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In-memory sample in Azure SQL Database

  • Artikkeli

Applies to: Azure SQL Database

In-memory technologies in Azure SQL Database enable you to improve performance of your application, and potentially reduce cost of your database. By using in-memory technologies in Azure SQL Database, you can achieve performance improvements with various workloads.

Two samples in this article illustrate the use of In-Memory OLTP as well as columnstore indexes in Azure SQL Database.

For more information, see:

For an introductory demo of In-Memory OLTP, see:

1. Install the In-Memory OLTP sample

You can create the AdventureWorksLT sample database with a few steps in the Azure portal. Then, use the steps in this section to add In-Memory OLTP objects to your AdventureWorksLT database and demonstrate performance benefits.

Installation steps

  1. In the Azure portal, create a Premium (DTU) or Business Critical (vCore) database on a logical server. Set the Source to the AdventureWorksLT sample database. For detailed instructions, see Create your first database in Azure SQL Database.

  2. Connect to the database with SQL Server Management Studio (SSMS).

  3. Copy the In-Memory OLTP Transact-SQL script to your clipboard. The T-SQL script creates the necessary in-memory objects in the AdventureWorksLT sample database that you created in step 1.

  4. Paste the T-SQL script into SSMS, and then execute the script. The MEMORY_OPTIMIZED = ON clause in the CREATE TABLE statements is crucial. For example:

    CREATE TABLE [SalesLT].[SalesOrderHeader_inmem](
    [SalesOrderID] int IDENTITY NOT NULL PRIMARY KEY NONCLUSTERED ...,
    ...
) WITH (MEMORY_OPTIMIZED = ON);

Error 40536

If you get error 40536 when you run the T-SQL script, run the following T-SQL script to verify whether the database supports in-memory objects:

SELECT DATABASEPROPERTYEX(DB_NAME(), 'IsXTPSupported');

A result of 0 means that In-Memory OLTP isn't supported, and 1 means that it is supported. In-Memory OLTP is available in Azure SQL Database Premium (DTU) and Business Critical (vCore) tiers.

About the created memory-optimized items

Tables: The sample contains the following memory-optimized tables:

  • SalesLT.Product_inmem
  • SalesLT.SalesOrderHeader_inmem
  • SalesLT.SalesOrderDetail_inmem
  • Demo.DemoSalesOrderHeaderSeed
  • Demo.DemoSalesOrderDetailSeed

You can filter to show only memory-optimized tables in Object Explorer in SSMS. When you right-click on Tables, then navigate to > Filter > Filter Settings > Is Memory Optimized. The value equals 1.

Or you can query catalog views, such as:

SELECT is_memory_optimized, name, type_desc, durability_desc
    FROM sys.tables
    WHERE is_memory_optimized = 1;

Natively compiled stored procedure: You can inspect SalesLT.usp_InsertSalesOrder_inmem through a catalog view query:

SELECT uses_native_compilation, OBJECT_NAME(object_id) AS module_name, definition
    FROM sys.sql_modules
    WHERE uses_native_compilation = 1;

2. Run the sample OLTP workload

The only difference between the following two stored procedures is that the first procedure uses memory-optimized tables, while the second procedure uses the regular on-disk tables:

  • SalesLT.usp_InsertSalesOrder_inmem
  • SalesLT.usp_InsertSalesOrder_ondisk

In this section, you see how to use the ostress.exe utility to execute the two stored procedures. You can compare how long it takes for the two stress runs to finish.

Install RML utilities and ostress

Preferably, you should run ostress.exe on an Azure virtual machine (VM). You would create an Azure VM in the same Azure region where your AdventureWorksLT database resides. You can also run ostress.exe on your local machine instead if you can connect to your Azure SQL database. However, network latency between your machine and the database in Azure might reduce performance benefits of In-Memory OLTP.

On the VM, or on whatever host you choose, install the Replay Markup Language (RML) utilities. The utilities include ostress.exe.

For more information, see:

Script for ostress.exe

This section displays the T-SQL script that is embedded in our ostress.exe command line. The script uses items that were created by the T-SQL script that you installed earlier.

When you run ostress.exe, we recommend that you pass parameter values designed to stress the workload using both of the following strategies:

  • Run a large number of concurrent connections, by using -n100.
  • Have each connection repeat hundreds of times, by using -r500.

