Usage

Downloading datasets

To download the current IMDb datsets to the current folder, run:

pimdb download all

(This downloads about 1 GB of data and might take a couple of minutes).

Transferring datasets into tables

To import them in a local SQLite database pimdb.db located in the current folder, run:

pimdb transfer all

(This will take a while. On a reasonably modern laptop with a local database you can expect about 2 hours).

The resulting database contains one tables for each dataset. The table names are PascalCase variants of the dataset name. For example, the date from the dataset title.basics are stored in the table TitleBasics. The column names in the table match the names from the datasets, for example TitleBasics.primaryTitle. A short description of all the datasets and columns can be found at the download page for the IMDb datasets.

Querying tables

To query the tables, you can use any database tool that supports SQLite, for example the freely available and platform independent community edition of DBeaver or the command line shell for SQLite.

For simple queries you can also use pimdb’s built-in query command, for example:

pimdb query "select count(1) from TitleBasics"

The result is shown on the standard output and can be redirected to a file, for example:

pimdb query "select primaryTitle, startYear from TitleBasics limit 10" >some.tsv

You can also store a query in a text file and specify the path:

pimdb query --file some-select.sql >some.tsv

Databases other than SQLite

--database DATABASE

Optionally you can specify a different database using the --database option with an SQLAlchemy engine configuration, which generally uses the template “dialect+driver://username:password@host:port/database”. SQLAlchemy supports several SQL dialects out of the box, and there are external dialects available for other SQL databases and other forms of tabular data.

Here’s an example for using a PostgreSQL database:

pimdb transfer --database "postgresql://user:password@localhost:5432/mydatabase" all

Building normalized tables

The tables so far are almost verbatim copies of the IMDb datasets with the exception that possible duplicate rows have been removed. This means that NameBasics.nconst and TitleBasics.tconst are unique, which sadly is not always (but still sometimes) the case for the datasets in the .tsv.gz files.

This data model already allows to perform several kinds of queries quite easily and efficiently.

However, the IMDb datasets do not offer a simple way to query N:M relations. For example, the column NameBasics.knownForTitles contains a comma separated list of tconsts like “tt2076794,tt0116514,tt0118577,tt0086491”.

To perform such queries efficiently you can build strictly normalized tables derived from the dataset tables by running:

pimdb build

If you did specify a --database for the transfer command before, you have to specify the same value for build in order to find the source data. These tables generally use snake_case names for both tables and columns, for example title_allias.is_original.

Querying normalized tables

N:M relations are stored in tables using the naming template some_to_other, for example name_to_known_for_title. These relation tables contain only the numeric ID’s to the respective actual data and a numeric column ordering to remember the sort order of the comma separated list in the IMDb dataset column.

For example, here is an SQL query to list the titles Alan Smithee is known for:

select
    title.primary_title,
    title.start_year
from
    name_to_known_for_title
    join name on
        name.id = name_to_known_for_title.name_id
    join title on
        title.id = name_to_known_for_title.title_id
where
    name.primary_name = 'Alan Smithee'

To list all movies and actors that played a character named “James Bond”:

Movies with a character named “James Bond” and the respective actor

select
    title.primary_title as "Title",
    title.start_year as "Year",
    name.primary_name as "Actor",
    "character".name as "Character"
from
    "character"
    join participation_to_character on
        participation_to_character.character_id = "character".id
    join participation on
        participation.id = participation_to_character.participation_id
    join name on
        name.id = participation.name_id
    join title on
        title.id = participation.title_id
    join title_type on
        title_type.id = title.title_type_id
where
    "character".name = 'James Bond'
    and title_type.name = 'movie'
order by
    title.start_year,
    name.primary_name,
    title.primary_title

Reference

To get an overview of general command line options and available commands run:

pimdb --help

To learn the available command line options for a specific command run for example:

pimdb transfer --help