battmatt · March 21, 2024 20:50
diff --git a/ForkJoinAPI.py b/ForkJoinAPI.py
 # (c) Copyright 2018 Zymergen, Inc.
 # All Rights Reserved
 """

 The following is example code used for a technology blog post: https://medium.com/@ZymergenTechBlog/building-a-parallel-task-api-with-celery-dbae5ced4e28

 The ForkJoin class can be used to generate a ZWork task that contains a single
 distributed processing step.  Your job should have 3 parts. An initial setup step 
 responsible for splitting of inputs into workable chunks. A process step that can
 process each chunk in a forked execution process and a join step that puts it all
 together and returns the final result.
 """
 import copy
 import functools
 from types import GeneratorType

 import cerberus
 from celery import chain, group, shared_task

 # Parallelization settings
 PARALLEL_DEFAULT_GROUP_SIZE = 32


 class InputValidationError(TypeError):
    """Exception representing a failure to validate job inputs against a Cerberus schema.

    Attributes:
        inputs: The inputs that failed validation
        schema: The Cerberus schema
        errors: The list of validation errors
    """

    def __init__(self, inputs, schema, errors):
        self.inputs = inputs
        self.schema = schema
        self.errors = errors
        self.warnings = None
        err_template = "Inputs did not match schema.\n\tInputs: {}\n\tSchema: {}\n\tErrors: {}"
        err_msg = err_template.format(self.inputs, self.schema, self.errors)
        super(InputValidationError, self).__init__(err_msg)


 def chunks(l, n):
    """A utility function for splitting a list into n roughly-equal sized chunks.
    """
    return [l[i::n] for i in xrange(n)]


 def flatten(xs):
    """Flatten a list.

    Turns a nested list into a flat list.
    Only goes one layer deep, not fully nested.

    Args:
        xs: a list of elements and lists
    Returns:
        A list with those same (and more) elements, but no lists
    """
    for x in xs:
        if isinstance(x, list) or isinstance(x, GeneratorType):
            for elem in x:
                yield elem
        else:
            yield x


 def validate_inputs(ins, schema):
    """Validates JSON inputs against a Cerberus schema.
    """
    validator = cerberus.Validator()
    is_valid = validator.validate(ins, schema)
    if not is_valid:
        raise InputValidationError(ins, schema, validator.errors)


 # def strips_extra_args(fn):
 #     """Makes a function able to accept arbitrary extra keyword arguments.
 #
 #     Returns a version of fn that can safely receive extra arguments, and raises
 #     an exception upon incorrect argumentation that is more useful than Python's
 #     default.
 #
 #     Details omitted
 #
 #     Args:
 #         fn: any function
 #     Returns:
 #         fn, but able to accept extra keyword arguments
 #     Raises:
 #         TypeError is raised whenever too few arguments are passed in.
 #     """
 #     return fn


 class ForkJoinTask(object):
    """Represents a task that can be run in parallel.

    To implement your own parallel task:
        1. Write a new class, MyTask, that inherits from this one
        2. Override the command and job_inputs properties
        3. Write business logic functions outside of the MyTask class
            - a "setup" for dividing inputs into units of work, each unit is a dictionary
              with keys that can be used as arg names for the process step
            - a "processer" for performing a single unit of work defined by the setup step
            - a "joiner" for recombining results from the work units
        4. Decorate those functions with MyTask.setup(), MyTask.process(), and MyTask.join()
            - see their docstrings below for details, restrictions, and expectations
        5. Input args are saved as 'orig_input_args`, which is dict of the original args
           that can be accessed in individual method signatures (e.g., process, join).

    Why do I need to implement a new static singleton? Can't this be more dynamic?
        No. This limitation comes from Celery, not us.
    """
    queue = 'parallel'

    def run_without_celery(self, job_args):
        """
        Run this task as a job without using the celery backend. This entire task will run in a
        single process, and there is no serialization/deserialization to/from JSON between steps,
        but otherwise should mimic the celery case exactly.

