PROJECT WILL BE OBSOLETE SEPTEMBER 10, 2017
IMDb will be discontinuing their ancient FTP service and will instead switch to a TSV format that is hosted on Amazon S3. See their announcement for more details: https://getsatisfaction.com/imdb/topics/imdb-data-now-available-in-amazon-s3
The existing FTP sites have already gone offline, but they've put up new temporary FTP sites that will go offline September 10, 2017. Until then, you can still update goim using one of the tempoary FTP sites. For example:
goim load ftp://ftp.funet.fi/pub/mirrors/ftp.imdb.com/pub/temporaryaccess/Goim is a command line utility for maintaining and querying the Internet Movie Database (IMDb). Goim automatically downloads IMDb's data in plain text format and loads it into a relational database. Goim can then interact with the data in the database in various ways: fuzzy (with trigrams) searching, simple renaming of media files (including TV episodes), view information like plots, credits, goofs, quotes, IMDb rankings, trivia, release dates, film locations, prequels/sequels, etc.
Goim currently supports both SQLite and PostgreSQL. By default, Goim uses SQLite---which is more of a convenience for users that don't want to run a database server. Using PostgreSQL should be faster, and more importantly, will give you insanely fast fuzzy searching.
For Go programmers, the
imdb
sub-package contains types and functions for handling data in the database. The
imdb/search
sub-package exposes the full power and flexibility of Goim's searching via an
API.
Goim is relased under the UNLICENSE.
Goim depends on Go and is go-gettable. Assuming you have Go installed and your
GOPATH is set, then the following
will install Goim into $GOPATH/bin:
go get github.com/BurntSushi/goimBy default, this will attempt to install SQLite. If you don't want SQLite or can't install it easily, then install Goim with CGO disabled:
CGO_ENABLED=0 go get github.com/BurntSushi/goimWhen CGO is disabled, Goim will only work with PostgreSQL.
If you want to give Goim a quick spin, it's easy to create a SQLite database with a subset of IMDb's data:
goim load -db goim.sqliteThis command downloads a list of all movies, TV shows and episodes and creates a new SQLite database in goim.sqlite. Depending on your system and internet connection, this might take anywhere from 1 minute to 5 minutes (including building indices).
Now you can find all episodes of The Simpsons that have "maggie" in the title:
# goim search -db goim.sqlite '%maggie%' {show:the simpsons}
1. episode And Maggie Makes Three (1995) (TV show: The Simpsons, #6.13)
2. episode Gone Maggie Gone (2009) (TV show: The Simpsons, #20.13)If you add IMDb user rankings (should take less than a minute):
goim load -db goim.sqlite -lists ratingsThen you can find the top ten ranked Simpsons episodes with at least 500 votes:
# time goim search -db goim.sqlite {show:the simpsons} {votes:500-} {sort:rank desc} {limit:10}
1. episode Homer the Smithers (1996) (TV show: The Simpsons, #7.17) (rank: 90/100, votes: 840)
2. episode Homer's Enemy (1997) (TV show: The Simpsons, #8.23) (rank: 89/100, votes: 1217)
3. episode The City of New York vs. Homer Simpson (1997) (TV show: The Simpsons, #9.1) (rank: 89/100, votes: 1160)
4. episode Boy Scoutz 'n the Hood (1993) (TV show: The Simpsons, #5.8) (rank: 88/100, votes: 874)
5. episode Homer Badman (1994) (TV show: The Simpsons, #6.9) (rank: 88/100, votes: 960)
6. episode Homer the Heretic (1992) (TV show: The Simpsons, #4.3) (rank: 88/100, votes: 1090)
7. episode Homer's Phobia (1997) (TV show: The Simpsons, #8.15) (rank: 88/100, votes: 1031)
8. episode Homer's Triple Bypass (1992) (TV show: The Simpsons, #4.11) (rank: 88/100, votes: 895)
9. episode Hurricane Neddy (1996) (TV show: The Simpsons, #8.8) (rank: 88/100, votes: 855)
10. episode King Size Homer (1995) (TV show: The Simpsons, #7.7) (rank: 88/100, votes: 997)Dig deeper by adding plot information to your database (takes minutes):
goim load -db goim.sqlite -lists plotAnd check out the plot for King Size Homer:
# goim plots -db goim.sqlite king size homer
Plot summaries for King Size Homer (1995)
=========================================
Mr. Burns institutes a new calisthenics program at work. Most employees enjoy
the morning workout, except Homer, who is too lazy. He finds out that if he
goes in disability, he will be exempt from the exercises. He finds hyper-obesity
among the list of disability, so he gorges himself on food to balloon up to 300
pounds.
-- AnonymousYou can read more examples and see a complete list of search options by running
goim help search. For example, if you load the actors list, you can search
the credits of movies and episodes.
Also, see goim help for a list of all commands, which includes a command for
each type of information available.
You will need to install a PostgreSQL server and have it running on your machine. This can be done for Windows, Mac or Linux. Start here.