However, you might want to start with much smaller values like -n10 and -r50 to ensure that everything is working.

The following script inserts a sample sales order with five line items into the following memory-optimized tables:

  • SalesLT.SalesOrderHeader_inmem
  • SalesLT.SalesOrderDetail_inmem
DECLARE
    @i int = 0,
    @od SalesLT.SalesOrderDetailType_inmem,
    @SalesOrderID int,
    @DueDate datetime2 = sysdatetime(),
    @CustomerID int = rand() * 8000,
    @BillToAddressID int = rand() * 10000,
    @ShipToAddressID int = rand() * 10000;

INSERT INTO @od
    SELECT OrderQty, ProductID
    FROM Demo.DemoSalesOrderDetailSeed
    WHERE OrderID= cast((rand()*60) as int);

WHILE (@i < 20)
BEGIN;
    EXECUTE SalesLT.usp_InsertSalesOrder_inmem @SalesOrderID OUTPUT,
        @DueDate, @CustomerID, @BillToAddressID, @ShipToAddressID, @od;
    SET @i = @i + 1;
END

To make the _ondisk version of the preceding T-SQL script for ostress.exe, you would replace both occurrences of the _inmem substring with _ondisk. These replacements affect the names of tables and stored procedures.

Run the _inmem stress workload first

You can use an RML Cmd Prompt window to run ostress.exe. The command-line parameters direct ostress to:

  • Run 100 connections concurrently (-n100).
  • Have each connection run the T-SQL script 50 times (-r50).
ostress.exe -n100 -r50 -S<servername>.database.windows.net -U<login> -P<password> -d<database> -q -Q"DECLARE @i int = 0, @od SalesLT.SalesOrderDetailType_inmem, @SalesOrderID int, @DueDate datetime2 = sysdatetime(), @CustomerID int = rand() * 8000, @BillToAddressID int = rand() * 10000, @ShipToAddressID int = rand()* 10000; INSERT INTO @od SELECT OrderQty, ProductID FROM Demo.DemoSalesOrderDetailSeed WHERE OrderID= cast((rand()*60) as int); WHILE (@i < 20) begin; EXECUTE SalesLT.usp_InsertSalesOrder_inmem @SalesOrderID OUTPUT, @DueDate, @CustomerID, @BillToAddressID, @ShipToAddressID, @od; set @i += 1; end"

To run the preceding ostress.exe command line:

  1. Reset the database data content by running the following command in SSMS, to delete all the data that was inserted by any previous runs:

    EXECUTE Demo.usp_DemoReset;

  2. Copy the text of the preceding ostress.exe command line to your clipboard.

  3. Replace the <placeholders> for the parameters -S -U -P -d with the correct values.

  4. Run your edited command line in an RML Cmd window.

Result is a duration

When ostress.exe finishes, it writes the run duration as its final line of output in the RML Cmd window. For example, a shorter test run lasted about 1.5 minutes:

11/12/15 00:35:00.873 [0x000030A8] OSTRESS exiting normally, elapsed time: 00:01:31.867

Reset, edit for _ondisk, then rerun

After you have the result from the _inmem run, perform the following steps for the _ondisk run:

  1. Reset the database by running the following command in SSMS to delete all the data that was inserted by the previous run:

    EXECUTE Demo.usp_DemoReset;

  2. Edit the ostress.exe command line to replace all _inmem with _ondisk.

  3. Rerun ostress.exe for the second time, and capture the duration result.

  4. Again, reset the database.

Expected comparison results

Our In-Memory OLTP tests have shown that performance improved by nine times for this simplistic workload, with ostress.exe running on an Azure VM in the same Azure region as the database.

3. Install the in-memory analytics sample

In this section, you compare the IO and statistics results when you're using a columnstore index versus a traditional b-tree index.

For real-time analytics on an OLTP workload, it's often best to use a nonclustered columnstore index. For details, see Columnstore Indexes Described.

Prepare the columnstore analytics test

  1. Use the Azure portal to create a fresh AdventureWorksLT database from the sample. Use any service objective that supports columnstore indexes.

  2. Copy the sql_in-memory_analytics_sample to your clipboard.

    • The T-SQL script creates the necessary objects in the AdventureWorksLT sample database that you created in step 1.
    • The script creates dimension tables and two fact tables. The fact tables are populated with 3.5 million rows each.
    • On smaller service objectives, the script might take 15 minutes or longer to complete.

