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Отличия SQL 2012 и 2014
Редакции SQL Server 2008 R2
Differences in R Features between Editions of SQL Server
Support for machine learning is available in the following editions of SQL Server and SQL Server Includes R Services, for in-database analytics in SQL Server. Also includes R Server Standalone , which can be used to connect to a variety of databases and pull data for analysis at scale, but does not run in-database. In SQL Server , the equivalent features are Machine Learning Services In-Database and Machine Learning Server Standalone. Optimized performance and scalability through parallelization and streaming. Supports analysis of large datasets that do not fit in the available memory, by using enhanced R packages, streaming, and parallel execution. Newer editions of Microsoft R Server include an improved version of the operationalization engine formerly known as DeployR that supports rapid, secure deployment and sharing of R solutions. For more information, see Operationalize. In-database analytics in SQL Server supports resource governance of external scripts to customize server resource usage. Same capabilities as Enterprise Edition; however, Developer Edition cannot be used in production environments. Has all the capabilities of in-database analytics included with Enterprise Edition, except for resource governance. Performance and scale is also limited: Only Express Edition with Advanced Services includes the machine learning features. The performance limitations are similar to Standard Edition. For more information about other product features, see Editions and Supported Features for SQL Server Performance of machine learning solutions in SQL Server is expected to generally be better than any conventional implementation using R, given the same hardware. That is because, in SQL Server, R solutions can be run using server resources and sometimes distributed to multiple processes using the RevoScaleR functions. Performance has not been assessed for Python solutions, as the feature is still under development, but some of the same benefits are expected to apply. Users can also expect to see considerable differences in performance and scalability for the same machine learning solution if run in Enterprise Edition vs. Reasons include support for parallel processing, streaming, and increased threads available for R worker processing. However, performance even on identical hardware can be affected by many factors outside the R or Python code. These factors include competing demands on server resources, the type of query plan that is created, schema changes, the need to update statistics or create a new query plan, fragmentation, and more. It is possible that a stored procedure containing R or Python code might run in seconds under one workload, but take minutes when there are other services running. Therefore, we recommend that you monitor multiple aspects of server performance, including networking for remote compute contexts, when measuring machine learning performance. We also recommend that you configure Resource Governor available in Enterprise Edition to customize the way that external script jobs are prioritized or handled under heavy server workloads. You can define classifier functions to specify the source of the external script job and prioritize certain workloads, limit the amount of memory used by SQL queries, and control the number of parallel processes used on a workload basis. Developer Edition provides performance equivalent to that of Enterprise Edition; however, use of Developer Edition is not supported for production environments. Even Standard Edition should offer some performance benefit, in comparison to standard R packages, given the same hardware configuration. However, Standard Edition does not support Resource Governor. Using resource governance is the best way to customize server resources to support varied workloads such as model training and scoring. Standard Edition also provides limited performance and scalability in comparison to Enterprise and Developer Editions. All the ScaleR functions and packages are included with Standard Edition, but the service that launches and manages R scripts is limited in the number of processes it can use. Moreover, data processed by the script must fit in memory. The same restrictions apply to solutions that use revoscalepy. Editions and Supported Features for SQL Server Differences in R Features between Editions of SQL Server 3 min to read Contributors. Enterprise Edition Includes R Services, for in-database analytics in SQL Server. Developer Edition Same capabilities as Enterprise Edition; however, Developer Edition cannot be used in production environments. Standard Edition Has all the capabilities of in-database analytics included with Enterprise Edition, except for resource governance. Express And Web Editions Only Express Edition with Advanced Services includes the machine learning features. For more information about other product features, see Editions and Supported Features for SQL Server Enterprise Edition Performance of machine learning solutions in SQL Server is expected to generally be better than any conventional implementation using R, given the same hardware. Developer Edition Developer Edition provides performance equivalent to that of Enterprise Edition; however, use of Developer Edition is not supported for production environments. Standard Edition Even Standard Edition should offer some performance benefit, in comparison to standard R packages, given the same hardware configuration. Express Edition with Advanced Services Express Edition is subject to the same limitations as Standard Edition. All Editions The following machine learning languages are supported for all editions: R SQL Server R and Python Microsoft R Open is included with all editions. Microsoft R Client can work with all editions. See Also Editions and Supported Features for SQL Server Comments Edit Share Twitter.
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