Eager loading makes it so that you can load all associated records for a set of objects in a single query, instead of a separate query for each object.
Two separate implementations are provided. eager should be used most of the time, as it loads associated records using one query per association. However, it does not allow you the ability to filter or order based on columns in associated tables. eager_graph loads all records in a single query using JOINs, allowing you to filter or order based on columns in associated tables. However, eager_graph is usually slower than eager, especially if multiple one_to_many or many_to_many associations are joined.
You can cascade the eager loading (loading associations on associated objects) with no limit to the depth of the cascades. You do this by passing a hash to eager or eager_graph with the keys being associations of the current model and values being associations of the model associated with the current model via the key.
The arguments can be symbols or hashes with symbol keys (for cascaded eager loading). Examples:
Album.eager(:artist).all Album.eager_graph(:artist).all Album.eager(:artist, :genre).all Album.eager_graph(:artist, :genre).all Album.eager(:artist).eager(:genre).all Album.eager_graph(:artist).eager(:genre).all Artist.eager(:albums=>:tracks).all Artist.eager_graph(:albums=>:tracks).all Artist.eager(:albums=>{:tracks=>:genre}).all Artist.eager_graph(:albums=>{:tracks=>:genre}).all
You can also pass a callback as a hash value in order to customize the dataset being eager loaded at query time, analogous to the way the :eager_block association option allows you to customize it at association definition time. For example, if you wanted artists with their albums since 1990:
Artist.eager(:albums => proc{|ds| ds.where{year > 1990}})
Or if you needed albums and their artist's name only, using a single query:
Albums.eager_graph(:artist => proc{|ds| ds.select(:name)})
To cascade eager loading while using a callback, you substitute the cascaded associations with a single entry hash that has the proc callback as the key and the cascaded associations as the value. This will load artists with their albums since 1990, and also the tracks on those albums and the genre for those tracks:
Artist.eager(:albums => {proc{|ds| ds.where{year > 1990}}=>{:tracks => :genre}})
Adds one or more INNER JOINs to the existing dataset using the keys and conditions specified by the given association. The following methods also exist for specifying a different type of JOIN:
association_full_join |
FULL JOIN |
association_inner_join |
INNER JOIN |
association_left_join |
LEFT JOIN |
association_right_join |
RIGHT JOIN |
# File lib/sequel/model/associations.rb, line 2422 def association_join(*associations) association_inner_join(*associations) end
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.
# File lib/sequel/model/associations.rb, line 2433 def complex_expression_sql_append(sql, op, args) r = args.at(1) if (((op == :'=' || op == :'!=') and r.is_a?(Sequel::Model)) || (multiple = ((op == :IN || op == :'NOT IN') and ((is_ds = r.is_a?(Sequel::Dataset)) or r.all?{|x| x.is_a?(Sequel::Model)})))) l = args.at(0) if ar = model.association_reflections[l] if multiple klass = ar.associated_class if is_ds if r.respond_to?(:model) unless r.model <= klass # A dataset for a different model class, could be a valid regular query return super end else # Not a model dataset, could be a valid regular query return super end else unless r.all?{|x| x.is_a?(klass)} raise Sequel::Error, "invalid association class for one object for association #{l.inspect} used in dataset filter for model #{model.inspect}, expected class #{klass.inspect}" end end elsif !r.is_a?(ar.associated_class) raise Sequel::Error, "invalid association class #{r.class.inspect} for association #{l.inspect} used in dataset filter for model #{model.inspect}, expected class #{ar.associated_class.inspect}" end if exp = association_filter_expression(op, ar, r) literal_append(sql, exp) else raise Sequel::Error, "invalid association type #{ar[:type].inspect} for association #{l.inspect} used in dataset filter for model #{model.inspect}" end elsif multiple && (is_ds || r.empty?) # Not a query designed for this support, could be a valid regular query super else raise Sequel::Error, "invalid association #{l.inspect} used in dataset filter for model #{model.inspect}" end else super end end
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 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.
# File lib/sequel/model/associations.rb, line 2501 def eager(*associations) opts = @opts[:eager] association_opts = eager_options_for_associations(associations) opts = opts ? opts.merge(association_opts) : association_opts clone(:eager=>opts) end
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.
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 you have a large database.
Each association's order, if definied, 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.
