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-primarily based tables, transactions on memory-optimized tables are absolutely atomic, consistent, isolated, and durable (ACID). Memory-optimized tables and natively compiled stored procedures support only a subset of Transact-SQL features. Beginning with SQL Server 2016, and in Azure SQL Database, there aren't any limitations for collations or code pages that are particular to In-Memory OLTP. The primary storage for memory-optimized tables is the main memory. Rows in the desk are read from and written to memory. A second copy of the desk knowledge is maintained on disk, but just for sturdiness purposes. See Creating and Managing Storage for Memory-Optimized Objects for extra information about durable tables. Data in memory-optimized tables is only read from disk during database recovery (for instance, after a server restart). For even larger performance positive aspects, In-Memory OLTP helps durable tables with transaction durability delayed. Delayed durable transactions are saved to disk quickly after the transaction commits and management is returned to the shopper.<br>
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+<br>In trade for the elevated efficiency, dedicated transactions that aren't persisted to disk are lost in a server crash or fail over. Apart from the default durable memory-optimized tables,  [MemoryWave Community](https://www.werbefotografie-koeln.de/events/jessen-lee-gallery-show/) SQL Server additionally [helps non-durable](https://www.houzz.com/photos/query/helps%20non-durable) memory-optimized tables, which aren't logged and their knowledge is not persisted on disk. Which means transactions on these tables don't require any disk IO, however the data is lost if there's a server crash or failover. In-Memory OLTP is integrated with SQL Server to supply a seamless expertise in all areas akin to growth, deployment, manageability, and supportability. A database can contain in-memory in addition to disk-based mostly objects. Rows in memory-optimized tables are versioned. This means that every row within the table probably has a number of versions. All row versions are maintained in the identical table data 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 next determine illustrates multi-versioning. The figure shows a desk with three rows and each row has different variations. The table has three rows: r1, r2, and r3. 1 has three variations, r2 has two variations, and r3 has 4 versions. Totally different variations of the identical row do not essentially occupy consecutive memory locations. The totally different row variations can be dispersed throughout the desk knowledge structure. The memory-optimized table data construction will be seen as a collection of row versions. Rows in disk-primarily based tables are organized in pages and extents, and individual rows addressed using page number and page offset, row variations in memory-optimized tables are addressed utilizing 8-byte memory pointers. Through natively compiled stored procedures. By means of interpreted Transact-SQL, outside of a natively compiled stored process. These Transact-SQL statements could also be both inside interpreted stored procedures or they may be advert hoc Transact-SQL statements. Memory-optimized tables can be accessed most effectively from natively compiled stored procedures (Natively Compiled Stored Procedures).<br>
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+<br>Memory-optimized tables will also be accessed with (conventional) interpreted Transact-SQL. Interpreted Transact-SQL refers to accessing memory-optimized tables with out a natively compiled stored procedure. Some examples of interpreted Transact-SQL entry embody accessing a [Memory Wave](http://rotaket.fr/actualite/article-lecitrailer/)-optimized desk from a DML trigger, ad hoc Transact-SQL batch, view, and desk-valued operate. The following desk summarizes native and interpreted Transact-SQL access for various 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 can access memory-optimized tables and natively compiled stored procedures. Sensitive information in memory-optimized tables can be protected through the use of Always Encrypted. When utilizing Always Encrypted with secure enclaves, the usage of enclave-enabled keys for columns in memory-optimized tables is not supported. Which means in-place encryption can't be used, and the initial encryption is completed on the shopper.<br>
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+<br>Always Encrypted isn't supported for any column in a memory-optimized table when the table is referenced in a natively compiled module. Communication: An software using many brief stored process calls would possibly see a smaller performance achieve in comparison with an application with fewer calls and extra performance implemented in each stored process. Transact-SQL Execution: In-Memory OLTP achieves one of the best performance when utilizing natively compiled stored procedures somewhat than interpreted stored procedures or query execution. There is usually a benefit to accessing memory-optimized tables from such saved procedures. Range Scan vs Point Lookup: Memory-optimized nonclustered indexes assist vary scans and ordered scans. For  [Memory Wave](https://mediawiki.laisvlaanderen.ehb.be/index.php/Distant_Direct_Memory_Entry_RDMA) level lookups, memory-optimized hash indexes have better performance than memory-optimized nonclustered indexes. Memory-optimized nonclustered indexes have higher efficiency than disk-based mostly indexes. Beginning in SQL Server 2016, the question plan for a memory-optimized desk can scan the desk in parallel. This improves the performance of analytical queries. Index operations: Index operations aren't logged, they usually exist only in memory.<br>