Chapter 01-Restriicting Data Using The SQL SELECT Statemnt-01
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Objective
After completing this lesson,you should be able to do the following:
- List the capabilities of SQL statements.
- Execute a basic SELECT statement
Lesson Agenda
- Basic SELECT statement
- Arithmetic expressions and NULL values in the SELECT statement
- Column aliases
- Use of concatenation operator,literal character strings,alternative quote operator,and the DISTINCT keyword
- DESCRIBE command
Capabilities of SQL SELECT Statements
Basic SELECT Statement
SELECT *|{[DISTINCT] column|expression [alias],...} FROM table;
- SELECT identifies the columns to be displayed.
- FROM identifies the table containing those columns.
Selecting All Columns
View Code
SQL> SELECT * FROM departments; DEPARTMENT_ID DEPARTMENT_NAME MANAGER_ID LOCATION_ID ------------- ------------------------------ ---------- ----------- 10 Administration 200 1700 20 Marketing 201 1800 30 Purchasing 114 1700 40 Human Resources 203 2400 50 Shipping 121 1500 60 IT 103 1400 70 Public Relations 204 2700 80 Sales 145 2500 90 Executive 100 1700 100 Finance 108 1700 110 Accounting 205 1700 DEPARTMENT_ID DEPARTMENT_NAME MANAGER_ID LOCATION_ID ------------- ------------------------------ ---------- ----------- 120 Treasury 1700 130 Corporate Tax 1700 140 Control And Credit 1700 150 Shareholder Services 1700 160 Benefits 1700 170 Manufacturing 1700 180 Construction 1700 190 Contracting 1700 200 Operations 1700 210 IT Support 1700 220 NOC 1700 DEPARTMENT_ID DEPARTMENT_NAME MANAGER_ID LOCATION_ID ------------- ------------------------------ ---------- ----------- 230 IT Helpdesk 1700 240 Government Sales 1700 250 Retail Sales 1700 260 Recruiting 1700 270 Payroll 1700 27 rows selected.
Selecting Specific Columns
View Code
SQL> SELECT department_id,location_id FROM departments; DEPARTMENT_ID LOCATION_ID ------------- ----------- 10 1700 20 1800 30 1700 40 2400 50 1500 60 1400 70 2700 80 2500 90 1700 100 1700 110 1700 DEPARTMENT_ID LOCATION_ID ------------- ----------- 120 1700 130 1700 140 1700 150 1700 160 1700 170 1700 180 1700 190 1700 200 1700 210 1700 220 1700 DEPARTMENT_ID LOCATION_ID ------------- ----------- 230 1700 240 1700 250 1700 260 1700 270 1700 27 rows selected.
Writting SQL Statements
- SQL statements are not case-sensitive.(约定俗称的东西，关键字大写，表名称、字段名称小写.)
- SQL statements can be entered on one or more lines.
- Keywords cannot be abbreviated or split across lines.
- Clauses are usually placed on separate lines.
- Idents are used to enhance readbility.
- In SQL Developer,SQL statements can optionally be terminated by a semicolon(;).Semicolons are required when you execute multiple SQL statements.
- In SQL*Plus,you are required to end each SQL statement with a semicolon(;).Or you can do this(end each SQL statement with a /);
Column Heading Defaults
- SQL Developer:
- -Default heading alignment:Left-aligned
- -Default heading display:Uppercase
- SQL*Plus:
- -Character and Date column headings are left-aligned.
- -Number column headings are right-aligned.
- -Default heading display:Uppercase
Arithmetic Expressions
Create expressions with number and data by using arithmetic operators.
| OPerator | Description |
| + | Add |
| - | Subtract |
| * | Multiply |
| / | Divide |
Using Arithmetic Operators
View Code
SQL> SELECT last_name,salary,salary+300 FROM employees; LAST_NAME SALARY SALARY+300 ------------------------- ---------- ---------- OConnell 2600 2900 Grant 2600 2900 Whalen 4400 4700 Hartstein 13000 13300 Fay 6000 6300 Mavris 6500 6800 Baer 10000 10300 Higgins 12008 12308 Gietz 8300 8600 King 24000 24300 Kochhar 17000 17300 LAST_NAME SALARY SALARY+300 ------------------------- ---------- ---------- De Haan 17000 17300 Hunold 9000 9300 Ernst 6000 6300 Austin 4800 5100 Pataballa 4800 5100 Lorentz 4200 4500 Greenberg 12008 12308 Faviet 9000 9300 Chen 8200 8500 Sciarra 7700 8000 Urman 7800 8100 LAST_NAME SALARY SALARY+300 ------------------------- ---------- ---------- Popp 6900 7200 Raphaely 11000 11300 Khoo 3100 3400 Baida 2900 3200 Tobias 2800 3100 Himuro 2600 2900 Colmenares 2500 2800 Weiss 8000 8300 Fripp 8200 8500 Kaufling 7900 8200 Vollman 6500 6800 LAST_NAME SALARY SALARY+300 ------------------------- ---------- ---------- Mourgos 5800 6100 Nayer 3200 3500 Mikkilineni 2700 3000 Landry 2400 2700 Markle 2200 2500 Bissot 3300 3600 Atkinson 2800 3100 Marlow 2500 2800 Olson 2100 2400 Mallin 3300 3600 Rogers 2900 