Optimize performance by using In-Memory technologies in SQL Database
By using In-Memory technologies in Azure SQL Database, you can achieve performance improvements with various workloads: transactional (online transactional processing (OLTP)), analytics (online analytical processing (OLAP)), and mixed (hybrid transaction/analytical processing (HTAP)). Because of the more efficient query and transaction processing, In-Memory technologies also help you to reduce cost. You typically don't need to upgrade the pricing tier of the database to achieve performance gains. In some cases, you might even be able reduce the pricing tier, while still seeing performance improvements with In-Memory technologies.
Here are two examples of how In-Memory OLTP helped to significantly improve performance:
- By using In-Memory OLTP, Quorum Business Solutions was able to double their workload while improving DTUs by 70%.
- DTU means database transaction unit, and it includes a measurement of resource consumption.
- The following video demonstrates significant improvement in resource consumption with a sample workload: In-Memory OLTP in Azure SQL Database Video.
- For more information, see the blog post: In-Memory OLTP in Azure SQL Database Blog Post
In-Memory technologies are available in all databases in the Premium tier, including databases in Premium elastic pools.
The following video explains potential performance gains with In-Memory technologies in Azure SQL Database. Remember that the performance gain that you see always depends on many factors, including the nature of the workload and data, access pattern of the database, and so on.
Azure SQL Database has the following In-Memory technologies:
- In-Memory OLTP increases transaction and reduces latency for transaction processing. Scenarios that benefit from In-Memory OLTP are: high-throughput transaction processing such as trading and gaming, data ingestion from events or IoT devices, caching, data load, and temporary table and table variable scenarios.
- Clustered columnstore indexes reduce your storage footprint (up to 10 times) and improve performance for reporting and analytics queries. You can use it with fact tables in your data marts to fit more data in your database and improve performance. Also, you can use it with historical data in your operational database to archive and be able to query up to 10 times more data.
- Nonclustered columnstore indexes for HTAP help you to gain real-time insights into your business through querying the operational database directly, without the need to run an expensive extract, transform, and load (ETL) process and wait for the data warehouse to be populated. Nonclustered columnstore indexes allow very fast execution of analytics queries on the OLTP database, while reducing the impact on the operational workload.
- You can also have the combination of a memory-optimized table with a columnstore index. This combination enables you to perform very fast transaction processing, and to concurrently run analytics queries very quickly on the same data.
Both columnstore indexes and In-Memory OLTP have been part of the SQL Server product since 2012 and 2014, respectively. Azure SQL Database and SQL Server share the same implementation of In-Memory technologies. Going forward, new capabilities for these technologies are released in Azure SQL Database first, before they are released in SQL Server.
This article describes aspects of In-Memory OLTP and columnstore indexes that are specific to Azure SQL Database and also includes samples:
- You'll see the impact of these technologies on storage and data size limits.
- You'll see how to manage the movement of databases that use these technologies between the different pricing tiers.
- You'll see two samples that illustrate the use of In-Memory OLTP, as well as columnstore indexes in Azure SQL Database.
See the following resources for more information.
In-depth information about the technologies:
- In-Memory OLTP Overview and Usage Scenarios (includes references to customer case studies and information to get started)
- Documentation for In-Memory OLTP
- Columnstore Indexes Guide
- Hybrid transactional/analytical processing (HTAP), also known as real-time operational analytics
A quick primer on In-Memory OLTP: Quick Start 1: In-Memory OLTP Technologies for Faster T-SQL Performance (another article to help you get started)
In-depth videos about the technologies:
- In-Memory OLTP in Azure SQL Database (which contains a demo of performance benefits and steps to reproduce these results yourself)
- In-Memory OLTP Videos: What it is and When/How to use it
- Columnstore Index: In-Memory Analytics Videos from Ignite 2016
Storage and data size
Data size and storage cap for In-Memory OLTP
In-Memory OLTP includes memory-optimized tables, which are used for storing user data. These tables are required to fit in memory. Because you manage memory directly in the SQL Database service, we have the concept of a quota for user data. This idea is referred to as In-Memory OLTP storage.
Each supported single database pricing tier and each elastic pool pricing tier includes a certain amount of In-Memory OLTP storage. See DTU-based resource limits - single database, DTU-based resource limits - elastic pools,vCore-based resource limits - single databases and vCore-based resource limits - elastic pools.
The following items count toward your In-Memory OLTP storage cap:
- Active user data rows in memory-optimized tables and table variables. Note that old row versions don't count toward the cap.
- Indexes on memory-optimized tables.
- Operational overhead of ALTER TABLE operations.
If you hit the cap, you receive an out-of-quota error, and you are no longer able to insert or update data. To mitigate this error, delete data or increase the pricing tier of the database or pool.
For details about monitoring In-Memory OLTP storage utilization and configuring alerts when you almost hit the cap, see Monitor In-Memory storage.
