Add 'Introduction to Memory-Optimized Tables'

Introduction-to-Memory-Optimized-Tables.md

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+<br>Memory-optimized tables are created using CREATE Desk (Transact-SQL). Memory-optimized tables are absolutely durable by default, and, like transactions on (traditional) disk-based mostly tables, transactions on memory-optimized tables are fully atomic, consistent, isolated, and durable (ACID). Memory-optimized tables and natively compiled stored procedures support only a subset of Transact-SQL features. Starting with SQL Server 2016, and in Azure SQL Database, there are not any limitations for collations or code pages which might be particular to In-Memory OLTP. The primary storage for memory-optimized tables is the principle memory. Rows in the table are read from and written to memory. A second copy of the table information is maintained on disk, but just for sturdiness functions. See Creating and Managing Storage for Memory-Optimized Objects for more information about durable tables. Data in memory-optimized tables is barely read from disk during database recovery (for instance, after a server restart). For  Memory Wave even larger performance features, In-Memory OLTP helps durable tables with transaction durability delayed. Delayed durable [transactions](https://www.britannica.com/search?query=transactions) are saved to disk soon after the transaction commits and management is returned to the consumer.<br>
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+<br>In trade for the increased efficiency, dedicated transactions that are not persisted to disk are lost in a [server crash](https://www.express.co.uk/search?s=server%20crash) or fail over. Moreover the default durable memory-optimized tables, SQL Server also supports non-durable memory-optimized tables, which aren't logged and their knowledge isn't persisted on disk. Which means transactions on these tables don't require any disk IO, but the data is lost if there is a server crash or failover. In-Memory OLTP is integrated with SQL Server to provide a seamless experience in all areas resembling improvement, deployment, manageability, and supportability. A database can include in-memory in addition to disk-based mostly objects. Rows in memory-optimized tables are versioned. This implies that each row within the desk probably has a number of variations. All row versions are maintained in the same table information structure. Row versioning is used to permit concurrent reads and writes on the identical row. For extra details about concurrent reads and writes on the identical row, see Transactions with Memory-Optimized Tables.<br>
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+<br>The following determine illustrates multi-versioning. The determine shows a table with three rows and each row has different variations. The desk has three rows:  [boost brain function](http://118.25.58.110:10880/curtissteere3/curtis2005/wiki/Fujitsu+Memory+Upgrades+%25E2%259C%2593%25C2%25AD+Guaranteed+Compatible) r1, r2, and r3. 1 has three versions, r2 has two versions, and r3 has four versions. Totally different variations of the identical row do not essentially occupy consecutive memory places. The different row versions may be dispersed throughout the table knowledge structure. The memory-optimized table information construction could 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 using page number and page offset, row variations in memory-optimized tables are addressed utilizing 8-byte memory pointers. Via natively compiled saved procedures. By interpreted Transact-SQL, outdoors of a natively compiled stored process. These Transact-SQL statements may be either inside interpreted saved procedures or they could also be ad hoc Transact-SQL statements. Memory-optimized tables might be accessed most efficiently from natively compiled saved procedures (Natively Compiled Saved Procedures).<br>
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+<br>Memory-optimized tables can be accessed with (traditional) interpreted Transact-SQL. Interpreted Transact-SQL refers to accessing memory-optimized tables with no natively compiled stored procedure. Some examples of interpreted Transact-SQL entry embody accessing a memory-optimized table from a DML set off, ad hoc Transact-SQL batch, view, and table-valued operate. The following table summarizes native and interpreted Transact-SQL entry for various objects. 1You can't entry a memory-optimized table or natively compiled saved procedure from the context connection (the connection from SQL Server when executing a CLR module). You'll be able to, however, create and open one other connection from which you'll access memory-optimized tables and  [boost brain function](https://soundhall.shop/bbs/board.php?bo_table=free&wr_id=88434) natively compiled saved procedures. Sensitive knowledge in memory-optimized tables can be protected by utilizing All the time Encrypted. When using All the time Encrypted with safe enclaves, the usage of enclave-enabled keys for columns in memory-optimized tables isn't supported. Which means in-place encryption can't be used, and the preliminary encryption is completed on the consumer.<br>
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+<br>All the time Encrypted is not supported for any column in a memory-optimized table when the desk is referenced in a natively compiled module. Communication: An application using many quick stored procedure calls may see a smaller efficiency achieve compared to an software with fewer calls and extra performance implemented in every stored procedure. Transact-SQL Execution: In-Memory OLTP achieves the perfect efficiency when using natively compiled saved procedures slightly than interpreted stored procedures or question execution. There generally is a benefit to accessing memory-optimized tables from such stored procedures. Vary Scan vs Point Lookup: Memory-optimized nonclustered indexes support vary scans and ordered scans. For level lookups, memory-optimized hash indexes have higher performance than memory-optimized nonclustered indexes. Memory-optimized nonclustered indexes have better efficiency than disk-based indexes. Starting in SQL Server 2016, the question plan for a memory-optimized table can scan the table in parallel. This improves the efficiency of analytical queries. Index operations: Index operations aren't logged, they usually exist only in memory.<br>