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Chapter: Fundamentals of Database Systems - Query Processing and Optimization, and Database Tuning - Physical Database Design and Tuning

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Factors That Influence Physical Database Design

Physical design is an activity where the goal is not only to create the appropriate structuring of data in storage, but also to do so in a way that guarantees good performance.

Factors That Influence Physical Database Design

 

Physical design is an activity where the goal is not only to create the appropriate structuring of data in storage, but also to do so in a way that guarantees good performance. For a given conceptual schema, there are many physical design alternatives in a given DBMS. It is not possible to make meaningful physical design decisions and performance analyses until the database designer knows the mix of queries, transactions, and applications that are expected to run on the database. This is called the job mix for the particular set of database system applications. The database administrators/designers must analyze these applications, their expected frequencies of invocation, any timing constraints on their execution speed, the expected frequency of update operations, and any unique constraints on attributes. We discuss each of these factors next.

 

A. Analyzing the Database Queries and Transactions. Before undertaking the physical database design, we must have a good idea of the intended use of the database by defining in a high-level form the queries and transactions that are expected to run on the database. For each retrieval query, the following information about the query would be needed:

 

        The files that will be accessed by the query.

        The attributes on which any selection conditions for the query are specified.

 

        Whether the selection condition is an equality, inequality, or a range condi-tion.

 

        The attributes on which any join conditions or conditions to link multiple tables or objects for the query are specified.

 

        The attributes whose values will be retrieved by the query.

 

The attributes listed in items 2 and 4 above are candidates for the definition of access structures, such as indexes, hash keys, or sorting of the file.

 

For each update operation or update transaction, the following information would be needed:

 

        The files that will be updated.

 

        The type of operation on each file (insert, update, or delete).

 

        The attributes on which selection conditions for a delete or update are spec-ified.

 

        The attributes whose values will be changed by an update operation.

 

Again, the attributes listed in item 3 are candidates for access structures on the files, because they would be used to locate the records that will be updated or deleted. On the other hand, the attributes listed in item 4 are candidates for avoiding an access structure, since modifying them will require updating the access structures.

 

B. Analyzing the Expected Frequency of Invocation of Queries and Transactions. Besides identifying the characteristics of expected retrieval queries and update transactions, we must consider their expected rates of invocation. This frequency information, along with the attribute information collected on each query and transaction, is used to compile a cumulative list of the expected fre-quency of use for all queries and transactions. This is expressed as the expected fre-quency of using each attribute in each file as a selection attribute or a join attribute, over all the queries and transactions. Generally, for large volumes of processing, the informal 80–20 rule can be used: approximately 80 percent of the processing is accounted for by only 20 percent of the queries and transactions. Therefore, in prac-tical situations, it is rarely necessary to collect exhaustive statistics and invocation rates on all the queries and transactions; it is sufficient to determine the 20 percent or so most important ones.

 

       Analyzing the Time Constraints of Queries and Transactions. Some queries and transactions may have stringent performance constraints. For example, a transaction may have the constraint that it should terminate within 5 seconds on 95 percent of the occasions when it is invoked, and that it should never take more than 20 seconds. Such timing constraints place further priorities on the attributes that are candidates for access paths. The selection attributes used by queries and transactions with time constraints become higher-priority candidates for primary access structures for the files, because the primary access structures are generally the most efficient for locating records in a file.

 

      Analyzing the Expected Frequencies of Update Operations. A minimum number of access paths should be specified for a file that is frequently updated, because updating the access paths themselves slows down the update operations. For example, if a file that has frequent record insertions has 10 indexes on 10 different attributes, each of these indexes must be updated whenever a new record is inserted. The overhead for updating 10 indexes can slow down the insert operations.

 

      Analyzing the Uniqueness Constraints on Attributes. Access paths should be specified on all candidate key attributes—or sets of attributes—that are either the primary key of a file or unique attributes. The existence of an index (or other access path) makes it sufficient to only search the index when checking this uniqueness constraint, since all values of the attribute will exist in the leaf nodes of the index. For example, when inserting a new record, if a key attribute value of the new record already exists in the index, the insertion of the new record should be rejected, since it would violate the uniqueness constraint on the attribute.

 

Once the preceding information is compiled, it is possible to address the physical database design decisions, which consist mainly of deciding on the storage structures and access paths for the database files.

 

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