Parallel Execution Plans

1. Parallel Execution Plans Joe Chang jchang6@yahoo.com www.qdpma.com

2. About Joe Chang • SQL Server Execution Plan Cost Model • True cost structure by system architecture • Decoding statblob (distribution statistics) • SQL Clone – statistics-only database • Tools • ExecStats – cross-reference index use by SQL-execution plan • Performance Monitoring, • Profiler/Trace aggregation

3. So you bought a 64+ core box Now • Learn all about Parallel Execution • All guns (cores) blazing • Negative scaling • Super-scaling • High degree of parallelism & small SQL • Anomalies, execution plan changes etc • Compression • Partitioning Yes, this can happen, how will you know No I have not been smoking pot How much in CPU do I pay for this? Great management tool, what else?

4. Parallel Execution Plans Reference: Adam Machanic PASS

5. Execution Plan Quickie I/O and CPU Cost components F4 Estimated Execution Plan Cost is duration in seconds on some reference platform IO Cost for scan: 1 = 10,800KB/s, 810 implies 8,748,000KB IO in Nested Loops Join: 1 = 320/s, multiple of 0.003125

6. Index + Key Lookup - Scan Actual CPU Time (Data in memory) LU 1919 1919 Scan 8736 8727 (926.67- 323655 * 0.0001581) / 0.003125 = 280160 (86.6%) True cross-over approx 1,400,000 rows 1 row : page 1,093,729 pages/1350 = 810.17 (8,748MB)

7. Index + Key Lookup - Scan Actual CPU Time LU 2138 321 Scan 18622 658 (817- 280326 * 0.0001581) / 0.003125 = 247259 (88%) 8748000KB/8/1350 = 810

8. Actual Execution Plan Estimated Actual Note Actual Number of Rows, Rebinds, Rewinds Estimated Actual

9. Row Count and Executions Outer Inner Source For Loop Join inner source and Key Lookup, Actual Num Rows = Num of Exec × Num of Rows

11. Parallel Plans

12. Parallelism Operations • Distribute Streams • Non-parallel source, parallel destination • Repartition Streams • Parallel source and destination • Gather Streams • Destination is non-parallel

13. Parallel Execution Plans Note: gold circle with double arrow, and parallelism operations

14. Parallel Scan (and Index Seek) 2X IO Cost same CPU reduce by degree of parallelism, except no reduction for DOP 16 DOP 1 DOP 2 8X 4X IO contributes most of cost! DOP 8 DOP 4

15. Parallel Scan 2 DOP 16

16. Hash Match Aggregate CPU cost only reduces By 2X,

17. Parallel Scan • IO Cost is the same • CPU cost reduced in proportion to degree of parallelism, last 2X excluded? On a weak storage system, a single thread can saturate the IO channel, Additional threads will not increase IO (reduce IO duration). A very powerful storage system can provide IO proportional to the number of threads. It might be nice if this was optimizer option? The IO component can be a very large portion of the overall plan cost Not reducing IO cost in parallel plan may inhibit generating favorable plan, i.e., not sufficient to offset the contribution from the Parallelism operations. A parallel execution plan is more likely on larger systems (-P to fake it?)

18. Actual Execution Plan - Parallel

19. More Parallel Plan Details

20. Parallel Plan - Actual

21. Parallelism – Hash Joins

22. Hash Join Cost DOP 4 DOP 1 DOP 2 Search: Understanding Hash Joins For In-memory, Grace, Recursive DOP 8

23. Hash Join Cost CPU Cost is linear with number of rows, outer and inner source See BOL on Hash Joins for In-Memory, Grace, Recursive IO Cost is zero for small intermediate data size, beyond set point proportional to server memory(?) IO is proportional to excess data (beyond in-memory limit) Parallel Plan: Memory allocation is per thread! Summary: Hash Join plan cost depends on memory if IO component is not zero, in which case is disproportionately lower with parallel plans. Does not reflect real cost?

