class SaveTestCase(TestCase):
def serve(self, path):
return '{0}\n'.format(path)
def test_save_map(self):
input = range(10)
self.job = SaveMapJob().run(input=self.test_server.urls(input))
results = sorted(self.results(self.job))
self.tag = self.disco.results(self.job.name)[1][0]
# Previously, each map would save one blob into DDFS. Now,
# the pipeline termination does it, using the output of the
# shuffle stage. So now, the number of blobs in the tag
# depends on the grouping used for shuffle, and also the
# number of nodes used. Hence, we cannot anymore assert on
# the number of blobs in the tag.
# self.assertEquals(len(list(self.ddfs.blobs(self.tag))), len(input))
self.assertEquals(results, [(str_to_bytes(str(e)+'!'), '') for e in input])
def test_save(self):
ducks = ['dewey', 'huey', 'louie']
a, b = SaveJob1(), SaveJob2()
self.job = JobChain({a: self.test_server.urls(ducks),
b: a})
self.job.wait()
self.tag = self.disco.results(b)[1][0]
self.assertAllEqual(sorted(self.results(b)),
[(str_to_bytes('{0}!?!?'.format(d)), '') for d in ducks])
def tearDown(self):
super(SaveTestCase, self).tearDown()
if hasattr(self, 'tag'):
self.ddfs.delete(self.tag)
class DavinChainTestCase(TestCase):
def runTest(self):
a, b, c = DavinChainJobA(), DavinChainJobA(), DavinChainJobC()
self.job = JobChain({a: ['raw://0', 'raw://1', 'raw://2'],
b: ['raw://3', 'raw://4', 'raw://5'],
c: [a, b]})
self.job.wait()
self.assertAllEqual(sorted(self.results(c)),
((str(x), '') for x in range(6)))
class SchemesTestCase(TestCase):
animals = ['horse', 'sheep', 'whale', 'tiger']
def serve(self, path):
return '\n'.join(self.animals)
def test_scheme_disco(self):
a, b = SchemesJobA(), SchemesJobB()
self.job = JobChain({a: self.test_server.urls([''] * 10), b: a})
self.job.wait()
for key, value in self.results(b):
self.assert_(key in self.animals)
self.assertEquals(value, None)
class AsyncTestCase(TestCase):
def sample(self, n):
from random import sample
return sample(range(n * 10), n * 2)
def serve(self, path):
return '\n'.join([path] * 10)
def runTest(self):
N = self.num_workers
self.job = JobChain((AsyncJob(), self.test_server.urls(self.sample(N)))
for x in range(5))
self.job.wait()
for job in self.job:
self.assertEquals(sum(1 for result in self.results(job)), N * 20)
class ChainTestCase(TestCase):
animals = [b'horse', b'sheep', b'whale', b'tiger']
def serve(self, path):
return b'\n'.join(self.animals)
def runTest(self):
a, b = ChainJobA(), ChainJobB()
self.job = JobChain({a: self.test_server.urls([''] * 100),
b: a})
self.job.wait()
for key, value in self.results(b):
self.assert_(key[:5] in self.animals)
self.assertEquals(key[5:], b'0-1-')
self.assertEquals(value, 1)
class AsyncTestCase(TestCase):
def sample(self, n):
from random import sample
return sample(range(n * 10), n * 2)
def serve(self, path):
return "\n".join([path] * 10)
def runTest(self):
N = self.num_workers
self.job = JobChain((AsyncJob(), self.test_server.urls(self.sample(N))) for x in range(5))
self.job.wait()
for job in self.job:
self.assertEquals(sum(1 for result in self.results(job)), N * 20)
def runTest(self):
input = self.test_server.urls([''] * 5)
a, b, c = WaitJob1(), WaitJob1(), WaitJob2()
self.job = JobChain({a: input, b: input, c: input})
self.assertRaises(JobError, self.job.wait)
valid = JobChain({a: input, b: input})
self.assertEquals(valid.wait(), valid)
class InputTestCase(TestCase):
def serve(self, path):
return b'smoothies'
def test_empty_map(self):
self.job = MapJob().run(input=[])
self.assertResults(self.job, [])
def test_empty_reduce(self):
self.job = ReduceJob().run(input=[])
self.assertResults(self.job, [])
def test_empty_mapreduce(self):
self.job = MapReduceJob().run(input=[])
self.assertResults(self.job, [])
def test_partitioned_map(self):
self.job = MapJob().run(input=['raw://organic_vodka'], partitions=2)
self.assertResults(self.job, [('organic_vodka', 'against_me')])
def test_nonpartitioned_map(self):
self.job = MapJob().run(input=['raw://organic_vodka'], partitions=None)
self.assertResults(self.job, [('organic_vodka', 'against_me')])
def test_nonpartitioned_reduce(self):
self.job = ReduceJob().run(input=self.test_server.urls(['test']),
partitions=None,
reduce_reader=None)
self.assertResults(self.job, [(b'smoothies', 'mmm')])
def test_partitioned_mapreduce(self):
self.job = MapReduceJob().run(input=self.test_server.urls(['test']),
partitions=8,
reduce_reader=task_io.chain_reader)
self.assertResults(self.job, [((b'smoothies', 'against_me'), 'mmm')])
def test_partitioned_reduce(self):
beers = ['sam_adams', 'trader_jose', 'boont_esb']
input = ['raw://{0}'.format(beer) for beer in beers]
a, b, c, d = MapJob(), MapJob(), ReduceJob(), MergeReduceJob()
self.job = JobChain({a: input,
b: input,
c: [a, b],
d: [a, b]})
self.job.wait()
self.assertAllEqual(sorted(self.results(c)), sorted(self.results(d)))
def runTest(self):
input = self.test_server.urls([''] * 5)
a, b, c = WaitJob1(), WaitJob1(), WaitJob2()
self.job = JobChain({a: input,
b: input,
c: input})
self.assertRaises(JobError, self.job.wait)
valid = JobChain({a: input, b:input})
self.assertEquals(valid.wait(), valid)
class SaveTestCase(TestCase):
def serve(self, path):
return '{0}\n'.format(path)
def test_save_map(self):
input = range(10)
self.job = SaveMapJob().run(input=self.test_server.urls(input))
results = sorted(self.results(self.job))
self.tag = self.disco.results(self.job.name)[1][0]
# Previously, each map would save one blob into DDFS. Now,
# the pipeline termination does it, using the output of the
# shuffle stage. So now, the number of blobs in the tag
# depends on the grouping used for shuffle, and also the
# number of nodes used. Hence, we cannot anymore assert on
# the number of blobs in the tag.
# self.assertEquals(len(list(self.ddfs.blobs(self.tag))), len(input))
self.assertEquals(results,
[(str_to_bytes(str(e) + '!'), '') for e in input])
def test_save(self):
ducks = ['dewey', 'huey', 'louie']
a, b = SaveJob1(), SaveJob2()
self.job = JobChain({a: self.test_server.urls(ducks), b: a})
self.job.wait()
self.tag = self.disco.results(b)[1][0]
self.assertAllEqual(sorted(self.results(b)),
[(str_to_bytes('{0}!?!?'.format(d)), '')
for d in ducks])
def tearDown(self):
super(SaveTestCase, self).tearDown()
if hasattr(self, 'tag'):
self.ddfs.delete(self.tag)