  3. Paste the T-SQL script into SSMS, and then execute the script. The COLUMNSTORE keyword in the CREATE INDEX statement is crucial: CREATE NONCLUSTERED COLUMNSTORE INDEX ...;

  4. Set AdventureWorksLT to the latest compatibility level, SQL Server 2022 (160): ALTER DATABASE AdventureworksLT SET compatibility_level = 160;

Key tables and columnstore indexes

  • dbo.FactResellerSalesXL_CCI is a table that has a clustered columnstore index, which has advanced compression at the data level.

  • dbo.FactResellerSalesXL_PageCompressed is a table that has an equivalent regular clustered index, compressed only at the page level.

4. Key queries to compare the columnstore index

There are several T-SQL query types that you can run to see performance improvements. In step 2 in the T-SQL script, pay attention to this pair of queries. They differ only on one line:

  • FROM FactResellerSalesXL_PageCompressed AS a
  • FROM FactResellerSalesXL_CCI AS a

A clustered columnstore index is on the FactResellerSalesXL_CCI table.

The following T-SQL script prints the logical I/O activity and time statistics, using SET STATISTICS IO and SET STATISTICS TIME for each query.

/*********************************************************************
Step 2 -- Overview
-- Page compressed BTree table vs Columnstore table performance differences
-- Enable actual query plan in order to see Plan differences when executing.
*/
-- Ensure the database uses the latest compatibility level
ALTER DATABASE AdventureworksLT SET compatibility_level = 160
GO

-- Execute a typical query that joins the fact table with dimension tables.
-- Note this query will run on the page compressed table. Note down the time.
SET STATISTICS IO ON
SET STATISTICS TIME ON
GO

SELECT c.Year
    ,e.ProductCategoryKey
    ,FirstName + ' ' + LastName AS FullName
    ,COUNT(SalesOrderNumber) AS NumSales
    ,SUM(SalesAmount) AS TotalSalesAmt
    ,AVG(SalesAmount) AS AvgSalesAmt
    ,COUNT(DISTINCT SalesOrderNumber) AS NumOrders
    ,COUNT(DISTINCT a.CustomerKey) AS CountCustomers
FROM FactResellerSalesXL_PageCompressed AS a
INNER JOIN DimProduct AS b ON b.ProductKey = a.ProductKey
INNER JOIN DimCustomer AS d ON d.CustomerKey = a.CustomerKey
INNER JOIN DimProductSubCategory AS e on e.ProductSubcategoryKey = b.ProductSubcategoryKey
INNER JOIN DimDate AS c ON c.DateKey = a.OrderDateKey
GROUP BY e.ProductCategoryKey,c.Year,d.CustomerKey,d.FirstName,d.LastName
GO

SET STATISTICS IO OFF
SET STATISTICS TIME OFF
GO


-- This is the same query on a table with a clustered columnstore index (CCI).
-- The comparison numbers are the more pronounced the larger the table is (this is an 11 million row table).
SET STATISTICS IO ON
SET STATISTICS TIME ON
GO
SELECT c.Year
    ,e.ProductCategoryKey
    ,FirstName + ' ' + LastName AS FullName
    ,COUNT(SalesOrderNumber) AS NumSales
    ,SUM(SalesAmount) AS TotalSalesAmt
    ,AVG(SalesAmount) AS AvgSalesAmt
    ,COUNT(DISTINCT SalesOrderNumber) AS NumOrders
    ,COUNT(DISTINCT a.CustomerKey) AS CountCustomers
FROM FactResellerSalesXL_CCI AS a
INNER JOIN DimProduct AS b ON b.ProductKey = a.ProductKey
INNER JOIN DimCustomer AS d ON d.CustomerKey = a.CustomerKey
INNER JOIN DimProductSubCategory AS e on e.ProductSubcategoryKey = b.ProductSubcategoryKey
INNER JOIN DimDate AS c ON c.DateKey = a.OrderDateKey
GROUP BY e.ProductCategoryKey,c.Year,d.CustomerKey,d.FirstName,d.LastName
GO

SET STATISTICS IO OFF
SET STATISTICS TIME OFF
GO

In a database using the P2 service objective, you can expect about nine times the performance gain for this query by using the clustered columnstore index compared with the traditional rowstore index. With the P15 service objective, you can expect about 57 times the performance gain by using the columnstore index.

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