Movielogr

movie poster

View on: IMDb | TMDb

Pain & Gain (2013)

Directed by Michael Bay

Overview

Daniel Lugo, manager of the Sun Gym in 1990s Miami, decides that there is only one way to achieve his version of the American dream: extortion. To achieve his goal, he recruits musclemen Paul and Adrian as accomplices. After several failed attempts, they abduct rich businessman Victor Kershaw and convince him to sign over all his assets to them. But when Kershaw makes it out alive, authorities are reluctant to believe his story.

Rated R | Length 130 minutes

Actors

Peter Stormare | Ed Harris | Tony Shalhoub | Larry Hankin | Mark Wahlberg | Michael Rispoli | Dwayne Johnson | Tony Plana | Bill Kelly | Wladimir Klitschko | Rob Corddry | Anthony Mackie | Ken Jeong | Keili Lefkovitz | Kurt Angle | Yolanthe Sneijder-Cabau | Patrick Bristow | Nikki Benz | Brian Stepanek | Rebel Wilson | Vivi Pineda | Bar Paly | Christopher Jestin Langstaff | Gustavo Quiroz Jr. | Corinne Ferrer | Zack Moore | Kory Getman | Emily Rutherfurd | Persei Caputo | Gwendalyn Barker | Vannessa Nevader | Anthony 'Ace' Thomas | Andrea Bennetti | Jennifer Nicole Lee | Jessica Dykstra

  BENCHMARKS  
Loading Time: Base Classes  0.0019
Controller Execution Time ( Overview / Movie Detail )  0.0296
Total Execution Time  0.0316
  GET DATA  
No GET data exists
  MEMORY USAGE  
514,568 bytes
  POST DATA  
No POST data exists
  URI STRING  
overview/movie_detail/5960
  CLASS/METHOD  
overview/movie_detail
  DATABASE:  movielogr_dev (Overview:$db)   QUERIES: 10 (0.0096 seconds)  (Hide)
0.0004  
SELECT 1
FROM `ml_sessions`
WHERE `id` = '72acabe6ff47792a911a272bda83ec2a3f585cf5'
AND `ip_address` = '216.73.216.6' ?>
0.0002  
SELECT GET_LOCK('2811fa5ed3393eac9ce943e8aebd41f9', 300) AS ci_session_lock ?>
0.0003  
SELECT `data`
FROM `ml_sessions`
WHERE `id` = '72acabe6ff47792a911a272bda83ec2a3f585cf5'
AND `ip_address` = '216.73.216.6' ?>
0.0006  
SELECT `MT`.`title_id`, `MT`.`tmdb_id`
FROM `ml_movie_titles` `MT`
WHERE `MT`.`title_id` = 5960
 LIMIT 1 ?>
0.0018  
SET SESSION group_concat_max_len = 12288 ?>
0.0055  
SELECT `MT`.`title_id`, `title`, `prefix`, `year`, `imdb_link`, `imdb_id`, `MT`.`tmdb_id`, `overview`, `certification`, `runtime`, MAX(MP.filename) AS filename, GROUP_CONCAT(DISTINCT(DR.director_name) ORDER BY DR.tmdb_director_id ASC SEPARATOR "|") AS directors, GROUP_CONCAT(DISTINCT(DR.tmdb_director_id) ORDER BY DR.tmdb_director_id ASC SEPARATOR "|") AS director_ids, GROUP_CONCAT(DISTINCT(ACT.actor_name) ORDER BY ACT.tmdb_actor_id ASC SEPARATOR "|") AS actors, GROUP_CONCAT(DISTINCT(ACT.tmdb_actor_id) ORDER BY ACT.tmdb_actor_id ASC SEPARATOR "|") AS actor_ids
FROM `ml_movie_titles` `MT`
LEFT JOIN `ml_movie_posters` `MP` ON `MT`.`title_id` = `MP`.`title_id`
LEFT JOIN `ml_junction_movies_directors` `JMD` ON `MT`.`title_id` = `JMD`.`title_id`
LEFT JOIN `ml_directors_new` `DR` ON `JMD`.`tmdb_director_id` = `DR`.`tmdb_director_id`
LEFT JOIN `ml_junction_movies_actors` `JMA` ON `MT`.`title_id` = `JMA`.`title_id`
LEFT JOIN `ml_actors_new` `ACT` ON `JMA`.`tmdb_actor_id` = `ACT`.`tmdb_actor_id`
WHERE `MT`.`title_id` = 5960
GROUP BY `title_id` ?>
0.0002  
SET SESSION group_concat_max_len = 1024 ?>
0.0003  
SELECT `username`, `date_viewed`, `MV`.`movie_id`
FROM `ml_users` `MU`
JOIN `ml_movies` `MV` ON `MV`.`user_id` = `MU`.`user_id`
JOIN `ml_movie_titles` `MT` ON `MT`.`title_id` = `MV`.`title_id`
WHERE `MT`.`title_id` = 5960
ORDER BY `date_viewed` DESC
 LIMIT 10 ?>
0.0002  
SELECT AVG(NULLIF(rating_id, 2)) AS `avg_rating`
FROM `ml_movies`
WHERE `title_id` = 5960 ?>
0.0002  
SELECT `star_rating`
FROM `ml_movie_ratings_ten_star`
WHERE `rating_id` = 0 ?>
  HTTP HEADERS  (Show)
  SESSION DATA  (Show)
  CONFIG VARIABLES  (Show)