Practical Performance

TuningFor Your

Progress OpenEdge Database

A Few Words About The Speaker

• Tom Bascom, Roaming DBA & Progress User since 1987

• President, DBAppraise, LLC– Remote Database Management Service.– Simplifying the job of Managing and Monitoring the world’s best

business applications.– tom@dbappraise.com

• VP, White Star Software, LLC– Expert Consulting Services related to all aspects of Progress and

OpenEdge. – tom@wss.com

Tom’s Top TwentyTuning Tips

In No Particular Order.

{inc/disclaimer.i}

• Your kilometerage will vary.• These tips are in no special order. But

sometimes order does matter.• In an ideal world you will change one thing at

a time, test and remove changes that do not have the desired effect.

• The real world isn’t very ideal.

#20 Dump & Load

• You shouldn’t need to routinely dump & load.• If you’re on OE10,• Using type 2 areas,• That have been well defined,• And you have a well-behaved application.

• The rest of us benefit from an occasional D&L.

#20 Dump & Load

SRC DST

Control

dlctl.p dlmon.p

dlx.p

dlx.p

dlx.p

dlx.p

status

config

initialize

status

dump load

Spawn

DB-20 Highly Parallel Dump & Loadhttp://www.psdn.com/ak_download/media/exch_audio/2007/DB/DB-20_Bascom.pdf

#19 Stay Current

• Up to date releases of Progress, your OS and your application are essential components of a well tuned system.

• You cannot take advantage of the best techniques, improved algorithms or new hardware without staying up to date.

• Failure to stay up to date may mean poor performance, increased costs and uncompetitiveness.

#19 Stay Current

• TRX throughput in v8 -> v9 (CCLP)• Major 4GL execution speed improvements in

9.1E.• 64 bit platform support and large memory.• 10.1A Automatic Defragmentation.• 10.1B Workgroup/Multicore bug addressed.• Significant improvements in DB read

performance in 10.1C (-spin enhancements).

#19 The Impact of an Upgrade

conv89

#18 Parallelize

• Step outside of the box and consider what portions of your system could benefit from being parallelized:

• MRP Runs• Nightly Processing• Reports• Data Extracts• Data Imports

#18 Parallelize$ mbpro dbname –p exp.p –param “01|0,3000”$ mbpro dbname –p exp.p –param “02|3000,6000”$ mbpro dbname –p exp.p –param “03|6000,9999”

/* exp.p */

define variable startCust as integer no-undo.define variable endCust as integer no-undo.

startCust = integer( entry( 1, entry( 2, session:parameter, “|” ))).endCust = integer( entry( 2, entry( 2, session:parameter, “|” ))).

output to value( “export.” + entry( 1, session:parameter, “|” ).for each customer no-lock where custNum >= startCust and custNum < endCust: export customer.end.output close.quit.

#17 Update Statistics

• SQL-92 uses a cost based optimizer…• But it cannot optimize without knowledge of the

cost! (data distribution).• Weekly or monthly “update statistics” is appropriate

for most people.• Or when 20% of your data has changed.• This is a data intense process:

– Run it during off hours if you can.– You might want to only do a few tables/indexes at a

time.

#17 Update Statistics

$ cat customer.sqlUPDATE TABLE STATISTICS AND INDEX STATISTICS AND ALL COLUMN STATISTICS FOR PUB.Customer;

COMMIT WORK;$DLC/bin/sqlexp \ -db dbname \ -S portnum \ -infile customer.sql \ -outfile customer.out \ -user username \ -password passwd >> customer.err 2>&1

For More Information: OpenEdge Data Management: SQL Development Chapter 10, Optimizing Query Performance

#16 Progress AppServer

• Used to reduce network traffic and latency.• When properly implemented it will minimize

the path length between business logic and data persistence layers.

• IOW, for best performance, the AppServer should live on the same server as the database and use a self-service connection.

• Exception: An AppServer which is dedicated to making Windows API calls.

procedure asproc … for each … end.end.

#16 Progress AppServer

run asproc on …

procedure asproc … for each … end.end.

#15 fork() & exec()

• Very expensive system calls.• Multiple context switches, process creation &

tear-down, IO to load executables etc.• Complex shell scripts (the old UNIX “lp”

subsystem).• Long PATH variables.• 4GL usage of UNIX, DOS, OS-COMMAND and

INPUT THROUGH.

#15 fork() & exec()define variable i as integer no-undo.

define variable fSize as integer no-undo.

etime( yes ).do i = 1 to 1000:input through value( "ls -ld .." ). import ^ ^ ^ ^ fSize. input close.end.display etime fSize.

define variable i as integer no-undo.

define variable fSize as integer no-undo.

etime( yes ).do i = 1 to 1000: file-info:file-name = "..". fSize = file-info:file-size.end.display etime fSize.

3140ms, at least 1000 calls to each ofopen(), close(), fork(), exec(), read()complete with multiple context switchesper invocation.

16ms, 1,000 stat() calls.

#14 -spin

• Almost all new machines, even desktops & laptops are now multi-core.

• Do NOT use the old X * # of CPUs rule to set –spin. It is bogus.