# File lib/sequel/model/associations.rb, line 2528 def eager_graph(*associations) eager_graph_with_options(associations) end
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:
:join_type |
Override the join type specified in the association | ||||||||||
:limit_strategy |
Use a strategy for handling limits on associations. Appropriate :limit_strategy values are:
This can also be a hash with association name symbol keys and one of the above values, to use different strategies per association. The default is the :ruby strategy. Choosing a different strategy can make your code significantly slower in some cases (perhaps even the majority of cases), so you should only use this if you have benchmarked that it is faster for your use cases. |
# File lib/sequel/model/associations.rb, line 2552 def eager_graph_with_options(associations, opts=OPTS) associations = [associations] unless associations.is_a?(Array) if eg = @opts[:eager_graph] eg = eg.dup [:requirements, :reflections, :reciprocals, :limits].each{|k| eg[k] = eg[k].dup} eg[:local] = opts ds = clone(:eager_graph=>eg) ds.eager_graph_associations(ds, model, ds.opts[:eager_graph][:master], [], *associations) else # Each of the following have a symbol key for the table alias, with the following values: # :reciprocals :: the reciprocal value to use for this association # :reflections :: AssociationReflection instance related to this association # :requirements :: array of requirements for this association # :limits :: Any limit/offset array slicing that need to be handled in ruby land after loading opts = {:requirements=>{}, :master=>alias_symbol(first_source), :reflections=>{}, :reciprocals=>{}, :limits=>{}, :local=>opts, :cartesian_product_number=>0, :row_proc=>row_proc} ds = clone(:eager_graph=>opts) ds.eager_graph_associations(ds, model, ds.opts[:eager_graph][:master], [], *associations).naked end end
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.
# File lib/sequel/model/associations.rb, line 2576 def ungraphed ds = super.clone(:eager_graph=>nil) if (eg = @opts[:eager_graph]) && (rp = eg[:row_proc]) ds.row_proc = rp end ds end
Call graph on the association with the correct arguments, update the eager_graph data structure, and recurse into eager_graph_associations if there are any passed in associations (which would be dependencies of the current association)
Arguments:
ds |
Current dataset |
model |
Current Model |
ta |
table_alias used for the parent association |
requirements |
an array, used as a stack for requirements |
r |
association reflection for the current association, or an SQL::AliasedExpression with the reflection as the expression and the alias base as the aliaz. |
*associations |
any associations dependent on this one |
# File lib/sequel/model/associations.rb, line 2599 def eager_graph_association(ds, model, ta, requirements, r, *associations) if r.is_a?(SQL::AliasedExpression) alias_base = r.alias r = r.expression else alias_base = r[:graph_alias_base] end assoc_table_alias = ds.unused_table_alias(alias_base) loader = r[:eager_grapher] if !associations.empty? if associations.first.respond_to?(:call) callback = associations.first associations = {} elsif associations.length == 1 && (assocs = associations.first).is_a?(Hash) && assocs.length == 1 && (pr_assoc = assocs.to_a.first) && pr_assoc.first.respond_to?(:call) callback, assoc = pr_assoc associations = assoc.is_a?(Array) ? assoc : [assoc] end end local_opts = ds.opts[:eager_graph][:local] limit_strategy = r.eager_graph_limit_strategy(local_opts[:limit_strategy]) ds = loader.call(:self=>ds, :table_alias=>assoc_table_alias, :implicit_qualifier=>(ta == ds.opts[:eager_graph][:master]) ? first_source : qualifier_from_alias_symbol(ta, first_source), :callback=>callback, :join_type=>local_opts[:join_type], :join_only=>local_opts[:join_only], :limit_strategy=>limit_strategy, :from_self_alias=>ds.opts[:eager_graph][:master]) if r[:order_eager_graph] && (order = r.fetch(:graph_order, r[:order])) ds = ds.order_more(*qualified_expression(order, assoc_table_alias)) end eager_graph = ds.opts[:eager_graph] eager_graph[:requirements][assoc_table_alias] = requirements.dup eager_graph[:reflections][assoc_table_alias] = r if limit_strategy == :ruby eager_graph[:limits][assoc_table_alias] = r.limit_and_offset end eager_graph[:cartesian_product_number] += r[:cartesian_product_number] || 2 ds = ds.eager_graph_associations(ds, r.associated_class, assoc_table_alias, requirements + [assoc_table_alias], *associations) unless associations.empty? ds end
Check the associations are valid for the given model. Call eager_graph_association on each association.
Arguments:
ds |
Current dataset |
model |
Current Model |
ta |
table_alias used for the parent association |
requirements |
an array, used as a stack for requirements |
*associations |
the associations to add to the graph |
# File lib/sequel/model/associations.rb, line 2643 def eager_graph_associations(ds, model, ta, requirements, *associations) return ds if associations.empty? associations.flatten.each do |association| ds = case association when Symbol, SQL::AliasedExpression ds.eager_graph_association(ds, model, ta, requirements, eager_graph_check_association(model, association)) when Hash association.each do |assoc, assoc_assocs| ds = ds.eager_graph_association(ds, model, ta, requirements, eager_graph_check_association(model, assoc), assoc_assocs) end ds else raise(Sequel::Error, 'Associations must be in the form of a symbol or hash') end end ds end
Replace the array of plain hashes with an array of model objects will all eager_graphed associations set in the associations cache for each object.
# File lib/sequel/model/associations.rb, line 2663 def eager_graph_build_associations(hashes) hashes.replace(EagerGraphLoader.new(self).load(hashes)) end
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