Columnstore Index in SQL Server Customer Success Stories

Columnstore Index in SQL Server 2016 - Customer Success Stories
Sunil Agarwal

Agenda Columnstore Overview Customer Success Stories
Benefits of Columnstore Indexes Recent improvements in columnstore indexes (SQL Server 2016 and SQL DB) Customer Success Stories Daman (Health Insurance) First American Title FIS M-Files

Demo – Value Proposition
Microsoft 2016 8/31/2018 9:04 PM Demo – Value Proposition Sunil Agarwal © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

SQL Server 2016: Columnstore Improvements
Performance String Predicate Pushdown to Scan Node Aggregate Pushdown New BatchMode Operators SIMD (Single Instruction Multiple Data) aware Functionality: Data Warehouse (DW) Nonclustered Index (NCI) – Efficient lookup and short-range queries Row-level locking and RCSI/SI support Offloading reporting to Readable Secondary replica in AlwaysON Functionality: Real-Time Operational Analytics Updateable nonclustered columnstore index (NCCI) Columnstore index on In-Memory OLTP tables Ability to remove deleted rows in compressed rowgroups (Index Defragmentation)

Traditional Data Warehouse
Microsoft Ignite 2016 8/31/2018 9:04 PM Traditional Data Warehouse © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

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

Customer – Daman Requirements/Challenges
More Performance = More Insight Leverage * Insight = Impact Query response time in seconds for common queries and few minutes for complex queries Challenges Current solution uses a competitive technology. Unable to keep with increased demands of the workload Looking for a solution that can keep with the workload for next 3-5 years

Customer – Daman: Benchmark Results
Solution - CCI Fact table with CCI and nonclustered index (NCI) Dimension tables – all CCI and with one PK POC was completed in 60 days including performance testing

Results – Production Daily Monthly Weekly Significant Improvement
Microsoft Ignite 2016 8/31/2018 9:04 PM Results – Production 100s with 30 concurrent users AdHoc Analytics Significant Improvement Daily Many queries < 20 seconds Runs at EOM Business Tool 5 x improvement SQL 2016: 10h Sybase IQ: 2 d Monthly 50 reports weekly Scheduled Reports 3x Improvement SQL 2016: 10 h Sybase IQ: 28 h Weekly © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

Demo – Recorded by Daman
Microsoft 2016 8/31/2018 9:04 PM Demo – Recorded by Daman Sunil Agarwal © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

Customer Daman: Learnings
ETL Multiple Delete and Update Statements resulted into multiple scans of columnstore Consolidated all deletes and Inserts into staging tables and then completed operation in one sweep Optimizer Many queries ran better with old cardinality estimator (In Production with old CE) Found an issues where SORT BatchMode execution regressed the query significantly. Fixed in RTM NCI/CCI Discovered an issue with REORGANIZE command that led to corruption. Issue was fixed

Scenario – Real Estate Property Search
Microsoft Ignite 2016 8/31/2018 9:04 PM Scenario – Real Estate Property Search © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

First American - Business Problem
Tech Ready 15 First American - Business Problem 8/31/2018 Data: 150+ Million Properties in USA Functional Overview Should support Dynamic search across 150 columns in different permutations Application requires counts to be displayed instantaneously with search criteria No Aggregates Query can return 10+ columns with 100s of rows Interactive Map based search in combination with other data elements Daily ETL Updating > 1 million rows Not a traditional Columnstore Index Scenario Failed Solution Can’t index all 150 columns row store Limited time for data preparation (ETL). Multiple indexes added to the overhead © 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

First American - Requirements
Tech Ready 15 8/31/2018 First American - Requirements Success Criteria 95% of the Queries should take less than 5 Seconds None of the queries should take more than 10 seconds. Should be able support 400 concurrent users Examples of Slow SQLs SELECT <select-list> FROM Property WHERE [HmEqtyPercentage] = 50 And [HmeEquityValue] = And [RecentMortgageAmount] BETWEEN AND And [EstimatedValue] BETWEEN AND SELECT <select-list> FROM NProperty WHERE [StreetNumber] = 918 And [StreetDir] = 'E' And [StreetName] = '90th' And ( [City] = 'Los Angeles' Or [PlaceName] = 'Los Angeles') Query Run Time – 10 seconds Query Run Time – 8 minutes © 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