3200 LAST_NAME SALARY SALARY+300 ------------------------- ---------- ---------- Gee 2400 2700 Philtanker 2200 2500 Ladwig 3600 3900 Stiles 3200 3500 Seo 2700 3000 Patel 2500 2800 Rajs 3500 3800 Davies 3100 3400 Matos 2600 2900 Vargas 2500 2800 Russell 14000 14300 LAST_NAME SALARY SALARY+300 ------------------------- ---------- ---------- Partners 13500 13800 Errazuriz 12000 12300 Cambrault 11000 11300 Zlotkey 10500 10800 Tucker 10000 10300 Bernstein 9500 9800 Hall 9000 9300 Olsen 8000 8300 Cambrault 7500 7800 Tuvault 7000 7300 King 10000 10300 LAST_NAME SALARY SALARY+300 ------------------------- ---------- ---------- Sully 9500 9800 McEwen 9000 9300 Smith 8000 8300 Doran 7500 7800 Sewall 7000 7300 Vishney 10500 10800 Greene 9500 9800 Marvins 7200 7500 Lee 6800 7100 Ande 6400 6700 Banda 6200 6500 LAST_NAME SALARY SALARY+300 ------------------------- ---------- ---------- Ozer 11500 11800 Bloom 10000 10300 Fox 9600 9900 Smith 7400 7700 Bates 7300 7600 Kumar 6100 6400 Abel 11000 11300 Hutton 8800 9100 Taylor 8600 8900 Livingston 8400 8700 Grant 7000 7300 LAST_NAME SALARY SALARY+300 ------------------------- ---------- ---------- Johnson 6200 6500 Taylor 3200 3500 Fleaur 3100 3400 Sullivan 2500 2800 Geoni 2800 3100 Sarchand 4200 4500 Bull 4100 4400 Dellinger 3400 3700 Cabrio 3000 3300 Chung 3800 4100 Dilly 3600 3900 LAST_NAME SALARY SALARY+300 ------------------------- ---------- ---------- Gates 2900 3200 Perkins 2500 2800 Bell 4000 4300 Everett 3900 4200 McCain 3200 3500 Jones 2800 3100 Walsh 3100 3400 Feeney 3000 3300 107 rows selected.
Operator Precedence
View Code
SQL> SELECT last_name,salary,12*salary + 100 FROM employees; LAST_NAME SALARY 12*SALARY+100 ------------------------- ---------- ------------- OConnell 2600 31300 Grant 2600 31300 Whalen 4400 52900 Hartstein 13000 156100 Fay 6000 72100 Mavris 6500 78100 Baer 10000 120100 Higgins 12008 144196 Gietz 8300 99700 King 24000 288100 Kochhar 17000 204100 LAST_NAME SALARY 12*SALARY+100 ------------------------- ---------- ------------- De Haan 17000 204100 Hunold 9000 108100 Ernst 6000 72100 Austin 4800 57700 Pataballa 4800 57700 Lorentz 4200 50500 Greenberg 12008 144196 Faviet 9000 108100 Chen 8200 98500 Sciarra 7700 92500 Urman 7800 93700 LAST_NAME SALARY 12*SALARY+100 ------------------------- ---------- ------------- Popp 6900 82900 Raphaely 11000 132100 Khoo 3100 37300 Baida 2900 34900 Tobias 2800 33700 Himuro 2600 31300 Colmenares 2500 30100 Weiss 8000 96100 Fripp 8200 98500 Kaufling 7900 94900 Vollman 6500 78100 LAST_NAME SALARY 12*SALARY+100 ------------------------- ---------- ------------- Mourgos 5800 69700 Nayer 3200 38500 Mikkilineni 2700 32500 Landry 2400 28900 Markle 2200 26500 Bissot 3300 39700 Atkinson 2800 33700 Marlow 2500 30100 Olson 2100 25300 Mallin 3300 39700 Rogers 2900 34900 LAST_NAME SALARY 12*SALARY+100 ------------------------- ---------- ------------- Gee 2400 28900 Philtanker 2200 26500 Ladwig 3600 43300 Stiles 3200 38500 Seo 2700 32500 Patel 2500 30100 Rajs 3500 42100 Davies 3100 37300 Matos 2600 31300 Vargas 2500 30100 Russell 14000 168100 LAST_NAME SALARY 12*SALARY+100 ------------------------- ---------- ------------- Partners 13500 162100 Errazuriz 12000 144100 Cambrault 11000 132100 Zlotkey 10500 126100 Tucker 10000 120100 Bernstein 9500 114100 Hall 9000 108100 Olsen 8000 96100 Cambrault 7500 90100 Tuvault 7000 84100 King 10000 120100 LAST_NAME SALARY 12*SALARY+100 ------------------------- ---------- ------------- Sully 9500 114100 McEwen 9000 108100 Smith 8000 96100 Doran 7500 90100 Sewall 7000 84100 Vishney 10500 126100 Greene 9500 114100 Marvins 7200 86500 Lee 6800 81700 Ande 6400 76900 Banda 6200 74500 LAST_NAME SALARY 12*SALARY+100 ------------------------- ---------- ------------- Ozer 11500 138100 Bloom 10000 120100 Fox 9600 115300 Smith 7400 88900 Bates 7300 87700 Kumar 6100 73300 Abel 11000 132100 Hutton 8800 105700 Taylor 8600 103300 Livingston 8400 100900 Grant 7000 84100 LAST_NAME SALARY 12*SALARY+100 ------------------------- ---------- ------------- Johnson 6200 74500 Taylor 3200 38500 Fleaur 3100 37300 Sullivan 2500 30100 Geoni 2800 33700 Sarchand 4200 50500 Bull 4100 49300 Dellinger 3400 40900 Cabrio 3000 36100 Chung 3800 45700 Dilly 3600 43300 LAST_NAME SALARY 12*SALARY+100 ------------------------- ---------- ------------- Gates 2900 34900 Perkins 2500 30100 Bell 4000 48100 Everett 3900 46900 McCain 3200 38500 Jones 2800 33700 Walsh 3100 37300 Feeney 3000 36100 107 rows selected.
View Code
SQL> SELECT last_name,salary,12*(salary + 100) FROM employees; LAST_NAME SALARY 12*(SALARY+100) ------------------------- ---------- --------------- OConnell 2600 32400 Grant 2600 32400 Whalen 4400 54000 Hartstein 13000 157200 Fay 6000 73200 Mavris 6500 79200 Baer 10000 121200 Higgins 12008 145296 Gietz 8300 100800 King 24000 289200 Kochhar 17000 205200 LAST_NAME SALARY 12*(SALARY+100) ------------------------- ---------- --------------- De Haan 17000 205200 Hunold 9000 109200 Ernst 6000 73200 Austin 4800 58800 Pataballa 4800 58800 Lorentz 4200 51600 Greenberg 12008 145296 Faviet 9000 109200 Chen 8200 99600 Sciarra 7700 93600 Urman 7800 94800 LAST_NAME SALARY 12*(SALARY+100) ------------------------- ---------- --------------- Popp 6900 84000 Raphaely 11000 133200 Khoo 3100 38400 Baida 2900 36000 Tobias 2800 34800 Himuro 2600 32400 Colmenares 2500 31200 Weiss 8000 97200 Fripp 8200 99600 Kaufling 7900 96000 Vollman 6500 79200 LAST_NAME SALARY 12*(SALARY+100) ------------------------- ---------- --------------- Mourgos 5800 70800 Nayer 3200 39600 Mikkilineni 2700 33600 Landry 2400 30000 Markle 2200 27600 Bissot 3300 40800 Atkinson 2800 34800 Marlow 2500 31200 Olson 2100 26400 Mallin 3300 40800 Rogers 2900 36000 LAST_NAME SALARY 12*(SALARY+100) ------------------------- ---------- --------------- Gee 2400 30000 Philtanker 2200 27600 Ladwig 3600 44400 Stiles 3200 39600 Seo 2700 33600 Patel 2500 31200 Rajs 3500 43200 Davies 3100 38400 Matos 2600 32400 Vargas 2500 31200 Russell 14000 169200 LAST_NAME SALARY 12*(SALARY+100) ------------------------- ---------- --------------- Partners 13500 163200 Errazuriz 12000 145200 Cambrault 11000 133200 Zlotkey 10500 127200 Tucker 10000 121200 Bernstein 9500 115200 Hall 9000 109200 Olsen 8000 97200 Cambrault 7500 91200 Tuvault 7000 85200 King 10000 121200 LAST_NAME SALARY 12*(SALARY+100) ------------------------- ---------- --------------- Sully 9500 115200 McEwen 9000 109200 Smith 8000 97200 Doran 7500 91200 Sewall 7000 85200 Vishney 10500 127200 Greene 9500 115200 Marvins 7200 87600 Lee 6800 82800 Ande 6400 78000 Banda 6200 75600 LAST_NAME SALARY 12*(SALARY+100) ------------------------- ---------- --------------- Ozer 11500 139200 Bloom 10000 121200 Fox 9600 116400 Smith 7400 90000 Bates 7300 88800 Kumar 6100 74400 Abel 11000 133200 Hutton 8800 106800 Taylor 8600 104400 Livingston 8400 102000 Grant 7000 85200 LAST_NAME SALARY 12*(SALARY+100) ------------------------- ---------- --------------- Johnson 6200 75600 Taylor 3200 39600 Fleaur 3100 38400 Sullivan 2500 31200 Geoni 2800 34800 Sarchand 4200 51600 Bull 4100 50400 Dellinger 3400 42000 Cabrio 3000 37200 Chung 3800 46800 Dilly 3600 44400 LAST_NAME SALARY 12*(SALARY+100) ------------------------- ---------- --------------- Gates 2900 36000 Perkins 2500 31200 Bell 4000 49200 Everett 3900 48000 McCain 3200 39600 Jones 2800 34800 Walsh 3100 38400 Feeney 3000 37200 107 rows selected.
Defining a Null Value
- Null is a value that is unavailable,unassigned,unknow,or inapplicable.
- Null is not the same as zero or a blank space.
View Code
SQL> SELECT last_name,job_id,salary,commission_pct FROM employees; LAST_NAME JOB_ID SALARY COMMISSION_PCT ------------------------- ---------- ---------- -------------- OConnell SH_CLERK 2600 Grant SH_CLERK 2600 Whalen AD_ASST 4400 Hartstein MK_MAN 13000 Fay MK_REP 6000 Mavris HR_REP 6500 Baer PR_REP 10000 Higgins AC_MGR 12008 Gietz AC_ACCOUNT 8300 King AD_PRES 24000 Kochhar AD_VP 17000 LAST_NAME JOB_ID SALARY COMMISSION_PCT ------------------------- ---------- ---------- -------------- De Haan AD_VP 17000 Hunold IT_PROG 9000 Ernst IT_PROG 6000 Austin IT_PROG 4800 Pataballa IT_PROG 4800 Lorentz IT_PROG 4200 Greenberg FI_MGR 12008 Faviet FI_ACCOUNT 9000 Chen FI_ACCOUNT 8200 Sciarra FI_ACCOUNT 7700 Urman FI_ACCOUNT 7800 LAST_NAME JOB_ID SALARY COMMISSION_PCT ------------------------- ---------- ---------- -------------- Popp FI_ACCOUNT 6900 Raphaely PU_MAN 11000 Khoo PU_CLERK 3100 Baida PU_CLERK 2900 Tobias PU_CLERK 2800 Himuro PU_CLERK 2600 Colmenares PU_CLERK 2500 Weiss ST_MAN 8000 Fripp ST_MAN 8200 Kaufling ST_MAN 7900 Vollman ST_MAN 6500 LAST_NAME JOB_ID SALARY COMMISSION_PCT ------------------------- ---------- ---------- -------------- Mourgos ST_MAN 5800 Nayer ST_CLERK 3200 Mikkilineni ST_CLERK 2700 Landry ST_CLERK 2400 Markle ST_CLERK 2200 Bissot ST_CLERK 3300 Atkinson ST_CLERK 2800 Marlow ST_CLERK 2500 Olson ST_CLERK 2100 Mallin ST_CLERK 3300 Rogers ST_CLERK 2900 LAST_NAME JOB_ID SALARY COMMISSION_PCT ------------------------- ---------- ---------- -------------- Gee ST_CLERK 2400 Philtanker ST_CLERK 2200 Ladwig ST_CLERK 3600 Stiles ST_CLERK 3200 Seo ST_CLERK 2700 Patel ST_CLERK 2500 Rajs ST_CLERK 3500 Davies ST_CLERK 3100 Matos ST_CLERK 2600 Vargas ST_CLERK 2500 Russell SA_MAN 14000 .4 LAST_NAME JOB_ID SALARY COMMISSION_PCT ------------------------- ---------- ---------- -------------- Partners SA_MAN 13500 .3 Errazuriz SA_MAN 12000 .3 Cambrault SA_MAN 11000 .3 Zlotkey SA_MAN 10500 .2 Tucker SA_REP 10000 .3 Bernstein SA_REP 9500 .25 Hall SA_REP 9000 .25 Olsen SA_REP 8000 .2 Cambrault SA_REP 7500 .2 Tuvault SA_REP 7000 .15 King SA_REP 10000 .35 LAST_NAME JOB_ID SALARY COMMISSION_PCT ------------------------- ---------- ---------- -------------- Sully SA_REP 9500 .35 McEwen SA_REP 9000 .35 Smith SA_REP 8000 .3 Doran SA_REP 7500 .3 Sewall SA_REP 7000 .25 Vishney SA_REP 10500 .25 Greene SA_REP 9500 .15 Marvins SA_REP 7200 .1 Lee SA_REP 6800 .1 Ande SA_REP 6400 .1 Banda SA_REP 6200 .1 LAST_NAME JOB_ID SALARY COMMISSION_PCT ------------------------- ---------- ---------- -------------- Ozer SA_REP 11500 .25 Bloom SA_REP 10000 .2 Fox SA_REP 9600 .2 Smith SA_REP 7400 .15 Bates SA_REP 7300 .15 Kumar SA_REP 6100 .1 Abel SA_REP 11000 .3 Hutton SA_REP 8800 .25 Taylor SA_REP 8600 .2 Livingston SA_REP 8400 .2 Grant SA_REP 7000 .15 LAST_NAME JOB_ID SALARY COMMISSION_PCT ------------------------- ---------- ---------- -------------- Johnson SA_REP 6200 .1 Taylor SH_CLERK 3200 Fleaur SH_CLERK 3100 Sullivan SH_CLERK 2500 Geoni SH_CLERK 2800 Sarchand SH_CLERK 4200 Bull SH_CLERK 4100 Dellinger SH_CLERK 3400 Cabrio SH_CLERK 3000 Chung SH_CLERK 3800 Dilly SH_CLERK 3600 LAST_NAME JOB_ID SALARY COMMISSION_PCT ------------------------- ---------- ---------- -------------- Gates SH_CLERK 2900 Perkins SH_CLERK 2500 Bell SH_CLERK 4000 Everett SH_CLERK 3900 McCain SH_CLERK 3200 Jones SH_CLERK 2800 Walsh SH_CLERK 3100 Feeney SH_CLERK 3000 107 rows selected.
