module Sequel::Model::Associations::DatasetMethods

If the dataset is being eagerly loaded, default to calling all instead of each.

Adds one or more INNER JOINs to the existing dataset using the keys and conditions specified by the given association(s). Take the same arguments as eager_graph, and operates similarly, but only adds the joins as opposed to making the other changes (such as adding selected columns and setting up eager loading).

The following methods also exist for specifying a different type of JOIN:

Examples:

# For each album, association_join load the artist
Album.association_join(:artist).all
# SELECT *
# FROM albums
# INNER JOIN artists AS artist ON (artists.id = albums.artist_id)

# For each album, association_join load the artist, using a specified alias
Album.association_join(Sequel[:artist].as(:a)).all
# SELECT *
# FROM albums
# INNER JOIN artists AS a ON (a.id = albums.artist_id)

# For each album, association_join load the artist and genre
Album.association_join(:artist, :genre).all
Album.association_join(:artist).association_join(:genre).all
# SELECT *
# FROM albums
# INNER JOIN artists AS artist ON (artist.id = albums.artist_id)
# INNER JOIN genres AS genre ON (genre.id = albums.genre_id)

# For each artist, association_join load albums and tracks for each album
Artist.association_join(albums: :tracks).all
# SELECT *
# FROM artists
# INNER JOIN albums ON (albums.artist_id = artists.id)
# INNER JOIN tracks ON (tracks.album_id = albums.id)

# For each artist, association_join load albums, tracks for each album, and genre for each track
Artist.association_join(albums: {tracks: :genre}).all
# SELECT *
# FROM artists
# INNER JOIN albums ON (albums.artist_id = artists.id)
# INNER JOIN tracks ON (tracks.album_id = albums.id)
# INNER JOIN genres AS genre ON (genre.id = tracks.genre_id)

# For each artist, association_join load albums with year > 1990
Artist.association_join(albums: proc{|ds| ds.where{year > 1990}}).all
# SELECT *
# FROM artists
# INNER JOIN (
#   SELECT * FROM albums WHERE (year > 1990)
# ) AS albums ON (albums.artist_id = artists.id)

# For each artist, association_join load albums and tracks 1-10 for each album
Artist.association_join(albums: {tracks: proc{|ds| ds.where(number: 1..10)}}).all
# SELECT *
# FROM artists
# INNER JOIN albums ON (albums.artist_id = artists.id)
# INNER JOIN (
#   SELECT * FROM tracks WHERE ((number >= 1) AND (number <= 10))
# ) AS tracks ON (tracks.albums_id = albums.id)

# For each artist, association_join load albums with year > 1990, and tracks for those albums
Artist.association_join(albums: {proc{|ds| ds.where{year > 1990}}=>:tracks}).all
# SELECT *
# FROM artists
# INNER JOIN (
#   SELECT * FROM albums WHERE (year > 1990)
# ) AS albums ON (albums.artist_id = artists.id)
# INNER JOIN tracks ON (tracks.album_id = albums.id)

If the expression is in the form x = y where y is a Sequel::Model instance, array of Sequel::Model instances, or a Sequel::Model dataset, assume x is an association symbol and look up the association reflection via the dataset's model. From there, return the appropriate SQL based on the type of association and the values of the foreign/primary keys of y. For most association types, this is a simple transformation, but for many_to_many associations this creates a subquery to the join table.

The preferred eager loading method. Loads all associated records using one query for each association.

The basic idea for how it works is that the dataset is first loaded normally. Then it goes through all associations that have been specified via eager. It loads each of those associations separately, then associates them back to the original dataset via primary/foreign keys. Due to the necessity of all objects being present, you need to use all to use eager loading, as it can't work with each.

This implementation avoids the complexity of extracting an object graph out of a single dataset, by building the object graph out of multiple datasets, one for each association. By using a separate dataset for each association, it avoids problems such as aliasing conflicts and creating cartesian product result sets if multiple one_to_many or many_to_many eager associations are requested.

One limitation of using this method is that you cannot filter the current dataset based on values of columns in an associated table, since the associations are loaded in separate queries. To do that you need to load all associations in the same query, and extract an object graph from the results of that query. If you need to filter based on columns in associated tables, look at eager_graph or join the tables you need to filter on manually.

Each association's order, if defined, is respected. If the association uses a block or has an :eager_block argument, it is used.

To modify the associated dataset that will be used for the eager load, you should use a hash for the association, with the key being the association name symbol, and the value being a callable object that is called with the associated dataset and should return a modified dataset. If that association also has dependent associations, instead of a callable object, use a hash with the callable object being the key, and the dependent association(s) as the value.

Examples:

# For each album, eager load the artist
Album.eager(:artist).all
# SELECT * FROM albums
# SELECT * FROM artists WHERE (id IN (...))

# For each album, eager load the artist and genre
Album.eager(:artist, :genre).all
Album.eager(:artist).eager(:genre).all
# SELECT * FROM albums
# SELECT * FROM artists WHERE (id IN (...))
# SELECT * FROM genres WHERE (id IN (...))

# For each artist, eager load albums and tracks for each album
Artist.eager(albums: :tracks).all
# SELECT * FROM artists
# SELECT * FROM albums WHERE (artist_id IN (...))
# SELECT * FROM tracks WHERE (album_id IN (...))

# For each artist, eager load albums, tracks for each album, and genre for each track
Artist.eager(albums: {tracks: :genre}).all
# SELECT * FROM artists
# SELECT * FROM albums WHERE (artist_id IN (...))
# SELECT * FROM tracks WHERE (album_id IN (...))
# SELECT * FROM genre WHERE (id IN (...))

