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SQL Server In-Memory OLTP Case Studies
- 2. Microsoft Confidential Version SessionState Performance Technology Bottleneck SQL Server 2012 12,000 batch requests/sec SQL Server Interpreted T-SQL Latch contentions SQL Server 2014 300,000 batch requests/sec Memory-Optimized Table, Interpreted T-SQL, Handling of Split LOBs CPU SQL Server 2016 1,200,000 batch requests/sec Memory-Optimized Table with LOB support, Natively Compiled stored procedures CPU In-Memory OLTP performance (bwin) Source: https://blogs.msdn.microsoft.com/sqlcat/2016/10/26/how-bwin-is-using-sql-server-2016-in-memory-oltp-to-achieve-unprecedented-performance-and-scale/ Version Cache System Performance Hardware nodes Mid-Tier Cache solution without SQL Server 150,000 batch requests/sec 19 Solution using SQL Server 2016 1,200,000 batch requests/sec 1
- 3. Microsoft Confidential Version SessionState Performance Technology Bottleneck SQL Server 2012 12,000 batch requests/sec SQL Server Interpreted T-SQL Latch contentions SQL Server 2014 300,000 batch requests/sec Memory-Optimized Table, Interpreted T-SQL, Handling of Split LOBs CPU SQL Server 2016 1,200,000 batch requests/sec Memory-Optimized Table with LOB support, Natively Compiled stored procedures CPU In-Memory OLTP performance (bwin) Source: https://blogs.msdn.microsoft.com/sqlcat/2016/10/26/how-bwin-is-using-sql-server-2016-in-memory-oltp-to-achieve-unprecedented-performance-and-scale/ Version Cache System Performance Hardware nodes Mid-Tier Cache solution without SQL Server 150,000 batch requests/sec 19 Solution using SQL Server 2016 1,200,000 batch requests/sec 1
- 7. CREATE TABLE [Customer]( [CustomerID] INT NOT NULL PRIMARY KEY NONCLUSTERED, [Name] NVARCHAR(250) NOT NULL, [CustomerSince] DATETIME2 NULL, INDEX [ICustomerSince] NONCLUSTERED (CustomerID, CustomerSince) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA); This table is durable (default). Non-durable tables: DURABILITY=SCHEMA_ONLY
- 9. CREATE PROCEDURE [dbo].[InsertOrder] @id INT, @date DATETIME2 WITH NATIVE_COMPILATION, SCHEMABINDING AS BEGIN ATOMIC WITH (TRANSACTION ISOLATION LEVEL = SNAPSHOT, LANGUAGE = N‘Dutch') -- insert T-SQL here END
- 11. Durability level Configuration Pros/Cons Scenarios Full durability Default DURABILITY=SCHEMA_AND_DATA Pro: • Every committed change is guaranteed to survive failure Con: • Latency impact: every commit requires log IO Default: most scenarios need full durability Delayed durability • Transaction commit time COMMIT WITH (DELAYED_DURABILITY=ON) • Atomic block of native procedure BEGIN ATOMIC WITH (DELAYED_DURABILITY=ON, …) • Database level ALTER DATABASE CURRENT SET DELAYED_DURABILITY=FORCED Pro: • Low latency due to no log IO in transaction execution path • Efficient log IO due to batching Con: • Limited data loss on failure (usually ~60K or ~1ms worth) Low latency requirements Can accept some data loss OR Copy of recent data exists elsewhere in case of failure AlwaysOn auto-failover (sync replicas) with low latency Non- durable tables Table creation DURABILITY=SCHEMA_ONLY Pro: • No IO at all Con: • Lose data on failure • Transient data such as session state • Caching • ETL (staging tables)
- 12. Durability level Configuration Pros/Cons Scenarios Full durability Default DURABILITY=SCHEMA_AND_DATA Pro: • Every committed change is guaranteed to survive failure Con: • Latency impact: every commit requires log IO Default: most scenarios need full durability Delayed durability • Transaction commit time COMMIT WITH (DELAYED_DURABILITY=ON) • Atomic block of native procedure BEGIN ATOMIC WITH (DELAYED_DURABILITY=ON, …) • Database level ALTER DATABASE CURRENT SET DELAYED_DURABILITY=FORCED Pro: • Low latency due to no log IO in transaction execution path • Efficient log IO due to batching Con: • Limited data loss on failure (usually ~60K or ~1ms worth) Low latency requirements Can accept some data loss OR Copy of recent data exists elsewhere in case of failure AlwaysOn auto-failover (sync replicas) with low latency Non- durable tables Table creation DURABILITY=SCHEMA_ONLY Pro: • No IO at all Con: • Lose data on failure • Transient data such as session state • Caching • ETL (staging tables)
- 13. Durability level Configuration Pros/Cons Scenarios Full durability Default DURABILITY=SCHEMA_AND_DATA Pro: • Every committed change is guaranteed to survive failure Con: • Latency impact: every commit requires log IO Default: most scenarios need full durability Delayed durability • Transaction commit time COMMIT WITH (DELAYED_DURABILITY=ON) • Atomic block of native procedure BEGIN ATOMIC WITH (DELAYED_DURABILITY=ON, …) • Database level ALTER DATABASE CURRENT SET DELAYED_DURABILITY=FORCED Pro: • Low latency due to no log IO in transaction execution path • Efficient log IO due to batching Con: • Limited data loss on failure (usually ~60K or ~1ms worth) Low latency requirements Can accept some data loss OR Copy of recent data exists elsewhere in case of failure AlwaysOn auto-failover (sync replicas) with low latency Non- durable tables Table creation DURABILITY=SCHEMA_ONLY Pro: • No IO at all Con: • Lose data on failure • Transient data such as session state • Caching • ETL (staging tables)
- 14. In-Memory OLTP perf demo
- 29. Applications / Web servers
- 31. ##temp
- 33. Memory-optimized table variables and temp tables
- 34. Data Warehouse – SQL Server 2016
- 36. Resources • Samples Perf demo WideWorldImporters sample DB IoT sample leveraging temporal tables release page Azure DB Sample Enable database for In-Memory OLTP SQL Server Samples GitHub Repository • Documentation Blog on In-Memory OLTP in SQL 2016 Memory-optimized table variables and temp tables MSDN Documentation for In-Memory OLTP Azure DB documentation for In-Memory What’s new in SQL2016 • Migrating apps In-Memory OLTP Common Workloads and Migration Considerations Migrating to In-Memory OLTP Guidelines for using Indexes • Related videos Microsoft Virtual Academy talks Recent investments in In-Memory OLTP