        Args:
            job_args: The input dictionary to the job

        Returns:
            The job results dictionary.
        """
        # avoid changing the global state of original job args
        nested_job_args = job_args.copy()
        nested_job_args['orig_input_args'] = job_args.copy()

        setup_results = self.setup_step((), **nested_job_args)
        process_results = [self.process_step(setup_results, i, **nested_job_args)
                           for i in range(len(setup_results))]
        return self.join_step(process_results, **nested_job_args)

    @classmethod
    def get_input_schema(cls, *args, **kwargs):
        """This method provides a Cerberus schema for job input validation.
        Returns:
            A Cerberus schema that will be used to validate job inputs.
        """
        raise NotImplementedError("Tasks MUST implement a schema")

    @classmethod
    def split(cls, work_units):
        """Override this to explicitly control which workers do which work.

        You can almost certainly use this default.

        Args:
            work_units (list<dict>): The inputs for each possible process() step
                This is the output from the setup() step.

        Returns:
            A list of list<dict>s. Each parent list corresponds to work for one worker.
        """
        expected_workers = PARALLEL_DEFAULT_GROUP_SIZE
        return chunks(work_units, min(expected_workers, len(work_units)))

    @classmethod
    def setup(cls, task):
        """Decorates a function that divides job inputs into units of work.

        This decorator MUST be used EXACTLY ONCE per job like this:

            @MyTask.setup
            def divide_job_inputs(arg0_from_schema, arg1_from_schema, ...):
                return [{'arg': 'val0'}, {'arg': 'val1'}]

        Args (passed into your decorated function):
            **kwargs (dict): Corresponds to parsed and validated job inputs.
                These should match keys from your jobs' get_input_schema().

        Return (expected from your decorated function):
            A list of dicts. Each dict represents a subset of inputs needed to do one
            unit of parallel work. The keys in this dict should match the params
            from your process() step function.  Remaining inputs to procces() are 
            provided by parsed/validated job inputs.
        """
        @functools.wraps(task)
        def setter_upper(*args, **kwargs):
            # We use cerberus for schema validation
            schema = strips_extra_args(cls.get_input_schema)(**kwargs)
            validate_inputs(kwargs, schema)
            normalized_inputs = cerberus.Validator().normalized(kwargs, schema)

            result = strips_extra_args(task)(**normalized_inputs)

            split_results = cls.split(result)

            return split_results

        cls.setup_step = lifecycle_task(setter_upper, queue=cls.queue)
        return cls.setup_step

    @classmethod
    def process(cls, task):
        """Decorates a function that performs one unit of parallel work.

        This decorator MUST be used EXACTLY ONCE per job like this:

            @MyTask.process
            def do_parallel_work(param0, param1, ...):
                return "whatever I want"

        Args (passed into your decorated function):
            **kwargs (dict): All the parsed/validated job inputs, PLUS all the
                arguments from one of setup()'s outputs. Your function will be
                passed only the arguments explicitly claimed in its signature.
                In the event of key collision, arguments from one of setup()'s
                outputs take precedent over parsed/validated job inputs.

        Return (expected from your decorated function):
            Your function can return any serializable object.
        """
        @functools.wraps(task)
        def process_inputs(divided_inputs, group_index, **kwargs):
            try:
                my_share_of_work = divided_inputs[group_index]
            except IndexError:
                return []

            outs = []

            schema = strips_extra_args(cls.get_input_schema)(**kwargs)
            normalized_kwargs = cerberus.Validator().normalized(kwargs, schema)
            for work_unit in my_share_of_work:
                work_unit_kwargs = copy.deepcopy(normalized_kwargs)
                work_unit_kwargs.update(work_unit)
                result = strips_extra_args(task)(**work_unit_kwargs)
                outs.append(result)

            return outs
        cls.process_step = lifecycle_task(process_inputs, queue=cls.queue)
        return cls.process_step

    @classmethod
    def join(cls, task):
        """Decorates a function that recombines parallel results.

        This decorator MUST be used EXACTLY ONCE per job like this:

            @MyTask.join
            def recombine_parallel_results(parallel_results, **kwargs):
                return "whatever I want"

        Args (passed into your decorated function):
            parallel_results (list): A list of all values that were returned from
                an invocation of the process() step.
            **kwargs (dict): All the parsed/validated job inputs.

