1. CS 435 Database Systems

2. Chapter 1 An Overview of Database Management

3. What is a database? “An electronic filing cabinet” “A repository for a collection of computerized data files” A collection of interrelated data. “Descriptions”-- not definitions

4. So, how is that different from a file?

5. File processing systems Independent systems Each has its own definition of data Each has its own data formats

6. File processing systems Independent systems. Each has its own definition of data. Each has its own data formats Faculty Data File Payroll System Reports Class Data File Class Scheduling System Reports Student Data File Grade Posting System Reports

7. File processing systems Faculty Data File Payroll System Reports Class Data File Class Scheduling System Reports Problems of inconsistency. May need faculty member name in each file. May be recorded differently in each.

8. Database systems A single data definition All data (potentially) accessible from each application Less paperwork exchange between applications

9. Database systems A single data definition All data (potentially) accessible from each application Faculty Data Class Data Student Data Data Definition Database Management System Payroll System Reports Class Scheduling System Reports Grade Posting System Reports Less paperwork exchange between applications

10. So, a database is a collection of "files," or at least a collection of data that would otherwise usually exist in multiple files.

11. What is a database management system? The software that makes it possible for multiple applications and multiple users to access the same (single) set of data. The software that enables users to access and share the single set of integrated data without concern about files and file structure.

12. What is a database system? The data The database software (database management system) The other software (applications) The hardware where the data and software reside (and execute) The users who use the system

13. data Computer hardware

14. software data Computer hardware users

15. A database system is the collection of data, the software to provide access to that data, (and the hardware upon which the data and software reside and execute.) To that we can also add the users. They are also part of the "system."

16. An example of a database and some "database operations.” (The CELLAR example) http://www.csupomona.edu/~hnriley/web_mysql/cellar.html

17. +-----+----------------+---------------+------+---------+-------+ | bin | wine | producer | year | bottles | ready | +-----+----------------+---------------+------+---------+-------+ | 2 | Chardonnay | Buena Vista | 2001 | 1 | 2003 | | 3 | Chardonnay | Geyser Peak | 2001 | 5 | 2003 | | 6 | Chardonnay | Simi | 2000 | 4 | 2002 | | 12 | Joh. Riesling | Jekel | 2002 | 1 | 2003 | | 21 | Fume Blanc | Ch. St. Jean | 2001 | 4 | 2003 | | 22 | Fume Blanc | Robt. Mondavi | 2000 | 2 | 2002 | | 30 | Gewurztraminer | Ch. St. Jean | 2002 | 3 | 2003 | | 43 | Cab. Sauvignon | Windsor | 1995 | 12 | 2004 | | 45 | Cab. Sauvignon | Geyser Peak | 1998 | 12 | 2006 | | 48 | Cab. Sauvignon | Robt. Mondavi | 1997 | 12 | 2008 | | 50 | Pinot Noir | Gary Farrell | 2000 | 3 | 2003 | | 51 | Pinot Noir | Fetzer | 1997 | 3 | 2004 | | 52 | Pinot Noir | Dehlinger | 1999 | 2 | 2002 | | 58 | Merlot | Clos du Bois | 1998 | 9 | 2004 | | 64 | Zinfandel | Cline | 1998 | 9 | 2007 | | 72 | Zinfandel | Rafanelli | 1999 | 2 | 2007 | +-----+----------------+---------------+------+---------+-------+ Date’s “CELLAR” Example

18. Retrieval: select wine, bin_num, producer from Cellar where ready = '2000' ; Result: +----------------+-----+--------------+ | wine | bin | producer | +----------------+-----+--------------+ | Cab. Sauvignon | 43 | Windsor | | Pinot Noir | 51 | Fetzer | | Merlot | 58 | Clos du Bois | +----------------+-----+--------------+ 3 rows in set (0.00 sec)

19. Date’s “CELLAR” Example Inserting new data: insert into Cellar values (53, 'Pinot Noir', 'Saintsbury', 2003, 6, 2008);

20. Date’s “CELLAR” Example Changing existing data: update Cellar set bottles = 4 where bin_num = 3; Deleting existing data: delete from cellar where bin_num = 2;

