def test_map_combiner_reduce(self):
factory = TFactory(combiner=TReducer)
sas = SortAndShuffle()
run_task(factory, istream=self.stream, ostream=sas)
with self._mkf('foo_map_combiner_reduce.out') as o:
run_task(factory, istream=sas, ostream=o,
private_encoding=False)
self.check_result('foo_map_combiner_reduce.out', STREAM_1)
def test_timer(self):
factory = TFactory(mapper=SleepingMapper)
exp_count = {
'registerCounter': 1,
'incrementCounter': Counter(
[_[0] for _ in STREAM_1]
)[TextWriter.MAP_ITEM]
}
with self._mkf('foo_map_only.out') as o:
run_task(factory, istream=self.stream1, ostream=o)
self.check_counts(o.name, exp_count)
def test_map_only(self):
factory = TFactory()
fname = self._mkfn('foo_map_only.out')
with open(fname, 'w') as o:
run_task(factory, istream=self.stream1, ostream=o)
exp_count = {
'done': 1,
'progress': 1,
'output': sum(len(_[2].split())
for _ in STREAM_1 if _[0] is TextWriter.MAP_ITEM)
}
self.check_counts(fname, exp_count)
def test_timer(self):
factory = TFactory(mapper=SleepingMapper)
with self._mkf('foo_map_only.out') as o:
run_task(factory, istream=self.stream1, ostream=o)
count = Counter()
with open(o.name) as f:
for line in f:
count[line.strip().split('\t', 1)[0]] += 1
exp_count = {
'registerCounter': 2,
'incrementCounter': 2 * Counter([_[0] for _ in STREAM_1])['mapItem']
}
for k, v in exp_count.iteritems():
self.assertTrue(k in count)
self.assertEqual(count[k], v)
def __run_test(self, mode, mapper_class, context_class):
cmd_file = self.__write_cmd_file(mode)
pp.run_task(
pp.Factory(mapper_class=mapper_class), private_encoding=False,
context_class=context_class, cmd_file=cmd_file)
out_fn = cmd_file + '.out'
out_records = []
with open(out_fn, 'rb') as f:
bf = BinaryDownStreamAdapter(f)
for cmd, args in bf:
if cmd == bf.OUTPUT:
name, color = args
out_records.append({'name': name, 'favorite_color': color})
self.assertEqual(len(out_records), len(self.records))
for out_r, r in zip(out_records, self.records):
for k, v in iteritems(out_r):
self.assertEqual(v.decode('UTF-8'), r[k])
def __run_test(self, mode, mapper_class, context_class):
cmd_file = self.__write_cmd_file(mode)
pp.run_task(
pp.Factory(mapper_class=mapper_class), private_encoding=False,
context_class=context_class, cmd_file=cmd_file
)
out_fn = cmd_file + '.out'
out_records = []
with open(out_fn) as ostream:
for cmd, args in BinaryDownStreamFilter(ostream):
if cmd == 'output':
name, color = args
out_records.append({'name': name, 'favorite_color': color})
self.assertEqual(len(out_records), len(self.records))
for out_r, r in zip(out_records, self.records):
for k, v in out_r.iteritems():
self.assertEqual(v, r[k])
def _test_map_reduce_with_private_encoding_helper(self, factory,
fast_combiner=False):
self.stream3.close()
cmd_file = self.stream3.name
out_file = cmd_file + '.out'
reduce_infile = cmd_file + '.reduce'
reduce_outfile = reduce_infile + '.out'
run_task(factory, cmd_file=cmd_file, private_encoding=True,
fast_combiner=fast_combiner)
data = {}
bw = BinaryWriter
with open(out_file, 'rb') as f:
bf = BinaryDownStreamAdapter(f)
for cmd, args in bf:
if cmd == bw.OUTPUT:
data.setdefault(args[0], []).append(args[1])
stream = []
stream.append((bw.START_MESSAGE, 0))
stream.append((bw.SET_JOB_CONF, 'key1', 'value1', 'key2', 'value2'))
stream.append((bw.RUN_REDUCE, 0, 0))
for k in data:
stream.append((bw.REDUCE_KEY, k))
for v in data[k]:
stream.append((bw.REDUCE_VALUE, v))
stream.append((bw.CLOSE,))
binary_stream_writer(reduce_infile, stream)
run_task(factory, cmd_file=reduce_infile, private_encoding=True)
with open(reduce_outfile, 'rb') as f:
with self._mkf('foo.out', mode='w') as o:
bf = BinaryUpStreamDecoder(f)
for cmd, args in bf:
if cmd == bw.PROGRESS:
o.write('progress\t%s\n' % args[0])
elif cmd == bw.OUTPUT:
o.write('output\t%s\n' %
'\t'.join([x.decode('utf-8') for x in args]))
elif cmd == bw.DONE:
o.write('done\n')
self.check_result('foo.out', STREAM_2)
def _test_map_reduce_with_private_encoding_helper(self, factory,
fast_combiner=False):
self.stream3.close()
cmd_file = self.stream3.name
out_file = cmd_file + '.out'
reduce_infile = cmd_file + '.reduce'
reduce_outfile = reduce_infile + '.out'
run_task(factory, cmd_file=cmd_file, private_encoding=True,
fast_combiner=fast_combiner)
data = {}
with open(out_file) as f:
