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Introduction to Memory-Optimized Tables

SQL Server 2014

Memory-optimized tables are tables, created using CREATE TABLE (SQL Server).

Memory-optimized tables reside in memory. Rows in the table are read from and written to memory. The entire table resides in memory. A second copy of the table data is maintained on disk, but only for durability purposes.

In-Memory OLTP is integrated with SQL Server to provide a seamless experience in all areas such as development, deployment, manageability, and supportability. A database can contain in-memory as well as disk-based objects.

Rows in memory-optimized tables are versioned. This means that each row in the table potentially has multiple versions. All row versions are maintained in the same table data structure. Row versioning is used to allow concurrent reads and writes on the same row. For more information about concurrent reads and writes on the same row, see Transactions in Memory-Optimized Tables.

The following figure illustrates multi-versioning. The figure shows a table with three rows and each row has different versions.


The table has three rows: r1, r2, and r3. r1 has three versions, r2 has two versions, and r3 has four versions. Note that different versions of the same row do not necessarily occupy consecutive memory locations. The different row versions can be dispersed throughout the table data structure.

The memory-optimized table data structure can be seen as a collection of row versions. Rows in disk-based tables are organized in pages and extents, and individual rows addressed using page number and page offset, row versions in memory-optimized tables are addressed using 8-byte memory pointers.

Memory-optimized tables are fully durable by default, and, like transactions on (traditional) disk-based tables, fully durable transactions on memory-optimized tables are fully atomic, consistent, isolated, and durable (ACID). Memory-optimized tables and natively compiled stored procedures support a subset of Transact-SQL.

In-Memory OLTP supports durable tables with transaction durability delayed. Delayed durable transactions are saved to disk soon after the transaction has committed. In exchange for the increased performance, committed transactions that have not saved to disk are lost in a server crash or failover.

Besides the default durable memory-optimized tables, SQL Server also supports non-durable memory-optimized tables, which are not logged and their data is not persisted on disk. This means that transactions on these tables do not require any disk IO, but the data will not be recovered if there is a server crash or failover.

Data in memory-optimized tables is accessed in two ways:

  • Through interpreted Transact-SQL (outside of a natively-compiled stored procedure). These Transact-SQL statements may be either inside interpreted stored procedures or they may be ad-hoc Transact-SQL statements.

  • Through natively compiled stored procedures.

Memory-optimized tables can be accessed most efficiently from natively compiled stored procedures (Natively Compiled Stored Procedures). Memory-optimized tables can also be accessed with (traditional) interpreted Transact-SQL. Interpreted Transact-SQL refers to accessing memory-optimized tables without a natively compiled stored procedure. Some examples of interpreted Transact-SQL access include accessing a memory-optimized table from a DML trigger, ad hoc Transact-SQL batch, view, and table-valued function.

The following table summarizes native and interpreted Transact-SQL access for various objects.


Access Using a Natively Compiled Stored Procedure

Interpreted Transact-SQL Access

CLR Access

Memory-optimized tables



No 1

Memory-Optimized Table Variables




Natively Compiled Stored Procedures

You cannot use the EXECUTE statement to execute any stored procedure from a natively compiled stored procedure.


No 1

1 You cannot access a memory-optimized table or natively compiled stored procedure from the context connection (the connection from SQL Server when executing a CLR module). You can, however, create and open another connection from which you can access memory-optimized tables and natively compiled stored procedures. For more information, see Regular vs. Context Connections.

The following factors will affect the performance gains that can be achieved with In-Memory OLTP:


An application with many calls to short stored procedures may see a smaller performance gain compared to an application with fewer calls and more functionality implemented in each stored procedure.

Transact-SQL Execution

In-Memory OLTP achieves the best performance when using natively compiled stored procedures rather than interpreted stored procedures or query execution. Stored procedures that execute other stored procedures cannot be natively compiled, but there can be a benefit to accessing memory-optimized tables from such stored procedures.

Range Scan vs Point Lookup

Memory-optimized nonclustered indexes support range scans and ordered scans. For point lookups, memory-optimized hash indexes have better performance than memory-optimized nonclustered indexes. Memory-optimized nonclustered indexes have better performance than disk-based indexes.

Index operations are not logged and they exist only in memory.


Applications whose performance is affected by engine-level concurrency, such as latch contention or blocking, improves significantly when the application moves to In-Memory OLTP.

The following table lists the performance and scalability issues that are commonly found in relational databases and how In-Memory OLTP can improve performance.


In-Memory OLTP Impact


High resource (CPU, I/O, network or memory) usage.


Natively compiled stored procedures can lower CPU usage significantly because they require significantly fewer instructions to execute a Transact-SQL statement compared to interpreted stored procedures.

In-Memory OLTP can help reduce the hardware investment in scaled-out workloads because one server can potentially deliver the throughput of five to ten servers.


If you encounter an I/O bottleneck from processing to data or index pages, In-Memory OLTP may reduce the bottleneck. Additionally, the checkpointing of In-Memory OLTP objects is continuous and does not lead to sudden increases in I/O operations. However, if the working set of the performance critical tables does not fit in memory, In-Memory OLTP will not improve performance because it requires data to be memory resident. If you encounter an I/O bottleneck in logging, In-Memory OLTP can reduce the bottleneck because it does less logging. If one or more memory-optimized tables are configured as non-durable tables, you can eliminate logging for data.


In-Memory OLTP does not offer any performance benefit. In-Memory OLTP can put extra pressure on memory as the objects need to be memory resident.


In-Memory OLTP does not offer any performance benefit. The data needs to be communicated from data tier to application tier.


Most scaling issues in SQL Server applications are caused by concurrency issues such as contention in locks, latches, and spinlocks.

Latch Contention

A typical scenario is contention on the last page of an index when inserting rows concurrently in key order. Because In-Memory OLTP does not take latches when accessing data, the scalability issues related to latch contentions are fully removed.

Spinlock Contention

Because In-Memory OLTP does not take latches when accessing data, the scalability issues related to spinlock contentions are fully removed.

Locking Related Contention

If your database application encounters blocking issues between read and write operations, In-Memory OLTP removes the blocking issues because it uses a new form of optimistic concurrency control to implement all transaction isolation levels. In-Memory OLTP does not use TempDB to store row versions.

If the scaling issue is caused by conflict between two write operations, such as two concurrent transactions trying to update the same row, In-Memory OLTP lets one transaction succeed and fails the other transaction. The failed transaction must be re-submitted either explicitly or implicitly, re-trying the transaction. In either case, you need to make changes to the application.

If your application experiences frequent conflicts between two write operations, the value of optimistic locking is diminished. The application is not suitable for In-Memory OLTP. Most OLTP applications don’t have a write conflicts unless the conflict is induced by lock escalation.

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