the incoming requests to appropriate application server in the enterprise, and within the application server to send the request to an appropriate application server process, PSAPPSRV. (I'm not 100% sure but I think application server balances the load among application server processes in a round robin fashion on *nix platforms whereas on Windows, it forwards all the requests to a single application server process until it reaches the configured limit before moving on to the next available application server process.). However this in-built load balancing is not perfect. Most of the times, the number of requests processed by each of the identically configured application server processes [running on different application server nodes in the enterprise] may not be even. This minor shortcoming could lead to uneven resource usage across different nodes in the PeopleSoft deployment. You can notice this in the CPU and memory usage reported for the two app server nodes in the benchmark environment (check the benchmark results white paper).

Sun Flash Accelerator F20 PCIe Card

To reduce I/O latency, hot tables and hot indexes were placed on a Sun Flash Accelerator F20 PCIe Card in this benchmark. The F20 card has a total capacity of 96 GB with 4 x 24GB Flash Modules (FMODs). Although this workload is moderately I/O intensive, the batch processes in this benchmark generate a lot of I/O for few minutes in the steady state of the benchmark. The flash accelerator handled the burst of I/O activity pretty well, and as a result the performance of the batch processesing was improved.

Check the white paper Best Practices for Oracle PeopleSoft Enterprise Payroll for North America using the Sun Storage F5100 Flash Array or Sun Flash Accelerator F20 PCIe Card to know more about the top flash products offered by Oracle|Sun and how they can be deployed in a PeopleSoft environment for maximum benefit.

Solaris specific Tuning

Almost on all versions of Solaris 10, the kernel uses 4M as the maximum page size despite the fact that the underlying hardware supports as high as 256M pages. However large pages may improve the performance of some of the memory intensive workloads such as Oracle database by reducing the number of virtual <=> physical translations there by reducing the expensive dTLB/iTLB misses. In the benchmark environment, the following values were set in the /etc/system configuration file of the database server node to enable 256MB pages for the process heap and ISM.

* 256M pages for process heap
set max_uheap_lpsize=0x10000000

* 256M pages for ISM
set mmu_ism_pagesize=0x10000000


While we are on the same topic, Linux configuration is out-of-the-box. No OS tuning was performed in this benchmark.

Tuning Tip for Solaris Customers

Even though we did not set up the middle-tier on a Solaris box in this benchmark, this particular tuning tip is still valid and may help all those customers running the application server on Solaris. Consider lowering the shell limit for the file descriptors to a value of 512 or less if it was set to any value greater than 512. As of today (until the release of PeopleTools 8.50), there are certain parts of code in PeopleSoft calls the file control routine, fcntl(), and the file close routine, fclose(), in a loop "ulimit -n" number of times to close a bunch of files which were opened to perform a specific task. In general, PeopleSoft processes won't open hundreds of files. Hence the above mentioned behavior results in ton of dummy calls that error out. Besides, those system calls are not cheap -- they consume CPU cycles. It gets worse when there are a number of PeopleSoft processes that exhibit this kind of behavior simultaneously. (high system CPU% is one of the symptoms that helps identifying this behavior). Oracle|PeopleSoft is currently trying to address this performance issue. Meanwhile customers can lower the file descriptors shell limit to reduce its intensity and impact.

We have not observed this behavior on OEL when running the benchmark. But be sure to trace the system calls and figure out if the shell limit for the file descriptors need be lowered even on Linux or other supported platforms.

______________________________________

Footnotes:



1. PeopleSoft benchmarks on Sun platform in year 2009-2010

  1. PeopleSoft HRMS 8.9 SELF-SERVICE Using ORACLE on Sun SPARC Enterprise M3000 and Enterprise T5120 Servers -- online transactions (OLTP)
  2. PeopleSoft Enterprise Payroll 9.0 using Oracle for Solaris on a Sun SPARC Enterprise M4000 (8 streams) -- batch workload
  3. PeopleSoft Enterprise Payroll 9.0 using Oracle for Solaris on a Sun SPARC Enterprise M4000 (16 streams) -- batch workload


2. *HP's benchmark results white paper did not show the CPU and memory breakdown numbers separately for each of the application server nodes. It only shows the average of average CPU and memory utilization for all app server nodes under "App Servers". Sun's average CPU, memory numbers [shown in the above table] were calculated in the same way for consistency.

(Copied from the original post at Oracle|Sun blogs @
http://blogs.sun.com/mandalika/entry/peoplesoft_campus_solutions_9_0
)
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Thursday, 11 February 2010

Extracting DDL Statements from a PeopleSoft Data Mover exported DAT file

Posted on 01:07 by Unknown
Case in hand: Given a PeopleSoft Data Mover exported data file (db or dat file), how to extract the DDL statements [from that data file] which gets executed as part of the Data Mover's data import process?

Here is a quick way to do it:

  1. Insert the SET EXTRACT statements in the Data Mover script (DMS) before the IMPORT .. statement.

    eg.,

    % cat /tmp/retrieveddl.dms

    ..
    SET EXTRACT OUTPUT /tmp/ddl_stmts.log;
    SET EXTRACT DDL;

    ..

    IMPORT *;


    It is mandatory that the SET EXTRACT OUPUT statement must appear before any SET EXTRACT statements.

  2. Run the Data Mover utility with the modified DMS script as an argument.

    eg., OS: Solaris


    % psdmtx -CT ORACLE -CD NAP11 -CO NAP11 -CP NAP11 -CI people -CW peop1e -FP /tmp/retrieveddl.dms


    On successful completion, you will find the DDL statements in /tmp/retrieveddl.dms file.

Check chapter #2 "Using PeopleSoft Data Mover" in Enterprise PeopleTools x.xx PeopleBook: Data Management document for more ideas.

______
(Originally posted on blogs.sun.com at:
http://blogs.sun.com/mandalika/entry/extracting_ddl_statements_from_a
)
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