bf = BinaryDownStreamFilter(f)
for cmd, args in bf:
if cmd == 'output':
data.setdefault(args[0], []).append(args[1])
stream = []
stream.append(('start', 0))
stream.append(('setJobConf', ('key1', 'value1', 'key2', 'value2')))
stream.append(('runReduce', 0, False))
for k in data:
stream.append(('reduceKey', k))
for v in data[k]:
stream.append(('reduceValue', v))
stream.append(('close',))
binary_stream_writer(reduce_infile, stream)
run_task(factory, cmd_file=reduce_infile, private_encoding=True)
with open(reduce_outfile) as f, self._mkf('foo.out', mode='w') as o:
bf = BinaryUpStreamDecoder(f)
for cmd, args in bf:
if cmd == 'progress':
o.write('progress\t%s\n' % args[0])
elif cmd == 'output':
o.write('output\t%s\n' % '\t'.join(args))
elif cmd == 'done':
o.write('done\n')
self.check_result('foo.out', STREAM_3)
def __main__():
factory = pp.Factory(Mapper, Reducer)
pp.run_task(factory)
def __main__():
pp.run_task(factory)
def main():
return run_task(Factory(Mapper, Reducer, combiner_class=Reducer))
from pydoop.mapreduce.pipes import run_task, Factory
from pydoop.mapreduce.api import Mapper, Reducer
class FilterMapper(Mapper):
"""
Process a wordcount output stream, emitting only records relative to
words whose count is equal to or above the configured threshold.
"""
def __init__(self, context):
super(FilterMapper, self).__init__(context)
jc = context.job_conf
self.threshold = jc.get_int("filter.occurrence.threshold")
def map(self, context):
word, occurrence = context.key, context.value
occurrence = struct.unpack(">i", occurrence)[0]
if occurrence >= self.threshold:
context.emit(word, str(occurrence))
class FilterReducer(Reducer):
def reduce(self, context):
pass
if __name__ == "__main__":
run_task(Factory(FilterMapper, FilterReducer))
def __main__():
pp.run_task(pp.Factory(
mapper_class=Mapper,
reducer_class=Reducer,
combiner_class=Reducer
))
def __main__():
pp.run_task(factory)
def main():
pp.run_task(FACTORY)
pass
class ColorWriter(AvroWriter):
schema = avro.schema.parse(open("stats.avsc").read())
def emit(self, key, value):
self.writer.append({'office': key, 'counts': value})
class ColorPick(api.Mapper):
def map(self, ctx):
user = ctx.value
color = user['favorite_color']
if color is not None:
ctx.emit(user['office'], Counter({color: 1}))
class ColorCount(api.Reducer):
def reduce(self, ctx):
s = sum(ctx.values, Counter())
ctx.emit(ctx.key, s)
pp.run_task(pp.Factory(mapper_class=ColorPick,
reducer_class=ColorCount,
record_reader_class=UserReader,
record_writer_class=ColorWriter),
private_encoding=True)
def __main__():
pipes.run_task(pipes.Factory(mapper_class=Mapper))
def __main__():
factory = pp.Factory(Mapper, Reducer)
pp.run_task(factory, context_class=Context)
def __main__():
factory = pp.Factory(Mapper, Reducer)
pp.run_task(factory, private_encoding=True)
def main():
pipes.run_task(FACTORY)
def __main__():
pp.run_task(factory, auto_serialize=False)
import struct
from pydoop.mapreduce.pipes import run_task, Factory
from pydoop.mapreduce.api import Mapper, Reducer
"""
Count followers of each node
Input : directed graph
e.g.) "3 4" indicates that person 3 has 4 followers.
Output : (destination, follower count)
e.g.) "4 2" node 4 has 2 followers.
"""
class DstCountMapper(Mapper):
def map(self, context):
# Implements your codes
line = context.value.split()
context.emit(line[1], 1)
class DstCountReducer(Reducer):
def reduce(self, context):
# Implements your codes
s = sum(context.values)
context.emit(context.key.encode("utf-8"), struct.pack(">i", s))
if __name__ == "__main__":
factory = Factory(DstCountMapper, DstCountReducer)
run_task(factory, auto_serialize=False)
def __main__():
pp.run_task(pp.Factory(mapper_class=Mapper), context_class=AvroContext)
def __main__():
"""Main function to be executed by pydoop framework"""
factory = pp.Factory(mapper_class=Mapper,
reducer_class=Reducer,
record_reader_class=Reader)
pp.run_task(factory, private_encoding=True)
def __main__():
pp.run_task(pp.Factory(Mapper, Reducer))
import struct
from pydoop.mapreduce.pipes import run_task, Factory
from pydoop.mapreduce.api import Mapper, Reducer
class FilterMapper(Mapper):
"""
Process a wordcount output stream, emitting only records relative to
words whose count is equal to or above the configured threshold.