21. +-----+----------------+---------------+------+---------+-------+ | bin | wine | producer | year | bottles | ready | +-----+----------------+---------------+------+---------+-------+ | 3 | Chardonnay | Geyser Peak | 2001 | 4 | 2003 | | 6 | Chardonnay | Simi | 2000 | 4 | 2002 | | 12 | Joh. Riesling | Jekel | 2002 | 1 | 2003 | | 21 | Fume Blanc | Ch. St. Jean | 2001 | 4 | 2003 | | 22 | Fume Blanc | Robt. Mondavi | 2000 | 2 | 2002 | | 30 | Gewurztraminer | Ch. St. Jean | 2002 | 3 | 2003 | | 43 | Cab. Sauvignon | Windsor | 1995 | 12 | 2004 | | 45 | Cab. Sauvignon | Geyser Peak | 1998 | 12 | 2006 | | 48 | Cab. Sauvignon | Robt. Mondavi | 1997 | 12 | 2008 | | 50 | Pinot Noir | Gary Farrell | 2000 | 3 | 2003 | | 51 | Pinot Noir | Fetzer | 1997 | 3 | 2004 | | 52 | Pinot Noir | Dehlinger | 1999 | 2 | 2002 | | 58 | Merlot | Clos du Bois | 1998 | 9 | 2004 | | 64 | Zinfandel | Cline | 1998 | 9 | 2007 | | 72 | Zinfandel | Rafanelli | 1999 | 2 | 2007 | | 53 | Pinot Noir | Saintsbury | 2003 | 6 | 2008 | +-----+----------------+---------------+------+---------+-------+ CELLAR changed from 5 to 4 row for bin 2 deleted new row inserted

22. Note that the “CELLAR database" looks like a "table," and in fact,that is what it is. In particular it is a relational table, or just a "relation."

23. Aside regarding tables… and, “looks like a table.”

24. +-----+----------------+---------------+------+---------+-------+ | bin | wine | producer | year | bottles | ready | +-----+----------------+---------------+------+---------+-------+ | 2 | Chardonnay | Buena Vista | 2001 | 1 | 2003 | | 3 | Chardonnay | Geyser Peak | 2001 | 5 | 2003 | | 6 | Chardonnay | Simi | 2000 | 4 | 2002 | | 12 | Joh. Riesling | Jekel | 2002 | 1 | 2003 | | 21 | Fume Blanc | Ch. St. Jean | 2001 | 4 | 2003 | | 22 | Fume Blanc | Robt. Mondavi | 2000 | 2 | 2002 | | 30 | Gewurztraminer | Ch. St. Jean | 2002 | 3 | 2003 | | 43 | Cab. Sauvignon | Windsor | 1995 | 12 | 2004 | | 45 | Cab. Sauvignon | Geyser Peak | 1998 | 12 | 2006 | | 48 | Cab. Sauvignon | Robt. Mondavi | 1997 | 12 | 2008 | | 50 | Pinot Noir | Gary Farrell | 2000 | 3 | 2003 | | 51 | Pinot Noir | Fetzer | 1997 | 3 | 2004 | | 52 | Pinot Noir | Dehlinger | 1999 | 2 | 2002 | | 58 | Merlot | Clos du Bois | 1998 | 9 | 2004 | | 64 | Zinfandel | Cline | 1998 | 9 | 2007 | | 72 | Zinfandel | Rafanelli | 1999 | 2 | 2007 | +-----+----------------+---------------+------+---------+-------+ “Tables” rows column “headings” columns Columns are aligned: i.e., strings left justified numbers right justified

25. +-----+----------------+---------------+------+---------+-------+ | bin | wine | producer | year | bottles | ready | +-----+----------------+---------------+------+---------+-------+ | 2 | Chardonnay | Buena Vista | 2001 | 1 | 2003 | | 3 | Chardonnay | Geyser Peak | 2001 | 5 | 2003 | | 6 | Chardonnay | Simi | 2000 | 4 | 2002 | | 12 | Joh. Riesling | Jekel | 2002 | 1 | 2003 | | 21 | Fume Blanc | Ch. St. Jean | 2001 | 4 | 2003 | | 22 | Fume Blanc | Robt. Mondavi | 2000 | 2 | 2002 | | 30 | Gewurztraminer | Ch. St. Jean | 2002 | 3 | 2003 | | 43 | Cab. Sauvignon | Windsor | 1995 | 12 | 2004 | | 45 | Cab. Sauvignon | Geyser Peak | 1998 | 12 | 2006 | | 48 | Cab. Sauvignon | Robt. Mondavi | 1997 | 12 | 2008 | | 50 | Pinot Noir | Gary Farrell | 2000 | 3 | 2003 | | 51 | Pinot Noir | Fetzer | 1997 | 3 | 2004 | | 52 | Pinot Noir | Dehlinger | 1999 | 2 | 2002 | | 58 | Merlot | Clos du Bois | 1998 | 9 | 2004 | | 64 | Zinfandel | Cline | 1998 | 9 | 2007 | | 72 | Zinfandel | Rafanelli | 1999 | 2 | 2007 | +-----+----------------+---------------+------+---------+-------+ Separating lines provided by MySQL