About elastic pools
With elastic pools, the In-Memory OLTP storage is shared across all databases in the pool. Therefore, the usage in one database can potentially affect other databases. Two mitigations for this are:
- Configure a
MaxvCorefor databases that is lower than the eDTU or vCore count for the pool as a whole. This maximum caps the In-Memory OLTP storage utilization, in any database in the pool, to the size that corresponds to the eDTU count.
- Configure a
MinvCorethat is greater than 0. This minimum guarantees that each database in the pool has the amount of available In-Memory OLTP storage that corresponds to the configured
Data size and storage for columnstore indexes
Columnstore indexes aren't required to fit in memory. Therefore, the only cap on the size of the indexes is the maximum overall database size, which is documented in the DTU-based purchasing model and vCore-based purchasing model articles.
When you use clustered columnstore indexes, columnar compression is used for the base table storage. This compression can significantly reduce the storage footprint of your user data, which means that you can fit more data in the database. And the compression can be further increased with [columnar archival compression](https://msdn.microsoft.com/library/cc280449.aspx#Using Columnstore and Columnstore Archive Compression). The amount of compression that you can achieve depends on the nature of the data, but 10 times the compression is not uncommon.
For example, if you have a database with a maximum size of 1 terabyte (TB) and you achieve 10 times the compression by using columnstore indexes, you can fit a total of 10 TB of user data in the database.
When you use nonclustered columnstore indexes, the base table is still stored in the traditional rowstore format. Therefore, the storage savings aren't as big as with clustered columnstore indexes. However, if you're replacing a number of traditional nonclustered indexes with a single columnstore index, you can still see an overall savings in the storage footprint for the table.
Moving databases that use In-Memory technologies between pricing tiers
There are never any incompatibilities or other problems when you upgrade to a higher pricing tier, such as from Standard to Premium. The available functionality and resources only increase.
But downgrading the pricing tier can negatively impact your database. The impact is especially apparent when you downgrade from Premium to Standard or Basic when your database contains In-Memory OLTP objects. Memory-optimized tables are unavailable after the downgrade (even if they remain visible). The same considerations apply when you're lowering the pricing tier of an elastic pool, or moving a database with In-Memory technologies, into a Standard or Basic elastic pool.
Downgrading to Basic/Standard: In-Memory OLTP isn't supported in databases in the Standard or Basic tier. In addition, it isn't possible to move a database that has any In-Memory OLTP objects to the Standard or Basic tier.
There is a programmatic way to understand whether a given database supports In-Memory OLTP. You can execute the following Transact-SQL query:
SELECT DatabasePropertyEx(DB_NAME(), 'IsXTPSupported');
If the query returns 1, In-Memory OLTP is supported in this database.
Before you downgrade the database to Standard/Basic, remove all memory-optimized tables and table types, as well as all natively compiled T-SQL modules. The following queries identify all objects that need to be removed before a database can be downgraded to Standard/Basic:
SELECT * FROM sys.tables WHERE is_memory_optimized=1 SELECT * FROM sys.table_types WHERE is_memory_optimized=1 SELECT * FROM sys.sql_modules WHERE uses_native_compilation=1
Downgrading to a lower Premium tier: Data in memory-optimized tables must fit within the In-Memory OLTP storage that is associated with the pricing tier of the database or is available in the elastic pool. If you try to lower the pricing tier or move the database into a pool that doesn't have enough available In-Memory OLTP storage, the operation fails.
Downgrading to Basic or Standard: Columnstore indexes are supported only on the Premium pricing tier and on the Standard tier, S3 and above, and not on the Basic tier. When you downgrade your database to an unsupported tier or level, your columnstore index becomes unavailable. The system maintains your columnstore index, but it never leverages the index. If you later upgrade back to a supported tier or level, your columnstore index is immediately ready to be leveraged again.
If you have a clustered columnstore index, the whole table becomes unavailable after the downgrade. Therefore we recommend that you drop all clustered columnstore indexes before you downgrade your database to an unsupported tier or level.
Downgrading to a lower supported tier or level: This downgrade succeeds if the whole database fits within the maximum database size for the target pricing tier, or within the available storage in the elastic pool. There is no specific impact from the columnstore indexes.
1. Install the In-Memory OLTP sample
You can create the AdventureWorksLT sample database with a few clicks in the Azure portal. Then, the steps in this section explain how you can enrich your AdventureWorksLT database with In-Memory OLTP objects and demonstrate performance benefits.
For a more simplistic, but more visually appealing performance demo for In-Memory OLTP, see:
In the Azure portal, create a Premium or Business Critical database on a server. Set the Source to the AdventureWorksLT sample database. For detailed instructions, see Create your first Azure SQL database.
Connect to the database with SQL Server Management Studio (SSMS.exe).
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.
Paste the T-SQL script into SSMS, and then execute the script. The
MEMORY_OPTIMIZED = ONclause CREATE TABLE statements are crucial. For example:
CREATE TABLE [SalesLT].[SalesOrderHeader_inmem]( [SalesOrderID] int IDENTITY NOT NULL PRIMARY KEY NONCLUSTERED ..., ... ) WITH (MEMORY_OPTIMIZED = ON);
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:
SELECT DatabasePropertyEx(DB_Name(), 'IsXTPSupported');
A result of 0 means that In-Memory isn't supported, and 1 means that it is supported. To diagnose the problem, ensure that the database is at the Premium service tier.