• Bigger is not always better with –spin!• Modest values (5,000 to 10,000) generally provide the

best and most consistent results for the vast majority of people.

• Use readprobe.p to explore.• Check out Rich Banville’s Superb Exchange 2008

Presentation!

#14 -spinOPS-28 A New Spin on Some Old Latcheshttp://www.psdn.com/ak_download/media/exch_audio/2008/OPS/OPS-28_Banville.ppt

Readprobe Data Access Results

50,000

100,000

150,000

200,000

250,000

300,000

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

Rec

ord

s R

ead

10.1C

10.1B

#13 bi cluster size

• The idea is to reduce the number and frequency of checkpoints giving APWs plenty of time to work.

• Larger bi clusters permit spikes in the workload to take place without ambushing the APWs.

• Easy to overlook when building new db via prostrct create…• 512 is the default OE 10 bi cluster size.• 8192 is good for small systems.• 16384 is “a good start” for larger systems.• Longer REDO phase on startup so don’t get crazy.• NOT a good idea for “Workgroup” database licenses. For

WG small values (512 or 1024) are better.

#13 bi cluster size

$ grep ‘(4250)’ dbname.lg(4250)Before-Image Cluster Size: 524288.

$ proutil dbname -C truncate bi -bi 16384…(1620) Before-image cluster size set to 16384 kb.(4251)Before-Image Cluster Size: 16777216.

$ proutil dbname -C -bigrow 8

#12 APW, AIW, BIW & WDOG

• Always start a BIW• Always start an AIW• Start WDOG• Two APWs are usually enough:

– Too many is just a (small) waste of CPU cycles.– If you are consistently flushing buffers at

checkpoints increase bi cluster size and add an APW (one at a time until buffer flushes stop).

#12 APW, AIW, BIW & WDOG

APW Data

BIW & AIW Data

#11 Larger db Blocks

• Larger blocks result in much more efficient IO.• Fewer IO ops mean less contention for disk.• Moving from 1k to 4k is huge. 4k to 8k is relatively

less huge but still very valuable.• 8k works best in most cases. Especially read-heavy

workloads.• Better disk space efficiency (tighter packing, less

overhead).

• Don’t forget to adjust –B and Rows Per Block!

#11 Larger db Blocks

• Large Blocks reduce IO, fewer operations are needed to move the same amount of data.

• More data can be packed into the same space because there is proportionally less overhead.

• Because a large block can contain more data it has improved odds of being a cache “hit”.

• Large blocks enable HW features to be leveraged. Especially SAN HW.

#11 Larger db Blocks

8k Blocks

8k Blocks

New Server

#10 Type 2 Storage Areas

• Data Clusters – contiguous blocks of data that are homogenous (just one table).

• 64 bit ROWID.• Variable (by area) rows per block.• All data should be in type 2 areas – until you

prove otherwise.• Storage Optimization Strategies!

#10 Type 2 Storage Areas

Storage areas

#9 Transactions

• Distinguish between a “business transaction” and a “database transaction”.

• Do not try to abuse a database transaction to enforce a business rule:– You may need to create “reversing (business)

transactions”.– Or restartable transactions.

• For large database operations “chunk” your transactions.

#9 “Chunking” Transactions

define variable i as integer no-undo. outer: do for customer transaction while true: inner: do while true: i = i + 1. find next customer exclusive-lock no-error. if not available customer then leave outer. discount = 0. if i modulo 100 = 0 then next outer. end. end.

#8 Minimize Network Traffic

• Use FIELD-LIST in queries.• Use –cache and –pls.• NO-LOCK queries pack multiple records into a

request and eliminate lock downgrade requests.• Watch out for client-side sorting and selection on

queries.• Remember that CAN-DO is evaluated on the

CLIENT (yet another reason not to use it).• Use -noautoresultlist/FORWARD-ONLY.

#8 Minimize Network Traffic

• Use a secondary broker to isolate high activity clients (such as reports).

• Consider setting –Mm to 8192 or larger.• Use –Mn to keep the number of clients per

server low (3 or less).• Use –Mi 1 to spread connections across

servers.

#7 Runaways, Orphans, Traps & Kills

• Consume entire cores doing nothing useful.• These are sometimes caused by bugs.• But that is rare.• More likely is a poorly conceived policy of

restricting user logins.• The UNIX “trap” command is often at the

bottom of these problems.

#7 Runaways, Orphans, Traps & Killshttp://dbappraise.com/traps.html

#6 The Buffer Cache

• The cure for disk IO is RAM.• Use RAM to buffer and cache IO ops.• Efficiency of –B:

– Is loosely measured by hit ratio.– Changes follow an inverse square law.– So to make a noticeable change in hit ratio you must

make a large change to –B.• 100,000 is “a good start” (800MB @ 8k blocks).

#6 The Buffer Cache

Layer Time # of Recs

# of Ops Cost per Op

Relative

Progress to –B 0.96 100,000 203,473 0.000005 1-B to FS Cache 10.24 100,000 26,711 0.000383 75

FS Cache to SAN 5.93 100,000 26,711 0.000222 45-B to SAN Cache* 11.17 100,000 26,711 0.000605 120SAN Cache to Disk 200.35 100,000 26,711 0.007500 1500

-B to Disk 211.52 100,000 26,711 0.007919 1585

* Used concurrent IO to eliminate FS cache

In Big B You Should Trust!

