def test_max_output_files(self):
job = MRWordFreqCount(['-r', 'spark', '--max-output-files', '1'])
job.sandbox(stdin=BytesIO(b'one two one\n two three\n'))
with job.make_runner() as runner:
runner.run()
self.assertEqual(self._num_output_files(runner), 1)
def test_max_output_files(self):
job = MRWordFreqCount(['-r', 'spark', '--max-output-files', '1'])
job.sandbox(stdin=BytesIO(b'one two one\n two three\n'))
with job.make_runner() as runner:
runner.run()
self.assertEqual(self._num_output_files(runner), 1)
def test_num_reducers(self):
jobconf_args = ['--jobconf', 'mapreduce.job.reduces=1']
job = MRWordFreqCount(['-r', 'spark'] + jobconf_args)
job.sandbox(stdin=BytesIO(b'one two one\n two three\n'))
with job.make_runner() as runner:
runner.run()
self.assertEqual(self._num_output_files(runner), 1)
def test_basic_job(self):
job = MRWordFreqCount(['-r', 'spark'])
job.sandbox(stdin=BytesIO(
b'one fish\ntwo fish\nred fish\nblue fish\n'))
with job.make_runner() as runner:
runner.run()
output = dict(job.parse_output(runner.cat_output()))
self.assertEqual(output, dict(blue=1, fish=4, one=1, red=1, two=1))
def test_num_reducers(self):
jobconf_args = ['--jobconf', 'mapreduce.job.reduces=1']
job = MRWordFreqCount(['-r', 'spark'] + jobconf_args)
job.sandbox(stdin=BytesIO(b'one two one\n two three\n'))
with job.make_runner() as runner:
runner.run()
num_output_files = sum(1 for f in listdir(runner.get_output_dir())
if f.startswith('part'))
self.assertEqual(num_output_files, 1)
def test_num_reducers(self):
jobconf_args = [
'--jobconf', 'mapreduce.job.reduces=1'
]
job = MRWordFreqCount(['-r', 'spark'] + jobconf_args)
job.sandbox(stdin=BytesIO(b'one two one\n two three\n'))
with job.make_runner() as runner:
runner.run()
self.assertEqual(self._num_output_files(runner), 1)
def test_max_output_files_is_cmd_line_only(self):
self.start(mrjob_conf_patcher(
dict(runners=dict(spark=dict(max_output_files=1)))))
log = self.start(patch('mrjob.runner.log'))
job = MRWordFreqCount(['-r', 'spark'])
job.sandbox(stdin=BytesIO(b'one two one\n two three\n'))
with job.make_runner() as runner:
runner.run()
# by default there should be at least 2 output files
self.assertNotEqual(self._num_output_files(runner), 1)
self.assertTrue(log.warning.called)
def test_max_output_files_is_cmd_line_only(self):
self.start(mrjob_conf_patcher(
dict(runners=dict(spark=dict(max_output_files=1)))))
log = self.start(patch('mrjob.runner.log'))
job = MRWordFreqCount(['-r', 'spark'])
job.sandbox(stdin=BytesIO(b'one two one\n two three\n'))
with job.make_runner() as runner:
runner.run()
# by default there should be at least 2 output files
self.assertNotEqual(self._num_output_files(runner), 1)
self.assertTrue(log.warning.called)
class MRWordFreqCount(MRJob):
OUTPUT_PROTOCOL = protocol.JSONProtocol
def mapper(self, _, line):
for word in WORD_RE.findall(line):
yield (word.lower(), 1)
def reducer(self, word, counts):
yield (word, sum(counts))
if __name__ == '__main__':
MRWordFreqCount().run()
def test_compression(self):
# deliberately mix Hadoop 1 and 2 config properties
jobconf_args = [
'--jobconf',
'mapred.output.compression.codec='\
'org.apache.hadoop.io.compress.GzipCodec',
'--jobconf',
'mapreduce.output.fileoutputformat.compress=true',
]
job = MRWordFreqCount(['-r', 'spark'] + jobconf_args)
job.sandbox(stdin=BytesIO(b'fa la la la la\nla la la la\n'))
with job.make_runner() as runner:
runner.run()
self.assertTrue(runner.fs.exists(
join(runner.get_output_dir(), 'part*.gz')))
self.assertEqual(dict(job.parse_output(runner.cat_output())),
dict(fa=1, la=8))
def test_basic_job(self):
job = MRWordFreqCount(['-r', 'spark'])
job.sandbox(
stdin=BytesIO(b'one fish\ntwo fish\nred fish\nblue fish\n'))
with job.make_runner() as runner:
runner.run()
output = dict(job.parse_output(runner.cat_output()))
self.assertEqual(output, dict(blue=1, fish=4, one=1, red=1, two=1))
def test_file_uris_as_input(self):
input1 = self.makefile('input1.txt', b'cat rat bat')
input2 = 'file://' + self.makefile('input2.txt', b'dog dog dog')
job = MRWordFreqCount([input1, input2])
job.sandbox()
with job.make_runner() as runner:
runner.run()
self.assertEqual(dict(job.parse_output(runner.cat_output())),
dict(bat=1, cat=1, dog=3, rat=1))
def test_the_wheels_on_the_bus(self):
RAW_INPUT = b"""
The wheels on the bus go round and round,
round and round, round and round
The wheels on the bus go round and round,
all through the town.
"""
EXPECTED_OUTPUT = {
u'all': 1,
u'and': 4,
u'bus': 2,
u'go': 2,
u'on': 2,
u'round': 8,
u'the': 5,
u'through': 1,
u'town': 1,
u'wheels': 2,
}
self.assertEqual(run_job(MRWordFreqCount(), RAW_INPUT),
EXPECTED_OUTPUT)
def test_compression(self):
# deliberately mix Hadoop 1 and 2 config properties
jobconf_args = [
'--jobconf',
('mapred.output.compression.codec='
'org.apache.hadoop.io.compress.GzipCodec'),
'--jobconf',
'mapreduce.output.fileoutputformat.compress=true',
]
job = MRWordFreqCount(['-r', 'spark'] + jobconf_args)
job.sandbox(stdin=BytesIO(b'fa la la la la\nla la la la\n'))
with job.make_runner() as runner:
runner.run()
self.assertTrue(
runner.fs.exists(join(runner.get_output_dir(), 'part*.gz')))
self.assertEqual(dict(job.parse_output(runner.cat_output())),
dict(fa=1, la=8))
def test_empty(self):
self.assertEqual(run_job(MRWordFreqCount()), {})
from mrjob.job import MRJob
from mrjob.step import MRStep
import re
from mrjob.examples.mr_word_freq_count import MRWordFreqCount
import sys
from operator import itemgetter, attrgetter, methodcaller
WORD_RE = re.compile(r"[\w']+")
list = []
if __name__ == '__main__':
job = MRWordFreqCount(args=sys.argv[1:])
with job.make_runner() as runner:
runner.run()
for line in runner.stream_output():
key, value = job.parse_output_line(line)
object = (key, value)
list.append(object)
sorted_list = sorted(list, key=itemgetter(1), reverse=True)
print sorted_list[0:10]
