def count_items(input, output):
"""Sum all the items in the input data set"""
# Intermediate file name
inter = output + '_inter'
# Run the task with specified mapper and reducer methods
prince.run(count_mapper, count_reducer, input, inter, inputformat='text', outputformat='text', files=__file__)
prince.run(sum_mapper, count_reducer, inter + '/part*', output, inputformat='text', outputformat='text', files=__file__)
# Read the output file and print it
file = prince.dfs.read(output + '/part*', first=1)
return int(file.split()[1])
def count_items(input, output):
"""Sum all the items in the input data set"""
# Intermediate file name
inter = output + '_inter'
# Run the task with specified mapper and reducer methods
prince.run(count_mapper,
count_reducer,
input,
inter,
inputformat='text',
outputformat='text',
files=__file__)
prince.run(sum_mapper,
count_reducer,
inter + '/part*',
output,
inputformat='text',
outputformat='text',
files=__file__)
# Read the output file and print it
file = prince.dfs.read(output + '/part*', first=1)
return int(file.split()[1])
part = '/part-00000'
options = {'damping': damping, 'precision': precision, 'nb_nodes': len(graph)}
# Create the initial values
pagerank_current = pagerank % iteration_start
if iteration_start == 1:
pagerank_values = [(n, make_value(pr_init, pr_init, n_adjacent)) for n, n_adjacent in graph.items()]
prince.dfs.write(pagerank_current + part, pagerank_values)
iteration_start += 1
stop = False
iteration = iteration_start
while not stop and iteration < iteration_max:
# Update file names
pagerank_previous = pagerank_current
pagerank_current = pagerank % iteration
term_current = term % iteration
# Compute the new PageRank values
prince.run(pagerank_mapper, pagerank_reducer, pagerank_previous + suffix, pagerank_current,
[], options, 'text', 'text')
# Termination: check if all PageRank values are stable
prince.run(term_mapper, term_reducer, pagerank_current + suffix, term_current,
[], options, 'text', 'text')
term_value = prince.dfs.read(term_current + suffix)
stop = int(term_value.split()[1])
# Get ready for the next iteration
iteration += 1
import prince
# Methods from wordcount are now in the local name space
from wordcount import *
def display_usage():
print 'usage: %s input output' % sys.argv[0]
print ' input: input file on the DFS'
print ' output: output file on the DFS'
if __name__ == "__main__":
# Always call prince.init() at the beginning of the program
prince.init()
if len(sys.argv) != 3:
display_usage()
sys.exit(0)
input = sys.argv[1]
output = sys.argv[2]
# Run the task with the mapper and reducer methods from the wordcount.py file
# Note that the file wordcount.py is added to the 'files' argument
prince.run(wc_mapper, wc_reducer, input, output, inputformat='text', outputformat='text', files='wordcount.py')
# Read the output file and print it
file = prince.dfs.read(output + '/part*')
print file
if __name__ == "__main__":
# Always call prince.init() at the beginning of the program
prince.init()
if len(sys.argv) != 3:
display_usage()
sys.exit(0)
input = sys.argv[1]
output = sys.argv[2] + '%04d'
sorted = sys.argv[2] + '_sorted'
suffix = '/part*'
# Create the initial buckets from the data
prince.run(init_mapper, init_reducer, input, output % 0, inputformat='text', outputformat='text')
stop = False
iteration = 1
while not stop:
# Merge current buckets
previous = output % (iteration - 1)
current = output % iteration
prince.run(merge_mapper, merge_reducer, previous + suffix, current, inputformat='text', outputformat='text')
# Check if sort is done
state = prince.dfs.read(current + suffix, last=1)
if int(state.split()[0]) == 0:
stop = True
iteration += 1
def display_usage():
print 'usage: %s input output' % sys.argv[0]
print ' input: input file on the DFS'
print ' output: output file on the DFS'
if __name__ == "__main__":
# Always call prince.init() at the beginning of the program
prince.init()
if len(sys.argv) != 3:
display_usage()
sys.exit(0)
input = sys.argv[1]
output = sys.argv[2]
