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SQL exercises on movie Database: Generate a report which contain the columns movie title, name of the female actor, year of the movie, role, movie genres, the director, date of release, and rating of that movie

SQL movie Database: Join Exercise-24 with Solution

24. From the following tables, write a query in SQL to generate a report, which contain the fields movie title, name of the female actor, year of the movie, role, movie genres, the director, date of release, and rating of that movie.

Sample table: movie
 mov_id |                     mov_title                      | mov_year | mov_time |    mov_lang     | mov_dt_rel | mov_rel_country
--------+----------------------------------------------------+----------+----------+-----------------+------------+-----------------
    901 | Vertigo                                            |     1958 |      128 | English         | 1958-08-24 | UK
    902 | The Innocents                                      |     1961 |      100 | English         | 1962-02-19 | SW
    903 | Lawrence of Arabia                                 |     1962 |      216 | English         | 1962-12-11 | UK
    904 | The Deer Hunter                                    |     1978 |      183 | English         | 1979-03-08 | UK
    905 | Amadeus                                            |     1984 |      160 | English         | 1985-01-07 | UK
    906 | Blade Runner                                       |     1982 |      117 | English         | 1982-09-09 | UK
    907 | Eyes Wide Shut                                     |     1999 |      159 | English         |            | UK
    908 | The Usual Suspects                                 |     1995 |      106 | English         | 1995-08-25 | UK
    909 | Chinatown                                          |     1974 |      130 | English         | 1974-08-09 | UK
    910 | Boogie Nights                                      |     1997 |      155 | English         | 1998-02-16 | UK
    911 | Annie Hall                                         |     1977 |       93 | English         | 1977-04-20 | USA
    912 | Princess Mononoke                                  |     1997 |      134 | Japanese        | 2001-10-19 | UK
    913 | The Shawshank Redemption                           |     1994 |      142 | English         | 1995-02-17 | UK
    914 | American Beauty                                    |     1999 |      122 | English         |            | UK
    915 | Titanic                                            |     1997 |      194 | English         | 1998-01-23 | UK
    916 | Good Will Hunting                                  |     1997 |      126 | English         | 1998-06-03 | UK
    917 | Deliverance                                        |     1972 |      109 | English         | 1982-10-05 | UK
    918 | Trainspotting                                      |     1996 |       94 | English         | 1996-02-23 | UK
    919 | The Prestige                                       |     2006 |      130 | English         | 2006-11-10 | UK
    920 | Donnie Darko                                       |     2001 |      113 | English         |            | UK
    921 | Slumdog Millionaire                                |     2008 |      120 | English         | 2009-01-09 | UK
    922 | Aliens                                             |     1986 |      137 | English         | 1986-08-29 | UK
    923 | Beyond the Sea                                     |     2004 |      118 | English         | 2004-11-26 | UK
    924 | Avatar                                             |     2009 |      162 | English         | 2009-12-17 | UK
    926 | Seven Samurai                                      |     1954 |      207 | Japanese        | 1954-04-26 | JP
    927 | Spirited Away                                      |     2001 |      125 | Japanese        | 2003-09-12 | UK
    928 | Back to the Future                                 |     1985 |      116 | English         | 1985-12-04 | UK
    925 | Braveheart                                         |     1995 |      178 | English         | 1995-09-08 | UK
Sample table: genres
 gen_id |      gen_title
--------+----------------------
   1001 | Action
   1002 | Adventure
   1003 | Animation
   1004 | Biography
   1005 | Comedy
   1006 | Crime
   1007 | Drama
   1008 | Horror
   1009 | Music
   1010 | Mystery
   1011 | Romance
   1012 | Thriller
   1013 | War

