Movielogr

movie poster

Rating: stars

Seen 1 time

Seen on: 10/30/2004

View on: IMDb | TMDb

Ginger Snaps (2000)

Directed by John Fawcett

Horror | Comedy

Most recently watched by noahphex, sleestakk, sensoria

Overview

The story of two outcast sisters, Ginger and Brigitte, in the mindless suburban town of Bailey Downs. On the night of Ginger’s first period, she is savagely attacked by a wild creature. Ginger’s wounds miraculously heal but something is not quite right. Now Brigitte must save her sister and save herself.

Rated R | Length 108 minutes

Actors

Mimi Rogers | Kris Lemche | Lucy Lawless | Katharine Isabelle | Christopher Redman | John Bourgeois | Jesse Moss | Danielle Hampton | Emily Perkins | Peter Keleghan | Ho Pak-Kwong | Wendii Fulford | Jimmy MacInnis | Lindsay Leese | Steven Taylor | Graeme Robertson | Nick Nolan | Bryon Bully | Ann Baggley | Maxwell Robertson

Comments

No comments yet. Log in and be the first!

  BENCHMARKS  
Loading Time: Base Classes  0.0016
Controller Execution Time ( Members / Movie Detail )  0.0302
Total Execution Time  0.0319
  GET DATA  
No GET data exists
  MEMORY USAGE  
531,288 bytes
  POST DATA  
No POST data exists
  URI STRING  
members/sensoria/movie_detail/9950
  CLASS/METHOD  
members/movie_detail
  DATABASE:  movielogr_dev (Members:$db)   QUERIES: 17 (0.0068 seconds)  (Hide)
0.0003  
SELECT 1
FROM `ml_sessions`
WHERE `id` = 'f0b6ba9b0c88a9cf48c773201e2fbc7d802ccf1c'
AND `ip_address` = '216.73.216.6' ?>
0.0002  
SELECT GET_LOCK('7bb4a2c2349e491347f69f67e8490820', 300) AS ci_session_lock ?>
0.0004  
SELECT `data`
FROM `ml_sessions`
WHERE `id` = 'f0b6ba9b0c88a9cf48c773201e2fbc7d802ccf1c'
AND `ip_address` = '216.73.216.6' ?>
0.0004  
SELECT `username`, `user_id`
FROM `ml_users`
WHERE `username` = 'sensoria'
 LIMIT 1 ?>
0.0003  
SELECT `MV`.`title_id`, `MT`.`tmdb_id`
FROM `ml_movie_titles` `MT`
LEFT JOIN `ml_movies` `MV` ON `MV`.`title_id` = `MT`.`title_id`
WHERE `MV`.`movie_id` = 9950
AND `MV`.`user_id` = 1
 LIMIT 1 ?>
0.0004  
SELECT `star_rating`
FROM `ml_users`
WHERE `ml_users`.`user_id` = 1
 LIMIT 1 ?>
0.0002  
SET SESSION group_concat_max_len = 12288 ?>
0.0030  
SELECT `MV`.`title_id`, `MV`.`movie_id`, COUNT(DISTINCT MV.title_id) AS mv_count, `title`, `prefix`, `year`, `imdb_link`, `MT`.`imdb_id`, `MT`.`tmdb_id`, `overview`, `certification`, `runtime`, `genre_id_01`, `genre_id_02`, `genre_id_03`, MAX(MP.filename) AS filename, `G1`.`genre_name` AS `genre_name_01`, `G2`.`genre_name` AS `genre_name_02`, `G3`.`genre_name` AS `genre_name_03`, `date_viewed`, `notes`, `MV`.`format_id`, `FMT`.`format_name`, `MV`.`source_id`, `SRC`.`source_name`, `MV`.`device_id`, `DVC`.`device_name`, `MV`.`rating_id`, `STR`.`star_rating`, `rewatch`, 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_movies` `MV` ON `MV`.`title_id` = `MT`.`title_id`
LEFT JOIN `ml_movie_posters` `MP` ON `MV`.`title_id` = `MP`.`title_id`
LEFT JOIN `ml_lookup_genres` `G1` ON `MV`.`genre_id_01` = `G1`.`genre_id`
LEFT JOIN `ml_lookup_genres` `G2` ON `MV`.`genre_id_02` = `G2`.`genre_id`
LEFT JOIN `ml_lookup_genres` `G3` ON `MV`.`genre_id_03` = `G3`.`genre_id`
LEFT JOIN `ml_lookup_formats` `FMT` ON `MV`.`format_id` = `FMT`.`format_id`
LEFT JOIN `ml_lookup_sources` `SRC` ON `MV`.`source_id` = `SRC`.`source_id`
LEFT JOIN `ml_lookup_devices` `DVC` ON `MV`.`device_id` = `DVC`.`device_id`
LEFT JOIN `ml_movie_ratings_ten_star` `STR` ON `MV`.`rating_id` = `STR`.`rating_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 `MV`.`movie_id` = 9950
AND `MV`.`user_id` = 1
ORDER BY `date_viewed` DESC ?>
0.0001  
SET SESSION group_concat_max_len = 1024 ?>
0.0002  
SELECT `event_title`, `JME`.`event_id`
FROM `ml_junction_movies_events` `JME`
LEFT JOIN `ml_movie_events` `EV` ON `JME`.`event_id` = `EV`.`event_id`
WHERE `JME`.`movie_id` = 9950 ?>
0.0002  
SELECT `tag`, `JMT`.`tag_id`
FROM `ml_junction_movies_tags` `JMT`
LEFT JOIN `ml_tags` `MT` ON `JMT`.`tag_id` = `MT`.`tag_id`
WHERE `JMT`.`movie_id` = 9950 ?>
0.0001  
SELECT `star_rating`
FROM `ml_users`
WHERE `ml_users`.`user_id` = 1
 LIMIT 1 ?>
0.0001  
SELECT `rating_id`
FROM `ml_movies` `MV`
WHERE `MV`.`user_id` = 1
AND `MV`.`movie_id` = 9950
 LIMIT 1 ?>
0.0001  
SELECT `star_rating`
FROM `ml_movie_ratings_ten_star` `MR`
WHERE `MR`.`rating_id` IS NULL
 LIMIT 1 ?>
0.0003  
SELECT `MV2`.`date_viewed`, `MV2`.`movie_id`
FROM `ml_movies` `MV`
LEFT JOIN `ml_movies` `MV2` ON `MV`.`title_id` = `MV2`.`title_id` AND `MV`.`user_id` = `MV2`.`user_id`
WHERE `MV`.`user_id` = 1
AND `MV`.`movie_id` = 9950
GROUP BY `MV2`.`movie_id`
ORDER BY `MV2`.`date_viewed` DESC ?>
0.0002  
SELECT `comment_id`, `comment`, `commenter_id`, `timestamp`, `username`, `email_address`
FROM `ml_comments` `MC`
JOIN `ml_users` `MU` ON `MU`.`user_id` = `MC`.`commenter_id`
WHERE `movie_id` = 9950 ?>
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` = 2518
ORDER BY `date_viewed` DESC
 LIMIT 10 ?>
  HTTP HEADERS  (Show)
  SESSION DATA  (Show)
  CONFIG VARIABLES  (Show)