Null Values in Arithmetic Expressions
Arithmetic expressions containing a null value evaluate to null.(只要表达式里面的值有NULL存在，整个表达式的值都将为NULL;但是NULL与字符串合并的时候，合并后的值为字符串本身;)
View Code
SQL> SELECT last_name,12*salary*commission_pct FROM employees; LAST_NAME 12*SALARY*COMMISSION_PCT ------------------------- ------------------------ OConnell Grant Whalen Hartstein Fay Mavris Baer Higgins Gietz King Kochhar LAST_NAME 12*SALARY*COMMISSION_PCT ------------------------- ------------------------ De Haan Hunold Ernst Austin Pataballa Lorentz Greenberg Faviet Chen Sciarra Urman LAST_NAME 12*SALARY*COMMISSION_PCT ------------------------- ------------------------ Popp Raphaely Khoo Baida Tobias Himuro Colmenares Weiss Fripp Kaufling Vollman LAST_NAME 12*SALARY*COMMISSION_PCT ------------------------- ------------------------ Mourgos Nayer Mikkilineni Landry Markle Bissot Atkinson Marlow Olson Mallin Rogers LAST_NAME 12*SALARY*COMMISSION_PCT ------------------------- ------------------------ Gee Philtanker Ladwig Stiles Seo Patel Rajs Davies Matos Vargas Russell 67200 LAST_NAME 12*SALARY*COMMISSION_PCT ------------------------- ------------------------ Partners 48600 Errazuriz 43200 Cambrault 39600 Zlotkey 25200 Tucker 36000 Bernstein 28500 Hall 27000 Olsen 19200 Cambrault 18000 Tuvault 12600 King 42000 LAST_NAME 12*SALARY*COMMISSION_PCT ------------------------- ------------------------ Sully 39900 McEwen 37800 Smith 28800 Doran 27000 Sewall 21000 Vishney 31500 Greene 17100 Marvins 8640 Lee 8160 Ande 7680 Banda 7440 LAST_NAME 12*SALARY*COMMISSION_PCT ------------------------- ------------------------ Ozer 34500 Bloom 24000 Fox 23040 Smith 13320 Bates 13140 Kumar 7320 Abel 39600 Hutton 26400 Taylor 20640 Livingston 20160 Grant 12600 LAST_NAME 12*SALARY*COMMISSION_PCT ------------------------- ------------------------ Johnson 7440 Taylor Fleaur Sullivan Geoni Sarchand Bull Dellinger Cabrio Chung Dilly LAST_NAME 12*SALARY*COMMISSION_PCT ------------------------- ------------------------ Gates Perkins Bell Everett McCain Jones Walsh Feeney 107 rows selected.
如果在算术表达式里面，存在NULL值，那么整个算术表达式的值将为NULL;
Defining a Column Alias
- A column alias:
- Renamees a column heading.
- Is usefull with alculations.
- Immediately follows the column name(There can also be the options AS keyword between the column name and alias.)
- Requires double quotation marks if it contains spaces or specila characters,or if it is case-sensitive.
Using Column Aliases
View Code
SQL> SELECT last_name AS name,commission_pct comm FROM employees; NAME COMM ------------------------- ---------- OConnell Grant Whalen Hartstein Fay Mavris Baer Higgins Gietz King Kochhar NAME COMM ------------------------- ---------- De Haan Hunold Ernst Austin Pataballa Lorentz Greenberg Faviet Chen Sciarra Urman NAME COMM ------------------------- ---------- Popp Raphaely Khoo Baida Tobias Himuro Colmenares Weiss Fripp Kaufling Vollman NAME COMM ------------------------- ---------- Mourgos Nayer Mikkilineni Landry Markle Bissot Atkinson Marlow Olson Mallin Rogers NAME COMM ------------------------- ---------- Gee Philtanker Ladwig Stiles Seo Patel Rajs Davies Matos Vargas Russell .4 NAME COMM ------------------------- ---------- Partners .3 Errazuriz .3 Cambrault .3 Zlotkey .2 Tucker .3 Bernstein .25 Hall .25 Olsen .2 Cambrault .2 Tuvault .15 King .35 NAME COMM ------------------------- ---------- Sully .35 McEwen .35 Smith .3 Doran .3 Sewall .25 Vishney .25 Greene .15 Marvins .1 Lee .1 Ande .1 Banda .1 NAME COMM ------------------------- ---------- Ozer .25 Bloom .2 Fox .2 Smith .15 Bates .15 Kumar .1 Abel .3 Hutton .25 Taylor .2 Livingston .2 Grant .15 NAME COMM ------------------------- ---------- Johnson .1 Taylor Fleaur Sullivan Geoni Sarchand Bull Dellinger Cabrio Chung Dilly NAME COMM ------------------------- ---------- Gates Perkins Bell Everett McCain Jones Walsh Feeney 107 rows selected.
View Code
SQL> SELECT last_name "Name",salary*12 "Annual Salary" FROM employees; Name Annual Salary ------------------------- ------------- OConnell 31200 Grant 31200 Whalen 52800 Hartstein 156000 Fay 72000 Mavris 78000 Baer 120000 Higgins 144096 Gietz 99600 King 288000 Kochhar 204000 Name Annual Salary ------------------------- ------------- De Haan 204000 Hunold 108000 Ernst 72000 Austin 57600 Pataballa 57600 Lorentz 50400 Greenberg 144096 Faviet 108000 Chen 98400 Sciarra 92400 Urman 93600 Name Annual Salary ------------------------- ------------- Popp 82800 Raphaely 132000 Khoo 37200 Baida 34800 Tobias 33600 Himuro 31200 Colmenares 30000 Weiss 96000 Fripp 98400 Kaufling 94800 Vollman 78000 Name Annual Salary ------------------------- ------------- Mourgos 69600 Nayer 38400 Mikkilineni 32400 Landry 28800 Markle 26400 Bissot 39600 Atkinson 33600 Marlow 30000 Olson 25200 Mallin 39600 Rogers 34800 Name Annual Salary ------------------------- ------------- Gee 28800 Philtanker 26400 Ladwig 43200 Stiles 38400 Seo 32400 Patel 30000 Rajs 42000 Davies 37200 Matos 31200 Vargas 30000 Russell 168000 Name Annual Salary ------------------------- ------------- Partners 162000 Errazuriz 144000 Cambrault 132000 Zlotkey 126000 Tucker 120000 Bernstein 114000 Hall 108000 Olsen 96000 Cambrault 90000 Tuvault 84000 King 120000 Name Annual Salary ------------------------- ------------- Sully 114000 McEwen 108000 Smith 96000 Doran 90000 Sewall 84000 Vishney 126000 Greene 114000 Marvins 86400 Lee 81600 Ande 76800 Banda 74400 Name Annual Salary ------------------------- ------------- Ozer 138000 Bloom 120000 Fox 115200 Smith 88800 Bates 87600 Kumar 73200 Abel 132000 Hutton 105600 Taylor 103200 Livingston 100800 Grant 84000 Name Annual Salary ------------------------- ------------- Johnson 74400 Taylor 38400 Fleaur 37200 Sullivan 30000 Geoni 33600 Sarchand 50400 Bull 49200 Dellinger 40800 Cabrio 36000 Chung 45600 Dilly 43200 Name Annual Salary ------------------------- ------------- Gates 34800 Perkins 30000 Bell 48000 Everett 46800 McCain 38400 Jones 33600 Walsh 37200 Feeney 36000 107 rows selected.
Concatenation Operator
A concatenation operator:
- Links coluns or character strings to other columns
- Is represented by two vertical bars(||)
- Creates a resultant column that is a character expression.
View Code
SQL> SELECT last_name || job_id AS "Employees" FROM employees; Employees ----------------------------------- AbelSA_REP AndeSA_REP AtkinsonST_CLERK AustinIT_PROG BaerPR_REP BaidaPU_CLERK BandaSA_REP BatesSA_REP BellSH_CLERK BernsteinSA_REP BissotST_CLERK Employees ----------------------------------- BloomSA_REP BullSH_CLERK CabrioSH_CLERK CambraultSA_MAN CambraultSA_REP ChenFI_ACCOUNT ChungSH_CLERK ColmenaresPU_CLERK DaviesST_CLERK De HaanAD_VP DellingerSH_CLERK Employees ----------------------------------- DillySH_CLERK DoranSA_REP ErnstIT_PROG ErrazurizSA_MAN EverettSH_CLERK FavietFI_ACCOUNT FayMK_REP FeeneySH_CLERK FleaurSH_CLERK FoxSA_REP FrippST_MAN Employees ----------------------------------- GatesSH_CLERK GeeST_CLERK GeoniSH_CLERK GietzAC_ACCOUNT GrantSH_CLERK GrantSA_REP GreenbergFI_MGR GreeneSA_REP HallSA_REP HartsteinMK_MAN HigginsAC_MGR Employees ----------------------------------- HimuroPU_CLERK HunoldIT_PROG HuttonSA_REP JohnsonSA_REP JonesSH_CLERK KauflingST_MAN KhooPU_CLERK KingSA_REP KingAD_PRES KochharAD_VP KumarSA_REP Employees ----------------------------------- LadwigST_CLERK LandryST_CLERK LeeSA_REP LivingstonSA_REP LorentzIT_PROG MallinST_CLERK MarkleST_CLERK MarlowST_CLERK MarvinsSA_REP MatosST_CLERK MavrisHR_REP Employees ----------------------------------- McCainSH_CLERK McEwenSA_REP MikkilineniST_CLERK MourgosST_MAN NayerST_CLERK OConnellSH_CLERK OlsenSA_REP OlsonST_CLERK OzerSA_REP PartnersSA_MAN PataballaIT_PROG Employees ----------------------------------- PatelST_CLERK PerkinsSH_CLERK PhiltankerST_CLERK PoppFI_ACCOUNT RajsST_CLERK RaphaelyPU_MAN RogersST_CLERK RussellSA_MAN SarchandSH_CLERK SciarraFI_ACCOUNT SeoST_CLERK Employees ----------------------------------- SewallSA_REP SmithSA_REP SmithSA_REP StilesST_CLERK SullivanSH_CLERK SullySA_REP TaylorSA_REP TaylorSH_CLERK TobiasPU_CLERK TuckerSA_REP TuvaultSA_REP Employees ----------------------------------- UrmanFI_ACCOUNT VargasST_CLERK VishneySA_REP VollmanST_MAN WalshSH_CLERK WeissST_MAN WhalenAD_ASST ZlotkeySA_MAN 107 rows selected.
Literal Character Strings
- A literal is a character,a number,or a date that is included in the SELECT statement.
- Date and character literal values must be enclosed within single quotation marks.
- Each character string is output onec for each row returned.
