def map_reduce(self,
map_function,
map_iterdata,
reduce_function,
chunksize=None,
worker_processes=None,
extra_args=None,
extra_env=None,
map_runtime_memory=None,
obj_chunk_size=None,
obj_chunk_number=None,
reduce_runtime_memory=None,
chunk_size=None,
chunk_n=None,
timeout=None,
invoke_pool_threads=None,
reducer_one_per_object=False,
reducer_wait_local=False,
include_modules=[],
exclude_modules=[]):
"""
Map the map_function over the data and apply the reduce_function across all futures.
This method is executed all within CF.
:param map_function: the function to map over the data
:param map_iterdata: An iterable of input data
:param chunksize: Split map_iteradata in chunks of this size.
Lithops spawns 1 worker per resulting chunk. Default 1
:param worker_processes: Number of concurrent/parallel processes in each worker Default 1
:param reduce_function: the function to reduce over the futures
:param extra_env: Additional environment variables for action environment. Default None.
:param extra_args: Additional arguments to pass to function activation. Default None.
:param map_runtime_memory: Memory to use to run the map function. Default None (loaded from config).
:param reduce_runtime_memory: Memory to use to run the reduce function. Default None (loaded from config).
:param obj_chunk_size: the size of the data chunks to split each object. 'None' for processing
the whole file in one function activation.
:param obj_chunk_number: Number of chunks to split each object. 'None' for processing the whole
file in one function activation.
:param remote_invocation: Enable or disable remote_invocation mechanism. Default 'False'
:param timeout: Time that the functions have to complete their execution before raising a timeout.
:param reducer_one_per_object: Set one reducer per object after running the partitioner
:param reducer_wait_local: Wait for results locally
:param invoke_pool_threads: Number of threads to use to invoke.
:param include_modules: Explicitly pickle these dependencies.
:param exclude_modules: Explicitly keep these modules from pickled dependencies.
:return: A list with size `len(map_iterdata)` of futures.
"""
self.last_call = 'map_reduce'
map_job_id = self._create_job_id('M')
runtime_meta = self.invoker.select_runtime(map_job_id,
map_runtime_memory)
map_job = create_map_job(self.config,
self.internal_storage,
self.executor_id,
map_job_id,
map_function=map_function,
iterdata=map_iterdata,
chunksize=chunksize,
worker_processes=worker_processes,
runtime_meta=runtime_meta,
runtime_memory=map_runtime_memory,
extra_args=extra_args,
extra_env=extra_env,
chunk_size=chunk_size,
chunk_n=chunk_n,
obj_chunk_size=obj_chunk_size,
obj_chunk_number=obj_chunk_number,
include_modules=include_modules,
exclude_modules=exclude_modules,
execution_timeout=timeout,
invoke_pool_threads=invoke_pool_threads)
map_futures = self.invoker.run_job(map_job)
self.futures.extend(map_futures)
if reducer_wait_local:
wait(fs=map_futures,
internal_storage=self.internal_storage,
job_monitor=self.job_monitor)
reduce_job_id = map_job_id.replace('M', 'R')
runtime_meta = self.invoker.select_runtime(reduce_job_id,
reduce_runtime_memory)
reduce_job = create_reduce_job(
self.config,
self.internal_storage,
self.executor_id,
reduce_job_id,
reduce_function,
map_job,
map_futures,
runtime_meta=runtime_meta,
runtime_memory=reduce_runtime_memory,
reducer_one_per_object=reducer_one_per_object,
extra_env=extra_env,
include_modules=include_modules,
exclude_modules=exclude_modules)
reduce_futures = self.invoker.run_job(reduce_job)
self.futures.extend(reduce_futures)
for f in map_futures:
f._produce_output = False
return map_futures + reduce_futures
def map_reduce(self,
map_function: Callable,
map_iterdata: List[Union[List[Any], Tuple[Any, ...],
Dict[str, Any]]],
reduce_function: Callable,
chunksize: Optional[int] = None,
extra_args: Optional[Union[List[Any], Tuple[Any, ...],
Dict[str, Any]]] = None,
extra_env: Optional[Dict[str, str]] = None,
map_runtime_memory: Optional[int] = None,
reduce_runtime_memory: Optional[int] = None,
obj_chunk_size: Optional[int] = None,
obj_chunk_number: Optional[int] = None,
timeout: Optional[int] = None,
reducer_one_per_object: Optional[bool] = False,
spawn_reducer: Optional[int] = 20,
include_modules: Optional[List[str]] = [],
exclude_modules: Optional[List[str]] = []) -> FuturesList:
"""
Map the map_function over the data and apply the reduce_function across all futures.
