Memory-optimized tables are created utilizing CREATE Table (Transact-SQL). Memory-optimized tables are totally durable by default, and, like transactions on (conventional) disk-primarily based tables, transactions on memory-optimized tables are totally atomic, constant, isolated, and durable (ACID). Memory-optimized tables and natively compiled saved procedures help only a subset of Transact-SQL options. Starting with SQL Server 2016, and in Azure SQL Database, there are no limitations for collations or code pages which are specific to In-Memory OLTP. The primary storage for memory-optimized tables is the main memory. Rows in the table are learn from and written to memory. A second copy of the table data is maintained on disk, however only for sturdiness functions. See Creating and Managing Storage for Memory-Optimized Objects for more information about durable tables. Knowledge in memory-optimized tables is only read from disk during database recovery (for instance, after a server restart). For even greater efficiency beneficial properties, In-Memory OLTP helps durable tables with transaction sturdiness delayed. Delayed durable transactions are saved to disk quickly after the transaction commits and management is returned to the consumer.

In trade for the increased efficiency, dedicated transactions that are not persisted to disk are lost in a server crash or fail over. Apart from the default durable memory-optimized tables, SQL Server also helps non-durable memory-optimized tables, which are not logged and their knowledge is not persisted on disk. Which means that transactions on these tables do not require any disk IO, however the data is misplaced if there is a server crash or failover. In-Memory OLTP is built-in with SQL Server to supply a seamless experience in all areas comparable to growth, deployment, manageability, and supportability. A database can comprise in-memory as well as disk-primarily based objects. Rows in memory-optimized tables are versioned. This implies that each row in the table probably has a number of variations. All row variations are maintained in the same table information structure. Row versioning is used to allow concurrent reads and writes on the identical row. For extra information about concurrent reads and writes on the same row, see Transactions with Memory-Optimized Tables.
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The following determine illustrates multi-versioning. The figure shows a desk with three rows and every row has totally different variations. The desk has three rows: r1, r2, and r3. 1 has three versions, r2 has two variations, and r3 has 4 versions. Different versions of the same row don't essentially occupy consecutive memory areas. The completely different row variations will be dispersed all through the desk data structure. The memory-optimized table data structure will be seen as a group of row versions. Rows in disk-primarily based tables are organized in pages and extents, Memory Wave and particular person rows addressed utilizing web page number and web page offset, row versions in memory-optimized tables are addressed utilizing 8-byte memory pointers. Via natively compiled saved procedures. Through interpreted Transact-SQL, Memory Wave outdoors of a natively compiled stored process. These Transact-SQL statements may be either inside interpreted stored procedures or brainwave audio program they may be ad hoc Transact-SQL statements. Memory-optimized tables could be accessed most effectively from natively compiled saved procedures (Natively Compiled Saved Procedures).

Memory-optimized tables may also be accessed with (conventional) interpreted Transact-SQL. Interpreted Transact-SQL refers to accessing memory-optimized tables and not using a natively compiled saved process. Some examples of interpreted Transact-SQL entry embody accessing a memory-optimized desk from a DML set off, ad hoc Transact-SQL batch, view, and table-valued function. The next table summarizes native and interpreted Transact-SQL access for numerous objects. 1You can't access a memory-optimized desk or natively compiled saved process from the context connection (the connection from SQL Server when executing a CLR module). You can, nonetheless, create and open another connection from which you'll be able to access memory-optimized tables and natively compiled saved procedures. Sensitive knowledge in memory-optimized tables can be protected by using At all times Encrypted. When utilizing Always Encrypted with safe enclaves, the usage of enclave-enabled keys for columns in memory-optimized tables isn't supported. This means that in-place encryption cannot be used, and the preliminary encryption is done on the client.

At all times Encrypted is not supported for any column in a memory-optimized table when the desk is referenced in a natively compiled module. Communication: An utility using many brief saved process calls would possibly see a smaller efficiency acquire compared to an application with fewer calls and more functionality applied in each stored process. Transact-SQL Execution: In-Memory OLTP achieves the perfect performance when using natively compiled stored procedures slightly than interpreted stored procedures or question execution. There generally is a benefit to accessing memory-optimized tables from such stored procedures. Range Scan vs Level Lookup: Memory-optimized nonclustered indexes support vary scans and ordered scans. For level lookups, memory-optimized hash indexes have better performance than memory-optimized nonclustered indexes. Memory-optimized nonclustered indexes have higher efficiency than disk-primarily based indexes. Starting in SQL Server 2016, the question plan for a memory-optimized table can scan the table in parallel. This improves the performance of analytical queries. Index operations: Index operations aren't logged, and they exist solely in memory.