In one of my previous articles I briefly introduced In-Memory Optimization in SQL Server. In this article we will conduct a simple experiment utilizing this new technology and discussing the benefits. The experiment will be organized in the following steps:
- Create the In-Memory processing enabled database
- Create the tables (disk-based, memory-enabled)
- Populate the tables with sample data
- Prepare the processing code for three scenarios (execution modes):
- Disk-based processing
- In-Memory optimization
- In-Memory optimization with natively compiled stored procedure
- Run all three execution modes and measure execution times
- Discuss findings
Let’s Write Some Code!
OK, enough words, let’s write some code!
Step 1: Create the In-Memory processing enabled database
--Creates a memory-enabled database by adding a memory-optimized filegroup with a specified directory. --Note: This assumes that directory c:\tmp exists (the subdirectory is created by SQL Server). CREATE DATABASE InMemDB; GO ALTER DATABASE InMemDB ADD FILEGROUP [inMemOLTP_fg] CONTAINS MEMORY_OPTIMIZED_DATA ALTER DATABASE InMemDB ADD FILE ( name = [inMemOLTP_fg_dir], filename= 'c:\tmp\InMemOLTPDir') TO FILEGROUP [inMemOLTP_fg]; GO
Step 2: Create the tables (disk-based, memory-enabled)
--Create sample tables representing a scenario involving 200000 Products USE InMemDB; GO --Create traditional table (disk-based) CREATE TABLE tblDiskBased( id INT PRIMARY KEY, code VARCHAR(50), descr VARCHAR(250), price float ); GO --Create a durable (data will be persisted) memory-optimized table --Alternative choice: SCHEMA_ONLY = Data will be lost if the server turns off unexpectedly CREATE TABLE tblMemOptimized( id INT NOT NULL PRIMARY KEY NONCLUSTERED HASH WITH (BUCKET_COUNT=400000), code VARCHAR(50), descr VARCHAR(250), price float )WITH (MEMORY_OPTIMIZED=ON, DURABILITY = SCHEMA_AND_DATA); GO --Create a durable (data will be persisted) memory-optimized table --Alternative choice: SCHEMA_ONLY = Data will be lost if the server turns off unexpectedly --Note: This table will be used by the natively-compiled stored procedure CREATE TABLE tblMemOptimized2( id INT NOT NULL PRIMARY KEY NONCLUSTERED HASH WITH (BUCKET_COUNT=400000), code VARCHAR(50), descr VARCHAR(250), price float )WITH (MEMORY_OPTIMIZED=ON, DURABILITY = SCHEMA_AND_DATA); GO
So far: We have created a memory enabled database and data structures. Now, it’s time to populate the tables with sample data.
Step 3: Populate the tables with sample data
--Populate the tables with sample data (200K records) --tblMemOptimized DECLARE @i INT SET @i=0 WHILE (@i<200000) BEGIN INSERT INTO tblMemOptimized SELECT CAST(@i as varchar(6)) as id, ('Code '+ (CAST (@i AS varchar(6)))) as code, ('Description '+ (CAST (@i AS varchar(6)))) as descr, (10 + (500-10)*RAND()) as price SET @i=@i+1 END GO --Populate the tables with sample data (200K records) --tblDiskBased --tblMemOptimized2 INSERT INTO tblDiskBased SELECT * FROM tblMemOptimized; GO INSERT INTO tblMemOptimized2 SELECT * FROM tblMemOptimized; GO
All three tables now have same data (200K rows each). You can easily check it out!
3a. Disk-based processing:
--Disk-based processing CREATE PROCEDURE updateDiskBased as BEGIN UPDATE dbo.tblDiskBased SET price=price-(price*0.05) END GO
3b. In-Memory optimization:
--Memory-Optimized CREATE PROCEDURE updateMemoryOptimized as BEGIN UPDATE dbo.tblMemOptimized SET price=price-(price*0.05) END GO
3c. In-Memory optimization with natively compiled stored procedure:
--Natively-Compiled Stored Procedure CREATE PROCEDURE updateMemoryOptimizedNatively with native_compilation, schemabinding, execute as owner as begin atomic with (transaction isolation level = snapshot,language = N'English') UPDATE dbo.tblMemOptimized2 SET price=price-(price*0.05) END GO
So far: We have created a memory enabled database and data structures. We have populated the tables with sample data (all three tables have identical data for experiment consistency purposes) and we have prepared the stored procedures that target each one of the three tables and perform the processing, that is the update of the ‘price’ column by decreasing it by 5%.
Running the Example
Ok, now it’s time to execute the three stored procedures, thus update all tables and measure execution times and here they are:
Additionally, let’s check once more some records just to confirm that the output is the same in all three tables:
First of all, from the above figure we can see that the output of the three update operations is exactly the same which is something that validates the correctness of the three different execution modes.
As you can see from the screenshot with execution times (Figure 2), the slowest execution was the one that involved the disk-based table (elapsed time: 1924 ms) having also the highest CPU time (328 ms).
The fastest execution was the one of the memory-optimized table with the natively compiled stored procedure where the elapsed time was 329 ms and the CPU 93 ms!
The memory optimized execution was faster than the disk-based with 404 ms elapsed time and 250 ms CPU time but it was not faster than the memory-optimized scenario combined with the natively compiled stored procedure.
In-Memory optimization in SQL Server is a great new technology which can significantly improve performance in many cases. Especially in cases of certain workloads such as staging processes in Data Warehouse systems, high data insert rates (i.e. smart metering) and low latency execution scenarios (i.e. investment trading), In-Memory optimization can boost performance. Of course, before applying it on Production systems, a proper analysis is required along with testing.
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- In-Memory Optimization in SQL Server: A Simple Experiment
- Introducing In-Memory Optimization in SQL Server
- Getting Started with SQL Server In-Memory OLTP – Part 1
- Getting Started with SQL Server In-Memory OLTP – Part 2
- Where are temporary tables stored in SQL Server?
- SQL Server 2016: TempDB Enhancements
- tempdb growth
Check our other related SQL Server Performance articles.
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Reference: SQLNetHub.com (https://www.sqlnethub.com)