# For each artist, eager load albums with year > 1990
Artist.eager(albums: proc{|ds| ds.where{year > 1990}}).all
# SELECT * FROM artists
# SELECT * FROM albums WHERE ((year > 1990) AND (artist_id IN (...)))

# For each artist, eager load albums and tracks 1-10 for each album
Artist.eager(albums: {tracks: proc{|ds| ds.where(number: 1..10)}}).all
# SELECT * FROM artists
# SELECT * FROM albums WHERE (artist_id IN (...))
# SELECT * FROM tracks WHERE ((number >= 1) AND (number <= 10) AND (album_id IN (...)))

# For each artist, eager load albums with year > 1990, and tracks for those albums
Artist.eager(albums: {proc{|ds| ds.where{year > 1990}}=>:tracks}).all
# SELECT * FROM artists
# SELECT * FROM albums WHERE ((year > 1990) AND (artist_id IN (...)))
# SELECT * FROM albums WHERE (artist_id IN (...))

The secondary eager loading method. Loads all associations in a single query. This method should only be used if you need to filter or order based on columns in associated tables, or if you have done comparative benchmarking it and determined it is faster.

This method uses Dataset#graph to create appropriate aliases for columns in all the tables. Then it uses the graph's metadata to build the associations from the single hash, and finally replaces the array of hashes with an array model objects inside all.

Be very careful when using this with multiple one_to_many or many_to_many associations, as you can create large cartesian products. If you must graph multiple one_to_many and many_to_many associations, make sure your filters are narrow if the datasets are large.

Each association's order, if defined, is respected. eager_graph probably won't work correctly on a limited dataset, unless you are only graphing many_to_one, one_to_one, and one_through_one associations.

Does not use the block defined for the association, since it does a single query for all objects. You can use the :graph_* association options to modify the SQL query.

Like eager, you need to call all on the dataset for the eager loading to work. If you just call each, it will yield plain hashes, each containing all columns from all the tables.

To modify the associated dataset that will be joined to the current dataset, you should use a hash for the association, with the key being the association name symbol, and the value being a callable object that is called with the associated dataset and should return a modified dataset. If that association also has dependent associations, instead of a callable object, use a hash with the callable object being the key, and the dependent association(s) as the value.

You can specify an alias by providing a Sequel::SQL::AliasedExpression object instead of an a Symbol for the assocation name.

Examples:

# For each album, eager_graph load the artist
Album.eager_graph(:artist).all
# SELECT ...
# FROM albums
# LEFT OUTER JOIN artists AS artist ON (artists.id = albums.artist_id)

# For each album, eager_graph load the artist, using a specified alias
Album.eager_graph(Sequel[:artist].as(:a)).all
# SELECT ...
# FROM albums
# LEFT OUTER JOIN artists AS a ON (a.id = albums.artist_id)

# For each album, eager_graph load the artist and genre
Album.eager_graph(:artist, :genre).all
Album.eager_graph(:artist).eager_graph(:genre).all
# SELECT ...
# FROM albums
# LEFT OUTER JOIN artists AS artist ON (artist.id = albums.artist_id)
# LEFT OUTER JOIN genres AS genre ON (genre.id = albums.genre_id)

# For each artist, eager_graph load albums and tracks for each album
Artist.eager_graph(albums: :tracks).all
# SELECT ...
# FROM artists
# LEFT OUTER JOIN albums ON (albums.artist_id = artists.id)
# LEFT OUTER JOIN tracks ON (tracks.album_id = albums.id)

# For each artist, eager_graph load albums, tracks for each album, and genre for each track
Artist.eager_graph(albums: {tracks: :genre}).all
# SELECT ...
# FROM artists
# LEFT OUTER JOIN albums ON (albums.artist_id = artists.id)
# LEFT OUTER JOIN tracks ON (tracks.album_id = albums.id)
# LEFT OUTER JOIN genres AS genre ON (genre.id = tracks.genre_id)

# For each artist, eager_graph load albums with year > 1990
Artist.eager_graph(albums: proc{|ds| ds.where{year > 1990}}).all
# SELECT ...
# FROM artists
# LEFT OUTER JOIN (
#   SELECT * FROM albums WHERE (year > 1990)
# ) AS albums ON (albums.artist_id = artists.id)

# For each artist, eager_graph load albums and tracks 1-10 for each album
Artist.eager_graph(albums: {tracks: proc{|ds| ds.where(number: 1..10)}}).all
# SELECT ...
# FROM artists
# LEFT OUTER JOIN albums ON (albums.artist_id = artists.id)
# LEFT OUTER JOIN (
#   SELECT * FROM tracks WHERE ((number >= 1) AND (number <= 10))
# ) AS tracks ON (tracks.albums_id = albums.id)

# For each artist, eager_graph load albums with year > 1990, and tracks for those albums
Artist.eager_graph(albums: {proc{|ds| ds.where{year > 1990}}=>:tracks}).all
# SELECT ...
# FROM artists
# LEFT OUTER JOIN (
#   SELECT * FROM albums WHERE (year > 1990)
# ) AS albums ON (albums.artist_id = artists.id)
# LEFT OUTER JOIN tracks ON (tracks.album_id = albums.id)

Run eager_graph with some options specific to just this call. Unlike eager_graph, this takes the associations as a single argument instead of multiple arguments.

Options:

If the dataset is being eagerly loaded, default to calling all instead of each.

Do not attempt to split the result set into associations, just return results as simple objects. This is useful if you want to use eager_graph as a shortcut to have all of the joins and aliasing set up, but want to do something else with the dataset.