Class: GraphQL::Dataloader

Inherits:
Object
  • Object
show all
Defined in:
lib/graphql/dataloader.rb,
lib/graphql/dataloader/source.rb,
lib/graphql/dataloader/request.rb,
lib/graphql/dataloader/request_all.rb,
lib/graphql/dataloader/null_dataloader.rb

Overview

This plugin supports Fiber-based concurrency, along with Source.

Examples:

Installing Dataloader


class MySchema < GraphQL::Schema
  use GraphQL::Dataloader
end

Waiting for batch-loaded data in a GraphQL field


field :team, Types::Team, null: true

def team
  dataloader.with(Sources::Record, Team).load(object.team_id)
end

Direct Known Subclasses

NullDataloader

Defined Under Namespace

Classes: NullDataloader, Request, RequestAll, Source

Constant Summary collapse

AsyncDataloader = 
Class.new(self) { self.default_nonblocking = true }

Class Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(nonblocking: self.class.default_nonblocking) ⇒ Dataloader

Returns a new instance of Dataloader.



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# File 'lib/graphql/dataloader.rb', line 52

def initialize(nonblocking: self.class.default_nonblocking)
  @source_cache = Hash.new { |h, k| h[k] = {} }
  @pending_jobs = []
  if !nonblocking.nil?
    @nonblocking = nonblocking
  end
end

Class Attribute Details

.default_nonblocking ⇒ Object

Returns the value of attribute default_nonblocking.



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# File 'lib/graphql/dataloader.rb', line 27

def default_nonblocking
  @default_nonblocking
end

Class Method Details

.use(schema, nonblocking: nil) ⇒ Object 



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# File 'lib/graphql/dataloader.rb', line 32

def self.use(schema, nonblocking: nil)
  schema.dataloader_class = if nonblocking
    AsyncDataloader
  else
    self
  end
end

.with_dataloading(&block) ⇒ Object

Call the block with a Dataloader instance, then run all enqueued jobs and return the result of the block.



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# File 'lib/graphql/dataloader.rb', line 42

def self.with_dataloading(&block)
  dataloader = self.new
  result = nil
  dataloader.append_job {
    result = block.call(dataloader)
  }
  dataloader.run
  result
end

Instance Method Details

#append_job(&job) ⇒ Object 



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# File 'lib/graphql/dataloader.rb', line 126

def append_job(&job)
  # Given a block, queue it up to be worked through when `#run` is called.
  # (If the dataloader is already running, than a Fiber will pick this up later.)
  @pending_jobs.push(job)
  nil
end

#clear_cache ⇒ void

This method returns an undefined value.

Clear any already-loaded objects from Source caches



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# File 'lib/graphql/dataloader.rb', line 135

def clear_cache
  @source_cache.each do |_source_class, batched_sources|
    batched_sources.each_value(&:clear_cache)
  end
  nil
end

#get_fiber_variables ⇒ Hash<Symbol, Object>

This is called before the fiber is spawned, from the parent context (i.e. from the thread or fiber that it is scheduled from).

Returns:

  • (Hash<Symbol, Object>) —

    Current fiber-local variables



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# File 'lib/graphql/dataloader.rb', line 68

def get_fiber_variables
  fiber_vars = {}
  Thread.current.keys.each do |fiber_var_key|
    # This variable should be fresh in each new fiber
    if fiber_var_key != :__graphql_runtime_info
      fiber_vars[fiber_var_key] = Thread.current[fiber_var_key]
    end
  end
  fiber_vars
end

#join_queues(previous_queue, next_queue) ⇒ Object 



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# File 'lib/graphql/dataloader.rb', line 269

def join_queues(previous_queue, next_queue)
  if @nonblocking
    Fiber.scheduler.run
    next_queue.select!(&:alive?)
  end
  previous_queue.concat(next_queue)
end

#nonblocking? ⇒ Boolean

Returns:

  • (Boolean) 


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# File 'lib/graphql/dataloader.rb', line 60

def nonblocking?
  @nonblocking
end

#run ⇒ Object 



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# File 'lib/graphql/dataloader.rb', line 171