Sample table: movie_genres

 mov_id | gen_id
--------+--------
    922 |   1001
    917 |   1002
    903 |   1002
    912 |   1003
    911 |   1005
    908 |   1006
    913 |   1006
    926 |   1007
    928 |   1007
    918 |   1007
    921 |   1007
    902 |   1008
    923 |   1009
    907 |   1010
    927 |   1010
    901 |   1010
    914 |   1011
    906 |   1012
    904 |   1013
Sample table: rating
 mov_id | rev_id | rev_stars | num_o_ratings
--------+--------+-----------+---------------
    901 |   9001 |      8.40 |        263575
    902 |   9002 |      7.90 |         20207
    903 |   9003 |      8.30 |        202778
    906 |   9005 |      8.20 |        484746
    924 |   9006 |      7.30 |
    908 |   9007 |      8.60 |        779489
    909 |   9008 |           |        227235
    910 |   9009 |      3.00 |        195961
    911 |   9010 |      8.10 |        203875
    912 |   9011 |      8.40 |
    914 |   9013 |      7.00 |        862618
    915 |   9001 |      7.70 |        830095
    916 |   9014 |      4.00 |        642132
    925 |   9015 |      7.70 |         81328
    918 |   9016 |           |        580301
    920 |   9017 |      8.10 |        609451
    921 |   9018 |      8.00 |        667758
    922 |   9019 |      8.40 |        511613
    923 |   9020 |      6.70 |         13091
Sample table: actor
 act_id |      act_fname       |      act_lname       | act_gender
--------+----------------------+----------------------+------------
    101 | James                | Stewart              | M
    102 | Deborah              | Kerr                 | F
    103 | Peter                | OToole               | M
    104 | Robert               | De Niro              | M
    105 | F. Murray            | Abraham              | M
    106 | Harrison             | Ford                 | M
    107 | Nicole               | Kidman               | F
    108 | Stephen              | Baldwin              | M
    109 | Jack                 | Nicholson            | M
    110 | Mark                 | Wahlberg             | M
    111 | Woody                | Allen                | M
    112 | Claire               | Danes                | F
    113 | Tim                  | Robbins              | M
    114 | Kevin                | Spacey               | M
    115 | Kate                 | Winslet              | F
    116 | Robin                | Williams             | M
    117 | Jon                  | Voight               | M
    118 | Ewan                 | McGregor             | M
    119 | Christian            | Bale                 | M
    120 | Maggie               | Gyllenhaal           | F
    121 | Dev                  | Patel                | M
    122 | Sigourney            | Weaver               | F
    123 | David                | Aston                | M
    124 | Ali                  | Astin                | F
Sample table: director
 dir_id |      dir_fname       |      dir_lname
--------+----------------------+----------------------
    201 | Alfred               | Hitchcock
    202 | Jack                 | Clayton
    203 | David                | Lean
    204 | Michael              | Cimino
    205 | Milos                | Forman
    206 | Ridley               | Scott
    207 | Stanley              | Kubrick
    208 | Bryan                | Singer
    209 | Roman                | Polanski
    210 | Paul                 | Thomas Anderson
    211 | Woody                | Allen
    212 | Hayao                | Miyazaki
    213 | Frank                | Darabont
    214 | Sam                  | Mendes
    215 | James                | Cameron
    216 | Gus                  | Van Sant
    217 | John                 | Boorman
    218 | Danny                | Boyle
    219 | Christopher          | Nolan
    220 | Richard              | Kelly
    221 | Kevin                | Spacey
    222 | Andrei               | Tarkovsky
    223 | Peter                | Jackson
Sample table: movie_direction
 dir_id | mov_id
--------+--------
    201 |    901
    202 |    902
    203 |    903
    204 |    904
    205 |    905
    206 |    906
    207 |    907
    208 |    908
    209 |    909
    210 |    910
    211 |    911
    212 |    912
    213 |    913
    214 |    914
    215 |    915
    216 |    916
    217 |    917
    218 |    918
    219 |    919
    220 |    920
    218 |    921
    215 |    922
    221 |    923
Sample table: movie_cast
 act_id | mov_id |              role
--------+--------+--------------------------------
    101 |    901 | John Scottie Ferguson
    102 |    902 | Miss Giddens
    103 |    903 | T.E. Lawrence
    104 |    904 | Michael
    105 |    905 | Antonio Salieri
    106 |    906 | Rick Deckard
    107 |    907 | Alice Harford
    108 |    908 | McManus
    110 |    910 | Eddie Adams
    111 |    911 | Alvy Singer
    112 |    912 | San
    113 |    913 | Andy Dufresne
    114 |    914 | Lester Burnham
    115 |    915 | Rose DeWitt Bukater
    116 |    916 | Sean Maguire
    117 |    917 | Ed
    118 |    918 | Renton
    120 |    920 | Elizabeth Darko
    121 |    921 | Older Jamal
    122 |    922 | Ripley
    114 |    923 | Bobby Darin
    109 |    909 | J.J. Gittes
    119 |    919 | Alfred Borden

Sample Solution:

-- Selecting various columns from multiple tables: movie, movie_cast, actor, movie_genres, genres, movie_direction, director, and rating
SELECT mov_title, act_fname, act_lname, 
mov_year, role, gen_title, dir_fname, dir_lname, 
mov_dt_rel, rev_stars
-- Performing natural joins between the movie, movie_cast, actor, movie_genres, genres, movie_direction, director, and rating tables
FROM movie 
NATURAL JOIN movie_cast
NATURAL JOIN actor
NATURAL JOIN movie_genres
NATURAL JOIN genres
NATURAL JOIN movie_direction
NATURAL JOIN director
NATURAL JOIN rating
-- Filtering the result to include only records where act_gender is 'F'
WHERE act_gender='F';

