def map_function(self, job_name, chunk_size, input_file, no_of_reducers, start_index):
map_output = ""
if job_name == Jobs.WORD_COUNT_JOB:
values = splitter.read_chunk_by_word(input_file, start_index, chunk_size)
engine = mapreduce.Engine(values, WordCountMap, WordCountReduce, mapreduce.Partition, no_of_reducers)
Worker.engine = engine
map_output = engine.map_phase()
elif job_name == Jobs.SORTING_JOB:
values = splitter.read_chunk_by_word(input_file, start_index, chunk_size)
engine = mapreduce.Engine(values, SortingMap, SortingReduce, mapreduce.Partition, no_of_reducers)
Worker.engine = engine
map_output = engine.map_phase()
elif job_name == Jobs.HAMMING_ENCODE_JOB:
values = splitter.read_chunk(input_file, start_index, chunk_size)
engine = hamming_mapreduce.Engine(values, HammingEncodingMap, HammingEncodingReduce, mapreduce.Partition,
no_of_reducers, start_index)
Worker.engine = engine
map_output = engine.hamming_encode_map_phase()
elif job_name == Jobs.HAMMING_DECODE_JOB:
values = splitter.read_binary_chunk(input_file, start_index, chunk_size)
engine = hamming_mapreduce.Engine(values, HammingDecodingMap, HammingDecodingReduce, mapreduce.Partition,
no_of_reducers, start_index)
Worker.engine = engine
map_output = engine.hamming_decode_map_phase()
elif job_name == Jobs.HAMMING_ERROR_JOB:
values = splitter.read_binary_chunk(input_file, start_index, chunk_size)
engine = hamming_mapreduce.Engine(values, HammingErrorMap, HammingErrorReduce, mapreduce.Partition,
no_of_reducers, start_index)
Worker.engine = engine
map_output = engine.hamming_error_map_phase()
elif job_name == Jobs.HAMMING_CHECK_JOB:
values = splitter.read_binary_chunk(input_file, start_index, chunk_size)
engine = hamming_mapreduce.Engine(values, HammingCheckMap, HammingCheckReduce, mapreduce.Partition,
no_of_reducers, start_index)
Worker.engine = engine
map_output = engine.hamming_check_map_phase()
elif job_name == Jobs.HAMMING_FIX_JOB:
values = splitter.read_binary_chunk(input_file, start_index, chunk_size)
engine = hamming_mapreduce.Engine(values, HammingFixMap, HammingFixReduce, mapreduce.Partition,
no_of_reducers,
start_index)
Worker.engine = engine
map_output = engine.hamming_fix_map_phase()
else:
print "Invalid Job Name ............."
Worker.engine = engine
# elif job_name == Jobs.HAMMING_ENCODE_JOB:
return map_output
splits = splitter.split_file(input_file, chunk_size)
engine = None
def silentremove(filename):
try:
os.remove(filename)
except OSError as e: # this would be "except OSError, e:" before Python 2.6
# if e.errno != errno.ENOENT: # errno.ENOENT = no such file or directory
pass # re-raise exception if a different error occured
# Map Phase
for start_index in splits:
if job_name == jobs.Jobs.SORTING_JOB or job_name == jobs.Jobs.WORD_COUNT_JOB:
values = splitter.read_chunk_by_word(input_file, start_index,
chunk_size)
else:
values = splitter.read_chunk(input_file, start_index, chunk_size)
if (job_name == jobs.Jobs.WORD_COUNT_JOB):
engine = mapreduce.Engine(values, jobs.WordCountMap,
jobs.WordCountReduce, mapreduce.Partition,
no_of_reducers)
elif (job_name == jobs.Jobs.SORTING_JOB):
engine = mapreduce.Engine(values, jobs.SortingMap, jobs.SortingReduce,
mapreduce.Partition, no_of_reducers)
map_output = engine.map_phase()
completed_jobs[start_index] = map_output
print "------------- Completed Map Job_ID: {0}--------------".format(
start_index)
splits = splitter.split_file(input_file, chunk_size)
engine = None
def silentremove(filename):
try:
os.remove(filename)
except OSError as e: # this would be "except OSError, e:" before Python 2.6
# if e.errno != errno.ENOENT: # errno.ENOENT = no such file or directory
pass # re-raise exception if a different error occured
# Map Phase
for start_index in splits:
if job_name == jobs.Jobs.SORTING_JOB or job_name == jobs.Jobs.WORD_COUNT_JOB:
values = splitter.read_chunk_by_word(input_file, start_index, chunk_size)
else:
values = splitter.read_chunk(input_file, start_index, chunk_size)
if job_name == jobs.Jobs.WORD_COUNT_JOB:
engine = mapreduce.Engine(values, jobs.WordCountMap, jobs.WordCountReduce, mapreduce.Partition, no_of_reducers)
elif job_name == jobs.Jobs.SORTING_JOB:
engine = mapreduce.Engine(values, jobs.SortingMap, jobs.SortingReduce, mapreduce.Partition, no_of_reducers)
map_output = engine.map_phase()
completed_jobs[start_index] = map_output
print "------------- Completed Map Job_ID: {0}--------------".format(start_index)
silentremove(output_file)
# Shuffle and Reduce Phase
for reducer_id in reducer_list: