w3resource

SQL exercises on movie Database: Find those lowest duration movies along with the year, director's name, actor's name and his/her role in that production

SQL movie Database: Join Exercise-11 with Solution

11. From the following table, write a SQL query to find movies with the shortest duration. Return movie title, movie year, director first name, last name, actor first name, last name and role.

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: 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 specific columns from the tables movie, movie_direction, movie_cast, director, and actor
SELECT mov_title, mov_year, dir_fname, dir_lname, 
       act_fname, act_lname, role 
-- Performing natural joins between the movie, movie_direction, movie_cast, director, and actor tables
FROM  movie
NATURAL JOIN movie_direction
NATURAL JOIN movie_cast
NATURAL JOIN director
NATURAL JOIN actor
-- Filtering the result to include only records where mov_time is equal to the minimum mov_time in the movie table
WHERE mov_time = (SELECT MIN(mov_time) FROM movie);

Sample Output:

                     mov_title                      | mov_year |      dir_fname       |      dir_lname       |      act_fname       |      act_lname       |              role
----------------------------------------------------+----------+----------------------+----------------------+----------------------+----------------------+-------------------------------
 Annie Hall                                         |     1977 | Woody                | Allen                | Woody                | Allen                | Alvy Singer
(1 rows)

Code Explanation :

The describe query in SQL that selects the movie title, year, director first name and last name, actor first name and last name, and role for the movie(s) with the shortest duration. The results are obtained by joining the movie, movie_direction, movie_cast, director, and actor tables.
The NATURAL JOIN keywords joins the movie, movie_direction, movie_cast, director, and actor tables based on their common columns (movie ID, director ID, and actor ID).
The WHERE clause filters the results to include only those movies with the shortest duration, which is obtained by using a subquery, and that is to select the minimum duration from the movie table.

Alternative Solutions:

Using INNER JOIN and WHERE Clause:


SELECT m.mov_title, m.mov_year, d.dir_fname, d.dir_lname, a.act_fname, a.act_lname, mc.role
FROM movie m
JOIN movie_direction md ON m.mov_id = md.mov_id
JOIN movie_cast mc ON m.mov_id = mc.mov_id
JOIN director d ON md.dir_id = d.dir_id
JOIN actor a ON mc.act_id = a.act_id
WHERE m.mov_time = (SELECT MIN(mov_time) FROM movie);

Explanation:

This query uses INNER JOINs to combine the movie, movie_direction, movie_cast, director, and actor tables based on their respective IDs. It then applies a WHERE clause to filter for movies with the shortest runtime. It selects the movie title, year, director's first name, last name, actor's first name, last name, and role.

Using WHERE Clause with Table Aliases:


SELECT m.mov_title, m.mov_year, d.dir_fname, d.dir_lname, a.act_fname, a.act_lname, mc.role
FROM movie m, movie_direction md, movie_cast mc, director d, actor a
WHERE m.mov_id = md.mov_id
  AND m.mov_id = mc.mov_id
  AND md.dir_id = d.dir_id
  AND mc.act_id = a.act_id
  AND m.mov_time = (SELECT MIN(mov_time) FROM movie);

Explanation:

This query uses the older comma-separated syntax for joining tables and specifies the join conditions in the WHERE clause. It then applies a WHERE clause to filter for movies with the shortest runtime. It selects the movie title, year, director's first name, last name, actor's first name, last name, and role.

Practice Online


Movie database model

Query Visualization:

Duration:

Query visualization of Find those lowest duration movies along with the year, director's name, actor's name and his/her role in that production - Duration

Rows:

Query visualization of Find those lowest duration movies along with the year, director's name, actor's name and his/her role in that production - Rows

Cost:

Query visualization of Find those lowest duration movies along with the year, director's name, actor's name and his/her role in that production - Cost

Have another way to solve this solution? Contribute your code (and comments) through Disqus.

Previous: From the following tables, write a SQL query to compute the average time and count number of movies for each genre. Return genre title, average time and number of movies for each genre.
Next: From the following tables, write a SQL query to find those years when a movie received a rating of 3 or 4. Sort the result in increasing order on movie year. Return move year.

What is the difficulty level of this exercise?

Test your Programming skills with w3resource's quiz.



Become a Patron!

Follow us on Facebook and Twitter for latest update.

It will be nice if you may share this link in any developer community or anywhere else, from where other developers may find this content. Thanks.

https://w3resource.com/sql-exercises/movie-database-exercise/sql-exercise-movie-database-24.php