Sample Output:

                     mov_title                      |      act_fname       |      act_lname       | mov_year |              role              |      gen_title       |      dir_fname       |      dir_lname       | mov_dt_rel | rev_stars
----------------------------------------------------+----------------------+----------------------+----------+--------------------------------+----------------------+----------------------+----------------------+------------+-----------
 The Innocents                                      | Deborah              | Kerr                 |     1961 | Miss Giddens                   | Horror               | Jack                 | Clayton              | 1962-02-19 |      7.90
 Princess Mononoke                                  | Claire               | Danes                |     1997 | San                            | Animation            | Hayao                | Miyazaki             | 2001-10-19 |      8.40
 Aliens                                             | Sigourney            | Weaver               |     1986 | Ripley                         | Action               | James                | Cameron              | 1986-08-29 |      8.40
(3 rows)

Code Explanation :

The said query in SQL that retrieves the title, year, release date, and rating of movies, as well as the first and last name of the actresses who played a role in the movie, the genres of the movie, and the first and last name of the director of the movie.
1. The NATURAL JOIN clause joins the movie_cast table to the movie table based on matching column names in the two tables.
2. The actor table joined to the result of the previous join step 1. 3. The movie_genres table joined to the result of the previous join step 2.
4. The genres table joined to the result of the previous join step 3.
5. The movie_direction table joined to the result of the previous join step 4.
6. The director table joined to the result of the previous join step 5.
7. The rating table joined to the result of the previous join step 6.
The WHERE clause filter the result, where only actors with a gender of 'F' will be included in the result set.

Alternative Solutions:

Using Subquery with EXISTS:


SELECT mov_title, act_fname, act_lname, 
       mov_year, role, gen_title, dir_fname, dir_lname, 
       mov_dt_rel, rev_stars
FROM movie 
JOIN movie_cast ON movie.mov_id = movie_cast.mov_id
JOIN actor ON movie_cast.act_id = actor.act_id
JOIN movie_genres ON movie.mov_id = movie_genres.mov_id
JOIN genres ON movie_genres.gen_id = genres.gen_id
JOIN movie_direction ON movie.mov_id = movie_direction.mov_id
JOIN director ON movie_direction.dir_id = director.dir_id
JOIN rating ON movie.mov_id = rating.mov_id
WHERE EXISTS (
    SELECT 1
    FROM actor
    WHERE actor.act_id = movie_cast.act_id
    AND actor.act_gender = 'F'
);

Explanation:

This SQL query uses a subquery with EXISTS to check if there exists an actor with gender 'F' who is part of the cast. If such an actor exists, the main query includes the corresponding movie details.

Using INNER JOINs with Subquery:


SELECT mov_title, act_fname, act_lname, 
       mov_year, role, gen_title, dir_fname, dir_lname, 
       mov_dt_rel, rev_stars
FROM movie 
JOIN movie_cast ON movie.mov_id = movie_cast.mov_id
JOIN actor ON movie_cast.act_id = actor.act_id
JOIN movie_genres ON movie.mov_id = movie_genres.mov_id
JOIN genres ON movie_genres.gen_id = genres.gen_id
JOIN movie_direction ON movie.mov_id = movie_direction.mov_id
JOIN director ON movie_direction.dir_id = director.dir_id
JOIN rating ON movie.mov_id = rating.mov_id
WHERE actor.act_gender = 'F';

Explanation:

This SQL query combines INNER JOINs with a WHERE clause to directly filter for actors with gender 'F'.

Relational Algebra Expression:

Relational Algebra Expression: Generate a report which contain the columns movie title, name of the female actor, year of the movie, role, movie genres, the director, date of release, and rating of that movie.

Relational Algebra Tree:

Relational Algebra Tree: Generate a report which contain the columns movie title, name of the female actor, year of the movie, role, movie genres, the director, date of release, and rating of that movie.

Practice Online


Movie database model

Query Visualization:

Duration:

Query visualization of Generate a report which contain the columns movie title, name of the female actor, year of the movie, role, movie genres, the director, date of release, and rating of that movie - Duration

Rows:

Query visualization of Generate a report which contain the columns movie title, name of the female actor, year of the movie, role, movie genres, the director, date of release, and rating of that movie - Rows

Cost:

Query visualization of Generate a report which contain the columns movie title, name of the female actor, year of the movie, role, movie genres, the director, date of release, and rating of that movie - Cost

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Previous: From the following tables, write a SQL query to find the years when most of the ‘Mystery Movies’ produced. Count the number of generic title and compute their average rating. Group the result set on movie release year, generic title. Return movie year, generic title, number of generic title and average rating.
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