Using Literal Character Strings
View Code
SQL> SELECT last_name || ' is a ' || job_id FROM employees; LAST_NAME||'ISA'||JOB_ID ----------------------------------------- Abel is a SA_REP Ande is a SA_REP Atkinson is a ST_CLERK Austin is a IT_PROG Baer is a PR_REP Baida is a PU_CLERK Banda is a SA_REP Bates is a SA_REP Bell is a SH_CLERK Bernstein is a SA_REP Bissot is a ST_CLERK LAST_NAME||'ISA'||JOB_ID ----------------------------------------- Bloom is a SA_REP Bull is a SH_CLERK Cabrio is a SH_CLERK Cambrault is a SA_MAN Cambrault is a SA_REP Chen is a FI_ACCOUNT Chung is a SH_CLERK Colmenares is a PU_CLERK Davies is a ST_CLERK De Haan is a AD_VP Dellinger is a SH_CLERK LAST_NAME||'ISA'||JOB_ID ----------------------------------------- Dilly is a SH_CLERK Doran is a SA_REP Ernst is a IT_PROG Errazuriz is a SA_MAN Everett is a SH_CLERK Faviet is a FI_ACCOUNT Fay is a MK_REP Feeney is a SH_CLERK Fleaur is a SH_CLERK Fox is a SA_REP Fripp is a ST_MAN LAST_NAME||'ISA'||JOB_ID ----------------------------------------- Gates is a SH_CLERK Gee is a ST_CLERK Geoni is a SH_CLERK Gietz is a AC_ACCOUNT Grant is a SH_CLERK Grant is a SA_REP Greenberg is a FI_MGR Greene is a SA_REP Hall is a SA_REP Hartstein is a MK_MAN Higgins is a AC_MGR LAST_NAME||'ISA'||JOB_ID ----------------------------------------- Himuro is a PU_CLERK Hunold is a IT_PROG Hutton is a SA_REP Johnson is a SA_REP Jones is a SH_CLERK Kaufling is a ST_MAN Khoo is a PU_CLERK King is a SA_REP King is a AD_PRES Kochhar is a AD_VP Kumar is a SA_REP LAST_NAME||'ISA'||JOB_ID ----------------------------------------- Ladwig is a ST_CLERK Landry is a ST_CLERK Lee is a SA_REP Livingston is a SA_REP Lorentz is a IT_PROG Mallin is a ST_CLERK Markle is a ST_CLERK Marlow is a ST_CLERK Marvins is a SA_REP Matos is a ST_CLERK Mavris is a HR_REP LAST_NAME||'ISA'||JOB_ID ----------------------------------------- McCain is a SH_CLERK McEwen is a SA_REP Mikkilineni is a ST_CLERK Mourgos is a ST_MAN Nayer is a ST_CLERK OConnell is a SH_CLERK Olsen is a SA_REP Olson is a ST_CLERK Ozer is a SA_REP Partners is a SA_MAN Pataballa is a IT_PROG LAST_NAME||'ISA'||JOB_ID ----------------------------------------- Patel is a ST_CLERK Perkins is a SH_CLERK Philtanker is a ST_CLERK Popp is a FI_ACCOUNT Rajs is a ST_CLERK Raphaely is a PU_MAN Rogers is a ST_CLERK Russell is a SA_MAN Sarchand is a SH_CLERK Sciarra is a FI_ACCOUNT Seo is a ST_CLERK LAST_NAME||'ISA'||JOB_ID ----------------------------------------- Sewall is a SA_REP Smith is a SA_REP Smith is a SA_REP Stiles is a ST_CLERK Sullivan is a SH_CLERK Sully is a SA_REP Taylor is a SA_REP Taylor is a SH_CLERK Tobias is a PU_CLERK Tucker is a SA_REP Tuvault is a SA_REP LAST_NAME||'ISA'||JOB_ID ----------------------------------------- Urman is a FI_ACCOUNT Vargas is a ST_CLERK Vishney is a SA_REP Vollman is a ST_MAN Walsh is a SH_CLERK Weiss is a ST_MAN Whalen is a AD_ASST Zlotkey is a SA_MAN 107 rows selected.
Alternative Quote(q) Operator
- Specify your own quotation mark delimiter.
- Select any delimiter.
- Increase readability and useability.
View Code
SQL> SELECT department_name || ' Department' || q'[`s Manager Id is ]' || manager_id AS "Department and Manager" FROM departments; Department and Manager -------------------------------------------------------------------------------- Administration Department`s Manager Id is 200 Marketing Department`s Manager Id is 201 Purchasing Department`s Manager Id is 114 Human Resources Department`s Manager Id is 203 Shipping Department`s Manager Id is 121 IT Department`s Manager Id is 103 Public Relations Department`s Manager Id is 204 Sales Department`s Manager Id is 145 Executive Department`s Manager Id is 100 Finance Department`s Manager Id is 108 Accounting Department`s Manager Id is 205 Department and Manager -------------------------------------------------------------------------------- Treasury Department`s Manager Id is Corporate Tax Department`s Manager Id is Control And Credit Department`s Manager Id is Shareholder Services Department`s Manager Id is Benefits Department`s Manager Id is Manufacturing Department`s Manager Id is Construction Department`s Manager Id is Contracting Department`s Manager Id is Operations Department`s Manager Id is IT Support Department`s Manager Id is NOC Department`s Manager Id is Department and Manager -------------------------------------------------------------------------------- IT Helpdesk Department`s Manager Id is Government Sales Department`s Manager Id is Retail Sales Department`s Manager Id is Recruiting Department`s Manager Id is Payroll Department`s Manager Id is 27 rows selected.
Duplicate Rows
The default display of queries is all rows,including duplicate rows.
