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In ADONET 35 and 35 SP1, Microsoft introduced two models designed to abstract SQL statements into a high-level language and to operate on database data as objects The first, LINQ to SQL, is a lightweight mapping of LINQ (Language Integrated Query) calls to the SQL Server database The other model, ADONET Entity Framework (EF), consists of an object-relational mapping (ORM) framework as well as query and view services built over the ADONET provider model The Entity Framework has its own dialect of SQL (Entity SQL or ESQL) and can use ESQL statements or LINQ queries to access data Although neither framework uses vanilla T-SQL as its query language, both frameworks can generate SQL statements or use existing stored procedures to access the database.

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This chapter is not an introduction to these frameworks as I assume that you already know their basics, but I will discuss how these frameworks interact with SQL Server, especially with respect to performance One way to look at the performance of an abstraction layer is to examine and profile the ADONET code, but both EF and LINQ to SQL are T-SQL code generators (EF is not database-specific, but I m only talking about SQL Server here); therefore, another way to address performance is to examine the generated T-SQL code I will look at performance from the generated T-SQL code perspective Many programmers who specialize in query tuning salivate over the prospect of tuning the bad queries that will assuredly result from these two data access stacks In addition, many DBAs would like to ban LINQ or EF use when coding against their companies databases.

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Most people who profess a dislike for the generated code have never seen (or have seen very little of) the generated code For someone who writes and tunes T-SQL code, code generating programs and frameworks that rely on code generation can be worrisome if the code generation compromises database performance Both Entity Framework and LINQ to SQL have API calls that can expose their generated T-SQL; you can also use SQL Profiler to look at the.

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generated T-SQL. This chapter outlines some of the performance and manageability concerns that arise through the use of these models, beginning with the dynamic generation of SQL inside applications.

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CONCERN LINQ to SQL and EF will proliferate the use of SQL code in applications, and will almost surely produce suboptimal dynamic SQL, causing database performance

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Working Conditions Teachers in correctional, literacy, and community-based continuing education programs spend three to thirty hours a week in classes, and they carry heavy teaching loads as high as six to nine hours per day, several days per week That situation is not true of four-year college and university professors involved in continuing education, however They carry the traditional teaching load in higher education, generally twelve to fteen semester hours in colleges, six to twelve semester hours in universities, with only part of it in continuing education In most situations, teaching takes place in the late afternoon, in the evening, or on the weekend, and it ranges from one-shot sessions of two to three hours to courses that run the full semester, meeting one to three times a week

problems and plan cache pollution. It s almost dogma among database programmers that static SQL in stored procedures is better for security than dynamic SQL constructed using string concatenation. Besides the obvious association between dynamic SQL and SQL injection, using dynamic SQL means that all users must be given access to the underlying tables, unless you use LINQ to SQL/EF strictly with views and stored procedures. Using stored procedures, the DBA doesn t need to give access to underlying tables to each user, only to EXECUTE permission. Using views, the DBA gives permission to the view, not the underlying tables. Plan cache pollution refers to the fact that using many different variations of the same SQL statement produces multiple plans in the cache for what is the same query. For example,

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