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T OP N P ERFORMANCE T IPS Adam Backman Partner, White Star Software.

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Presentation on theme: "T OP N P ERFORMANCE T IPS Adam Backman Partner, White Star Software."— Presentation transcript:

1 T OP N P ERFORMANCE T IPS Adam Backman Partner, White Star Software

2 Components of Performance  Application  Network  Disk  Memory  CPU

3 Wise men once said… The best administrator in the world can be easily defeated by incompetent programmers -Adam Backman (2014) No amount of tuning can defeat crap code -Tom Bascom (2014)

4 Application Issues  Use indexes (Not: USE-INDEX)  Use NO-LOCK for reporting  Use NO-UNDO on variables  Use TEMP-TABLES  Use FIELD-LIST  Move Client/Server applications to use OpenEdge ® AppServers™

5 Methodology  After correcting obvious application issues you want to push the performance bottleneck to the fastest resource  Changes should be made one at a time so the effect of each change can be measured prior to the next change  The goal is to push the bottleneck to the next faster resource Network  Disk  Memory  CPU

6 OpenEdge Architecture  OpenEdge Architecture −Shared memory −Server-less −Multi-server  Networking −Primary broker −Secondary broker

7 7 OpenEdge Memory Architecture

8 OpenEdge Network Architecture Primary broker (–Mn –Mpb –Mi –Ma) Splitting clients across servers Single pool of servers for all users Secondary broker (–Mpb –Mi –Ma) Splitting clients across brokers Multiple pools of servers Call Center users General users Reports…

9 9 The OpenEdge Server – A process that accesses the database for 1 or more remote clients OpenEdge Architecture Client/Server Overview

10 Network I/O  Sources of Network I/O −Temporary files  -T pointing to network  Users’ home directories on network −Poorly constructed queries  Lock requests  Poor use of indexes  No use of field list on large tables  Solutions −-T to local drive (C:\TEMP or /tmp) −Use of AppServer for read-intensive queries −Use –pls with program libraries stored on the network

11 Disks  This is where to spend your money  Goal: Use all disks evenly  Buy as many physical disks as possible  RAID 5 is still bad in many cases, improvements have been made but test before you buy as there is a performance wall out there and it is closer with RAID 5

12 Planning – Careful Planning

13 Causes of Disk I/O  Database −User requests (reads) - Usually 90% of total load −Updates (This affects DB, BI and AI)  Temporary file I/O  Operating system - usually minimal provided enough memory is installed  Other I/O −Reporting to disk −Other application file −Archiving

14 Operating System Storage Considerations  Use RAID 10  Avoid RAID5 (There are exceptions but not many)  Use large stripe widths  Match OpenEdge and OS block size

15 Operating system Storage  Use RAID 10 (0+1) or Mirroring and Striping for best protection of data with optimal performance for the database  For the AI and BI RAID 10 still makes sense in most cases. Exception: Single database environments

16 RAID 10 vs. RAID 5 cache fill rate Typical Production DB Example: 4GB / ( 200 io/sec – 800 io/sec ) = cache doesn’t fill! fillTime = cacheSize / (requestRate – serviceRate) Maintenance Example: 4GB / ( 5000 io/sec – 3200 io/sec ) = 583 sec. (≈ 10 min.) (RAID10) 4GB / ( 5000 io/sec – 200 io/sec ) = 218 sec (≈ 4 min.) (RAID5) Heavy Update Production DB Example: 4GB / ( 1200 io/sec – 800 io/sec ) = 2621 sec. (≈ 44 min.) (RAID10) 4GB / ( 1200 io/sec – 200 io/sec ) = 1049 sec. (≈ 17 min.) (RAID5) 4 disks RAID10 vs RAID5 4KB db blocks 4GB RAM cache (1048576 blocks)

17 Database Storage Considerations  Database blocks  Type II storage areas  Database block size  Records per block  Fixed vs. variable extents  Enable large files

18 Database Blocks

19 Block Layout Block’s DBKEYTypeChainBackup Ctr Next DBKEY in ChainBlock Update Counter TopReserved Free Space …….... Compressed Index Entries... BotIndex No. Num EntriesBytes Used... Compressed Index Entries... Dummy Entry... Block’s DBKEYTypeChainBackup Ctr Next DBKEY in ChainBlock Update Counter Free Space Free Dirs. Rec 0 OffsetRec 1 Offset Rec 2 OffsetRec n Offset Num Dirs. Free Space Used Data Space row 0 row 2 row 1 Data Block Index Block

20 Performance Tuning General tuning methodology  Get yourself in the ballpark  Get baseline timings/measurements  Change one thing at a time to understand value of each change This is the only thing where everyone agrees 100%

21 Remember: Tuning is easy just follow our simple plan

22 Type I Storage Areas  Data blocks are social −They allow data from any table in the area to be stored within a single block −Index blocks only contain data for a single index  Data and index blocks can be tightly interleaved potentially causing scatter

23 Type II Storage Areas  Like data is clustered together  A cluster will only contain records from a single table  A cluster can contain 8, 64 or 512 blocks  Data scatter is reduced  Universally better performance −Disk array read-ahead −Better locality of data −Better value per physical I/O

24 Type II Clusters CustomerOrderOrder Index

25 Storage Areas Compared Data Block(1) Idx Block(1) Data Block(3) Data Block(2) Data Block(1) Idx Block(2) Idx Block(1) Data Block(3) Idx Block(2) Data Block(1) Idx Block(1) Type IType II

26 Database Block Size  Generally, 8k works best for Unix/Linux  4k works best for Windows  Remember to build file systems with larger block sizes (match if possible)  There are exceptions so a little testing goes a long way but if in doubt use the above guidelines

27 Determining Records per Block  Determine “Mean” record size −Use proutil -C dbanalys  Add 20 bytes for record and block overhead  Divide this product into your database block size  Choose the next HIGHER binary number −Must be between 1 and 256

28 Example: Records /Block  Mean record size = 90  Add 20 bytes for overhead (90 + 20 = 110)  Divide product into database blocksize −8192 ÷ 110 = 74.47  Choose next higher binary number 128  Default records per block is 64 in version 9 and 10

29 OpenEdge Memory Considerations  -B buffer pool  -B2 alternate buffer pool  BI cluster size  Before image buffers  Page writers  -spin

30 General Memory Rule - Locality of Reference  When data is referenced there is a high probability that it will be referenced again soon  If data is referenced there is a high probability that “nearby” data will be referenced soon  Locality of reference is why caching exists at all levels of computing

31 OpenEdge Buffer Pool (-B)  OpenEdge buffer pool provides a way to reduce operations to physical disks  A 90% buffer hit rate denotes 1 in 10 requests being passed to disk  At 95% 1 in 20 requests are passed to disk  At 99% only 1 of each 100 requests are passed to disk  At the high percentages small increases in buffer hit percentage significantly reduce I/Os to disk

32 OpenEdge Alternate Buffer Pool (-B2)  An excellent way to reduce/eliminate in-memory overhead associated with buffer pool operations  Tables that are candidates for the –B2 are: −Fixed size (not rapidly growing) −Very high utilization (master records)  The –B2 must be set larger than the storage requirement of the tables and indexes that are placed in this structure −Use a database analysis to determine size −I generally add 10% for good measure

33 What is a Checkpoint?  A synchronization point between memory and disk −The initial checkpoint schedules modified buffers (-B) to be written prior to the next checkpoint −Subsequent checkpoints first check to make sure all of the previously scheduled buffers have been written and then schedules  The goal is to have no buffers to be written at checkpoint

34 Checkpoint Efficiency  The before image cluster size determines the frequency of checkpoints  The ability APWs to write all of the modified blocks determines the efficiency  Buffers flushed at checkpoint can be caused by a disk bottleneck as well as improper settings of the cluster size and number of APWs  If changing the number of APWs does not reduce or eliminate buffers flushed at checkpoint it is likely a disk issue

35 Checkpoint Efficiency  Goals: −1 Checkpoint per 120 seconds (2 min.) during highest human update period of the day (BI cluster size) −Start with a single APW and add APWs until you can get through the highest update period of the day without buffers flushed at checkpoint −If you already have several APWs then look at writes by BIW and reduce the number of APWs one at a time  Writes by BIW should increase  Partial writes should reduce  Still no buffers flushed at checkpoint

36 Before Image Buffers -bibufs  Buffers used by BIW (and APWs) to do the writes to the before image file  The default value is too low  Generally, I set it to 50 for small to medium systems and 150 for large systems  Additionally, I generally set –aibufs to the same value as -bibufs

37 Page Writers  Every database that has updates should have a Before image writes (BIW)  Every database that has after imaging journaling enabled should have a after image writer (AIW)  As stated before: −Start with a single asynchronous page writer (APW) −After your cluster size is correct (1 CP/120 sec.) −Add additional APWs to eliminate writes at checkpoint in the activity screen (option 5) in promon

38 Setting –spin  The –spin parameter determines the number of retries a client will attempt while getting shared memory resources  The vast majority of installations can set it between 2000 and 10000  Others will need to do in-depth analysis and these sights are generally very high throughput  Putting highly read static tables in –B2 can provide additional performance without changing -spin

39 Conclusion  Move the bottleneck to the next faster resource  Setup disks for optimal performance −No, RAID 5 −Yes, RAID 10 −Large stripe width/chunk size −Match DB and OS block size  Database block size (generally 8k blocks are best)  Remember to change number of records per block

40 Conclusion - continued  Use type II storage areas  Get the best buffer hit rate that you can (95%+)  High read tables with little or no growth could go in –B2  One checkpoint per 2 minutes at peak (human peak)  APWs, start with 1 and add as needed  No buffers flushed at checkpoint  Use –spin generally between 2000 and 10000 works for most

41 Questions?

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