        Return (expected from your decorated function):
            Any serializable object. This object is the job's final result.
        """
        @functools.wraps(task)
        def joiner(distributed_results, *args, **kwargs):
            schema = strips_extra_args(cls.get_input_schema)(**kwargs)
            normalized_kwargs = cerberus.Validator().normalized(kwargs, schema)
            # The process step nested its results. Unnest them.
            unnested_results = list(flatten(distributed_results))
            results = strips_extra_args(task)(unnested_results, *args, **normalized_kwargs)
            return results

        # place the final step on a different queue so it's not blocked by other running
        # parallel jobs
        cls.join_step = lifecycle_task(joiner, queue='celery')
        return cls.join_step

    @classmethod
    def signature(cls, options):
        """Returns a Celery signature to describe the complete job-step sequence.

        Args:
            options (dict): The shared options for the job (auth, request, etc.)
        Returns:
            A Celery signature.
        """
        return fork_join_task(cls.setup_step, cls.process_step, cls.join_step, options)


 def fork_join_task(setup_step, process_step, join_step, bound_args):
    """Creates a parallel Celery fork/join task from provided functions.

    Args:
        setup_step (celery task): A "setup" step for the whole job
        process_step (celery task): A "process" step that runs in parallel after setup
        join_step (celery task): A "join" step to recombine parallel results
        bound_args (dict): Any bound arguments that can be accessed by all steps
    Returns:
        A new Celery job that performs a setup/process/join work pattern.
        The returned job's steps will all be partially applied over bound_args.
    """
    setup_sig = setup_step.signature(**bound_args)
    process_sig = parallel_processing_step(process_step, bound_args)
    join_sig = join_step.signature(**bound_args)
    return chain(setup_sig, process_sig, join_sig)


 def parallel_processing_step(
        process_step, bound_args, group_size=PARALLEL_DEFAULT_GROUP_SIZE):
    """Returns a "group" signature for a distributed application of process_fn.
    """
    signatures = [process_step.signature(group_index=i, **bound_args) for i in range(group_size)]
    return group(signatures)


 def lifecycle_task(task, queue):
    """Makes a Celery task from a Python function.

    This runner can act as a single step within a parallel job.

    Args:
        task (fn): the function to run
        queue (str): The name of the Celery queue to use.

    Returns:
        A Celery task that runs the provided function
    """
    name = "{}.{}".format(task.__module__, task.__name__)

    @shared_task(ignore_result=False,
                 name=name, queue=queue, options={'queue': queue})
    def internal_runner(*args, **kwargs):
        return task(*args, **kwargs)

    return internal_runner
	# (c) Copyright 2018 Zymergen, Inc.
	# All Rights Reserved
	"""

	The following is example code used for a technology blog post: https://medium.com/@ZymergenTechBlog/building-a-parallel-task-api-with-celery-dbae5ced4e28

	The ForkJoin class can be used to generate a ZWork task that contains a single
	distributed processing step. Your job should have 3 parts. An initial setup step
	responsible for splitting of inputs into workable chunks. A process step that can
	process each chunk in a forked execution process and a join step that puts it all
	together and returns the final result.
	"""
	import copy
	import functools
	from types import GeneratorType

	import cerberus
	from celery import chain, group, shared_task

	# Parallelization settings
	PARALLEL_DEFAULT_GROUP_SIZE = 32


	class InputValidationError(TypeError):
	"""Exception representing a failure to validate job inputs against a Cerberus schema.

	Attributes:
	inputs: The inputs that failed validation
	schema: The Cerberus schema
	errors: The list of validation errors
	"""

	def __init__(self, inputs, schema, errors):
	self.inputs = inputs
	self.schema = schema
	self.errors = errors
	self.warnings = None
	err_template = "Inputs did not match schema.\n\tInputs: {}\n\tSchema: {}\n\tErrors: {}"
	err_msg = err_template.format(self.inputs, self.schema, self.errors)
	super(InputValidationError, self).__init__(err_msg)


	def chunks(l, n):
	"""A utility function for splitting a list into n roughly-equal sized chunks.
	"""
	return [l[i::n] for i in xrange(n)]


	def flatten(xs):
	"""Flatten a list.

	Turns a nested list into a flat list.
	Only goes one layer deep, not fully nested.