26. Separating lines provided by textbook publisher

27. bin wine producer year bottles ready 2 Chardonnay Buena Vista 2001 1 2003 3 Chardonnay Geyser Peak 2001 5 2003 6 Chardonnay Simi 2000 4 2002 12 Joh. Riesling Jekel 2002 1 2003 21 Fume Blanc Ch. St. Jean 2001 4 2003 22 Fume Blanc Robt. Mondavi 2000 2 2002 30 Gewurztraminer Ch. St. Jean 2002 3 2003 43 Cab. Sauvignon Windsor 1995 12 2004 45 Cab. Sauvignon Geyser Peak 1998 12 2006 48 Cab. Sauvignon Robt. Mondavi 1997 12 2008 50 Pinot Noir Gary Farrell 2000 3 2003 51 Pinot Noir Fetzer 1997 3 2004 52 Pinot Noir Dehlinger 1999 2 2002 58 Merlot Clos du Bois 1998 9 2004 64 Zinfandel Cline 1998 9 2007 72 Zinfandel Rafanelli 1999 2 2007 No separating lines

28. So, a database is usually said to consist of tables rather than files. The rows of the tables would be the "records" of a file. The columns of the table are the "fields" of those records.

29. Note that the "database," the collection of tables, is a logical concept, a data structure.

30. The database software (the database manager, the DBMS) provides the mapping of the logical database into one or more logical files, and ultimately into a physical representation on disk.

31. Thus, there are stored files, stored records, and stored fields. (Sometimes the DBMS shares this mapping with the operating system)

32. Parts +------+-------+-------+--------+--------+ | pnum | pname | color | weight | city | +------+-------+-------+--------+--------+ | P1 | Nut | Red | 12.0 | London | | P2 | Bolt | Green | 17.0 | Paris | | P3 | Screw | Blue | 17.0 | Rome | | P4 | Screw | Red | 14.0 | London | | P5 | Cam | Blue | 12.0 | Paris | | P6 | Cog | Red | 19.0 | London | +------+-------+-------+--------+--------+ Say this parts table is stored in the database as a file

34. Stored database Other stored files P1 Nut Red 12.0 P2 Bolt Green 17.0 “Parts” stored file Two occurrences of the “part” stored record type. Stored field occurrences …and the table rows become records in the file, the columns fields within each record

35. P1 Nut Red 12.0 But, for example, the data for a part (a table row): P1 12.0 P1 Nut Red might be stored as two records: and

36. Stored database Other stored files P1 Nut Red 12.0 P2 Bolt Green 17.0 “Parts” stored file Parts +------+-------+-------+--------+--------+ | pnum | pname | color | weight | city | +------+-------+-------+--------+--------+ | P1 | Nut | Red | 12.0 | London | | P2 | Bolt | Green | 17.0 | Paris | | P3 | Screw | Blue | 17.0 | Rome | | P4 | Screw | Red | 14.0 | London | | P5 | Cam | Blue | 12.0 | Paris | | P6 | Cog | Red | 19.0 | London | +------+-------+-------+--------+--------+ DBMS Data independence--the immunity of applications to change in physical representation. The DBMS relieves the user of any concern about how the data is represented physically.

37. Look again at the CELLAR example to see how this table relates to other tables that might exist. http://www.csupomona.edu/~hnriley/web_mysql/cellar.html

38. +-----+----------------+---------------+------+---------+-------+ | bin | wine | producer | year | bottles | ready | +-----+----------------+---------------+------+---------+-------+ | 2 | Chardonnay | Buena Vista | 2001 | 1 | 2003 | | 3 | Chardonnay | Geyser Peak | 2001 | 5 | 2003 | | 6 | Chardonnay | Simi | 2000 | 4 | 2002 | | 12 | Joh. Riesling | Jekel | 2002 | 1 | 2003 | | 21 | Fume Blanc | Ch. St. Jean | 2001 | 4 | 2003 | | 22 | Fume Blanc | Robt. Mondavi | 2000 | 2 | 2002 | | 30 | Gewurztraminer | Ch. St. Jean | 2002 | 3 | 2003 | | 43 | Cab. Sauvignon | Windsor | 1995 | 12 | 2004 | | 45 | Cab. Sauvignon | Geyser Peak | 1998 | 12 | 2006 | | 48 | Cab. Sauvignon | Robt. Mondavi | 1997 | 12 | 2008 | | 50 | Pinot Noir | Gary Farrell | 2000 | 3 | 2003 | | 51 | Pinot Noir | Fetzer | 1997 | 3 | 2004 | | 52 | Pinot Noir | Dehlinger | 1999 | 2 | 2002 | | 58 | Merlot | Clos du Bois | 1998 | 9 | 2004 | | 64 | Zinfandel | Cline | 1998 | 9 | 2007 | | 72 | Zinfandel | Rafanelli | 1999 | 2 | 2007 | +-----+----------------+---------------+------+---------+-------+ CELLAR Suppose we want to add some information about each wine.

39. +-----+----------------+-------|---------------+------+-----+-------+ | bin | wine | type | producer | year | qty | ready | +-----+----------------+-------|---------------+------+-----+-------+ | 2 | Chardonnay | white | Buena Vista | 2001 | 1 | 2003 | | 3 | Chardonnay | white | Geyser Peak | 2001 | 5 | 2003 | | 6 | Chardonnay | white | Simi | 2000 | 4 | 2002 | | 12 | Joh. Riesling | white | Jekel | 2002 | 1 | 2003 | | 21 | Fume Blanc | white |Ch. St. Jean | 2001 | 4 | 2003 | | 22 | Fume Blanc | white | Robt. Mondavi | 2000 | 2 | 2002 | | 30 | Gewurztraminer | white | Ch. St. Jean | 2002 | 3 | 2003 | | 43 | Cab. Sauvignon | red | Windsor | 1995 | 12 | 2004 | | 45 | Cab. Sauvignon | red | Geyser Peak | 1998 | 12 | 2006 | | 48 | Cab. Sauvignon | red | Robt. Mondavi | 1997 | 12 | 2008 | | 50 | Pinot Noir | red | Gary Farrell | 2000 | 3 | 2003 | | 51 | Pinot Noir | red | Fetzer | 1997 | 3 | 2004 | | 52 | Pinot Noir | red | Dehlinger | 1999 | 2 | 2002 | | 58 | Merlot | red | Clos du Bois | 1998 | 9 | 2004 | | 64 | Zinfandel | red | Cline | 1998 | 9 | 2007 | | 72 | Zinfandel | red | Rafanelli | 1999 | 2 | 2007 | +-----+----------------+-------|---------------+------+-----+-------+ for example: “redundancies” Chardonnay is white--3 times Pinot Noir is red--3 times

40. So, more tables--for example: Wine: Name, Type, Description, Characteristic Producer: Name, Area, Appellation

41. Wines +----------------+-----------+------------------+---------------------+ | wine_name | wine_type | wine_description | wine_characteristic | +----------------+-----------+------------------+---------------------+ | Chardonnay | white | dry | buttery | | Joh. Riesling | white | semi-sweet | fruity | | Fume Blanc | white | dry | smoky | | Gewurztraminer | white | semi-sweet | spicy | | Cab. Sauvignon | red | dry | oaky | | Pinot Noir | red | dry | fruity | | Merlot | red | dry | plummy | | Zinfandel | red | dry | spicy | +----------------+-----------+------------------+---------------------+

42. Note that this gives us the ability to describe a wine as "Red" in one place, rather than adding it to the CELLAR table and repeating it each time that wine appears. This eliminates "redundancy."

43. Producers +--------------+-----------------+-------------+ | name | area | appellation | +--------------+-----------------+-------------+ | Fetzer | Hopland | Mendocino | | Gary Farrell | Russian River V | Sonoma | | Geyser Peak | Alexander Valle | Sonoma | | Jekel | Arroyo Seco | Monterey | |. |. |. | |. | etc. |. | +--------------+-----------------+-------------+ Similarly,

44. Thus the database, (the collection of tables) is "integrated," i.e., the entirety of the data is formed by use of all of the tables.

