def spark_mapper(current_range):
"""
Gets the paths to the file(s) in the current executor, then
declares the headers found.
Args:
current_range (tuple): A pair that contains the starting and
ending values of the current range.
Returns:
function: The map function to be executed on each executor,
complete with all headers needed for the analysis.
"""
# Get and declare headers on each worker
headers_on_executor = [
pyspark.SparkFiles.get(ntpath.basename(filepath))
for filepath in headers
]
Utils.declare_headers(headers_on_executor)
# Get and declare shared libraries on each worker
shared_libs_on_ex = [
pyspark.SparkFiles.get(ntpath.basename(filepath))
for filepath in shared_libraries
]
Utils.declare_shared_libraries(shared_libs_on_ex)
return mapper(current_range)
def distribute_shared_libraries(self, shared_libraries_paths):
"""
Includes the C++ shared libraries to be declared before execution. If
any pcm file is present in the same folder as the shared libraries, the
function will try to retrieve them and distribute them.
Args:
shared_libraries_paths (str, iter): A string or an iterable (such as
a list, set...) containing the paths to all necessary C++ shared
libraries as strings. This function accepts both paths to the
libraries themselves and paths to directories containing the
libraries.
"""
libraries_to_distribute = set()
pcm_to_distribute = set()
if isinstance(shared_libraries_paths, str):
pcm_to_distribute, libraries_to_distribute = (
Utils.check_pcm_in_library_path(shared_libraries_paths))
else:
for path_string in shared_libraries_paths:
pcm, libraries = Utils.check_pcm_in_library_path(path_string)
libraries_to_distribute.update(libraries)
pcm_to_distribute.update(pcm)
# Distribute shared libraries and pcm files to the workers
self.distribute_unique_paths(libraries_to_distribute)
self.distribute_unique_paths(pcm_to_distribute)
# Include shared libraries locally
Utils.declare_shared_libraries(libraries_to_distribute)
# Finally, add everything to the includes set
self.shared_libraries.update(libraries_to_distribute)
def dask_mapper(current_range):
"""
Gets the paths to the file(s) in the current executor, then
declares the headers found.
Args:
current_range (tuple): The current range of the dataset being
processed on the executor.
Returns:
function: The map function to be executed on each executor,
complete with all headers needed for the analysis.
"""
# Retrieve the current worker local directory
localdir = get_worker().local_directory
# Get and declare headers on each worker
headers_on_executor = [
os.path.join(localdir, os.path.basename(filepath))
for filepath in headers
]
Utils.declare_headers(headers_on_executor)
# Get and declare shared libraries on each worker
shared_libs_on_ex = [
os.path.join(localdir, os.path.basename(filepath))
for filepath in shared_libraries
]
Utils.declare_shared_libraries(shared_libs_on_ex)
return mapper(current_range)
