Getting the most from your SAN File and Filegroup design patterns. Stephen Archbold

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2 Getting the most from your SAN File and Filegroup design patterns Stephen Archbold

3 About me Microsoft Certified Master SQL Server 2008 Working with SQL Server for 6+ years Former Production DBA for 24/7 High volume operation Currently SQL Server consultant at Prodata, Ireland Specialising in Performance Tuning and Consolidation Blog at and Get me on LinkedIn

4 Agenda Data Filegroup/File Fundamentals Storage Design Patterns OLTP Data Warehousing Fast Track style Data Warehousing on a SAN What other go faster buttons have we got Case Study The unruly fact table How do we make the changes

5 Data Filegroup/File Fundamentals General Filegroup Recommended Practices. Separate for: Nothing but system tables in Primary I/O patterns Different volatility Data Age If using Multiple Files in a Filegroup Files must be equally sized Files must be equally full SQL does not redistribute data when adding more files

6 Pattern 1 - OLTP Transactional processing is all about speed You want to get the transaction recorded and the user out as quick as possible Metric for throughput becomes less about MB/Sec, and more about transactions and I/O s per second

7 Challenges of OLTP Solid State Disk becoming more commonplace These thrive on Random I/O As the databases can be small, file/filegroup layout can suffer from neglect Faster disk brings different challenges

8 PRIMARY MyDB.MDF Filegroup File Transactions File2.NDF File1.NDF PAGELATCH! Reference Ref.NDF Volatile Volatile.NDF

9 Behind the scenes Single File Two Files Wait Type % SOS_SCHEDULER_YIELD 55 PAGEIOLATCH_EX 17 PAGELATCH_SH 15 ASYNC_IO_COMPLETION 5 PAGELATCH_UP 5 Wait Type % SOS_SCHEDULER_YIELD 66 PAGEIOLATCH_EX 12 PAGELATCH_SH 10 ASYNC_IO_COMPLETION 7 SLEEP_BPOOL_FLUSH 2

10 Facts and Figures Average Batch Completion Time (Seconds) Single file Two Files Four Files Eight Files 310, , , , , , , , ,000 Transactions Per Second 303, , , ,410 Single file Two Files Four Files Eight Files

11 What can we take away from this? Resolving in memory contention lies with the file layout This is actually nothing new, TempDB has been tuned this way for years! Keep in mind, files are written to in a round robin fashion

12 Data Warehousing

13 Pattern 2 Fast Track Scenario Large Volume Star Schema Need to optimize for sequential throughput Scanning Entire Table Not Shared Storage

14 CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU Large Partitioned Fact Table Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6 Partition 7 Partition 8 HBA 1 HBA 2 Controller 1 Controller 2 Controller 1 Controller 2 Enclosure 1 MyFact_part1.NDF MyFact_part2.NDF MyFact_Part3.NDF Myfact_Part4.NDF Enclosure 2 MyFact_part5.NDF MyFact_part6.NDF MyFact_Part7.NDF Myfact_Part8.NDF CPU CPU Filegroup File / LUN Partition 9 Partition 10 Partition 11 Partition 12 HBA 3 Controller 1 Controller 2 Enclosure MyFact_part9.NDF MyFact_part10.NDF MyFact_Part11.NDF Myfact_Part12.NDF

15 Fast Track Pros and Cons Pros Easy to figure out your needs Simple, scalable and fast In depth guidance available from Microsoft Cons Not recommended for pinpoint queries Only really for processing entire data sets Need VERY understanding Infrastructure team

16 Pattern 3 Datawarehouse on SAN Large Volume Star Schema Cannot optimize for sequential throughput Shared Storage More mixed workload

17 Goal Large Request Size We need Read Ahead Enterprise edition is capable of issuing a request for 512KB on a single read ahead request (Standard you re stuck at 64K) It can issue several of these (outstanding I/O) at a time, up to 4MB But you may not even be close to 512KB

18 How close are you to the 512k Nirvana Run something like: And watch this guy:

19 Fragmentation - Party Foul Champion #1 killer of read ahead Read ahead size will be reduced if pages being requested aren t in logical order Being a diligent type, you rebuild your indexes Because SQL is awesome, it does this using parallelism! So what s the catch? If Read Ahead is your goal, MAXDOP 1 to rebuild your indexes!

20 Enclosure 1 PRIMARY MyDB.MDF Filegroup File / LUN

21 Primary Enclosure 1 MyDB.MDF Dimensions Dimensions.NDF Volatile Facts Staging.NDF Facts.NDF Filegroup File / LUN Large Fact Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6 Partition 7 Partition 8 Fact.NDF Partition1.NDF Partition2.NDF Partition3.NDF Partition4.NDF Partition5.NDF Partition6.NDF Partition7.NDF Partition8.NDF

22 Getting data out of your Data Warehouse for Analysis Services How does Analysis Services pull in data?

23 Do we have any go faster buttons? On read heavy workloads and Enterprise Edition, Compression If storing multiple Tables in a Filegroup: -E For Data Warehouses - This allocates 64 extents (4MB) per object, per file, rather than the standard 1 (64K) If using multiple Files in a Filegroup -T1117 For all - This ensures that if auto growth occurs on one file, it occurs on all others. Ensures round robin remains in place In General on dedicated SQL servers Evaluate -T834 Requires Lock Pages in memory enabled This enables large page allocations for the Buffer Pool (2Mb 16Mb) Can cause problems if memory is fragmented by other apps

24 Case Study The Unruly Fact Table 3 TeraByte Data Warehouse Table scan was topping out at 300 mb/sec Storage was capable of 1.7 GB/sec Table partitioning was in place All tables were in a single Filegroup Had to get creative on enhancing throughput

25 Test Conditions 16 core server, Hyper Threaded to 32 cores 128 GB of Memory SQLIO fully sequential, storage gives 2.2 GB/Sec 32 range scans started up to simulate workload Page compression was enabled, the T834 trace flag was enabled MAXDOP of 1 on the server to ensure # of threads were controlled

26 Facts and Figures 1200 Throughput MB/Sec I/O Request Size (Kb) Baseline Post Index Rebuild with MAXDOP 1 Single FG for large table(s) Multiple FG one per partition 150 Baseline Post Index Rebuild with MAXDOP 1 Single FG for large table(s) Multiple FG one per partition

27 Other metrics Scenario Time Secs Avg IO (K) Avg MB/Sec Max MB/Sec Baseline Post Index Rebuild with MAXDOP Single FG for large table(s) Multiple FG one per partition ,150 1,281

28 Thankfully easy - Index rebuilds! How do we make the changes For non partitioned tables, drop and re-create on the new Filegroup For partitioned tables Alter the partition scheme to point to the new FileGroup For heaps, create a Clustered Index on the table on the new filegroup, then drop it!

29 Summary File and Filegroup considerations can yield huge gains system wide Know your workload and optimise for it If you have a Hybrid workload, then have a Hybrid architecture! Don t neglect your SQL Settings Code changes and indexes aren t the only way to save the day!

30 Paul Randal Multi file/filegroup testing on Fusion IO Fast Track Configuration Guide Resolving Latch contention Useful links Maximizing Table Scan Speed on SQL 2008 R2 Specifying storage requirements (Find that sweet spot!) Fragmentation in Data Warehouses Partial Database Availability and Piecemeal restores

31 THANK YOU! For attending this session and PASS SQLRally Nordic 2013, Stockholm