45. Cellar +-----+----------------+---------------+------+---------+-------+ | bin | wine | producer | year | bottles | ready | +-----+----------------+---------------+------+---------+-------+ | 2 | Chardonnay | Buena Vista | 2001 | 1 | 2003 | | 3 | Chardonnay | Geyser Peak | 2001 | 5 | 2003 | | 6 | Chardonnay | Simi | 2000 | 4 | 2002 | | 12 | Joh. Riesling | Jekel | 2002 | 1 | 2003 | | 21 | Fume Blanc | Ch. St. Jean | 2001 | 4 | 2003 | | 22 | Fume Blanc | Robt. Mondavi | 2000 | 2 | 2002 | | 30 | Gewurztraminer | Ch. St. Jean | 2002 | 3 | 2003 | | 43 | Cab. Sauvignon | Windsor | 1995 | 12 | 2004 | | 45 | Cab. Sauvignon | Geyser Peak | 1998 | 12 | 2006 | | 48 | Cab. Sauvignon | Robt. Mondavi | 1997 | 12 | 2008 | | 50 | Pinot Noir | Gary Farrell | 2000 | 3 | 2003 | | 51 | Pinot Noir | Fetzer | 1997 | 3 | 2004 | | 52 | Pinot Noir | Dehlinger | 1999 | 2 | 2002 | | 58 | Merlot | Clos du Bois | 1998 | 9 | 2004 | | 64 | Zinfandel | Cline | 1998 | 9 | 2007 | | 72 | Zinfandel | Rafanelli | 1999 | 2 | 2007 | +-----+----------------+---------------+------+---------+-------+ Wines +----------------+-----------+------------------+---------------------+ | wine_name | wine_type | wine_description | wine_characteristic | +----------------+-----------+------------------+---------------------+ | Chardonnay | white | dry | buttery | | Joh. Riesling | white | semi-sweet | fruity | | Fume Blanc | white | dry | smoky | | Gewurztraminer | white | semi-sweet | spicy | | Cab. Sauvignon | red | dry | oaky | | Pinot Noir | red | dry | fruity | | Merlot | red | dry | plummy | | Zinfandel | red | dry | spicy | +----------------+-----------+------------------+---------------------+ Producers +--------------+-----------------+-------------+ | name | area | appellation | +--------------+-----------------+-------------+ | Fetzer | Hopland | Mendocino | | Gary Farrell | Russian River V | Sonoma | | Geyser Peak | Alexander Valle | Sonoma | | Jekel | Arroyo Seco | Monterey | |. |. |. | The database: a collection of "files," or at least a collection of data that would otherwise usually exist in multiple files.

46. And, we can add Maps to the wineries Photographs of wineries, wine bottles Recordings of our own “tasting notes” Etc.

47. The data can also be "shared," by programs or by users. (Single-user vs. multi-user systems.)

48. Persistent vs. Transient data

49. The persistent data. Once put into the database, it stays there until explicitly removed. Faculty Data Class Data Student Data Data Definition Database Management System Payroll System Reports Class Scheduling System Reports Grade Posting System Reports Transient or ephemeral data. Input, output, intermediate results.

50. Another definition: A database is a collection of persistent data that is used by the application systems of some given enterprise.

51. What are applications? Application programs? Application systems? What is an “enterprise”?

52. The Benefits of a Database Makes possible, supports, enhances –Rapid availability of current data. –Reduced redundancy. –Less inconsistency. –Sharing of data among users, applications –Enforcement of standards –Enforcement of security –Maintenance of integrity

53. Entity Relationship Modeling Entities: The “things” that we need to record data about. Relationships: How these things are related to one another.

54. Entities: The “things” that we need to record data about. People Products Places Processes Policies Paper (documents)

55. Relationships: How these things are related to one another--connections between and among the “things” and their data; Which peoplemake which products Which products are stored in which places What places use what processes What processes require what policies the relationships

56. Products People People make products. Products are made by people. Relationships are bi-directional. Entity Relationship

57. Products People Relationships are bi-directional. Entity Relationship For example: Given a person, find which products that person makes. Given a product, find which people make that product.

59. binary relationship ternary relationship recursive relationship

61. A “horizontal,” or row, subset of the table CELLAR A “vertical,” or column, subset Operations on tables produce only tables.

62. Relational Database Management Systems DB2 Ingres Informix Microsoft SQL Server Oracle Sybase

63. Other Database Management System “Models” (pre-relational) Hierarchical (tree structure) Network (graph structure) Inverted list

64. Other (new) Approaches (post-relational) Deductive Expert Extendable Object Oriented Semantic Universal Relation DBMSs

65. People Data Administrator High level position Responsible for defining the data to be maintained Makes policy (regarding security, etc.) Non-”technical” Database Administrator Creates the database Implements the policies IT professional

66. People Application Programmers Write the programs to maintain the database and provide access to it Need to know only the external view of the DB End Users Interact with the programs to enter data, change data, generate reports May not need to know anything about the DB

67. Application Programs 4GL Systems Interfaces Query Language Processor Command Driven Menu or Forms Driven

68. 1.1 Define the following terms: binary relationship menu-driven interface command-driven interfacemulti-user system concurrent access online application data administration persistent data database property database system query language data independence redundancy DBA relationship DBMS security entity sharing entity/relationship diagram stored field forms-driven interface stored file integration stored record integrity transaction