View Code
SQL> SELECT department_id FROM employees; DEPARTMENT_ID ------------- 50 50 10 20 20 40 70 110 110 90 90 DEPARTMENT_ID ------------- 90 60 60 60 60 60 100 100 100 100 100 DEPARTMENT_ID ------------- 100 30 30 30 30 30 30 50 50 50 50 DEPARTMENT_ID ------------- 50 50 50 50 50 50 50 50 50 50 50 DEPARTMENT_ID ------------- 50 50 50 50 50 50 50 50 50 50 80 DEPARTMENT_ID ------------- 80 80 80 80 80 80 80 80 80 80 80 DEPARTMENT_ID ------------- 80 80 80 80 80 80 80 80 80 80 80 DEPARTMENT_ID ------------- 80 80 80 80 80 80 80 80 80 80 DEPARTMENT_ID ------------- 80 50 50 50 50 50 50 50 50 50 50 DEPARTMENT_ID ------------- 50 50 50 50 50 50 50 50 107 rows selected. SQL> SELECT DISTINCT department_id FROM employees; DEPARTMENT_ID ------------- 100 30 20 70 90 110 50 40 80 10 DEPARTMENT_ID ------------- 60 12 rows selected.
只要使用了DISTICT keyword，那么DISTINCT后面的所有列都会受此影响，而不是距离DISTINCT keyword最近的那一列.
Displaying the Table Structure
- Use the DESCRIBE command to display the structure of a table
- Or,select the table in the Connections tree and use the Columns to view the table structure.
DESC[RIBE] tablename;
DESC不是SQL的命令，而是SQL*Plus的命令.
DEMO-01:查看表的基本结构信息
View Code
SQL> desc employees; Name Null? Type ----------------------------------------- -------- ---------------------------- EMPLOYEE_ID NOT NULL NUMBER(6) FIRST_NAME VARCHAR2(20) LAST_NAME NOT NULL VARCHAR2(25) EMAIL NOT NULL VARCHAR2(25) PHONE_NUMBER VARCHAR2(20) HIRE_DATE NOT NULL DATE JOB_ID NOT NULL VARCHAR2(10) SALARY NUMBER(8,2) COMMISSION_PCT NUMBER(2,2) MANAGER_ID NUMBER(6) DEPARTMENT_ID NUMBER(4)
DEMO-02:查看表的详细结构信息
View Code
SQL> SET LONG 9999 SQL> SELECT DBMS_METADATA.GET_DDL('TABLE','EMPLOYEES') FROM DUAL; DBMS_METADATA.GET_DDL('TABLE','EMPLOYEES') -------------------------------------------------------------------------------- CREATE TABLE "HR"."EMPLOYEES" ( "EMPLOYEE_ID" NUMBER(6,0), "FIRST_NAME" VARCHAR2(20), "LAST_NAME" VARCHAR2(25) CONSTRAINT "EMP_LAST_NAME_NN" NOT NULL ENABLE, "EMAIL" VARCHAR2(25) CONSTRAINT "EMP_EMAIL_NN" NOT NULL ENABLE, "PHONE_NUMBER" VARCHAR2(20), "HIRE_DATE" DATE CONSTRAINT "EMP_HIRE_DATE_NN" NOT NULL ENABLE, "JOB_ID" VARCHAR2(10) CONSTRAINT "EMP_JOB_NN" NOT NULL ENABLE, "SALARY" NUMBER(8,2), "COMMISSION_PCT" NUMBER(2,2), DBMS_METADATA.GET_DDL('TABLE','EMPLOYEES') -------------------------------------------------------------------------------- "MANAGER_ID" NUMBER(6,0), "DEPARTMENT_ID" NUMBER(4,0), CONSTRAINT "EMP_SALARY_MIN" CHECK (salary > 0) ENABLE, CONSTRAINT "EMP_EMAIL_UK" UNIQUE ("EMAIL") USING INDEX PCTFREE 10 INITRANS 2 MAXTRANS 255 NOLOGGING COMPUTE STATISTICS STORAGE(INITIAL 65536 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DE FAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "EXAMPLE" ENABLE, CONSTRAINT "EMP_EMP_ID_PK" PRIMARY KEY ("EMPLOYEE_ID") USING INDEX PCTFREE 10 INITRANS 2 MAXTRANS 255 NOLOGGING COMPUTE STATISTICS DBMS_METADATA.GET_DDL('TABLE','EMPLOYEES') -------------------------------------------------------------------------------- STORAGE(INITIAL 65536 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DE FAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "EXAMPLE" ENABLE, CONSTRAINT "EMP_MANAGER_FK" FOREIGN KEY ("MANAGER_ID") REFERENCES "HR"."EMPLOYEES" ("EMPLOYEE_ID") ENABLE, CONSTRAINT "EMP_JOB_FK" FOREIGN KEY ("JOB_ID") REFERENCES "HR"."JOBS" ("JOB_ID") ENABLE, CONSTRAINT "EMP_DEPT_FK" FOREIGN KEY ("DEPARTMENT_ID") REFERENCES "HR"."DEPARTMENTS" ("DEPARTMENT_ID") ENABLE ) SEGMENT CREATION IMMEDIATE DBMS_METADATA.GET_DDL('TABLE','EMPLOYEES') -------------------------------------------------------------------------------- PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS NOLOGGING STORAGE(INITIAL 65536 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DE FAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "EXAMPLE"
Summary
In this lesson,you should have learned how to:
- Write a SELECT statement that:
- -Returns all rows and columns from a table.
- -Returns specified columns form a table.
- -Uses column aliases to display more descriptive column headings
SELECT *|{DISTINCT column|expression [alias],...} FROM table;