# Run the task with the mapper and reducer methods from the wordcount.py file
# Note that the file wordcount.py is added to the 'files' argument
prince.run(wc_mapper,
wc_reducer,
input,
output,
inputformat='text',
outputformat='text',
files='wordcount.py')
# Read the output file and print it
file = prince.dfs.read(output + '/part*')
print file
# Create the initial values
pagerank_current = pagerank % iteration_start
if iteration_start == 1:
pagerank_values = [(n, make_value(pr_init, pr_init, n_adjacent))
for n, n_adjacent in graph.items()]
prince.dfs.write(pagerank_current + part, pagerank_values)
iteration_start += 1
stop = False
iteration = iteration_start
while not stop and iteration < iteration_max:
# Update file names
pagerank_previous = pagerank_current
pagerank_current = pagerank % iteration
term_current = term % iteration
# Compute the new PageRank values
prince.run(pagerank_mapper, pagerank_reducer,
pagerank_previous + suffix, pagerank_current, [], options,
'text', 'text')
# Termination: check if all PageRank values are stable
prince.run(term_mapper, term_reducer, pagerank_current + suffix,
term_current, [], options, 'text', 'text')
term_value = prince.dfs.read(term_current + suffix)
stop = int(term_value.split()[1])
# Get ready for the next iteration
iteration += 1
def wc_reducer(key, values):
"""Reducer method with 'key' a string and 'values' a generator of strings"""
try: yield key, sum([int(v) for v in values])
except ValueError: pass # discard non-numerical values
def display_usage():
print 'usage: ./%s input output' % sys.argv[0]
print ' input: input file on the DFS'
print ' output: output file on the DFS'
if __name__ == "__main__":
# Always call prince.init() at the beginning of the program
prince.init()
if len(sys.argv) != 3:
display_usage()
sys.exit(0)
input = sys.argv[1]
output = sys.argv[2]
# Run the task with specified mapper and reducer methods
prince.run(wc_mapper, wc_reducer, input, output, inputformat='text', outputformat='text')
# Read the output file and print it
file = prince.dfs.read(output + '/part*')
print file
options = {'graph': filename_graph, 'source': source_node}
# Create the initial frontier with the tuple (source, 0)
frontier_current = frontier % iteration_start
if iteration_start == 1:
prince.dfs.write(frontier_current + part, (source_node, '%d %d' % (sys.maxint, 0)))
iteration_start += 1
stop = False
iteration = iteration_start
while not stop and iteration < iteration_max:
# Update file names
frontier_previous = frontier_current
frontier_current = frontier % iteration
term_current = term % iteration
# Compute the new frontier
prince.run(frontier_mapper, frontier_reducer, frontier_previous + suffix, frontier_current,
filename_graph, options, 'text', 'text')
print prince.dfs.read(frontier_current + suffix)
# Termination: check if all distances are stable
prince.run(term_mapper, term_reducer, frontier_current + suffix, term_current,
filename_graph, options, 'text', 'text')
print prince.dfs.read(term_current + suffix)
term_value = prince.dfs.read(term_current + suffix)
stop = int(term_value.split()[1])
# Get ready for the next iteration
iteration += 1
# Create the initial frontier with the tuple (source, 0)
frontier_current = frontier % iteration_start
if iteration_start == 1:
prince.dfs.write(frontier_current + part,
(source_node, '%d %d' % (sys.maxint, 0)))
iteration_start += 1
stop = False
iteration = iteration_start
while not stop and iteration < iteration_max:
# Update file names
frontier_previous = frontier_current
frontier_current = frontier % iteration
term_current = term % iteration
# Compute the new frontier
prince.run(frontier_mapper, frontier_reducer,
frontier_previous + suffix, frontier_current,
filename_graph, options, 'text', 'text')
print prince.dfs.read(frontier_current + suffix)
# Termination: check if all distances are stable
prince.run(term_mapper, term_reducer, frontier_current + suffix,
term_current, filename_graph, options, 'text', 'text')
print prince.dfs.read(term_current + suffix)
term_value = prince.dfs.read(term_current + suffix)
stop = int(term_value.split()[1])
# Get ready for the next iteration
iteration += 1