Once you're all set up, create a database and enable the pg_trgm extension
(which is what provides fuzzy searching):
createdb imdb
psql -U postgres imdb -c 'CREATE EXTENSION pg_trgm;'Note that enabling an extension can only be done by a PostgreSQL superuser, which is what the '-U postgres' is for (you may use any user here that has superuser privileges).
Technically, you can use Goim with PostgreSQL without enabling the pg_trgm
extension, but it isn't recommended (and Goim will yell at you).
Now all you need to do is fill in your connection information. You can use the
-db flag, but typing in all your connection details every time is painful.
Instead, tell Goim to write a default config file:
goim write configNow edit and fill in your details (the comments in the config file should help):
$EDITOR ~/.config/goim/config.tomlNote that the config file can specify a SQLite database too.
With all of that out of the way, you can now follow the steps above for loading
and searching with SQLite. (Leave out the -db ... flag.) Also, with fuzzy
searching, you don't need to use the '%' wildcard any more (although you can).
For example, you can use goim search maggie {show:simpsons} to find all
episodes of The Simpsons with "maggie" in the title.
I just copied the first season of The Simpsons off my DVD box set, but I have a problem. All of my files look like this:
S01E01.mkv S01E04.mkv S01E07.mkv S01E10.mkv S01E13.mkv
S01E02.mkv S01E05.mkv S01E08.mkv S01E11.mkv
S01E03.mkv S01E06.mkv S01E09.mkv S01E12.mkvNo problem. Goim can rename these easily with the rename command:
# goim rename -tv 'the simpsons' *.mkv
Rename 'S01E01.mkv' to 'S01E01 - Simpsons Roasting on an Open F
Rename 'S01E02.mkv' to 'S01E02 - Bart the Genius.mkv'
Rename 'S01E03.mkv' to 'S01E03 - Homer's Odyssey.mkv'
Rename 'S01E04.mkv' to 'S01E04 - There's No Disgrace Like Home.
Rename 'S01E05.mkv' to 'S01E05 - Bart the General.mkv'
Rename 'S01E06.mkv' to 'S01E06 - Moaning Lisa.mkv'
Rename 'S01E07.mkv' to 'S01E07 - The Call of the Simpsons.mkv'
Rename 'S01E08.mkv' to 'S01E08 - The Telltale Head.mkv'
Rename 'S01E09.mkv' to 'S01E09 - Life on the Fast Lane.mkv'
Rename 'S01E10.mkv' to 'S01E10 - Homer's Night Out.mkv'
Rename 'S01E11.mkv' to 'S01E11 - The Crepes of Wrath.mkv'
Rename 'S01E12.mkv' to 'S01E12 - Krusty Gets Busted.mkv'
Rename 'S01E13.mkv' to 'S01E13 - Some Enchanted Evening.mkv'
Are you sure you want to rename these files? [y/n]: yAnd now my files look like this:
S01E01 - Simpsons Roasting on an Open Fire.mkv
S01E02 - Bart the Genius.mkv
S01E03 - Homer's Odyssey.mkv
S01E04 - There's No Disgrace Like Home.mkv
S01E05 - Bart the General.mkv
S01E06 - Moaning Lisa.mkv
S01E07 - The Call of the Simpsons.mkv
S01E08 - The Telltale Head.mkv
S01E09 - Life on the Fast Lane.mkv
S01E10 - Homer's Night Out.mkv
S01E11 - The Crepes of Wrath.mkv
S01E12 - Krusty Gets Busted.mkv
S01E13 - Some Enchanted Evening.mkvThe above command executes in less than a second on my machine. The exact same command could be used to rename an entire series at once.
The rename command is very flexible, and it can also rename movies and work
with different file name formats. Read more about it with goim help rename.
Whether you're loading data for the first time or updating an existing
database, you'll want to use Goim's load command. By default, data is
downloaded from one of IMDb's FTP mirrors, but it also supports HTTP
downloading or reading from the local file system.
The load command lets you pick and choose which lists you want. By default,
it only loads the movies list. But let's say you also want plots and
quotes:
goim load -lists plot,quotesSince plots and quotes are completely independent, this load will be done in parallel if you're using PostgreSQL.
If you want to add all attribute information (i.e., plots, quotes, trivia,
goofs, etc.), then you can use the special attr list (make sure movies has
already been loaded):
goim load -lists attrOr you can load all information available with the all list. (Warning:
loading actors can take a while!)
I haven't been clever enough to come up with a good way for updating the
database in place, so every update will truncate the corresponding table and
rebuild it from scratch. (This is done inside a transaction, so if something
bad happens, your old data should be preserved.) The only exceptions to
the truncating scheme are the atom and name table. The short story here is
that this will allow primary (surrogate) keys to persist across updates. Under
this scheme, you should never have to worry about stale data cluterring search
results.
(See DESIGN.md
for some elaboration on this point.)
