Movielogr

movie poster

Rating: 5 stars

Tags

snakes boats

Seen 1 time

Seen on: 03/08/2011

View on: IMDb | TMDb

Anaconda (1997)

Directed by Luis Llosa

Horror

Most recently watched by sensoria

Overview

A “National Geographic” film crew is taken hostage by an insane hunter, who takes them along on his quest to capture the world’s largest - and deadliest - snake.

Rated PG-13 | Length 89 minutes

Actors

Owen Wilson | Eric Stoltz | Jonathan Hyde | Ice Cube | Jon Voight | Danny Trejo | Frank Welker | Jennifer Lopez | Kari Wuhrer | Vincent Castellanos

Viewing Notes

I decided to revisit this movie since my library had both it and the sequel on DVD.

This has a crazy mismatched cast that includes Eric Stoltz, Jennifer Lopez, Ice Cube, Jon Voight, Owen Wilson (!!), Kari Wuhrer and a young Danny Trejo. So bizarre!

Voight is over the top with his Scarface accent and the CGI snake effects aren’t very effective, but as long as you don’t take anything too seriously, the movie is fun to watch.

Comments

No comments yet. Log in and be the first!

  BENCHMARKS  
Loading Time: Base Classes  0.0013
Controller Execution Time ( Members / Movie Detail )  0.0826
Total Execution Time  0.0839
  GET DATA  
No GET data exists
  MEMORY USAGE  
531,816 bytes
  POST DATA  
No POST data exists
  URI STRING  
members/sensoria/movie_detail/2268
  CLASS/METHOD  
members/movie_detail
  DATABASE:  movielogr_dev (Members:$db)   QUERIES: 17 (0.0631 seconds)  (Hide)
0.0004  
SELECT 1
FROM `ml_sessions`
WHERE `id` = 'ae1d47ded30db2d2014120e986cf50182b394974'
AND `ip_address` = '216.73.216.6' ?>
0.0574  
SELECT GET_LOCK('64f38c73ca2cf87b0773133221dc6eea', 300) AS ci_session_lock ?>
0.0004  
SELECT `data`
FROM `ml_sessions`
WHERE `id` = 'ae1d47ded30db2d2014120e986cf50182b394974'
AND `ip_address` = '216.73.216.6' ?>
0.0003  
SELECT `username`, `user_id`
FROM `ml_users`
WHERE `username` = 'sensoria'
 LIMIT 1 ?>
0.0004  
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` = 2268
AND `MV`.`user_id` = 1
 LIMIT 1 ?>
0.0003  
SELECT `star_rating`
FROM `ml_users`
WHERE `ml_users`.`user_id` = 1
 LIMIT 1 ?>
0.0002  
SET SESSION group_concat_max_len = 12288 ?>
0.0015  
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` = 2268
AND `MV`.`user_id` = 1
ORDER BY `date_viewed` DESC ?>
0.0002  
SET SESSION group_concat_max_len = 1024 ?>
0.0003  
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` = 2268 ?>
0.0003  
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` = 2268 ?>
0.0002  
SELECT `star_rating`
FROM `ml_users`
WHERE `ml_users`.`user_id` = 1
 LIMIT 1 ?>
0.0002  
SELECT `rating_id`
FROM `ml_movies` `MV`
WHERE `MV`.`user_id` = 1
AND `MV`.`movie_id` = 2268
 LIMIT 1 ?>
0.0002  
SELECT `star_rating`
FROM `ml_movie_ratings_ten_star` `MR`
WHERE `MR`.`rating_id` = '12'
 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` = 2268
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` = 2268 ?>
0.0002  
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` = 2070
ORDER BY `date_viewed` DESC
 LIMIT 10 ?>
  HTTP HEADERS  (Show)
  SESSION DATA  (Show)
  CONFIG VARIABLES  (Show)