Database Design Information System File File Information System File Information System Database (consolidated & integrated data from files) File Information System Information System INFORMATION SYSTEMS FRAMEWORK FOCUS ON SYSTEM DATA FOCUS ON SYSTEM PROCESSES FOCUS ON SYSTEM INTERFACES FOCUS ON SYSTEM GEOGRAPHY Business Subjects SYSTEM OWNERS (scope) Survey Phase (establish scope and project plan) Custom ers order zero, one, or m ore products. Products m ay be ordered by ze ro, one , or m ore custom ers. Study Phase (establish system improvement objectives) Data Requirements S Y S T E M A N A L Y S T S SYSTEM USERS (requirements) PRODUCT product-no product-name unit-of-measure unit-price quantity-available CUSTOMER customer-no customer-name customer-rating balance-due ORDER order-no order-date products-ordered quantities-ordered Definition Phase (establish and prioritize business system requirements) data models Database Schema SYSTEM DESIGNERS (specification) PRODUCT CUSTOMER product_no [Alpha(10)] INDEX customer_no [Alpha (10)] INDEX product_name [Alpha(32)] customer_name [Alpha(32)] unit_of_measure [Alpha(2)] customer_rating [Alpha(1)] INDEX unit_pri ce [Real(3,2)] balance_due [Real(5,2)] quantity_available [Integ er(4)] ORDER_PRODUCT ORDER order_no [Alpha(12)] INDEX ORDER. order_no order_date [Date(mmddyyyy) PRODUCT.product_no quantity_ordered [Integ er(2) CUSTOMER.customer_n o Design Phase (translate business requirements into a technical design) Database Programs SYSTEM BUILDERS (components) FAST Methodology CREATE TABLE CUSTOMER (customer_no CHAR(10) NOT NULL customer_name CHAR(32) NOT N ULL customer _rating CHAR (1) NOT NU LL balance_due DECIMAL(5,2) CREATE INDEX cust_no_idx on CUSTOMER CREATE INDEX cust_rt_idx on CUSTOMER Existing Databases and Technology Existing Applications and Technology Existing Interfaces and Technology Implementation Phase (translate technical design into code) Existing Networks and Technology Database Concepts for the Systems Analyst Fields Fields are common to both files and databases. A field is the implementation of a data attribute. • Fields are the smallest unit of meaningful data to be stored in a file or database. There are four types of fields that can be stored: primary keys, secondary keys, foreign keys, and descriptive fields. Primary keys are fields whose values identify one and only one record in a file. Secondary keys are alternate identifiers for an database. • A single file in a database may only have one primary key, but it may have several secondary keys. Database Concepts for the Systems Analyst Fields There are four types of fields that can be stored: primary keys, secondary keys, foreign keys, and descriptive fields. (continued) Foreign keys are pointers to the records of a different file in a database. • Foreign keys are how the database ‘links’ the records of one type to those of another type. Descriptive fields are any other fields that store business data. Database Concepts for the Systems Analyst Records Fields are organized into records. Like fields, records are common to both files and databases. A record is a collection of fields arranged in a predefined format. During systems design, records will be classified as either fixedlength or variable-length records. Most database systems impose a fixed-length record structure, meaning that each record instance has the same fields, same number of fields, and same logical size. Variable-length record structures allow different records in the same file to have different lengths. • Database systems typically disallow (or, at least, discourage) variable length records. Database Concepts for the Systems Analyst Records When a computer program ‘reads’ a record from a database, it actually retrieves a group or block of records at a time. This approach minimizes the number of actual disk accesses. A blocking factor is the number of logical records included in a single read or write operation (from the computer’s perspective). A block is sometimes called a physical record. Today, the blocking factor is usually determined and optimized by the chosen database technology, but a qualified database expert may be allowed to fine tune that blocking factor for performance. Database Concepts for the Systems Analyst Files and Tables Similar records are organized into groups called files. A file is the set of all occurrences of a given record structure. In database systems, a file corresponds to a set of similar records; usually called a table. A table is the relational database equivalent of a file. Some of the types of files and tables include: Master files or tables contain records that are relatively permanent. • Once a record has been added to a master file, it remains in the system indefinitely. • The values of fields for the record will change over its lifetime, but the individual records are retained indefinitely. Database Concepts for the Systems Analyst Files and Tables Some of the types of files and tables include: (continued) Transaction files or tables contain records that describe business events. • The data describing these events normally has a limited useful lifetime. • In information systems, transaction records are frequently retained on-line for some period of time. • Subsequent to their useful lifetime, they are archived off-line. Document files and tables contain stored copies of historical data for easy retrieval and review without the overhead of regenerating the document. Database Concepts for the Systems Analyst Files and Tables Some of the types of files and tables include: (continued) Archival files and tables contain master and transaction file records that have been deleted from on-line storage. • Records are rarely deleted; they are merely moved from on-line storage to off-line storage. • Archival requirements are dictated by government regulation and the need for subsequent audit or analysis. Table look-up files contain relatively static data that can be shared by applications to maintain consistency and improve performance. Database Concepts for the Systems Analyst Files and Tables Some of the types of files and tables include: (continued) Audit files are special records of updates to other files, especially master and transaction files. • They are used in conjunction with archive files to recover ``lost’’ data. • Audit trails are typically built into better database technologies. Database Concepts for the Systems Analyst Databases Databases provide for the technical implementation of entities and relationships. The history of information systems has led to one inescapable conclusion: Data is a resource that must be controlled and managed! Out of necessity, database technology was created so an organization could maintain and use its data as an integrated whole instead of as separate data files. Users and Programmers Information System File A legacy file-based information system Users and Programmers File (built in-house) Information System Information System Operational Database (built in-house) (built in-house) (built in-house) File End-User Tools File Data Warehouse End-User Applications File A legacy file-based information system Users and Programmers Users Personal DB File (purchased) File Operational Database Information System (purchased) Work-Group Database End-User Work Group Users and Programmers Database Concepts for the Systems Analyst Databases Database Architecture: Database architecture refers to the database technology including the database engine, database management utilities, database CASE tools for analysis and design, and database application development tools. The control center of a database architecture is its database management system. • A database management system (DBMS) is specialized computer software available from computer vendors that is used to create, access, control, and manage the database. The core of the DBMS is often called its database engine. The engine responds to specific commands to create database structures, and then to create, read, update, and delete records in the database. Database Concepts for the Systems Analyst Databases Database Architecture: A systems analyst, or database analyst, designs the structure of the data in terms of record types, fields contained in those record types, and relationships that exist between record types. These structures are defined to the database management system using its data definition language. • Data definition language (or DDL) is used by the DBMS to physically establish those record types, fields, and structural relationships. Additionally, the DDL defines views of the database. Views restrict the portion of a database that may be used or accessed by different users and programs. DDLs record the definitions in a permanent data repository. Programmers Systems Analysts and/or Database Designers End Users Host-based Transaction Processing Monitor (optional) Data Manipulation Language DML Data Definition Language DDL Internal TP Monitor (opt) Proprietary Data Manipulation Language and/or Report Writers Database Management System (DBMS) Stored Data Metadata Database Concepts for the Systems Analyst Databases Database Architecture: Some data dictionaries include formal, elaborate software that helps database specialists track metadata – the data about the data –such as record and field definitions, synonyms, data relationships, validation rules, help messages, and so forth. The database management system also provides a data manipulation language to access and use the database in applications. • A data manipulation language (or DML) is used to create, read, update, and delete records in the database, and to navigate between different records and types of records. The DBMS and DML hide the details concerning how records are organized and allocated to the disk. Database Concepts for the Systems Analyst Databases Database Architecture: Many DBMSs don’t require the use of a DDL to construct the database, or a DML to access the database. • They provide their own tools and commands to perform those tasks. This is especially true of PC-based DBMSs. Many DBMSs also include proprietary report writing and inquiry tools to allow users to access and format data without directly using the DML. Some DBMSs include a transaction processing monitor (or TP monitor) that manages on-line accesses to the database, and ensures that transactions that impact multiple tables are fully processed as a single unit. Database Concepts for the Systems Analyst Databases Relational Database Management Systems: There are several types of database management systems and they can be classified according to the way they structure records. Early database management systems organized records in hierarchies or networks implemented with indexes and linked lists. Relational databases implement data in a series of tables that are ‘related’ to one another via foreign keys. • Files are seen as simple two-dimensional tables, also known as relations. • The rows are records. • The columns correspond to fields. Customer places Order sells Ordered Product sold on Product Customers Table Customer Number Customer Name 10112 10113 10114 10117 Luck Star Pemrose Hartman K-Jack Industries Customer Balance … 1455.77 12.14 0.00 - 20.00 Orders Table Order Number Customer Number (foreign key) A633 A634 A635 10112 10114 10112 … Ordered Products Table Order Number (foreign key) Product Number (foreign key) Quantity Ordered A633 A633 A634 A634 A635 A635 77F02 77B12 77B13 77F01 77B12 77B15 1 500 100 5 300 15 … Products Table Product Number Product Description Quantity in Stock 77B12 77B13 77B15 77F01 77F02 Widget Widget Widget Gadget Gadget 8000 0 52 20 2 … Database Concepts for the Systems Analyst Databases Relational Database Management Systems: Both the DDL and DML of most relational databases is called SQL (which stands for Structured Query Language). • SQL supports not only queries, but complete database creation and maintenance. • A fundamental characteristic of relational SQL is that commands return ‘a set’ of records, not necessarily just a single record (as in non-relational database and file technology). Database Concepts for the Systems Analyst Databases Relational Database Management Systems: High-end relational databases also extend the SQL language to support triggers and stored procedures. • Triggers are programs embedded within a table that are automatically invoked by a updates to another table. • Stored procedures are programs embedded within a table that can be called from an application program. Both triggers and stored procedures are reusable because they are stored with the tables themselves. • This eliminates the need for application programmers to create the equivalent logic within each application that use the tables. Data Analysis for Database Design What is a Good Data Model? A good data model is simple. As a general rule, the data attributes that describe an entity should describe only that entity. A good data model is essentially non-redundant. This means that each data attribute, other than foreign keys, describes at most one entity. A good data model should be flexible and adaptable to future needs. We should make the data models as application-independent as possible to encourage database structures that can be extended or modified without impact to current programs. Data Analysis for Database Design Data Analysis The technique used to improve a data model in preparation for database design is called data analysis. Data analysis is a process that prepares a data model for implementation as a simple, non-redundant, flexible, and adaptable database. The specific technique is called normalization. • Normalization is a technique that organizes data attributes such that they are grouped together to form stable, flexible, and adaptive entities. Data Analysis for Database Design Data Analysis Normalization is a three-step technique that places the data model into first normal form, second normal form, and third normal form. An entity is in first normal form (1NF) if there are no attributes that can have more than one value for a single instance of the entity. An entity is in second normal form (2NF) if it is already in 1NF, and if the values of all non-primary key attributes are dependent on the full primary key – not just part of it. An entity is in third normal form (3NF) if it is already in 2NF, and if the values of its non-primary key attributes are not dependent on any other non-primary key attributes. Data Analysis for Database Design Normalization Example First Normal Form: The first step in data analysis is to place each entity into 1NF. sold PRODUCT ------------Key Data---------------Product-Number (PK1) Universal-Product-Code (PK2) --------Non-Key Data------------Quantity-in-Stock Product-Type Suggested-Retail-Price Club-Default-Unit-Price Current-Special-Unit-Price Current-Month-Units-Sold Current-Year-Units-Sold Total-Lifetime-Units-Sold MEMBER ORDER ------------------Key Data--------------------Order-Number (PK) ----------------Non-Key Data----------------Order-Creation-Date Order-Automatic-Fill-Date Member Number (FK1) Member-Name Member-Address Shipping-Address Shipping Instructions Club-Name (FK2) Promotion-Number (FK2) 0 { Ordered-Product-Description } n 0 { Ordered-Product-Title } n 1 { Quantity-Ordered } n 1 { Purchased-Unit-Price } n 1 { Extended-Price } n Order-Sub-Total-Cost Order-Sales-Tax Ship-Via-Method Shipping-Charge Order-Status Prepaid-Amount Method-of-Payment placed MEMBER ---------------------Key Data---------------------Member-Number (PK1) ------------------Non-Key Data------------------Member-Name Member-Status Member-Street-Address Member-Daytime-Phone-Number Date-of-Last-Order Member-Balance-Due Member-Bonus-Balance-Available Member-Credit-Card-Information 1 { Club-Name } n 1 { Agreement-Number } n 1 { Taste Code } n 1 { Media Preference } n 1 { Date-Enrolled } n 1 { Expiration-Date } n 1 { Number-of-Credits-Required } n 1 { Number of Credits-Earned } n enrolls in CLUB ------------------Key Data---------------------Club-Name (PK) --------------Non-Key Data-------------------Club-Description Club-Charter-Date 1 { Agreement-Number } n 1 { Agreement-Active-Date } n 1 { Agreement-Expiration-Date } n 1 { Obligation-Period } n 1 { Required-Number-of-Credits } n 1 { Bonus-Credits-After-Obligation } n sponsors is a generates MERCHANDISE -------------Key Data--------------Product-Number (PK1) Universal-Product-Code (PK1) ---------Non-Key Data-----------Merchandise-Name Merchandise-Description Merchandise-Size Merchasnise-Color Unit-of-Measure TITLE --------------Key Data-------------Product-Number (PK1) Universal-Product-Code (PK2) ----------Non-Key Data----------Title-of-Work Title-Cover Catalog-Description Copyright-Date Entertainment-Category Credit-Value features is a AUDIO TITLE -------------Key Data--------------Product-Number (PK1) Universal-Product-Code (PK1) ---------Non-Key Data-----------Artist Audio-Category Audio-Sub-Category Number-of-Units-in-Package Audio-Media-Code Content-Advisory-Code VIDEO TITLE -------------Key Data--------------Product-Number (PK1) Universal-Product-Code (PK1) ---------Non-Key Data-----------Producer Director Video-Category Video-Sub-Category Closed-Captioned Language Running-Time Video-media-Type Video-Encoding Screen-Aspect MPA-Rating-Code GAME TITLE -------------Key Data--------------Product-Number (PK1) Universal-Product-Code (PK1) ---------Non-Key Data-----------Manufacturer Game-Category Game-Sub-Category Game-Platform Game-Media-Type Number-of-Players Parent-Advisory-Code PROMOTION ---------Key Data------------Club-Name (PK1) Promotion-Number (PK1) -------Non-Key Data-------Product-Number (FK1) Promotion-Release-Date Promotion-Status Promotion-Type Automatic-Fill-Delay MEMBER ORDER (1NF) ------------------Key Data--------------------Order-Number (PK) ----------------Non-Key Data----------------Order-Creation-Date Order-Automatic-Fill-Date Member Number (FK1) Member-Name Member-Address Shipping-Address Shipping Instructions Club-Name (FK2) Order-Sub-Total-Cost Order-Sales-Tax Ship-Via-Method Shipping-Charge Order-Status Prepaid-Amount MEMBER ORDER (unnormalized) ------------------KeyData--------------------Order-Number (PK) ---------------Non-Key Data----------------Order-Creation-Date Order-Automatic-Fill-Date Member Number (FK1) Member-Name Member-Address Shipping-Address Shipping Instructions Club-Name (FK2) Promotion-Number (FK2) 0 { Ordered-Product-Description } n 0 { Ordered-Product-Title } n 1 { Quantity-Ordered } n 1 { Purchased-Unit-Price } n 1 { Extended-Price } n Order-Sub-Total-Cost Order-Sales-Tax Ship-Via-Method Shipping-Charge Order-Status Prepaid-Amount Method-of-Payment sells CORRECTION MEMBER ORDERED PRODUCT (1NF) ---------------Key Data-----------------Member-Number (PK1) (FK) Product-Number (PK1) (FK) -------------Non-Key Data------------Ordered-Product-Description Ordered-Product-Title Quantity-Ordered Purchased-Unit-Price Extended-Price sold as PRODUCT (1NF) ------------Key Data---------------Product-Number (PK1) Universal-Product-Code (PK2) --------Non-Key Data------------Quantity-in-Stock Product-Type Suggested-Retail-Price Club-Default-Unit-Price Current-Special-Unit-Price Current-Month-Units-Sold Current-Year-Units-Sold Total-Lifetime-Units-Sold CLUB (1NF) ------------------Key Data---------------------Club-Name (PK) --------------Non-Key Data-------------------Club-Description Club-Charter-Date establishes CLUB (unnormalized) ------------------Key Data---------------------Club-Name (PK) --------------Non-Key Data-------------------Club-Description Club-Charter-Date 1 { Agreement-Number } n 1 { Agreement-Active-Date } n 1 { Agreement-Expiration-Date } n 1 { Obligation-Period } n 1 { Required-Number-of-Credits } n 1 { Bonus-Credits-After-Obligation } n CORRECTION AGREEMENT (1NF) ----------Key Data----------------Club-Name (PK1) (FK) Agreement-Number (PK1) --------Non-Key Data------------Agreement-Active-Date Agreement-Expiration-Date Obligation-Period Required-Number-of-Credits Bonus-Credits-After-Obligation MEMBER (1NF) ---------------------Key Data---------------------Member-Number (PK1) ------------------Non-Key Data------------------Member-Name Member-Status Member-Street-Address Member-Daytime-Phone-Number Date-of-Last-Order Member-Balance-Due Member-Bonus-Balance-Available Member-Credit-Card-Information