"""
def __init__(self, context):
super(FilterMapper, self).__init__(context)
jc = context.job_conf
self.threshold = jc.get_int("filter.occurrence.threshold")
def map(self, context):
word, occurrence = context.key, context.value
occurrence = struct.unpack(">i", occurrence)[0]
if occurrence >= self.threshold:
context.emit(word, str(occurrence))
class FilterReducer(Reducer):
def reduce(self, context):
pass
if __name__ == "__main__":
run_task(Factory(FilterMapper, FilterReducer))
def test_map_only(self):
factory = TFactory()
with self._mkf('foo_map_only.out') as o:
run_task(factory, istream=self.stream1, ostream=o)
def main():
run_task(FACTORY)
def __main__():
"""Main function to be executed by pydoop framework"""
factory = pp.Factory(mapper_class=Mapper, reducer_class=Reducer, record_reader_class=Reader)
pp.run_task(factory, private_encoding=True)
def main():
pipes.run_task(FACTORY)
class ColorWriter(AvroWriter):
schema = avro.schema.parse(open("stats.avsc").read())
def emit(self, key, value):
self.writer.append({'office': key, 'counts': value})
class ColorPick(api.Mapper):
def map(self, ctx):
user = ctx.value
color = user['favorite_color']
if color is not None:
ctx.emit(user['office'], Counter({color: 1}))
class ColorCount(api.Reducer):
def reduce(self, ctx):
s = sum(ctx.values, Counter())
ctx.emit(ctx.key, s)
pp.run_task(pp.Factory(
mapper_class=ColorPick,
reducer_class=ColorCount,
record_reader_class=UserReader,
record_writer_class=ColorWriter
), private_encoding=True)
def __main__():
factory = pp.Factory(mapper_class=Mapper, reducer_class=Reducer)
pp.run_task(factory, private_encoding=True, context_class=AvroContext)
def __main__():
pipes.run_task(pipes.Factory(
mapper_class=Mapper,
record_writer_class=Writer,
))
def __main__():
factory = pp.Factory(mapper_class=Mapper)
pp.run_task(factory, context_class=AvroContext)
#!/usr/bin/env python
"""
Filter top-50 follower countings
"""
import struct
from pydoop.mapreduce.pipes import run_task, Factory
from pydoop.mapreduce.api import Mapper, Reducer
class FilterMapper(Mapper):
def map(self, context):
# Implements your codes
context.emit(context.key,context.value)
# pass
class FilterReducer(Reducer):
def reduce(self, context):
# Implements your codes
context.emit("","")
# pass
if __name__ == "__main__":
factory = Factory(FilterMapper, FilterReducer)
run_task(factory)
def main():
pp.run_task(FACTORY)
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
#
# END_COPYRIGHT
import struct
import re
from pydoop.mapreduce.pipes import run_task, Factory
from pydoop.mapreduce.api import Mapper, Reducer
class WordCountMapper(Mapper):
def map(self, context):
words = re.sub('[^0-9a-zA-Z]+', ' ', context.value).split()
for w in words:
context.emit(w, 1)
class WordCountReducer(Reducer):
def reduce(self, context):
s = sum(context.values)
context.emit(context.key, struct.pack(">i", s))
if __name__ == "__main__":
run_task(Factory(WordCountMapper, WordCountReducer))
def __main__():
pp.run_task(factory, private_encoding=False, auto_serialize=False)
super(AvroContext, self).set_job_conf(vals)
schema = avro.schema.parse(self._job_conf[AVRO_SCHEMA_KEY])
self.datum_reader = DatumReader(schema)
def get_input_value(self):
# FIXME reuse, reuse, reuse
sys.stderr.write('value: %r\n' % self._value)
f = StringIO(self._value)
dec = BinaryDecoder(f)
return self.datum_reader.read(dec)
class ColorPick(api.Mapper):
def map(self, ctx):
user = ctx.value
color = user['favorite_color']
sys.stderr.write('user: %r' % user)
if color is not None:
ctx.emit(user['office'], Counter({color: 1}))
class ColorCount(api.Reducer):
def reduce(self, ctx):
s = sum(ctx.values, Counter())
ctx.emit(ctx.key, "%r" % s)
pp.run_task(pp.Factory(mapper_class=ColorPick, reducer_class=ColorCount),
private_encoding=True,
context_class=AvroContext)
def __main__():
pp.run_task(pp.Factory(mapper_class=Mapper), context_class=AvroContext)
def main():
return run_task(Factory(Mapper, Reducer, combiner_class=Reducer))
def __main__():
pp.run_task(pp.Factory(Mapper, None))
def run_task(mapper_class, reducer_class=NoAvroColorCount):
pp.run_task(
pp.Factory(mapper_class=mapper_class, reducer_class=reducer_class),
private_encoding=True, context_class=AvroContext
)
def __main__():
pp.run_task(FACTORY, private_encoding=True, context_class=CONTEXT)
def __main__():
pp.run_task(pp.Factory(Mapper, None))