About the created memory-optimized items
Tables: The sample contains the following memory-optimized tables:
You can inspect memory-optimized tables through the Object Explorer in SSMS. Right-click Tables > Filter > Filter Settings > Is Memory Optimized. The value equals 1.
Or you can query the 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), definition FROM sys.sql_modules WHERE uses_native_compilation = 1;
Run the sample OLTP workload
The only difference between the following two stored procedures is that the first procedure uses memory-optimized versions of the tables, while the second procedure uses the regular on-disk tables:
In this section, you see how to use the handy ostress.exe utility to execute the two stored procedures at stressful levels. You can compare how long it takes for the two stress runs to finish.
When you run ostress.exe, we recommend that you pass parameter values designed for both of the following:
- Run a large number of concurrent connections, by using -n100.
- Have each connection loop 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.
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.
The following script inserts a sample sales order with five line items into the following memory-optimized tables:
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.
Install RML utilities and ostress
Ideally, you would plan to run ostress.exe on an Azure virtual machine (VM). You would create an Azure VM in the same Azure geographic region where your AdventureWorksLT database resides. But you can run ostress.exe on your laptop instead.
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:
- The ostress.exe discussion in Sample Database for In-Memory OLTP.
- Sample Database for In-Memory OLTP.
- The blog for installing ostress.exe.
Run the _inmem stress workload first
You can use an RML Cmd Prompt window to run our ostress.exe command line. 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:
Reset the database data content by running the following command in SSMS, to delete all the data that was inserted by any previous runs:
Copy the text of the preceding ostress.exe command line to your clipboard.
<placeholders>for the parameters -S -U -P -d with the correct real values.
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:
Reset the database by running the following command in SSMS to delete all the data that was inserted by the previous run:
Edit the ostress.exe command line to replace all _inmem with _ondisk.
Rerun ostress.exe for the second time, and capture the duration result.
Again, reset the database (for responsibly deleting what can be a large amount of test data).
Expected comparison results
Our In-Memory tests have shown that performance improved by nine times for this simplistic workload, with ostress running on an Azure VM in the same Azure region as the database.
2. 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
Use the Azure portal to create a fresh AdventureWorksLT database from the sample.
- Use that exact name.
- Choose any Premium service tier.
Copy the sql_in-memory_analytics_sample to your clipboard.
- The T-SQL script creates the necessary In-Memory objects in the AdventureWorksLT sample database that you created in step 1.
- The script creates the Dimension table and two fact tables. The fact tables are populated with 3.5 million rows each.
- The script might take 15 minutes to complete.
Paste the T-SQL script into SSMS, and then execute the script. The COLUMNSTORE keyword in the CREATE INDEX statement is crucial, as in:
CREATE NONCLUSTERED COLUMNSTORE INDEX ...;
Set AdventureWorksLT to compatibility level 130:
ALTER DATABASE AdventureworksLT SET compatibility_level = 130;
Level 130 is not directly related to In-Memory features. But level 130 generally provides faster query performance than 120.
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, which is compressed only at the page level.
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 a
FROM FactResellerSalesXL_CCI a
A clustered columnstore index is in the FactResellerSalesXL_CCI table.
The following T-SQL script excerpt prints statistics for IO and TIME for the query of each table.
/********************************************************************* Step 2 -- Overview -- Page Compressed BTree table v/s Columnstore table performance differences -- Enable actual Query Plan in order to see Plan differences when Executing */ -- Ensure Database is in 130 compatibility mode ALTER DATABASE AdventureworksLT SET compatibility_level = 130 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 a INNER JOIN DimProduct b ON b.ProductKey = a.ProductKey INNER JOIN DimCustomer d ON d.CustomerKey = a.CustomerKey Inner JOIN DimProductSubCategory e on e.ProductSubcategoryKey = b.ProductSubcategoryKey INNER JOIN DimDate 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 Prior query on a table with a clustered columnstore index CCI -- The comparison numbers are even more dramatic the larger the table is (this is an 11 million row table only) 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 a INNER JOIN DimProduct b ON b.ProductKey = a.ProductKey INNER JOIN DimCustomer d ON d.CustomerKey = a.CustomerKey Inner JOIN DimProductSubCategory e on e.ProductSubcategoryKey = b.ProductSubcategoryKey INNER JOIN DimDate 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 with the P2 pricing tier, you can expect about nine times the performance gain for this query by using the clustered columnstore index compared with the traditional index. With P15, you can expect about 57 times the performance gain by using the columnstore index.
Monitor In-Memory OLTP storage for In-Memory OLTP
See Common Workload Patterns and Migration Considerations (which describes workload patterns where In-Memory OLTP commonly provides significant performance gains)