#5 Rapid Readers

• Similar to a runaway – consumes a whole CPU• But is actually doing db IO• Usually caused by:

– Table scans– Poor index selection.– Unmonitored batch processes and app-servers.– Really bad algorithm choices.

#5 Rapid Readers

High Read Rate

Suspicious user IO

Code being run!

#4 Balance & Isolate IO

• Use more than one disk:– A fast disk can do 150 or so random IO Ops/sec.– Kbytes/sec is a measure of sequential IO.– OLTP is mostly random.

• Don’t waste time trying to “manually stripe”.• Instead, use “hardware” striping and mirroring.• Isolate AI extents for safety, not performance.• Isolate temp-file, application, OS and “other” IO.

#4 Balance & Isolate IOfillTime = cacheSize / (requestRate – serviceRate)

Typical Production DB Example (4k db blocks):

4GB / ( 200 io/sec – 800 io/sec ) = cache doesn’t fill!

Heavy Update Production DB Example:

4GB / ( 1200 io/sec – 800 io/sec ) = 2621 seconds (RAID10)4GB / ( 1200 io/sec – 200 io/sec ) = 1049 seconds (RAID5)

Maintenance Example:

4GB / ( 5000 io/sec – 3200 io/sec ) = 583 seconds (RAID10)4GB / ( 5000 io/sec – 200 io/sec ) = 218 seconds (RAID5)

#3 Manage Temp File IO

• Temp-file IO can exceed db IO.• Sometimes by 2:1, 3:1 or more!• -T isolates temp file IO.• -t helps you to crudely diagnose the source of IO.• -y provides some detail regarding r-code swapping.• -mmax buffers r-code, 4096 is a good start for ChUI,

16384 for GUI.• Memory mapped procedure libraries cache r-code.• Use –Bt & -tmpbsize to tune 4GL temp-tables.

#3 Manage Temp File IO-rw-r--r-- 1 VEILLELA users 579312 Oct 19 15:16 srtrAyhEb-rw-r--r-- 1 wrightb users 35697664 Oct 19 15:16 srtH6miqb-rw-r--r-- 1 STEELEJL users 36772864 Oct 19 15:16 srtz37kyb-rw-r--r-- 1 THERRIKS users 0 Oct 19 07:12 srt--Elab-rw-r--r-- 1 root users 17649 Oct 19 15:16 lbiV6Qp7a-rw-r--r-- 1 root users 34704 Oct 19 15:16 lbi-TymMa-rw-r--r-- 1 wrightb users 811008 Oct 19 15:16 DBIHDmiqc-rw-r--r-- 1 BECKERLM users 8192 Oct 19 11:06 DBI--Abac-rw-r--r-- 1 CALUBACJ users 8192 Oct 19 09:16 DBI--Abyc

CLIENT.MON (-y)Program access statistics: Times BytesReads from temp file: 0 0Writes to temp file: 0 0Loads of .r programs: 14 524594Saves of compilation .r's: 0 0Compilations of .p's: 0 0Checks of files with stat: 165 0

#2 Index Compact

• Compacts Indexes.• Removes deleted record placeholders.• Improves “utilization” = fewer levels & blocks

and more index entries per read.• Runs online or offline.• Available since version 9.

#2 Index Compact

• Do NOT set target % for 100!• Consider compacting when utilization < 70%• … and blocks > 1,000.

proutil dbname –C idxcompact table.index target%

INDEX BLOCK SUMMARY FOR AREA "APP_FLAGS_Idx" : 96-------------------------------------------------------Table Index Fields Levels Blocks Size %Util FactorPUB.APP_FLAGS AppNo 183 1 3 4764 37.1M 89.9 1.2 FaxDateTime 184 2 2 45 259.8K 72.4 1.6 FaxUserNotified 185 2 2 86 450.1K 65.6 1.7

INDEX BLOCK SUMMARY FOR AREA "Cust_Index" : 10-------------------------------------------------------Table Index Fields Levels Blocks Size % Util FactorPUB.Customer Comments 13 1 1 1 874.0B 21.5 1.0 CountryPost 14 2 2 4 9.0K 56.5 1.9 CustNum 12 1 2 4 9.9K 62.2 1.8 Name 15 1 2 9 22.5K 62.9 1.7 SalesRep 16 1 1 1 1.3K 33.6 1.0

#1 Stupid 4GL Tricks

• Bad code will defeat any amount of heroic tuning and excellent hardware.

• Luckily bad code is often advertised by the perpetrator as having been developed to “improve performance”.

• Just because a feature of the 4GL can do something doesn’t mean that it should be used to do it.

#1 Stupid 4GL Tricks/* SR#1234 – enhanced lookup to improve performance! */

update cName.

find first customer where cName matches customer.name use-index custNum no-error.

-Or –

find first customer where can-do( cName, name ) use-index custNum no-error.

Questions?

Thank You!