Muokkaa

Jaa

Optimize performance by using in-memory technologies in Azure SQL Database

  • Artikkeli

Applies to: Azure SQL Database

In-memory technologies enable you to improve performance of your application, and potentially reduce cost of your database.

When to use in-memory technologies

By using in-memory technologies, you can achieve performance improvements with various workloads:

  • Transactional (online transactional processing (OLTP)) where most of the requests read or update smaller set of data, for example, create/read/update/delete (CRUD) operations.
  • Analytic (online analytical processing (OLAP)) where most of the queries have complex calculations for reporting purposes, and also regularly scheduled processes that perform load (or bulk load) operations and/or write data changes to existing tables. Often, OLAP workloads are updated periodically from OLTP workloads.
  • Mixed (hybrid transaction/analytical processing (HTAP)) where both OLTP and OLAP queries are executed on the same set of data.

In-memory technologies can improve performance of these workloads by keeping the data that should be processed into the memory, using native compilation of the queries, or advanced processing such as batch processing and SIMD instructions that are available on the underlying hardware.

Overview

Azure SQL Database supports the following in-memory technologies:

  • In-Memory OLTP increases number of transactions per second 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 fast execution of analytics queries on the OLTP database, while reducing the impact on the operational workload.
  • Memory-optimized clustered columnstore indexes for HTAP enables you to perform fast transaction processing, and to concurrently run analytics queries very quickly on the same data.

Columnstore indexes and In-Memory OLTP were introduced to SQL Server in 2012 and 2014, respectively. Azure SQL Database, Azure SQL Managed Instance, and SQL Server share the same implementation of in-memory technologies.

Note

For a detailed step-by-step tutorial to demonstrate the performance advantages of In-Memory OLTP technology, using the AdventureWorksLT sample database and ostress.exe, see In-memory sample in Azure SQL Database.

Benefits of in-memory technology

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.

By using In-Memory OLTP, Quorum Business Solutions was able to double their workload while improving DTUs by 70%. For more information, see In-Memory OLTP in Azure SQL Database.

Note

In-Memory OLTP is available in the Premium (DTU) and Business Critical (vCore) service tiers of Azure SQL Database. The Hyperscale service tier supports a subset of In-Memory OLTP objects. For more information, see Hyperscale limitations.

Columnstore indexes are available in all service tiers except for the Basic tier, and the Standard tier when the service objective is below S3. For more information, see Change service tiers of databases containing columnstore indexes.

This article describes aspects of In-Memory OLTP and columnstore indexes that are specific to Azure SQL Database, and also includes samples that let you see:

  • The impact of these technologies on storage and data size limits.
  • How to manage the movement of databases that use these technologies between the different pricing tiers.
  • An illustrative use of In-Memory OLTP, as well as columnstore indexes.

For more information about in-memory technologies in SQL Server, see:

In-Memory OLTP

In-Memory OLTP technology provides extremely fast data access operations by keeping all data in memory. It also uses specialized indexes, native compilation of queries, and latch-free data-access to improve performance of the OLTP workload. There are two ways to organize your In-Memory OLTP data:

  • Memory-optimized rowstore format where every row is a separate memory object. This is a classic In-Memory OLTP format optimized for high-performance OLTP workloads. There are two types of memory-optimized tables that can be used in the memory-optimized rowstore format:

    • Durable tables (SCHEMA_AND_DATA) where the rows placed in memory are preserved after server restart. This type of tables behaves like a traditional rowstore table with the additional benefits of in-memory optimizations.
    • Nondurable tables (SCHEMA_ONLY) where the rows are not-preserved after restart. This type of table is designed for temporary data (for example, replacement of temp tables), or tables where you need to quickly load data before you move it to some persisted table (so called staging tables).

  • Memory-optimized columnstore format where data is organized in a columnar format. This structure is designed for HTAP scenarios where you need to run analytic queries on the same data structure where your OLTP workload is running.

Note

In-Memory OLTP technology is designed for the data structures that can fully reside in memory. Since the in-memory data cannot be offloaded to disk, make sure that you are using database that has enough memory. For more information, see Data size and storage cap for In-Memory OLTP.

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. Each service objective has a memory quota or cap for memory-optimized tables, known as In-Memory OLTP storage.

Each supported single database service objective and each elastic pool service objective includes a certain amount of In-Memory OLTP storage:

The following items count toward your In-Memory OLTP storage cap:

  • Active user data rows in memory-optimized tables and table variables. 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 service objective of the database or elastic pool.

