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Practical Performance Tuning

Practical Performance Tuning. For 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.

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Practical Performance Tuning

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  1. Practical Performance Tuning For Your Progress OpenEdge Database

  2. 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

  3. Tom’s Top TwentyTuning Tips • In No Particular Order.

  4. {inc/disclaimer.i} • Yourkilometeragewill 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.

  5. #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.

  6. #20 Dump & Load DB-20 Highly Parallel Dump & Loadhttp://www.psdn.com/ak_download/media/exch_audio/2007/DB/DB-20_Bascom.pdf

  7. #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.

  8. #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).

  9. #19 The Impact of an Upgrade conv89

  10. #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

  11. #18 Parallelize $ mbprodbname –p exp.p –param “01|0,3000” $ mbprodbname –p exp.p –param “02|3000,6000” $ mbprodbname –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.

  12. #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.

  13. #17 Update Statistics $ cat customer.sql UPDATE TABLE STATISTICS AND INDEX STATISTICS AND ALL COLUMN STATISTICS FOR PUB.Customer; COMMIT WORK; $DLC/bin/sqlexp \ -db dbname \ -S portnum \ -infile customer.sql \ -outfilecustomer.out \ -user username \ -password passwd >> customer.err 2>&1 For More Information: OpenEdge Data Management: SQL Development Chapter 10, Optimizing Query Performance

  14. #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.

  15. #16 Progress AppServer run asproc on … procedure asproc … for each … end. end. procedure asproc … for each … end. end.

  16. #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.

  17. #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 etimefSize. 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 etimefSize. 3140ms, at least 1000 calls to each of open(), close(), fork(), exec(), read() complete with multiple context switches per invocation. 16ms, 1,000 stat() calls.

  18. #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!

  19. #14 -spin OPS-28 A New Spin on Some Old Latches http://www.psdn.com/ak_download/media/exch_audio/2008/OPS/OPS-28_Banville.ppt

  20. #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.

  21. #13 bi cluster size $ grep ‘(4250)’ dbname.lg Before-Image Cluster Size: 524288. $ proutildbname -C truncate bi -bi 16384 … (1620) Before-image cluster size set to 16384 kb. Before-Image Cluster Size: 16777216. $ proutildbname -C -bigrow 8

  22. #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).

  23. #12 APW, AIW, BIW & WDOG APW Data BIW & AIW Data

  24. #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!

  25. #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.

  26. #11 Larger db Blocks 8k Blocks 8k Blocks New Server

  27. #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!

  28. #10 Type 2 Storage Areas Storage areas

  29. #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.

  30. #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.

  31. #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.

  32. #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.

  33. #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.

  34. #7 Runaways, Orphans, Traps & Kills http://dbappraise.com/traps.html

  35. #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).

  36. #6 The Buffer Cache In Big B You Should Trust! * Used concurrent IO to eliminate FS cache

  37. #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.

  38. #5 Rapid Readers High Read Rate Suspicious user IO Code being run!

  39. #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.

  40. #4 Balance & Isolate IO • fillTime = 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)

  41. #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.

  42. #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 Bytes • Reads from temp file: 0 0 • Writes to temp file: 0 0 • Loads of .r programs: 14 524594 • Saves of compilation .r's: 0 0 • Compilations of .p's: 0 0 • Checks of files with stat: 165 0

  43. #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.

  44. #2 Index Compact proutildbname –C idxcompacttable.index target% • Do NOT set target % for 100! • Consider compacting when utilization < 70% • … and blocks > 1,000. INDEX BLOCK SUMMARY FOR AREA "APP_FLAGS_Idx" : 96 ------------------------------------------------------- Table Index Fields Levels Blocks Size %Util Factor PUB.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 Factor PUB.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

  45. #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.

  46. #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.

  47. Questions?

  48. Thank You!

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