def run
  if @nonblocking && !Fiber.scheduler
    raise "`nonblocking: true` requires `Fiber.scheduler`, assign one with `Fiber.set_scheduler(...)` before executing GraphQL."
  end
  # At a high level, the algorithm is:
  #
  #  A) Inside Fibers, run jobs from the queue one-by-one
  #    - When one of the jobs yields to the dataloader (`Fiber.yield`), then that fiber will pause
  #    - In that case, if there are still pending jobs, a new Fiber will be created to run jobs
  #    - Continue until all jobs have been _started_ by a Fiber. (Any number of those Fibers may be waiting to be resumed, after their data is loaded)
  #  B) Once all known jobs have been run until they are complete or paused for data, run all pending data sources.
  #    - Similarly, create a Fiber to consume pending sources and tell them to load their data.
  #    - If one of those Fibers pauses, then create a new Fiber to continue working through remaining pending sources.
  #    - When a source causes another source to become pending, run the newly-pending source _first_, since it's a dependency of the previous one.
  #  C) After all pending sources have been completely loaded (there are no more pending sources), resume any Fibers that were waiting for data.
  #    - Those Fibers assume that source caches will have been populated with the data they were waiting for.
  #    - Those Fibers may request data from a source again, in which case they will yeilded and be added to a new pending fiber list.
  #  D) Once all pending fibers have been resumed once, return to `A` above.
  #
  # For whatever reason, the best implementation I could find was to order the steps `[D, A, B, C]`, with a special case for skipping `D`
  # on the first pass. I just couldn't find a better way to write the loops in a way that was DRY and easy to read.
  #
  pending_fibers = []
  next_fibers = []
  pending_source_fibers = []
  next_source_fibers = []
  first_pass = true

  while first_pass || (f = pending_fibers.shift)
    if first_pass
      first_pass = false
    else
      # These fibers were previously waiting for sources to load data,
      # resume them. (They might wait again, in which case, re-enqueue them.)
      resume(f)
      if f.alive?
        next_fibers << f
      end
    end

    while @pending_jobs.any?
      # Create a Fiber to consume jobs until one of the jobs yields
      # or jobs run out
      f = spawn_fiber {
        while (job = @pending_jobs.shift)
          job.call
        end
      }
      resume(f)
      # In this case, the job yielded. Queue it up to run again after
      # we load whatever it's waiting for.
      if f.alive?
        next_fibers << f
      end
    end

    if pending_fibers.empty?
      # Now, run all Sources which have become pending _before_ resuming GraphQL execution.
      # Sources might queue up other Sources, which is fine -- those will also run before resuming execution.
      #
      # This is where an evented approach would be even better -- can we tell which
      # fibers are ready to continue, and continue execution there?
      #
      if (first_source_fiber = create_source_fiber)
        pending_source_fibers << first_source_fiber
      end

      while pending_source_fibers.any?
        while (outer_source_fiber = pending_source_fibers.pop)
          resume(outer_source_fiber)
          if outer_source_fiber.alive?
            next_source_fibers << outer_source_fiber
          end
          if (next_source_fiber = create_source_fiber)
            pending_source_fibers << next_source_fiber
          end
        end
        join_queues(pending_source_fibers, next_source_fibers)
        next_source_fibers.clear
      end
      # Move newly-enqueued Fibers on to the list to be resumed.
      # Clear out the list of next-round Fibers, so that
      # any Fibers that pause can be put on it.
      join_queues(pending_fibers, next_fibers)
      next_fibers.clear
    end
  end

  if @pending_jobs.any?
    raise "Invariant: #{@pending_jobs.size} pending jobs"
  elsif pending_fibers.any?
    raise "Invariant: #{pending_fibers.size} pending fibers"
  elsif next_fibers.any?
    raise "Invariant: #{next_fibers.size} next fibers"
  end
  nil
end

#run_isolated ⇒ Object

Use a self-contained queue for the work in the block.



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# File 'lib/graphql/dataloader.rb', line 143

def run_isolated
  prev_queue = @pending_jobs
  prev_pending_keys = {}
  @source_cache.each do |source_class, batched_sources|
    batched_sources.each do |batch_args, batched_source_instance|
      if batched_source_instance.pending?
        prev_pending_keys[batched_source_instance] = batched_source_instance.pending.dup
        batched_source_instance.pending.clear
      end
    end
  end

  @pending_jobs = []
  res = nil
  # Make sure the block is inside a Fiber, so it can `Fiber.yield`
  append_job {
    res = yield
  }
  run
  res
ensure
  @pending_jobs = prev_queue
  prev_pending_keys.each do |source_instance, pending|
    source_instance.pending.merge!(pending)
  end
end

#set_fiber_variables(vars) ⇒ void

This method returns an undefined value.

Set up the fiber variables in a new fiber.

This is called within the fiber, right after it is spawned.

Parameters:



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# File 'lib/graphql/dataloader.rb', line 85

def set_fiber_variables(vars)
  vars.each { |k, v| Thread.current[k] = v }
  nil
end

#with(source_class, *batch_args, **batch_kwargs) ⇒ Object

truffle-ruby wasn't doing well with the implementation below



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# File 'lib/graphql/dataloader.rb', line 97

def with(source_class, *batch_args)
  batch_key = source_class.batch_key_for(*batch_args)
  @source_cache[source_class][batch_key] ||= begin
    source = source_class.new(*batch_args)
    source.setup(self)
    source
  end
end

#yield ⇒ void

This method returns an undefined value.

Tell the dataloader that this fiber is waiting for data.

Dataloader will resume the fiber after the requested data has been loaded (by another Fiber).



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# File 'lib/graphql/dataloader.rb', line 120

def yield
  Fiber.yield
  nil
end