For details about monitoring In-Memory OLTP storage utilization and configuring alerts when you almost hit the cap, see Monitor In-Memory OLTP 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 Max eDTU or Max vCore for databases that is lower than the eDTU or vCore count for the pool as a whole. This maximum also caps the In-Memory OLTP storage utilization in any database in the pool proportionally.
  • Configure a Min eDTU or Min vCore that 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 Min eDTU or Min vCore.

Change service tiers of databases that use In-Memory OLTP technologies

In-Memory OLTP isn't supported in the General Purpose, Standard, and Basic service tiers of Azure SQL Database. Therefore, it isn't possible to scale a database that has any In-Memory OLTP objects to one of these tiers. If you want to scale a database to one of these service tiers, remove all memory-optimized tables and table types as well as all natively compiled T-SQL modules, or convert them to disk-based objects and regular T-SQL modules.

When you scale down a Business Critical or a Premium database, data in the memory-optimized tables must fit within the In-Memory OLTP storage that is available in the destination service objective of the database or elastic pool. If you try to scale down the database or elastic pool, or move a database into an elastic pool, and the destination service objective doesn't have enough available In-Memory OLTP storage, the operation fails.

Determine whether In-Memory OLTP objects exist

There is a programmatic way to find 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.

The following queries identify all objects that need to be removed before a database can be scaled to the Hyperscale, General Purpose, Standard, or Basic service tier:

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;

In-memory columnstore

In-memory columnstore technology is enabling you to store and query a large amount of data in the tables. Columnstore technology uses column-based data storage format and batch query processing to achieve gain up to 10 times the query performance in OLAP workloads over traditional row-oriented storage. You can also achieve gains up to 10 times the data compression over the uncompressed data size.

There are two types of columnstore indexes that you can use to organize your data:

  • Clustered columnstore where all data in the table is organized in the columnar format. In this type of index, all rows in the table are placed in columnar format that highly compresses the data and enables you to execute fast analytical queries and reports on the table. Depending on the nature of your data, the size of your data might be decreased 10x-100x. Clustered columnstore indexes also enable fast ingestion of large amount of data (bulk-load) since large batches of data greater than 100,000 rows are compressed before they are stored on disk. This type of index is a good choice for the classic data warehouse scenarios.
  • Non-clustered columnstore where the data is stored in traditional rowstore table and there is an additional index in the columnstore format that is used for the analytical queries. This type of index enables Hybrid Transactional-Analytic Processing (HTAP): the ability to run fast real-time analytics on a transactional workload. OLTP queries are executed on rowstore table that is optimized for accessing a small set of rows, while OLAP queries are executed on columnstore index that is better choice for scans and analytics. The query optimizer dynamically chooses rowstore or columnstore format based on the query. Nonclustered columnstore indexes don't decrease the size of the data since original data-set is kept in the original rowstore table without any change. However, the size of the additional columnstore index is orders of magnitude smaller than the equivalent B-tree index.

Note

In-memory columnstore technology keeps only the data that is needed for processing in memory, while the data that cannot fit in memory is stored on disk. Therefore, the amount of data in columnstore structures can exceed the amount of available memory.

Data size and storage for columnstore indexes

Columnstore indexes aren't required to fully 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. Compression ratio can be further increased with columnar archival 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 significant as with clustered columnstore indexes. However, if you're replacing many traditional nonclustered indexes with a single columnstore index, you can still see an overall savings in the storage footprint for the table. You can also use rowstore data compression for the base table.

Change service tiers of databases containing columnstore indexes

If you use the DTU purchasing model and your database contains columnstore indexes, your application might stop working if you scale your database below the S3 service objective. Columnstore indexes are supported only in the Hyperscale, Business Critical, and Premium service tiers, as well as in the Standard service tier if using S3 and above. Columnstore indexes are not supported in the Basic service tier. When you scale your database to an unsupported service tier or service objective, your columnstore index becomes unavailable. The system maintains the index when you execute DML statements, but it never uses the index. If you later scale back to a supported service tier or service objective, your columnstore index is immediately ready to be used again.

If you have a clustered columnstore index, the entire table becomes unavailable if the database is scaled to an unsupported service tier or service objective. Drop all clustered columnstore indexes, replacing them with rowstore clustered indexes or heaps, before the scaling operation.

Related content