	Args:
	xs: a list of elements and lists
	Returns:
	A list with those same (and more) elements, but no lists
	"""
	for x in xs:
	if isinstance(x, list) or isinstance(x, GeneratorType):
	for elem in x:
	yield elem
	else:
	yield x


	def validate_inputs(ins, schema):
	"""Validates JSON inputs against a Cerberus schema.
	"""
	validator = cerberus.Validator()
	is_valid = validator.validate(ins, schema)
	if not is_valid:
	raise InputValidationError(ins, schema, validator.errors)


	# def strips_extra_args(fn):
	# """Makes a function able to accept arbitrary extra keyword arguments.
	#
	# Returns a version of fn that can safely receive extra arguments, and raises
	# an exception upon incorrect argumentation that is more useful than Python's
	# default.
	#
	# Details omitted
	#
	# Args:
	# fn: any function
	# Returns:
	# fn, but able to accept extra keyword arguments
	# Raises:
	# TypeError is raised whenever too few arguments are passed in.
	# """
	# return fn


	class ForkJoinTask(object):
	"""Represents a task that can be run in parallel.

	To implement your own parallel task:
	1. Write a new class, MyTask, that inherits from this one
	2. Override the command and job_inputs properties
	3. Write business logic functions outside of the MyTask class
	- a "setup" for dividing inputs into units of work, each unit is a dictionary
	with keys that can be used as arg names for the process step
	- a "processer" for performing a single unit of work defined by the setup step
	- a "joiner" for recombining results from the work units
	4. Decorate those functions with MyTask.setup(), MyTask.process(), and MyTask.join()
	- see their docstrings below for details, restrictions, and expectations
	5. Input args are saved as 'orig_input_args`, which is dict of the original args
	that can be accessed in individual method signatures (e.g., process, join).

	Why do I need to implement a new static singleton? Can't this be more dynamic?
	No. This limitation comes from Celery, not us.
	"""
	queue = 'parallel'

	def run_without_celery(self, job_args):
	"""
	Run this task as a job without using the celery backend. This entire task will run in a
	single process, and there is no serialization/deserialization to/from JSON between steps,
	but otherwise should mimic the celery case exactly.

	Args:
	job_args: The input dictionary to the job

	Returns:
	The job results dictionary.
	"""
	# avoid changing the global state of original job args
	nested_job_args = job_args.copy()
	nested_job_args['orig_input_args'] = job_args.copy()

	setup_results = self.setup_step((), **nested_job_args)
	process_results = [self.process_step(setup_results, i, **nested_job_args)
	for i in range(len(setup_results))]
	return self.join_step(process_results, **nested_job_args)

	@classmethod
	def get_input_schema(cls, args, *kwargs):
	"""This method provides a Cerberus schema for job input validation.
	Returns:
	A Cerberus schema that will be used to validate job inputs.
	"""
	raise NotImplementedError("Tasks MUST implement a schema")

	@classmethod
	def split(cls, work_units):
	"""Override this to explicitly control which workers do which work.

	You can almost certainly use this default.

	Args:
	work_units (list<dict>): The inputs for each possible process() step
	This is the output from the setup() step.

	Returns:
	A list of list<dict>s. Each parent list corresponds to work for one worker.
	"""
	expected_workers = PARALLEL_DEFAULT_GROUP_SIZE
	return chunks(work_units, min(expected_workers, len(work_units)))

	@classmethod
	def setup(cls, task):
	"""Decorates a function that divides job inputs into units of work.

	This decorator MUST be used EXACTLY ONCE per job like this:

	@MyTask.setup
	def divide_job_inputs(arg0_from_schema, arg1_from_schema, ...):
	return [{'arg': 'val0'}, {'arg': 'val1'}]

	Args (passed into your decorated function):
	**kwargs (dict): Corresponds to parsed and validated job inputs.
	These should match keys from your jobs' get_input_schema().

	Return (expected from your decorated function):
	A list of dicts. Each dict represents a subset of inputs needed to do one
	unit of parallel work. The keys in this dict should match the params
	from your process() step function. Remaining inputs to procces() are
	provided by parsed/validated job inputs.
	"""
	@functools.wraps(task)
	def setter_upper(args, *kwargs):
	# We use cerberus for schema validation
	schema = strips_extra_args(cls.get_input_schema)(**kwargs)
	validate_inputs(kwargs, schema)
	normalized_inputs = cerberus.Validator().normalized(kwargs, schema)

	result = strips_extra_args(task)(**normalized_inputs)

	split_results = cls.split(result)

	return split_results

	cls.setup_step = lifecycle_task(setter_upper, queue=cls.queue)
	return cls.setup_step

	@classmethod
	def process(cls, task):
	"""Decorates a function that performs one unit of parallel work.