24. Parallelism Repartition Streams DOP 8 DOP 2 DOP 4

25. Bitmap BOL: Optimizing Data Warehouse Query Performance Through Bitmap Filtering A bitmap filter uses a compact representation of a set of values from a table in one part of the operator tree to filter rows from a second table in another part of the tree. Essentially, the filter performs a semi-join reduction; that is, only the rows in the second table that qualify for the join to the first table are processed. SQL Server uses the Bitmap operator to implement bitmap filtering in parallel query plans. Bitmap filtering speeds up query execution by eliminating rows with key values that cannot produce any join records before passing rows through another operator such as the Parallelism operator. A bitmap filter uses a compact representation of a set of values from a table in one part of the operator tree to filter rows from a second table in another part of the tree. By removing unnecessary rows early in the query, subsequent operators have fewer rows to work with, and the overall performance of the query improves. The optimizer determines when a bitmap is selective enough to be useful and in which operators to apply the filter. For more information, see Optimizing Data Warehouse Query Performance Through Bitmap Filtering.

26. Parallel Execution Plan Summary • Queries with high IO cost may show little plan cost reduction on parallel execution • Plans with high portion hash or sort cost show large parallel plan cost reduction • Parallel plans may be inhibited by high row count in Parallelism Repartition Streams • Watch out for (Parallel) Merge Joins!

28. Scaling Theory

29. Parallel Execution Strategy • Partition work into little pieces • Ensures each thread has same amount • High overhead to coordinate • Partition into big pieces • May have uneven distribution between threads • Small table join to big table • Thread for each row from small table • Partitioned table options

30. What Should Scale? 3 2 2 Trivially parallelizable: 1) Split large chunk of work among threads, 2) Each thread works independently, 3) Small amount of coordination to consolidate threads

31. More Difficult? 4 3 2 3 2 Parallelizable: 1) Split large chunk of work among threads, 2) Each thread works on first stage 3) Large coordination effort between threads 4) More work … Consolidate

32. Partitioned Tables No Repartition Streams Regular Table Partitioned Tables No Repartition Streams operations!

33. Scaling Reality 8-way Quad-Core Opteron Windows Server 2008 R2 SQL Server 2008 SP1 + HF 27

34. Test Queries • TPC-H SF 10 database • Standard, Compressed, Partitioned (30) • Line Item Table SUM, 59M rows, 8.75GB • Orders Table 15M rows

35. CPU-sec Standard CPU-sec to SUM 1 or 2 columns in Line Item Compressed

36. Speed Up Standard Compressed

37. Line Item sum 1 column CPU-sec Speed up relative to DOP 1

38. Line Item Sum w/Group By CPU-sec Speedup

39. Hash Join CPU-sec Speedup

40. Key Lookup and Table Scan CPU-sec 1.4M rows Speedup

42. Parallel Execution Summary • Contention in queries w/low cost per page • Simple scan, • High Cost per Page – improves scaling! • Multiple Aggregates, Hash Join, Compression • Table Partitioning – • alternative query plans • Loop Joins – broken at high DOP • Merge Join – seriously broken (parallel)

43. Scaling DW Summary • Massive IO bandwidth • Parallel options for data load, updates etc • Investigate Parallel Execution Plans • Scaling from DOP 1, 2, 4, 8, 16, 32 etc • Scaling with and w/o HT • Strategy for limiting DOP with multiple users

44. Fixes from Microsoft Needed • Contention issues in parallel execution • Table scan, Nested Loops • Better plan cost model for scaling • Back-off on parallelism if gain is negligible • Fix throughput degradation with multiple users running big DW queries • Sybase and Oracle, Throughput is close to Power or better

45. Test Systems

46. Test Systems • 2-way quad-core Xeon 5430 2.66GHz • Windows Server 2008 R2, SQL 2008 R2 • 8-way dual-core Opteron 2.8GHz • Windows Server 2008 SP1, SQL 2008 SP1 • 8-way quad-core Opteron 2.7GHz Barcelona • Windows Server 2008 R2, SQL 2008 SP1 Build 2789 8-way systems were configured for AD- not good!

47. Test Methodology • Boot with all processors • Run queries at MAXDOP 1, 2, 4, 8, etc • Not the same as running on 1-way, 2-way, 4-way server • Interpret results with caution

48. References • Search Adam Machanic PASS