This document contains slides from a chapter on SQL success. It covers various SQL SELECT statements with different clauses like WHERE, BETWEEN, IN, LIKE, NULL, CASE, functions, and aggregates. It discusses selecting columns, filtering rows, string operations, date functions, and grouping or aggregating data. The slides provide examples of selecting movies by country and year, concatenating strings, calculating ages from dates, and using CASE and DISTINCT.

1. SQL Success
Chapter 3 slides
Stéphane Faroult
1

2. select * from tablename
2

3. select * from movies
3

4. 4

5. All columns
5

6. Row Data
variable1
variable2
variable3
6

7. select movie_id,
title,
year_released,
country_code
from movies
7

8. select * from table
≈
print table
8

9. Restriction
9

10. Restriction
10

11. Restriction
11

12. Restriction
12

13. select * from movies
13

14. select * from movies
where ...
14

15. select * from movies
where country = 'us'
15

16. select * from movies
where country = 'us'
16

17. select * from movies
where country = 'us'
17

18. select * from movies
where country = 'us'
18

19. 'constant'
19

20. column
20

21. where title = Jaws
21

22. where title = Jaws
22

23. where title ='Jaws'
23

24. Movies
24

25. Movies
25

26. US Movies
26

27. select * from movies
where country = 'us'
27

28. select *
from (select * from movies
select
where country = 'us' us_movies
'us')
where year_released between 1940 and 1949
28

29. ALL MOVIES
29

30. US MOVIES
30

31. US MOVIES, 1940s
31

32. select *
from (select * from movies
where year_released between 1940
and 1949)
movies_from_the_1940s
where country = 'us'
32

33. select *
from movies
where country = 'us'
and year_released between 1940 and 1949
33

34. select *
from movies
where country = 'us'
and year_released between 1940 and 1949
or
34

35. select *
from movies
where country = 'us'
and year_released between 1940 and 1949
or
not
35

36. select * from movies
where country = 'us'
or country = 'gb'
36

37. and > or
37

38. and > or
2+3*4
38

39. and > or
2+3*4
39

40. and > or
2+3*4
12
40

41. and > or
2+3*4
2 + 12
41

42. and > or
2+3*4
2 + 12
14
42

43. where country = 'us'
or country = 'gb'
and year_released between 1940
and 1949
43

44. where country = 'us'
or country = 'gb'
and year_released between 1940
and 1949
44

45. Movies
45

46. Movies
1940s
46

47. where (country = 'us'
or country = 'gb')
and year_released between 1940
and 1949
47

48. French movies from the 1940s
plus
American movies from the 1950s
48

49. select * from movies
where (country = 'fr'
and year_released between
and
or (country = 'us'
and year_released between
and
1940
1949)
1950
1959)
49

50. =
50

51. =
<> or !=
51

52. =
<> or !=
<
<=
52

53. =
<> or !=
<
<=
>
>=
53

54. 2 < 10
54

55. 2 < 10
'2' < '10'
55

56. 2 < 10
'2' < '10'
56

57. 2 < 10
'2' < '10'
'2-JUN-1883'>'1-DEC-2056'
57

58. 2 < 10
'2' < '10'
'2-JUN-1883'>'1-DEC-2056'
As strings!
58

59. DD/MM/YYYY
59

60. DD/MM/YYYY
MM/DD/YYYY
60

61. Convert
EXPLICITLY!
61

62. where issued = <some date>
62

63. where issued = <some date>
Flickr:Yoppy & Rudolf Schuba
63

64. where issued = <some date>
Flickr:Yoppy & Rudolf Schuba
64

65. 65

66. 66

67. where issued >=
and issued <=
67

68. where issued >= <Monday 00:00:00>
and issued <= <Friday 00:00:00>
68

69. where issued >= <Monday 00:00:00>
and issued <= <Friday 00:00:00>
69

70. 40
41
42
43
44
45
Sun Mon Tue Wed Thu
27 28 29 30
1
4
5
6
7
8
11 12 13 14 15
18 19 20 21 22
25 26 27 28 29
1
2
3
4
5
Fri
2
9
16
23
30
6
Sat
3
10
17
24
31
7
70

