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The first step in using LINQ with F# is to generate the code that implements the object/relational (O/R) mapping for the database tables to which you re connecting Let s first look at why you want to do this You learned previously how to create simple tables and store and retrieve data using SQL code However, this approach doesn t work well for large data schemas with multiple related tables and constraints For instance, creating a hierarchy of records in a number of related tables connected by foreign keys involves issuing multiple SQL statements to create the base data and to connect them in ways that obey the foreign-key and other constraints that may be in place Instead, it can often be much better to view records in tables as if they were objects This is part of what an O/R mapping provides.

In Kitty, statements can be separated by semicolons. This is handled in the StmtList grammar production whose semantic extract is a list of statements. Note that you could have written this rule in a head-recursive way: StmtList: | Stmt { [$1] } | Stmt SEMI StmtList { $1 @ [ $3 ] } Unlike in recursive-descent or any other LL parsing technique, the previous rule does not pose a problem for fsyacc, and thus no left-factoring is needed. However, it does create a copy of the statement list each time a new expression is appended to it. We have eliminated this by using the following productions: StmtList: | Stmt { [$1] } | StmtList SEMI Stmt { $3 :: $1 } combined with a List.rev where the rule is used. This rule consumes all statements and inserts each one by one to the singleton list that contains the first statement. As a result, the return list will be in reverse order, which is why you need to apply List.rev. You may want to define a separate rule to perform this operation. Another feature that is often needed is the ability to parse empty or optional lists. This can be easily accomplished using an empty (epsilon) symbol as in the following example: StmtListOpt: { [] } | StmtList { $1 } This rule matches an optional list of statements and returns an empty list if no statements can be parsed.

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You already saw that datasets can be used to fetch and manipulate data in a disconnected way These datasets are managed by data adapters that handle the gory details of producing the appropriate SQL statements and issuing them to the underlying database when fetching records or synchronizing the changes made to the dataset back to the database The main goal of a separate Data Access Layer (DAL) is to bridge the gap between two disparate domains: the database and your application logic Practically speaking, this frees you from having to write SQL code and mingle it with your application code O/R mappings use smart data objects that can load and persist record-level data Underneath, they use objects such as ADONET datasets that are filled and flushed on demand The tool this section focuses on is SqlMetal, which computes the O/R mapping for LINQ, part of the NET Framework 3.

All open source systems today draw their lineage from the foundations of the work that Stallman and others produced in an effort to create a software utopia in which Stallman believed organizations should generate revenue from selling services, not proprietary property rights There are several examples of Stallman s vision becoming reality The GNU/Linux (henceforth referred to as Linux) movement has spawned numerous successful (and profitable) companies, such as Red Hat and Slackware, that sell customized distributions and support for Linux Another example is MySQL, which has become the most successful open source database system Although the concept of a software utopia is arguably not a reality today, it is possible to download an entire suite of systems and tools to power a personal or business computer without spending any money on the software itself.

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