Solution – SQL Server 2016 POC
Microsoft Ignite 2016 8/31/2018 9:04 PM Solution – SQL Server 2016 POC Key Points Excluded spatial columns from NCCI as they are not supported One unique btree index on rowstore to enable full text index Rowgroup Elimination allowed efficient Filtering Spatial index on geo-columns No changes to the application Query Optimizer automatically chooses the right indexes Spatial Index Full Text Index Relational Table (disk-based) (Clustered Index) Btree index Nonclustered columnstore index (NCCI) © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

First American -SQL Server 2014 with CCI (Production)
Microsoft Ignite 2016 8/31/2018 9:04 PM First American -SQL Server 2014 with CCI (Production) Challenge CCI does not support Spatial or Full Text indexes Solution Create two copies of the data (Rowstore and CCI) Spatial/Full Text index on rowstore Hybrid Query Model Hybrid queries are directed appropriately Spatial Index Full Text Index Clustered columnstore index (CCI) Rowstore Table (disk-based) (Clustered Index/Heap) © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

First American - Hybrid Query
The count queries would be directed to Column Store The spatial queries would be directed to Row Store Full Owner Search would be directed to Row Store Record Retrieval Search uses the Hybrid Query (Rowstore and Columnstore) 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')

First American - Results with Columnstore Index
Tech Ready 15 8/31/2018 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) © 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

First American - Learnings
Support of all data types would have allowed us to use CCI Avoid implicit type conversion in predicate evaluation which can lead to index scan instead of seek Statistics on columnstore must be pre-created

Scenario – Hybrid Transaction / Analytical Processing (HTAP)
Microsoft Ignite 2016 8/31/2018 9:04 PM Scenario – Hybrid Transaction / Analytical Processing (HTAP) © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

FIS – Business Overview and Challenges
Microsoft Ignite 2016 8/31/2018 9:04 PM FIS – Business Overview and Challenges Application Overview Securities lending market Marketplace for Large institutions to lend securities on short term basis to Hedge funds OLTP Transactions capture the securities and Analytics provides access to trends, average loan age Current Solution Implemented using SQL Server 2012 using rowstore for transactional processing ETL data every 5 minutes to provide near real-time analytics Total transaction volume 1.2 million rows per 8 hours of trading Dash board to reflect trades/analytics in real-time © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

FIS – Application Challenges
Required real-time analytics but with minimal impact on transactional workload Simplify Operational workflow. For example, eliminate ETL every 5 minutes Columnstore Index Benefits? Total rows in the table 1.2 million starting with 0 every day Columnstore compression only 1 million rows. No benefit for most of the day What to do?

FIS – Solution Considered two configurations
Nonclustered columnstore index (NCCI) In-Memory OLTP with columnstore index Baseline with all 1.2 million rows compressed Analytics queries completed with sub-second response time Minimal impact on transactional workload in spite of columnstore index Conclusion – Columnstore could work if the data was compressed Delivered solution with NCCI with force compression Compressed the rows every 100k inserts Analytics performance still good Chose NCCI as a solution. Simple to built on disk-based rowstore

FIS – Performance Numbers
Microsoft Ignite 2016 8/31/2018 9:04 PM FIS – Performance Numbers Status - Application Live in Production FIS Case Study © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

Microsoft Ignite 2016 8/31/2018 9:04 PM Scenario – Traditional Hybrid Transaction / Analytical Processing (HTAP) © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

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

Document loading and Viewing
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

Requirements Metadata insert/update must complete < 1 sec
Microsoft Ignite 2016 8/31/2018 9:04 PM Requirements Metadata insert/update must complete < 1 sec Mass update to document metadata < 2-3 secs View listings must be produced real-time users modify and insert data (OLTP) point lookups users navigate views (OLAP) © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

Challenge with Previous solution
Unable to meet SLAs as number of documents grow OLAP queries performance slowed down Limit M-Files repository size to 5-10 million objects Customer requirements were higher  we set the goal to support 100 million objects in one vault in 2-3 years View listings ("OLAP queries" from the OLTP database) was the bottleneck Using a separate OLAP DB is not acceptable because of real-time requirements

Solution Evaluated Hadoop, HP Vertica, NoSQLs
Poor / non-existent support for ACID We wanted our solution to work on-premises and in the cloud SQL Server 2016: Use NCCI on metadata tables No application changes Metadata Table Btree Index Nonclustered Columnstore Index (NCCI)

Results Environment Performance with NCCI in SQL Server 2016
77 million rows per table (5 tables involved in a query) Total size of database tested: 5 TB without compression Performance with NCCI in SQL Server 2016 OLTP performance impact < 5% OLAP Query performance Query Before (sec) After (sec) Contracts by Pricing Type and Agency 6.5 0.5 Contracts by year and month 2.6 0.05

M-Files - Learnings Learnings
Couple of corruption issues discovered with NCI/CCI approach. Fixed in RTM REORGANIZE helped M-Files manage the data fragmentation.