71. 40
41
42
43
44
45
Sun Mon Tue Wed Thu
27 28 29 30
1
4
5
6
7
8
11 12 13 14 15
18 19 20 21 22
25 26 27 28 29
1
2
3
4
5
Fri
2
9
16
23
30
6
Sat
3
10
17
24
31
7
71

72. year_released between 1940 and 1949
72

73. year_released between 1940 and 1949
year_released >= 1940
and year_released <= 1949
73

74. where (country = 'us'
or country = 'gb')
and year_released between 1940 and 1949
74

75. where (country = 'us'
or country = 'gb')
and year_released between 1940 and 1949
where country in ('us', 'gb')
and year_released between 1940 and 1949
75

76. country not in ('us', 'gb')
76

77. like
77

78. like
%
78

79. like
%
_
79

80. select * from movies
where title not like '%A%'
and title not like '%a%'
80

81. select * from movies
where upper(title)
not like '%A%'
81

82. select * from movies
where upper(title)
not like '%A%'
82

83. Flickr:Daniel Moyle
83

84. if (ptr == NULL) {
…
84

85. if (ptr == NULL) {
…
85

86. NULL in SQL
is NOT a value …
86

87. where column_name = null
87

88. where column_name = null
88

89. where column_name <> null
89

90. where column_name <> null
90

91. where column_name is null
91

92. where column_name is null
where column_name is not null
92

93. Who are the people
in the database
who are alive?
93

94. 94

95. 95

96. select title, year_released
from movies
where country = 'us'
96

97. +
-
databases
97

98. -
databases
+
schemas
98

99. -
databases
+
-
schemas
+
tables
99

100. -
databases
+
-
schemas
+
-
tables
+
columns
100

101. desc movies;
101

102. desc movies;
describe table movies
102

103. desc movies;
describe table movies
d movies
103

104. desc movies;
describe table movies
d movies
.schema movies
104

105. compute
105

106. compute
derive
106

107. Flickr: Etsuko Nakamura
107

108. Flickr: Etsuko Nakamura
108

109. 'hello' + ' world'
109

110. 'hello' ||' world'
110

111. concat('hello' , ' world')
111

112. select title
|| ' was released in '
|| year_released movie_release
from movies
where country = 'us'
112

113. select title
|| ' was released in '
|| year_released movie_release
from movies
where country = 'us'
113

114. select title
+ ' was released in '
+ cast(year_released
as varchar) movie_release
from movies
where country = 'us'
114

115. select concat(title,
' was released in ',
year_released) movie_release
from movies
where country = 'us'
115

116. people
Age of people alive?
116

117. people
born
died
Age of people alive?
117

118. Alive
118

119. Alive
died is null
119

120. Alive
died is null
Age
120

121. Alive
died is null
Age
<this year> - born
121

122. select column1, ...
from some_table
where some_column = some_user_input
122

123. select f(column1), ...
from some_table
where some_column = some_user_input
123

124. select f(column1), ...
from some_table
where some_column = some_user_input
124

125. select column1, ...
from some_table
where some_column = f(some_user_input)
125

126. select column1, ...
from some_table
where f(some_column) = some_user_input
126

127. case
end
Flickr:Tony Austin
127

128. color
Y
n
case upper(color)
when 'Y' then 'Color'
when 'N' then 'B&W'
else '?'
end as color,
...
128

129. color
Y
n
case upper(color)
when 'Y' then 'Color'
when 'N' then 'B&W'
else '?'
end as color,
...
129

130. color
Y
n
Color
case upper(color)
when 'Y' then 'Color'
when 'N' then 'B&W'
else '?'
end as color,
...
130

131. color
Y
n
Color
case upper(color)
when 'Y' then 'Color'
when 'N' then 'B&W'
else '?'
end as color,
...
131

132. color
Y
Color
?
n
case upper(color)
when 'Y' then 'Color'
when 'N' then 'B&W'
else '?'
end as color,
...
132

133. color
Y
Color
?
n
B&W
case upper(color)
when 'Y' then 'Color'
when 'N' then 'B&W'
else '?'
end as color,
...
133

134. color
Y
Color
?
n
B&W
case upper(color)
when 'Y' then 'Color'
when 'N' then 'B&W'
else '?'
end as color,
...
134

