Real Time Operational Analytics with Microsoft Sql Server 2016 [Liviu Ieran]
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Bussines case for Real-Time Operational Analytics Using In-Memory Technology. Columnstore overview. Tips and tricks for achieving great performance
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Real Time Operational Analytics with Microsoft Sql Server 2016 [Liviu Ieran]
- 1. SQL Server 2016 Real Time Operational Analytics Liviu Ieran [email protected]
- 2. The explosion of data sources... …drives an explosion of data …which drives businesses to learn more and do more faster 2013-2020 CAGR = 41% 25B 4.0B1.3 B 2010 2013 2020 There’s an opportunity to drive smarter decisions with data Performance
- 3. 1 10 100 1000 10000 100000 1000000 1990 1991 1992 1993 1994 1994 1995 1996 1997 1998 1999 2000 2000 2001 2002 2004 2005 2007 2008 2009 2011 US$/GB $ per GB of PC Class Memory Meanwhile RAM cost continues to drop Moore’s Law on total CPU processing power holds but in parallel processing… CPU clock rate stalled… Because processors would melt… Hardware Trends New CPU won’t run a short transaction much faster Performance
- 4. SQL In-Memory Technologies Over 100x analytics query speed and significant data compression with In-Memory ColumnStore Up to 30x faster transaction processing with In-Memory OLTP Faster AnalyticsFaster Transactions IN-MEMORY OLTP IN-MEMORY DW Performance
- 5. Traditional operational/analytics architecture Key issues Complex implementation Requires two servers (capital expenditures and operational expenditures) Data latency in analytics High demand: requires real-time analytics IIS Server BI analysts Performance
- 6. Minimizing data latency for analytics Challenges Analytics queries are resource intensive and can cause blocking Minimizing impact on operational workloads Sub-optimal execution of analytics on relational schema Benefits No data latency No ETL No separate data warehouse IIS Server BI analysts Performance
- 7. Real-Time Analytics – What it is NOT for OLTP OLTP OLTP • Operational Data Coming from multiple sources • Extreme Analytics – Needs pre-aggregated cubes – Star-Schema • Challenge with OLTP schema – Data is normalized – Queries require multi-table joins Performance
- 8. Memory Optimized Tables: Row and Hash Index Structure 90, 150 Susan Bogota 50, ∞ Jane Prague Timestamps NameChain ptrs City Hash index on City Hash index on Name 100, 200 John Prague 200, ∞ John Beijing f(John) f(Jane) f(Beijing) f(Prague) f(Bogota) Performance
- 9. Columnstore Index: Why? Improved compression: • Data from same domain compress better • 10x compression Reduced I/O: • Fetch only columns needed … Data stored as rows Data stored as columns Efficient operation on small set of rows Ideal for OLTP C1 C2 C3 C5C4 Improved Performance: • More data fits in memory • Batch Mode execution • upto100x Ideal for DW Workload Performance
- 10. Operational analytics: columnstore on in-memory tables No explicit delta row group Rows (tail) not in columnstore stay in In-Memory OLTP table No columnstore index overhead when operating on tail Background task migrates rows from tail to columnstore in chunks of 1 million rows not changed in last 1 hour Columnstore data fully resident in memory Persisted together with operational data No application changes required In-Memory OLTP table Tail Range index Hash index Performance
- 11. DEMO
- 12. © 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, 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.
Editor's Notes
- #14: Dramatic Performance Gain is from RowStore to ColumnStore Great Scalability from 4S to 8S Newer Release with Better Performance: SQL 2012 to SQL 2014 In the near future, we will have new scale point with higher scale H/W, as well as new release with even higher performance. Q: Why 4S of SQL2012 is empty? A: We haven’t published a number for it.