Insurance Company Sunil Agarwal Microsoft Ignite 2016
8/31/2018 9:04 PM Insurance Company Sunil Agarwal © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

Customer – Insurance Company (Anonymous)
TechReady 23 8/31/2018 9:04 PM Customer – Insurance Company (Anonymous) Scenario Traditional DW – running on-premise (SQL 2008 R2) Requirements Move entire solution to the cloud. Compare IaaS v/s PaaS v/s Azure Data warehouse v/s AWS Queries must run in seconds. No degradation in performance in overall workload - Test workload of 530 Business object queries, 150 OLAP queries. Challenges Cost considerations: Need good performance at reasonable price. Comparison of which technology is best for this specific workload. Current on-prem DW as 750 GB page compressed – would it fit on Azure SQL DB with growth rates? The company’s insurance product line includes auto, motorcycle, travel, home, business, life, health, personal accident, and mortgage insurance. It provides its insurance products and solutions directly, as well as through phone or online. © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

Insurance Company: Decision and Learnings
TechReady 23 8/31/2018 9:04 PM Insurance Company: Decision and Learnings Azure IaaS Azure SQLDB Azure SQLDW AWS Customer Chose this for production deployment. Required VPN at the time of the POC DB size was a consideration Size was too small Requirement for concurrent queries was high Cost was cheaper Took a bet on Azure given the successful POC Learnings: Significant perf due to batch mode improvements in compat level 130 Azure SQL Database by default creates DB with compat level 120, have to change to 130. Used trace flag to disable batch sort, in some queries. NCI over CCI for pointed queries Blanket updates issued by ETL behave differently, on rowstore only modified values are updated, on CCI column values aren’t checked. SQLDW is best for larger sizes. Few 100 GB isn’t going to cut it from perf perspective as they shard data across nodes, so if small amounts you don’t get great perf if large amounts great perf ( MPP system ) SQL DW has a limit of 32 concurrent queries so dashboarding type queries are not the ideal candidate, usually folks put an SSAS cube in front of PDW/SQL DW to front end those dashboaridng queries DB Size as in before the columstore compression they were concerned about hitting the 1TB limit on Azure SQL DB Azure SQL DB does not support VPN.. When you update a column in a table which is rowstore ( Update tbl1 set col1 = 1 ) , log records for rows are generated ONLY for changed rows ( aka if no row changes its col1 value as the value is already 1, then update does nothing ) On columnstore any update ( whether a column value is changed or not) will generate new version of the row. Direct insurance ETL did blanket updates banking on prior behavior. © 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

Insurance Company : Solution and Results
Solution: Use Clustered Columnstore Index Results: 60% of queries had an improvement of 10X or greater. <10% of queries ran slower. Compression – 2-4x on top of PAGE compression

Summary Session objective(s): Key Takeaways:
Tech Ready 15 8/31/2018 Summary Session objective(s): Share customer success stories with Columnstore Index in SQL Server 2016 Key Takeaways: Columnstore Index in SQL Server 2016 is an industry leader SQL Server Columnstore provides competitive edge to real-world applications This slide is required. Do NOT delete. This should be the first slide after your Title Slide. This is an important year and we need to arm our attendees with the information they can use to Grow Share! Please ensure that your objectives are SMART (defined below) and that they will enable them to go in and win against the competition to grow share. If you have questions, please contact your Track PM for guidance. We have also posted guidance on writing good objectives, out on the Speaker Portal ( This slide should introduce the session by identifying how this information helps the attendee, partners and customers be more successful. Why is this content important? This slide should call out what’s important about the session (sort of the why should we care, why is this important and how will it help our customers/partners be successful) as well as the key takeaways/objectives associated with the session. Call out what attendees will be able to execute on using the information gained in this session. What will they be able to walk away from this session and execute on with their customers. Good Objectives should be SMART (specific, measurable, achievable, realistic, time-bound). Focus on the key takeaways and why this information is important to the attendee, our partners and our customers. Each session has objectives defined and published on please work with your Track PM to call these out here in the slide deck. If you have questions, please contact your Track PM. See slide 5 in this template for a complete list of Tracks and TPMs. © 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

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Columnstore Index in SQL Server Customer Success Stories

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