This document discusses how SQL Server 2016 columnstore indexes improved performance for several customers. It provides examples where columnstore indexes led to significant query performance improvements - from 50-60% faster scheduled reports for an insurance company to 5 times faster analytics for another customer. It also discusses how columnstore indexes helped a real estate company improve performance for hybrid queries on their large property data and enabled an application at a financial services company to realize columnstore index benefits for the first time.

2. Agenda

4. World Record Breaking Performance (TPC-H)
SQL Server Leading the TPC-H benchmark

5. SQL Server 2016: Columnstore Improvements
Performance
Functionality: Data Warehouse (DW)
Functionality: Real-Time Operational Analytics

6. World Record Breaking Performance (TPC-H)
SQL Server 2016 gives 40% improved
performance over SQL Server 2014

8. Customer – Daman (Health Insurance, UAE)
Scenario
• Traditional DW with ~100 target tables, ~200 source
tables several billion rows
• Multi-layered, holistic, dimensional model, used by all
users
• Queries involve multi-table joins

9. Customer – Daman Requirements/Challenges
Requirements
Challenges

10. Approach
JulJunMayAprMarFebJan
2015 2016
DecNov
Go: POC SQL Server 2016
POC SQL Server 2016 Go
Production
Go
In the cloud
6 contenders
Synthetic data
Same data model
Similar characteristics
On the premises
SQL Server 2016 vs.
Sybase IQ
Full productive data set Daily load on SQL Server 2016
Automated test framework
Full test end user tools

11. Customer – Daman: Benchmark Results
Solution - CCI

12. Results – testing & production
50 reports weekly
Scheduled Reports
60% improvement
SQL 2016: 10 h
Sybase IQ: 28 h
Weekly
Runs at EOM
Business Tool
5 x improvement
SQL 2016: 10h
Sybase IQ: 2 d
Monthly
100s with 30 concurrent users
AdHoc Analytics
Significant
Improvement
Daily
Many queries < 20
seconds

13. Demo – Screen Shots

14. Customer Daman: Learnings
ETL
Optimizer
Case Study
https://customers.microsoft.com/en-us/story/daman

16. First American - Business Problem
• Scope of Data:150 Million Properties in USA
• Querying
• Not a traditional Columnstore Index Scenario
• Failed Solution

17. First American - Requirements
Success Criteria
Examples of Slow SQLs
SELECT
FROM
WHERE
SELECT
FROM
WHERE

18. 19
Relational Table (disk-based)
(Clustered Index)

19. 20
Btree index
Rowstore Table (disk-based)
Hybrid queries are directed appropriately
Challenge
Solution

20. First American - Hybrid Query
SELECT <Select List>
FROM NormandySearch.Search.Property
WHERE propertyid in(SELECT propertyid
FROM NormandySearch.Search.PropertyCS
WHERE [NumericStreetNumber] = 416
and OwnerName like 'BOW%‘ And ( [City] = 'Bay Village'
Or [PlaceName] = 'Bay Village')

21. First American - Results with Columnstore Index
Data Compression
CPU Consumption
Query Performance with 100 concurrent users
With Rowstore (PAGE Compressed) With CCI
Database Size (including Indexes) 560 GB 44 GB (13x)

22. First American - Learnings

24. FIS – Business Overview and Challenges
Application Overview
Current Solution

25. FIS – Application Challenges
Challenge
No Columnstore Index Benefits
What to do?

26. FIS – Solution
Considered two configurations
Baseline with all 1.2 million rows compressed
Delivered solution with NCCI with force compression

27. FIS – Performance Numbers
Status - Application Live in Production
FIS Case Study

29. what something is instead of where it's stored.
Organize information based on
Proposal
ESTT Corporation
9/30/2016
Website Renewal
Y:
Proposals 2016
Projects
ESTT
Official docs
Website renewal
M-FILES – Document Management

30. Each document inserted
results in approximately
60 SQL inserts into
Metadata
Metadata change in a
document can result
updating metadata of
large number of other
documents
Document loading and Viewing

33. Metadata Table
Btree Index

34. Query Before (sec) After (sec)
Contracts by Pricing Type and Agency 6.5 0.5
Contracts by year and month 2.6 0.05

36. Thank you
for your time!