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THE RELATIONAL ENGINE AND THE STORAGE ENGINE
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The SQL Server database engine is made up of two main components: the relational engine and the storage engine. Unlike versions of SQL Server prior to 7, these two pieces are clearly separated, and their primary method of communication with each other is through OLE DB. The relational engine comprises all the components necessary to parse and optimize any query. It requests data from the storage engine in terms of OLE DB rowsets and then processes the rowsets returned. The storage engine comprises the components needed to actually access and modify data on disk.
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The command parser handles language events raised by ODS. It checks for proper syntax and translates Transact-SQL commands into an internal format that can be operated on. This internal format is known as a query tree. If the parser does not recognize the syntax, a syntax error is immediately raised. Starting with SQL Server 6, syntax error messages identify where the error occurred. However, nonsyntax error messages cannot be explicit about the exact source line that was the cause of the error. Because only the parser can access the source of the statement, the statement is no longer available in source format when the command is actually executed. Exceptions to the calling sequence for the command parser are EXECUTE( string ) and cursor operations. Both of these operations can recursively call the parser.
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The optimizer takes the query tree from the command parser and prepares it for execution. This module compiles an entire command batch, optimizes queries, and checks security. The query optimization and compilation result in an execution plan. The first step in producing such a plan is to normalize the query, which potentially breaks down a single query into multiple,
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fine-grained queries. After the optimizer normalizes the query, it optimizes it, which means that the optimizer determines a plan for executing that query. Query optimization is cost-based; the optimizer chooses the plan that it determines would cost the least, based on internal metrics that include estimated memory requirements, estimated CPU utilization, and the estimated number of required I/Os. It considers the type of statement requested, checks the amount of data in the various tables affected, looks at the indexes available for each table, and then looks at a sampling of the data values kept for each index or column referenced in the query. The sampling of the data values is called statistics. Based on the available information, the optimizer considers the various access methods and join strategies it could use to resolve a query and chooses the most cost-effective plan. The optimizer also decides which indexes, if any, should be used for each table in the query, and, in the case of a multitable query, the order in which the tables should be accessed and the join strategy to be used. The optimizer also uses pruning heuristics to ensure that more time is not spent optimizing a query than it would take to simply choose a plan and execute it. The optimizer does not necessarily do exhaustive optimization. Some products consider every possible plan and then choose the most cost-effective one. The advantage of this exhaustive optimization is that the syntax chosen for a query would theoretically never cause a performance difference, no matter what syntax the user employed. But if you deal with an involved query, it could take much longer to estimate the cost of every conceivable plan than it would to accept a good plan, even if not the best one, and execute it. For example, in one product review, SQL Server (and some other products) consistently executed one complex eight-table join faster than a product whose optimizer produced the same ideal execution plan each time, even though SQL Server s execution plan varied somewhat. This was a case in which a pruning technique produced faster results than pure exhaustive optimization. In general, though, you will typically get the same execution plan no matter what equivalent syntax you use to specify the query. Some products have no cost-based optimizer and rely purely on rules to determine the query execution plan. In such cases, the syntax of the
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