135. 135

136. case column_name
when null then
end
136

137. case column_name
when null then
end
137

138. case upper(color)
when 'Y' then 'Color'
when 'N' then 'B&W'
else '?'
end as color,
138

139. case died
when 1920
when 1921
when 1922
when 1923
when 1924
when 1925
when 1926
when 1927
when 1928
then
then
then
then
then
then
then
then
then
'passed
'passed
'passed
'passed
'passed
'passed
'passed
'passed
'passed
away'
away'
away'
away'
away'
away'
away'
away'
away'
139

140. case
when died is
'alive
else 'passed
end as status
null then
and kicking'
away'
140

141. Some useful
functions
more in appendix B ...
Flickr:Sanath Kumar
141

142. round()
142

143. round()
trunc()
143

144. round(3.141592, 0)
3
trunc()
144

145. round(3.141592, 0)
3
trunc(3.141592)
3
145

146. round(3.141592, 3)
3.142
trunc(3.141592)
3
146

147. round(3.141592, 3)
3.142
trunc(3.141592, 3)
3.141
147

148. upper(), lower()
148

149. upper(), lower()
substr()
149

150. upper(), lower()
substr('Citizen Kane', 5, 3)
150

151. upper(), lower()
substr()
trim( )
151

152. upper(), lower()
substr()
trim( ' Oops
')
'Oops'
152

153. upper(), lower()
substr()
trim( )
replace()
153

154. upper(), lower()
substr()
trim( )
replace('Sheep', 'ee', 'i')
'Ship'
154

155. upper(), lower()
155

156. upper(), lower()
substring()
156

157. upper(), lower()
substring('Citizen Kane', 5, 3)
157

158. upper(), lower()
substring()
ltrim()
158

159. upper(), lower()
substring()
ltrim(' Oops
')
'Oops
'
159

160. upper(), lower()
substring()
ltrim(' Oops
')
'Oops
'
rtrim()
160

161. upper(), lower()
substring()
ltrim()
rtrim()
replace()
161

162. upper(), lower()
substring()
ltrim()
rtrim()
replace('Sheep', 'ee', 'i')
'Ship'
162

163. Current date
163

164. Current date
Date Arithmetic
FEBRUARY
MARCH
164

165. Current date
Date Arithmetic
MARCH
+ 1 month
+ 30 days
165

166. dateadd(month, 1, date_col)
166

167. dateadd(month, 1, date_col)
date_col + 1 month
167

168. dateadd(month, 1, date_col)
date_col + 1 month
date_col + interval'1 month'
168

169. dateadd(month, 1, date_col)
date_col + 1 month
date_col + interval'1 month'
date_add(date_col, interval 1 month)
169

170. dateadd(month, 1, date_col)
date_col + 1 month
date_col + interval'1 month'
date_add(date_col, interval 1 month)
add_months(date_col, 1)
170

171. dateadd(month, 1, date_col)
date_col + 1 month
date_col + interval'1 month'
date_add(date_col, interval 1 month)
add_months(date_col, 1)
date_col + decimal_number
171

172. dateadd(month, 1, date_col)
date_col + 1 month
date_col + interval'1 month'
date_add(date_col, interval 1 month)
add_months(date_col, 1)
date_col + decimal_number
date(date_col, '1 month')
172

173. '28-DEC-1895'
173

174. '28-DEC-1895'
174

175. '28-DEC-1895'
DBMS date
175

176. '12/28/1895'
DBMS date
176

177. 177

178. cast(
as
)
178

179. x y z
179

180. z
(x
+
y)
180

181. z
(x
+
y)
181

182. No duplicates
182

183. No duplicates
Identifier
183

184. select country from movies
184

185. 185

186. 186

187. distinct
187

188. distinct
select distinct country
from movies
188

189. Aggregate functions
189

190. select country, year_released, title
from movies
190

191. select country, year_released, title
from movies
us
us
ru
us
us
hk
in
us
gb
gb
it
us
de
se
fr
it
jp
in
nz
fr
...
1942
1990
1925
1982
1977
1986
1975
1954
1962
1949
1948
1941
1985
1957
1997
1966
1954
1955
2001
1946
Casablanca
Goodfellas
Bronenosets Potyomkin
Blade Runner
Annie Hall
Ying hung boon sik
Sholay
On The Waterfront
Lawrence Of Arabia
The Third Man
Ladri di biciclette
Citizen Kane
Das Boot
Det sjunde inseglet
Le cinquième élément
Il buono, il brutto, il cattivo
Shichinin no Samurai
Pather Panchali
The Lord of the Rings
La belle et la bête
191