	This decorator MUST be used EXACTLY ONCE per job like this:

	@MyTask.process
	def do_parallel_work(param0, param1, ...):
	return "whatever I want"

	Args (passed into your decorated function):
	**kwargs (dict): All the parsed/validated job inputs, PLUS all the
	arguments from one of setup()'s outputs. Your function will be
	passed only the arguments explicitly claimed in its signature.
	In the event of key collision, arguments from one of setup()'s
	outputs take precedent over parsed/validated job inputs.

	Return (expected from your decorated function):
	Your function can return any serializable object.
	"""
	@functools.wraps(task)
	def process_inputs(divided_inputs, group_index, **kwargs):
	try:
	my_share_of_work = divided_inputs[group_index]
	except IndexError:
	return []

	outs = []

	schema = strips_extra_args(cls.get_input_schema)(**kwargs)
	normalized_kwargs = cerberus.Validator().normalized(kwargs, schema)
	for work_unit in my_share_of_work:
	work_unit_kwargs = copy.deepcopy(normalized_kwargs)
	work_unit_kwargs.update(work_unit)
	result = strips_extra_args(task)(**work_unit_kwargs)
	outs.append(result)

	return outs
	cls.process_step = lifecycle_task(process_inputs, queue=cls.queue)
	return cls.process_step

	@classmethod
	def join(cls, task):
	"""Decorates a function that recombines parallel results.

	This decorator MUST be used EXACTLY ONCE per job like this:

	@MyTask.join
	def recombine_parallel_results(parallel_results, **kwargs):
	return "whatever I want"

	Args (passed into your decorated function):
	parallel_results (list): A list of all values that were returned from
	an invocation of the process() step.
	**kwargs (dict): All the parsed/validated job inputs.

	Return (expected from your decorated function):
	Any serializable object. This object is the job's final result.
	"""
	@functools.wraps(task)
	def joiner(distributed_results, args, *kwargs):
	schema = strips_extra_args(cls.get_input_schema)(**kwargs)
	normalized_kwargs = cerberus.Validator().normalized(kwargs, schema)
	# The process step nested its results. Unnest them.
	unnested_results = list(flatten(distributed_results))
	results = strips_extra_args(task)(unnested_results, args, *normalized_kwargs)
	return results

	# place the final step on a different queue so it's not blocked by other running
	# parallel jobs
	cls.join_step = lifecycle_task(joiner, queue='celery')
	return cls.join_step

	@classmethod
	def signature(cls, options):
	"""Returns a Celery signature to describe the complete job-step sequence.

	Args:
	options (dict): The shared options for the job (auth, request, etc.)
	Returns:
	A Celery signature.
	"""
	return fork_join_task(cls.setup_step, cls.process_step, cls.join_step, options)


	def fork_join_task(setup_step, process_step, join_step, bound_args):
	"""Creates a parallel Celery fork/join task from provided functions.

	Args:
	setup_step (celery task): A "setup" step for the whole job
	process_step (celery task): A "process" step that runs in parallel after setup
	join_step (celery task): A "join" step to recombine parallel results
	bound_args (dict): Any bound arguments that can be accessed by all steps
	Returns:
	A new Celery job that performs a setup/process/join work pattern.
	The returned job's steps will all be partially applied over bound_args.
	"""
	setup_sig = setup_step.signature(**bound_args)
	process_sig = parallel_processing_step(process_step, bound_args)
	join_sig = join_step.signature(**bound_args)
	return chain(setup_sig, process_sig, join_sig)


	def parallel_processing_step(
	process_step, bound_args, group_size=PARALLEL_DEFAULT_GROUP_SIZE):
	"""Returns a "group" signature for a distributed application of process_fn.
	"""
	signatures = [process_step.signature(group_index=i, **bound_args) for i in range(group_size)]
	return group(signatures)


	def lifecycle_task(task, queue):
	"""Makes a Celery task from a Python function.

	This runner can act as a single step within a parallel job.

	Args:
	task (fn): the function to run
	queue (str): The name of the Celery queue to use.

	Returns:
	A Celery task that runs the provided function
	"""
	name = "{}.{}".format(task.__module__, task.__name__)

	@shared_task(ignore_result=False,
	name=name, queue=queue, options={'queue': queue})
	def internal_runner(args, *kwargs):
	return task(args, *kwargs)

	return internal_runner