MEMBER (unnormalized) ---------------------Key Data---------------------Member-Number (PK1) ------------------Non-Key Data------------------Member-Name Member-Status Member-Address Member-Daytime-Phone-Number Date-of-Last-Order Member-Balance-Due Member-Bonus-Balance-Available Member-Credit-Card-Information 1 { Club-Name } n 1 { Agreement-Number } n 1 { Taste Code } n 1 { Media Preference } n 1 { Date-Enrolled } n 1 { Expiration-Date } n 1 { Number-of-Credits-Required } n 1 { Number of Credits-Earned } n enrolls in CORRECTION CLUB MEMBERSHIP (1NF) -------------Key Data-------------Member-Number (PK1) (FK) Club-Name (PK1) (FK) Agreement-Number (PK1) (FK) ---------Non-Key Data----------Taste Code Media Preference Date-Enrolled Expiration-Date Number-of-Credits-Required Number of Credits-Earned binds AGREEMENT (1NF) ----------Key Data----------------Club-Name (PK1) (FK) Agreement-Number (PK1) --------Non-Key Data------------Agreement-Active-Date Agreement-Expiration-Date Obligation-Period Required-Number-of-Credits Bonus-Credits-After-Obligation establishes CLUB (1NF) ------------------Key Data---------------------Club-Name (PK) --------------Non-Key Data-------------------Club-Description Club-Charter-Date sponsors Data Analysis for Database Design Normalization Example Second Normal Form: The next step of data analysis is to place the entities into 2NF. • It is assumed that you have already placed all entities into 1NF. • 2NF looks for an anomaly called a partial dependency, meaning an attribute(s) whose value is determined by only part of the primary key. • Entities that have a single attribute primary key are already in 2NF. • Only those entities that have a concatenated key need to be checked. MEMBER ORDERED PRODUCT (1NF) ---------------Key Data-----------------Member-Number (PK1) (FK) Product-Number (PK1) (FK) -------------Non-Key Data------------Ordered-Product-Description Ordered-Product-Title Quantity-Ordered Purchased-Unit-Price Extended-Price CORRECTION MEMBER ORDERED PRODUCT (2NF) ---------------Key Data-----------------Member-Number (PK1) (FK) Product-Number (PK1) (FK) -------------Non-Key Data------------Quantity-Ordered Purchased-Unit-Price Extended-Price sold as PRODUCT (2NF) ------------Key Data---------------Product-Number (PK1) Universal-Product-Code (PK2) --------Non-Key Data------------Quantity-in-Stock Product-Type Suggested-Retail-Price Club-Default-Unit-Price Current-Special-Unit-Price Current-Month-Units-Sold Current-Year-Units-Sold Total-Lifetime-Units-Sold is a MERCHANDISE (2NF) -------------Key Data--------------Product-Number (PK1) Universal-Product-Code (PK1) ---------Non-Key Data-----------Merchandise-Name Merchandise-Description Merchandise-Size Merchasnise-Color Unit-of-Measure TITLE (2NF) --------------Key Data-------------Product-Number (PK1) Universal-Product-Code (PK2) ----------Non-Key Data----------Title-of-Work Title-Cover Catalog-Description Copyright-Date Entertainment-Category Credit-Value Data Analysis for Database Design Normalization Example Third Normal Form: Entities are assumed to be in 2NF before beginning 3NF analysis. Third normal form analysis looks for two types of problems, derived data and transitive dependencies. • In both cases, the fundamental error is that non key attributes are dependent on other non key attributes. • Derived attributes are those whose values can either be calculated from other attributes, or derived through logic from the values of other attributes. • A transitive dependency exists when a non-key attribute is dependent on another non-key attribute (other than by derivation). • Transitive analysis is only performed on those entities that do not have a concatenated key. Data Analysis for Database Design Normalization Example Third Normal Form: Third normal form analysis looks for two types of problems, derived data and transitive dependencies. (continued) • A transitive dependency exists when a non-key attribute is dependent on another non-key attribute (other than by derivation). – This error usually indicates that an undiscovered entity is still embedded within the problem entity. • Transitive analysis is only performed on those entities that do not have a concatenated key. “An entity is said to be in third normal form if every nonprimary key attribute is dependent on the primary key, the whole primary key, and nothing but the primary key.” MEMBER ORDERED PRODUCT (2NF) ---------------Key Data-----------------Member-Number (PK1) (FK) Product-Number (PK1) (FK) -------------Non-Key Data------------Quantity-Ordered Purchased-Unit-Price Extended-Price CORRECTION MEMBER ORDERED PRODUCT (3NF) ---------------Key Data-----------------Member-Number (PK1) (FK) Product-Number (PK1) (FK) -------------Non-Key Data------------Quantity-Ordered Purchased-Unit-Price Extended-Price MEMBER (3NF) ---------------------Key Data---------------------Member-Number (PK1) ------------------Non-Key Data------------------Member-Name Member-Status Member-Street-Address Member-Daytime-Phone-Number Date-of-Last-Order Member-Balance-Due Member-Bonus-Balance-Available Member-Credit-Card-Information placed MEMBER ORDER (2NF) ------------------Key Data--------------------Order-Number (PK) ----------------Non-Key Data----------------Order-Creation-Date Order-Automatic-Fill-Date Member Number (FK1) Member-Name Member-Address Shipping-Address Shipping Instructions Club-Name (FK2) Order-Sub-Total-Cost Order-Sales-Tax Ship-Via-Method Shipping-Charge Order-Status Prepaid-Amount CORRECTION MEMBER ORDER (3NF) ------------------Key Data--------------------Order-Number (PK) ----------------Non-Key Data----------------Order-Creation-Date Order-Automatic-Fill-Date Member Number (FK1) Member-Name Member-Address Shipping-Address Shipping Instructions Club-Name (FK2) Order-Sub-Total-Cost Order-Sales-Tax Ship-Via-Method Shipping-Charge Order-Status Prepaid-Amount Data Analysis for Database Design Normalization Example Simplification by Inspection: When several analysts work on a common application, it is not unusual to create problems that won’t be taken care of by normalization. • These problems are best solved through simplification by inspection, a process wherein a data entity in 3NF is further simplified by such efforts as addressing subtle data redundancy. Data Analysis for Database Design Normalization Example CASE Support for Normalization: Most CASE tools can only normalize to first normal form. • They accomplish this in one of two ways. – They look for many-to-many relationships and resolve those relationships into associative entities. – They look for attributes specifically described as having multiple values for a single entity instance. It is exceedingly difficult for a CASE tool to identify second and third normal form errors. • That would require the CASE tool to have the intelligence to recognize partial and transitive dependencies. Database Design Introduction The design of any database will usually involve the DBA and database staff. They will handle the technical details and cross-application issues. It is useful for the systems analyst to understand the basic design principles for relational databases. Database Design Goals and Prerequisites to Database Design The goals of database design are as follows: A database should provide for the efficient storage, update, and retrieval of data. A database should be reliable – the stored data should have high integrity to promote user trust in that data. A database should be adaptable and scaleable to new and unforeseen requirements and applications. Database Design Goals and Prerequisites to Database Design The data model may have to be divided into multiple data models to reflect database distribution and database replication decisions. Data distribution refers to the distribution of either specific tables, records, and/or fields to different physical databases. Data replication refers to the duplication of specific tables, records, and/or fields to multiple physical databases. Each sub-model or view should reflect the data to be stored on a single server. Database Design The Database Schema The design of a database is depicted as a special model called a database schema. A database schema is the physical model or blueprint for a database. It represents the technical implementation of the logical data model. A relational database schema defines the database structure in terms of tables, keys, indexes, and integrity rules. A database schema specifies details based on the capabilities, terminology, and constraints of the chosen database management system. Database Design The Database Schema Transforming the logical data model into a physical relational database schema rules and guidelines: 1 Each fundamental, associative, and weak entity is implemented as a separate table. • The primary key is identified as such and implemented as an index into the table. • Each secondary key is implemented as its own index into the table. • Each foreign key will be implemented as such. • Attributes will be implemented with fields. – These fields correspond to columns in the table. Database Design The Database Schema Transforming the logical data model into a physical relational database schema rules and guidelines: (continued) • The following technical details must usually be specified for each attribute. – Data type. Each DBMS supports different data types, and terms for those data types. – Size of the Field. Different DBMSs express precision of real numbers differently. – NULL or NOT NULL. Must the field have a value before the record can be committed to storage? – Domains. Many DBMSs can automatically edit data to ensure that fields contain legal data. – Default. Many DBMSs allow a default value to be automatically set in the event that a user or programmer submits a record without a value. Database Design The Database Schema Transforming the logical data model into a physical relational database schema rules and guidelines: (continued) 2 Supertype/subtype entities present additional options as follows: • Most CASE tools do not currently support object-like constructs such as supertypes and subtypes. • Most CASE tools default to creating a separate table for each entity supertype and subtype. • If the subtypes are of similar size and data content, a database administrator may elect to collapse the subtypes into the supertype to create a single table. 3 Evaluate and specify referential integrity constraints. Database Design Data and Referential Integrity There are at least three types of data integrity that must be designed into any database - key integrity, domain integrity and referential integrity. Key Integrity: Every table should have a primary key (which may be concatenated). • The primary key must be controlled such that no two records in the table have the same primary key value. • The primary key for a record must never be allowed to have a NULL value. Database Design Data and Referential Integrity Domain Integrity: Appropriate controls must be designed to ensure that no field takes on a value that is outside of the range of legal values. Referential Integrity: A referential integrity error exists when a foreign key value in one table has no matching primary key value in the related table. Database Design Data and Referential Integrity Referential Integrity: Referential integrity is specified in the form of deletion rules as follows: • No restriction. – Any record in the table may be deleted without regard to any records in any other tables. • Delete:Cascade. – A deletion of a record in the table must be automatically followed by the deletion of matching records in a related table. • Delete:Restrict. – A deletion of a record in the table must be disallowed until any matching records are deleted from a related table. Database Design Data and Referential Integrity Referential Integrity: Referential integrity is specified in the form of deletion rules as follows: (continued) • Delete:Set Null. – A deletion of a record in the table must be automatically followed by setting any matching keys in a related table to the value NULL. Database Design Roles Some database shops insist that no two fields have exactly the same name. This presents an obvious problem with foreign keys A role name is an alternate name for a foreign key that clearly distinguishes the purpose that the foreign key serves in the table. The decision to require role names or not is usually established by the data or database administrator. Database Design Database Prototypes Prototyping is not an alternative to carefully thought out database schemas. On the other hand, once the schema is completed, a prototype database can usually be generated very quickly. Most modern DBMSs include powerful, menu-driven database generators that automatically create a DDL and generate a prototype database from that DDL. A database can then be loaded with test data that will prove useful for prototyping and testing outputs, inputs, screens, and other systems components. Database Design Database Capacity Planning A database is stored on disk. The database administrator will want an estimate of disk capacity for the new database to ensure that sufficient disk space is available. Database capacity planning can be calculated with simple arithmetic as follows. 1 For each table, sum the field sizes. • This is the record size for the table. 2 For each table, multiply the record size times the number of entity instances to be included in the table. • This is the table size. Database Design Database Capacity Planning Database capacity planning can be calculated with simple arithmetic as follows. (continued) 3 Sum the table sizes. • This is the database size. 4 Optionally, add a slack capacity buffer (e.g., 10%) to account for unanticipated factors or inaccurate estimates above. • This is the anticipated database capacity. Database Design Database Structure Generation CASE tools are frequently capable of generating SQL code for the database directly from a CASE-based database schema. This code can be exported to the DBMS for compilation. Even a small database model can require 50 pages or more of SQL data definition language code to create the tables, indexes, keys, fields, and triggers. Clearly, a CASE tool’s ability to automatically generate syntactically correct code is an enormous productivity advantage. Furthermore, it almost always proves easier to modify the database schema and re-generate the code, than to maintain the code directly. The Next Generation of Database Design Introduction Relational database technology is widely deployed and used in contemporary information system shops. One new technology is slowly emerging that could ultimately change the landscape dramatically – object database management systems. The heir apparent to relational DBMSs, object database management systems store true objects, that is, encapsulated data and all of the processes that can act on that data. Because relational database management systems are so widely used, we don’t expect this change to happen quickly. • It is expected that these vendors will either build object technology into their existing relational DBMSs, or they will create new, object DBMSs and provide for the transition between relational and object models.