SQL Server 2016 gives 40% improved performance over SQL Server 2014

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3 Overview

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5 World Record Breaking Performance (TPC-H) SQL Server 06 gives 40% improved performance over SQL Server 04

6 SSAS 06 Query Exec Multi-Dimensional (MOLAP) Tabular (VertiPaq) Query Exec ETL

7 SSAS 06 Multi-Dimensional Tabular ETL ROLAP Query exec Direct Query

8 Columnstore Index: Why? Data stored as rows Data stored as columns C C C3 C4 C5 Ideal for OLTP Efficient operation on small set of rows Improved compression: Data from same domain compress better Reduced I/O: Fetch only columns needed Improved Performance: More data fits in memory Optimized for CPU utilization Ideal for DW Workload 8

9 OrderDateKey ProductKey StoreKey RegionKey Quantity SalesAmount

10 OrderDateKey ProductKey StoreKey RegionKey Quantity SalesAmount OrderDateKey ProductKey StoreKey RegionKey Quantity SalesAmount

11 OrderDateKey ProductKey StoreKey RegionKey 3 Quantity SalesAmount OrderDateKey ProductKey StoreKey RegionKey Quantity SalesAmount

12 OrderDateKey ProductKey StoreKey RegionKey 3 Quantity SalesAmount OrderDateKey ProductKey StoreKey RegionKey Quantity SalesAmount

13 Overview Data Load

14 Delete bitmap Data Loading into Columnstore: Bulk Import

15 Delete bitmap Data Loading into Columnstore: Staging Table Insert into <columnstore> select * from <rowstore-staging> SQL 06: Parallel Insert into Columnstore index

16 Overview Data Load New physical Structures

17 Delete bitmap Delete bitmap BTREE (NCI) Mapping Index(internal) No PK/FK constraints. Uniqueness can be enforced through Materialized Views

18 Overview Data Load New physical Structures Query Performance

19 Rowgroup Elimination SELECT ProductKey, SUM (SalesAmount) FROM SalesTable WHERE OrderDateKey < 0008 GROUP BY ProductKey OrderDateKey ProductKey StoreKey RegionKey 3 Quantity SalesAmount OrderDateKey ProductKey StoreKey RegionKey Quantity Column Elimination SalesAmount

20 Process multiple rows together for efficiency Significant reduction in instructions X X Batch object Column vectors New in SQL 06 X C C0 C4 Single Thread Batch Mode Execution Batch Anti-Semi-Join Multiple count DISTINCT Window Aggregates Sort X

21 Select SUM(sales) from <columnstore>

22 30 rows 0 rows (R)

23 Try to do in the ETL Try to do on a partition that will be switched IN Update only what you need Try to avoid Join might be better

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26 Overview Data Load New physical Structures Query Performance Supportability

27 Operation SQL 04 SQL 06 Removing Deleted Rows Requires Index REBUILD Index REORGANIZE Remove deleted from single compressed RG Merge one or more compressed RGs with deleted rows Done ONLINE Smaller RG size resulting from Smaller BATCHSIZE Memory Pressure Index build Residual Index REBUILD Ordering Rows Create clustered index Create columnstore index by dropping clustered Index Index REORGANIZE No Changes

28 Overview Data Load New physical Structures Query Performance Supportability Concurrency and High Availability

29 Operation SQL 04 SQL 06 Query Rowgroup granularity No support of RCSI or SI Recommendation: use Read Uncommitted Support of SI and RCSI (non blocking) Insert Lock at row level (trickle insert) Rowgroup level for set of rows No changes Delete Lock at Rowgroup level Row level lock in conjunction with NCI Update Lock at Rowgroup level Implemented as Delete/Insert Row level lock in conjunction with NCI 9

30 Operation SQL 04 SQL 06 Backup/Restore Just like any other index. Each SEGMENT is persisted using LOB datatype No changes AlwaysOn Failover Clustering (FCI) Fully Supported Fully Supported AlwaysON Availability Groups Index Create/Rebuild Fully supported except Readable Secondary Offline Offline Fully Supported with Readable Secondary 30

31 Overview Data Load New physical Structures Query Performance Supportability Concurrency and High Availability Operational Analytics

32 Insert into <transactions> values ( <upc-code>, flowers, $0.00) Select ProduceName, ExpiryDate, Quantity, Sum(Sales) From <transactions> Where ProduceType = perishables Group By ProduceName, ExpiryDate IIS Server ETL Key Issues Complex Implementation Requires two Servers (CapEx and OpEx) Data Latency in Analytics More businesses demand/require real-time Analytics

33 Insert into <transactions> values ( <upc-code>, flowers, $0.00) IIS Server Select ProduceName, ExpiryDate, Quantity, sum (sales) From <transactions> Where ProduceType = perishables Group By ProduceName, ExpiryDate Benefits No Data Latency No ETL No Separate DW This is Real-Time ANALYTICS Challenges Minimizing Impact on

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35 SQL Real-Time In-Memory Analytics Technologies Faster Transactions IN-MEMORY OLTP Up to 30x faster transaction processing with In-Memory OLTP + + In 06 and Azure DB Faster Analytics IN-MEMORY DW Over 00x analytics query speed and significant data compression with In-Memory ColumnStore Using the same tables

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37 Please fill online evaluation for both speakers and overall event. You have both links in the last EVENT UPDATE Session evaluation form: Overall event evaluation form:

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