:param map_function: The function to map over the data
:param map_iterdata: An iterable of input data
:param reduce_function: The function to reduce over the futures
:param chunksize: Split map_iteradata in chunks of this size. Lithops spawns 1 worker per resulting chunk. Default 1
:param extra_args: Additional arguments to pass to function activation. Default None
:param extra_env: Additional environment variables for action environment. Default None
:param map_runtime_memory: Memory to use to run the map function. Default None (loaded from config)
:param reduce_runtime_memory: Memory to use to run the reduce function. Default None (loaded from config)
:param obj_chunk_size: the size of the data chunks to split each object. 'None' for processing the whole file in one function activation
:param obj_chunk_number: Number of chunks to split each object. 'None' for processing the whole file in one function activation
:param timeout: Time that the functions have to complete their execution before raising a timeout
:param reducer_one_per_object: Set one reducer per object after running the partitioner
:param spawn_reducer: Percentage of done map functions before spawning the reduce function
:param include_modules: Explicitly pickle these dependencies.
:param exclude_modules: Explicitly keep these modules from pickled dependencies.
:return: A list with size `len(map_iterdata)` of futures.
"""
self.last_call = 'map_reduce'
map_job_id = self._create_job_id('M')
runtime_meta = self.invoker.select_runtime(map_job_id,
map_runtime_memory)
map_job = create_map_job(config=self.config,
internal_storage=self.internal_storage,
executor_id=self.executor_id,
job_id=map_job_id,
map_function=map_function,
iterdata=map_iterdata,
chunksize=chunksize,
runtime_meta=runtime_meta,
runtime_memory=map_runtime_memory,
extra_args=extra_args,
extra_env=extra_env,
obj_chunk_size=obj_chunk_size,
obj_chunk_number=obj_chunk_number,
include_modules=include_modules,
exclude_modules=exclude_modules,
execution_timeout=timeout)
map_futures = self.invoker.run_job(map_job)
self.futures.extend(map_futures)
if isinstance(map_iterdata, FuturesList):
for fut in map_iterdata:
fut._produce_output = False
if spawn_reducer != ALWAYS:
self.wait(map_futures, return_when=spawn_reducer)
logger.debug(
f'ExecutorID {self.executor_id} | JobID {map_job_id} - '
f'{spawn_reducer}% of map activations done. Spawning reduce stage'
)
reduce_job_id = map_job_id.replace('M', 'R')
runtime_meta = self.invoker.select_runtime(reduce_job_id,
reduce_runtime_memory)
reduce_job = create_reduce_job(
config=self.config,
internal_storage=self.internal_storage,
executor_id=self.executor_id,
reduce_job_id=reduce_job_id,
reduce_function=reduce_function,
map_job=map_job,
map_futures=map_futures,
runtime_meta=runtime_meta,
runtime_memory=reduce_runtime_memory,
reducer_one_per_object=reducer_one_per_object,
extra_env=extra_env,
include_modules=include_modules,
exclude_modules=exclude_modules)
reduce_futures = self.invoker.run_job(reduce_job)
self.futures.extend(reduce_futures)
for f in map_futures:
f._produce_output = False
return create_futures_list(map_futures + reduce_futures, self)