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
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
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)
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
28.
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
M-Files efficiently organizes information and documents and makes that content easy to find and share. One of the things that makes M-Files unique is our approach to storing and organizing content. Everything stored in M-Files is organized by what it is instead of where it's stored, as is the case when using a traditional folder-based approach.
Only a few details need to be entered when saving documents, such as its type or class (i.e., proposal, invoice, contract, etc.) and what it's related to (i.e., customer, project, contact, etc.). This metadata-driven approach is much more intuitive and precise as compared to guessing the folder where it should be stored.
users modify and insert data (OLTP)
point lookups
users navigate views (OLAP)