192. select country, year_released, title
from movies
de
fr
fr
fr
gb
gb
hk
in
in
it
it
jp
nz
ru
se
us
us
us
us
us
...
1985
1997
1946
1942
1962
1949
1986
1975
1955
1948
1966
1954
2001
1925
1957
1942
1990
1982
1977
1954
Das Boot
Le cinquième élément
La belle et la bête
Les Visiteurs du Soir
Lawrence Of Arabia
The Third Man
Ying hung boon sik
Sholay
Pather Panchali
Ladri di biciclette
Il buono, il brutto, il cattivo
Shichinin no Samurai
The Lord of the Rings
Bronenosets Potyomkin
Det sjunde inseglet
Casablanca
Goodfellas
Blade Runner
Annie Hall
On The Waterfront
192

193. select country, year_released, title
from movies
de
fr
fr
fr
gb
gb
hk
in
in
it
it
jp
nz
ru
se
us
us
us
us
us
...
1985
1997
1946
1942
1962
1949
1986
1975
1955
1948
1966
1954
2001
1925
1957
1942
1990
1982
1977
1954
Das Boot
Le cinquième élément
La belle et la bête
Les Visiteurs du Soir
Lawrence Of Arabia
The Third Man
Ying hung boon sik
Sholay
Pather Panchali
Ladri di biciclette
Il buono, il brutto, il cattivo
Shichinin no Samurai
The Lord of the Rings
Bronenosets Potyomkin
Det sjunde inseglet
Casablanca
Goodfellas
Blade Runner
Annie Hall
On The Waterfront
193

194. group by
194

195. group by
select country,
count(*) number_of_movies
from movies
group by country
195

196. group by
select country
country,
count(*) number_of_movies
from movies
group by country
196

197. group by
select country
country,
count(*) number_of_movies
from movies
group by country
197

198. select country, year_released, title
from movies
de
fr
fr
fr
gb
gb
hk
in
in
it
it
jp
nz
ru
se
us
us
us
us
us
...
1985
1997
1946
1942
1962
1949
1986
1975
1955
1948
1966
1954
2001
1925
1957
1942
1990
1982
1977
1954
Das Boot
Le cinquième élément
La belle et la bête
Les Visiteurs du Soir
Lawrence Of Arabia
The Third Man
Ying hung boon sik
Sholay
Pather Panchali
Ladri di biciclette
Il buono, il brutto, il cattivo
Shichinin no Samurai
The Lord of the Rings
Bronenosets Potyomkin
Det sjunde inseglet
Casablanca
Goodfellas
Blade Runner
Annie Hall
On The Waterfront
1
3
2
1
2
2
1
1
1
1
17
198

199. select country,
year_released,
count(*) number_of_movies
from movies
group by country,
year_released
199

200. select country,
year_released,
count(*) number_of_movies
from movies
group by country,
year_released
200

201. select count(*) number_of_movies
from movies
201

202. where
202

203. where
203

204. where
204

205. where
205

206. where
206

207. distinct, group by
207

208. distinct, group by
208

209. distinct, group by
209

210. distinct, group by
210

211. distinct, group by
211

212. count(*) count(col)
min(col)
max(col)
avg(col)
212

213. Earliest release year
by country?
213

214. Earliest release year
by country?
select country,
min(year_released) oldest_movie
from movies
group by country
214

215. select country,
min(year_released) oldest_movie
from movies
group by country
215

216. select *
from (
select country,
min(year_released) oldest_movie
from movies
group by country
) earliest_movies_per_country
where oldest_movie < 1940
216

217. having
select country,
min(year_released) oldest_movie
from movies
group by country
217

218. having
select country,
min(year_released) oldest_movie
from movies
group by country
having min(year_released) < 1940
218

219. SORT
Flickr: Randy Robertson
219

220. select country,
min(year_released) oldest_movie
from movies
group by country
having country = 'us'
220

221. select country,
min(year_released) oldest_movie
from movies
where country = 'us'
group by country
221

222. Flickr: Dano
222

223. having
Result of aggregate
223

224. Nulls?
224

225. Nulls?
known + unknown = unknown
225

226. Aggregate functions
ignore
Nulls
FLickr: Linda Åslund
226

227. select max(died) most_recent_death
from people
227

228. select max(died) most_recent_death
from people
where died is not null
228

229. count(*)
229

230. count(*)
230

231. count(col)
231

232. count(col)
232

233. select count(*) people_count,
count(born) birth_year_count,
count(died) death_year_count
from people
233

234. select count(colname)
234

235. select count(distinct colname)
235

236. select country,
count(distinct year_released)
number_of_years
from movies
group by country
236

237. select country,
count(*) number_of_years
from (select distinct country,
year_released
from movies) t
group by country
237

238. How many people
are both
actors and directors?
238

239. How many people
are both
actors and directors?
239

240. How many people
are both
actors and directors?
240

241. How many people
are both
actors and directors?
241

242. How many people
are both
actors and directors?
242

243. How many people
are both
actors and directors?
243

244. How many people
are both
actors and directors?
244

245. How many people
are both
actors and directors?
credits
245

246. movieid
peopleid
8
8
8
8
10
10
10
10
10
12
12
12
136
136
136
136
115
115
115
...
37
38
39
40
11
12
15
16
17
11
11
12
378
433
434
435
38
359
360
credited_as
D
A
A
A
A
A
D
A
A
A
D
A
D
A
A
A
A
D
A
246

247. movieid
peopleid
8
8
8
8
10
10
10
10
10
12
12
12
136
136
136
136
115
115
115
...
37
38
39
40
11
12
15
16
17
11
11
12
378
433
434
435
38
359
360
credited_as
D
A
A
A
A
A
D
A
A
A
D
A
D
A
A
A
A
D
A
select peopleid,
credited_as
from credits
247

248. movieid
peopleid
8
8
8
8
10
10
10
10
10
12
12
12
136
136
136
136
115
115
115
...
37
38
39
40
11
12
15
16
17
11
11
12
378
433
434
435
38
359
360
credited_as
D
A
A
A
A
A
D
A
A
A
D
A
D
A
A
A
A
D
A
select peopleid,
credited_as
from credits
248

249. movieid
peopleid
8
8
8
8
10
10
10
10
10
12
12
12
136
136
136
136
115
115
115
...
37
38
39
40
11
12
15
16
17
11
11
12
378
433
434
435
38
359
360
credited_as
D
A
A
A
A
A
D
A
A
A
D
A
D
A
A
A
A
D
A
select peopleid,
credited_as
from credits
249

250. peopleid
11
11
12
15
16
17
37
38
39
40
359
360
361
378
379
380
442
442
443
...
credited_as
A
D
A
D
A
A
D
A
A
A
D
A
A
D
A
A
A
D
A
select distinct
peopleid,
credited_as
from credits
where credited_as
in ('A', 'D')
250

251. select distinct
peopleid,
credited_as
from credits
where credited_as
in ('A', 'D')
251

252. ( select distinct
peopleid,
credited_as
from credits
where credited_as
in ('A', 'D')) all_actors_and_directors
252

253. select peopleid, count(*) as number_of_roles
from ( select distinct
peopleid,
credited_as
from credits
where credited_as
in ('A', 'D')) all_actors_and_directors
group by peopleid
having count(*) = 2
253

254. select count(*) number_of_acting_directors
from (
select peopleid, count(*) as number_of_roles
from ( select distinct
peopleid,
credited_as
from credits
where credited_as
in ('A', 'D')) all_actors_and_directors
group by peopleid
having count(*) = 2 ) acting_directors
254

255. Rows are selected with where + conditions.
Beware of or.
Beware of nulls.
Beware of dates.
255

256. Rows are selected with where + conditions.
Beware of or.
Beware of nulls.
Beware of dates.
Many functions. case ... end for conditional
display.
256

257. Rows are selected with where + conditions.
Beware of or.
Beware of nulls.
Beware of dates.
Many functions. case ... end for conditional
display.
You must use distinct or group by to remove
duplicates from a result set if there is no key.
257