Typically, IMDb updates its plain text data sets some time between Friday and Saturday morning, so there's no need to have Goim update your database more frequently than once a week.
The schema of the database is very simple, but I've made an ER diagram. It was automatically generated with erd and goim-write-erd.
The following benchmarks were measured with data downloaded from IMDb on February 3, 2014 (872MB compressed). The specs of my machine: Intel i7 3930K (12 logical CPUs) with 32GB of DDR3 1600MHz RAM. Both PostgreSQL and SQLite databases were stored on a Crucial M4 128GB solid state drive (CT128M4SSD2).
A complete database (with indices) for SQLite uses approximately 3GB
of space on disk. A complete load (with all IMDb data downloaded first) took
about 12 minutes. Note that since this is SQLite, this did not use any
concurrent updating. After completion, a search query of %Matrix% takes
approximately 0.5 seconds.
A complete database (with indices) for PostgreSQL 9.3 (using a default
configuration) uses approximately 5.5GB of space on disk. A complete load (with
all IMDb data downloaded first) took about 7.5 minutes. There is a significant
speed boost from parallel table updates, although about half the time is spent
building indices (the trigram indices take especially long). After completion,
a search query of %Matrix% takes approximately 0.18 seconds. A search query
of matrix (using the trigram indices) takes approximately 1 second. (Searches
were done only when the Postgres autovacuum appeared to be idling. On my
system, it tends to run for a few minutes after a full load of the database.)
Goim is smart about updating and will avoid rebuilding indices where appropriate. For example, while the initial load took 7.5 minutes, updating the database with new data took only 5.5 minutes on my machine.
A PostgreSQL database with just movies/TV shows/episodes takes about 1.5 minutes to load completely, including indices.
Loading all attribute lists (excludes only movies and actors/actresses)
into a PostgreSQL database takes about 2 minutes to load completely, including
indices.
The point of these benchmarks is not to be rigorous, but to give you a general ballpark of the sorts of resources used to load the database.
For reference, here is the output of goim size on a full database as of
February 26, 2014:
actor 2944464 rows (102 MB)
aka_title 364286 rows (28 MB)
alternate_version 18052 rows (5728 kB)
atom 5736653 rows (285 MB)
color_info 1382568 rows (49 MB)
credit 21316676 rows (1248 MB)
episode 1723386 rows (73 MB)
genre 1687771 rows (71 MB)
goof 189835 rows (46 MB)
language 1396121 rows (59 MB)
link 909486 rows (49 MB)
literature 123700 rows (18 MB)
location 716354 rows (54 MB)
movie 957817 rows (40 MB)
mpaa_rating 14884 rows (1256 kB)
name 5736653 rows (282 MB)
plot 387030 rows (227 MB)
quote 613856 rows (169 MB)
rating 517401 rows (22 MB)
release_date 3339695 rows (169 MB)
running_time 878488 rows (39 MB)
sound_mix 506885 rows (23 MB)
tagline 147611 rows (13 MB)
trivia 401191 rows (89 MB)
tvshow 99905 rows (4328 kB)
total 5737 MBrename command to general searching.
(Maybe. Not sure if I want to complicate the search too much more, but it
would be nice to give a file name and, e.g., get back a plot.)cmd_load_test.go.)I tend to acquire a lot of media and it's a pain to keep up with correctly
naming it. Many years ago, I spent a weekend hacking together a Python script
to parse the movies IMDb list into a MySQL database and used that to rename
files. But it was slow and the renaming script was terribly inflexible.
I wanted to make it better, so I embarked on a more disciplined approach to
storing IMDb's data. I also find it incredibly useful to access most of IMDb
instantly from the command line.
To the best of my knowledge, there are only two tools that claim to load a substantial fraction of IMDb's data into a relational database: IMDbPY and JMDB. The source code of JMDB doesn't appear to ever have been released, and it looks like some sort of GUI tool. Truthfully, I haven't tried it.
IMDbPY has been around for a long time and is pretty similar to Goim. However, I found its loading procedure to be a bit awkward (a fast load seems to require some mangling with CSV files), and generally slower than Goim although I haven't done any rigorous benchmarks. (And I don't know enough about IMDbPY to know if the comparison would be fair.)
IMDbPY also seems to support MySQL. Goim does not. (And I don't have any particular plans to support it, but I'm not against it.)
It is entirely possible that I could have used IMDbPY to load a database and then built tools on top of it to do searching and renaming, but I'm much happier with a smaller and simpler piece of software to do the work for me. Also, it's a lot more fun to design your own database. Plus, controlling the schema gives me the freedom to experiment with other neat ideas, like storing temporal data based on the diffs that IMDb provides for its plain text data.
While IMDb is generous enough to provide an easily parseable dump of a subset of their data, they are pretty finicky with their licensing.
This project is not a commerical project. The only source of IMDb data in Goim is through the "alternative interfaces" plain text data files, which are expressly provided for non-commercial uses.
Point-by-point: