def test_basics(self):
v = ('a' * 10, 'b' * 90)
pickler = coders.PickleCoder()
self.assertEquals(v, pickler.decode(pickler.encode(v)))
pickler = coders.Base64PickleCoder()
self.assertEquals(v, pickler.decode(pickler.encode(v)))
self.assertEquals(coders.Base64PickleCoder().encode(v),
base64.b64encode(coders.PickleCoder().encode(v)))
def test_should_sample(self):
# Order of magnitude more buckets than highest constant in code under test.
buckets = [0] * 300
# The seed is arbitrary and exists just to ensure this test is robust.
# If you don't like this seed, try your own; the test should still pass.
random.seed(1720)
# Do enough runs that the expected hits even in the last buckets
# is big enough to expect some statistical smoothing.
total_runs = 10 * len(buckets)
# Fill the buckets.
for _ in range(total_runs):
opcounts = OperationCounters(CounterFactory(), 'some-name',
coders.PickleCoder(), 0)
for i in range(len(buckets)):
if opcounts.should_sample():
buckets[i] += 1
# Look at the buckets to see if they are likely.
for i in range(10):
self.assertEqual(total_runs, buckets[i])
for i in range(10, len(buckets)):
self.assertTrue(
buckets[i] > 7 * total_runs / i,
'i=%d, buckets[i]=%d, expected=%d, ratio=%f' %
(i, buckets[i], 10 * total_runs / i, buckets[i] /
(10.0 * total_runs / i)))
self.assertTrue(
buckets[i] < 14 * total_runs / i,
'i=%d, buckets[i]=%d, expected=%d, ratio=%f' %
(i, buckets[i], 10 * total_runs / i, buckets[i] /
(10.0 * total_runs / i)))
def test_update_int(self):
opcounts = OperationCounters(CounterFactory(), 'some-name',
coders.PickleCoder(), 0)
self.verify_counters(opcounts, 0)
opcounts.update_from(GlobalWindows.windowed_value(1))
opcounts.update_collect()
self.verify_counters(opcounts, 1)
class SyncFn(beam.DoFn):
STATE = userstate.BagStateSpec('state', coders.PickleCoder())
def __init__(self, size):
assert size > 0, 'Must provide a positive size'
self.size = size
def process(self, element, state=beam.DoFn.StateParam(STATE)):
key, value = element
cache = list(state.read())
if cache:
cache = cache[0]
else:
cache = {}
values = cache.get(key, [])
values.append(value)
if len(values) == self.size:
if key in cache:
del cache[key]
yield tuple(values)
else:
cache[key] = values
state.clear()
if cache:
state.add(cache)
def test_update_str(self):
coder = coders.PickleCoder()
opcounts = OperationCounters(CounterFactory(), 'some-name', coder, 0)
self.verify_counters(opcounts, 0, float('nan'))
value = GlobalWindows.windowed_value('abcde')
opcounts.update_from(value)
estimated_size = coder.estimate_size(value)
self.verify_counters(opcounts, 1, estimated_size)
class IndexAssigningStatefulDoFn(beam.DoFn):
INDEX_STATE = CombiningValueStateSpec(name="index", coder=coders.PickleCoder(), combine_fn=sum)
def process(self, element, index=beam.DoFn.StateParam(INDEX_STATE)):
_, value = element
current_index = index.read()
index.add(1)
yield (current_index, value)
def test_update_old_object(self):
coder = coders.PickleCoder()
opcounts = OperationCounters(CounterFactory(), 'some-name', coder, 0)
self.verify_counters(opcounts, 0, float('nan'))
obj = OldClassThatDoesNotImplementLen()
value = GlobalWindows.windowed_value(obj)
opcounts.update_from(value)
estimated_size = coder.estimate_size(value)
self.verify_counters(opcounts, 1, estimated_size)
class _StatefulJobOutputsFn(beam.DoFn):
STATE = userstate.BagStateSpec('state', coders.PickleCoder())
def process(self, element, level, state=beam.DoFn.StateParam(STATE)):
assert level in JobAggregateLevel.STATEFUL
# example payload structure...
# {
# 'source': Any
# 'graphid': 0,
# 'jobtasks': {0: 3, 1: 3},
# 'jobid': 0,
# 'taskid': 2,
# 'output': [
# '/tmp/job-0_output-0.task-2.ext',
# '/tmp/job-0_output-1.task-2.ext',
# ],
# }
_, payload = element
# There are two values we will track that differ depending on the
# aggregation type/level desired.
#
# - key : aggregation per-unique value
# - size : total number of times expected to see `key`
key = payload[level]
if level == JobAggregateLevel.JOB:
# str(key) is to deal with json making all dict keys strings
size = payload['jobtasks'][str(key)]
elif level == JobAggregateLevel.GRAPH:
size = sum(payload['jobtasks'].values())
else:
raise NotImplementedError
cache = dict(state.read())
seen, data = cache.get(key, (0, []))
seen += 1
data.extend(payload['output'])
cache[key] = (seen, data)
state.clear()
for k, v in cache.items():
# size == seen
if size == v[0]:
# cprint('fire-{}: {}'.format(level, k), 'red', attrs=['bold'])
yield cache.pop(k)[1]
else:
state.add((k, v))
def test_update_multiple(self):
coder = coders.PickleCoder()
total_size = 0
opcounts = OperationCounters(CounterFactory(), 'some-name', coder, 0)
self.verify_counters(opcounts, 0, float('nan'))
value = GlobalWindows.windowed_value('abcde')
opcounts.update_from(value)
total_size += coder.estimate_size(value)
value = GlobalWindows.windowed_value('defghij')
opcounts.update_from(value)
total_size += coder.estimate_size(value)
self.verify_counters(opcounts, 2, float(total_size) / 2)
value = GlobalWindows.windowed_value('klmnop')
opcounts.update_from(value)
total_size += coder.estimate_size(value)
self.verify_counters(opcounts, 3, float(total_size) / 3)
def run_Create(self, transform_node):
transform = transform_node.transform
step = self._add_step(TransformNames.CREATE_PCOLLECTION,
transform_node.full_label, transform_node)
# TODO(silviuc): Eventually use a coder based on typecoders.
# Note that we base64-encode values here so that the service will accept
# the values.
element_coder = coders.PickleCoder()
step.add_property(
PropertyNames.ELEMENT,
[base64.b64encode(element_coder.encode(v))
for v in transform.value])
# The service expects a WindowedValueCoder here, so we wrap the actual
# encoding in a WindowedValueCoder.
step.encoding = self._get_cloud_encoding(
coders.WindowedValueCoder(element_coder))
step.add_property(
PropertyNames.OUTPUT_INFO,
[{PropertyNames.USER_NAME: (
'%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
PropertyNames.ENCODING: step.encoding,
PropertyNames.OUTPUT_NAME: PropertyNames.OUT}])
class SolveDoFn(beam.DoFn):
PREV_TIMESTAMP = BagStateSpec(name="timestamp_state", coder=coders.PickleCoder())
PREV_ELEMENTS = BagStateSpec(name="elements_state", coder=coders.PickleCoder())
PREV_MODEL = BagStateSpec(name="model_state", coder=coders.PickleCoder())
PREV_SAMPLESET = BagStateSpec(name="sampleset_state", coder=coders.PickleCoder())
def process(
self,
value,
timestamp=beam.DoFn.TimestampParam,
timestamp_state=beam.DoFn.StateParam(PREV_TIMESTAMP),
elements_state=beam.DoFn.StateParam(PREV_ELEMENTS),
model_state=beam.DoFn.StateParam(PREV_MODEL),
sampleset_state=beam.DoFn.StateParam(PREV_SAMPLESET),
algorithm=None,
algorithm_options=None,
map_fn=None,
solve_fn=None,
unmap_fn=None,
solver=LocalSolver(exact=False), # default solver
initial_mtype=sawatabi.constants.MODEL_ISING,
):
_, elements = value
# Sort with the event time.
# If we sort a list of tuples, the first element of the tuple is recognized as a key by default,
# so just `sorted` is enough.
sorted_elements = sorted(elements)
# generator into a list
timestamp_state_as_list = list(timestamp_state.read())
elements_state_as_list = list(elements_state.read())
model_state_as_list = list(model_state.read())
sampleset_state_as_list = list(sampleset_state.read())
# Extract the previous timestamp, elements, and model from state
if len(timestamp_state_as_list) == 0:
prev_timestamp = -1.0
else:
prev_timestamp = timestamp_state_as_list[-1]
if len(elements_state_as_list) == 0:
prev_elements = []
else:
prev_elements = elements_state_as_list[-1]
if len(model_state_as_list) == 0:
prev_model = sawatabi.model.LogicalModel(mtype=initial_mtype)
else:
prev_model = model_state_as_list[-1]
if len(sampleset_state_as_list) == 0:
prev_sampleset = None
else:
prev_sampleset = sampleset_state_as_list[-1]
# Sometimes, when we use the sliding window algorithm for a bounded data (such as a local file),
# we may receive an outdated event whose timestamp is older than timestamp of previously processed event.
if float(timestamp) < float(prev_timestamp):
yield (
f"The received event is outdated: Timestamp is {timestamp.to_utc_datetime()}, "
+ f"while an event with timestamp of {timestamp.to_utc_datetime()} has been already processed."
)
return
# Algorithm specific operations
# Incremental: Append current window into the all previous data.
if algorithm == sawatabi.constants.ALGORITHM_INCREMENTAL:
sorted_elements.extend(prev_elements)
sorted_elements = sorted(sorted_elements)
# Partial: Merge current window with the specified data.
elif algorithm == sawatabi.constants.ALGORITHM_PARTIAL:
filter_fn = algorithm_options["filter_fn"]
filtered = filter(filter_fn, prev_elements)
sorted_elements = list(filtered) + sorted_elements
sorted_elements = sorted(sorted_elements)
# Resolve outgoing elements in this iteration
def resolve_outgoing(prev_elements, sorted_elements):
outgoing = []
for p in prev_elements:
if p[0] >= sorted_elements[0][0]:
break
outgoing.append(p)
return outgoing
outgoing = resolve_outgoing(prev_elements, sorted_elements)
# Resolve incoming elements in this iteration
def resolve_incoming(prev_elements, sorted_elements):
incoming = []
if len(prev_elements) == 0:
incoming = sorted_elements
else:
for v in reversed(sorted_elements):
if v[0] <= prev_elements[-1][0]:
break
incoming.insert(0, v)
return incoming
incoming = resolve_incoming(prev_elements, sorted_elements)
# Clear the BagState so we can hold only the latest state, and
# Register new timestamp and elements to the states
timestamp_state.clear()
timestamp_state.add(timestamp)
elements_state.clear()
elements_state.add(sorted_elements)
# Map problem input to the model
try:
model = map_fn(prev_model, prev_sampleset, sorted_elements, incoming, outgoing)
except Exception as e:
yield f"Failed to map: {e}\n{traceback.format_exc()}"
return
# Clear the BagState so we can hold only the latest state, and
# Register new model to the state
model_state.clear()
model_state.add(model)
# Algorithm specific operations
# Attenuation: Update scale based on data timestamp.
if algorithm == sawatabi.constants.ALGORITHM_ATTENUATION:
model.to_physical() # Resolve removed interactions. TODO: Deal with placeholders.
ref_timestamp = model._interactions_array[algorithm_options["attenuation.key"]]
min_ts = min(ref_timestamp)
max_ts = max(ref_timestamp)
min_scale = algorithm_options["attenuation.min_scale"]
if min_ts < max_ts:
for i, t in enumerate(ref_timestamp):
new_scale = (1.0 - min_scale) / (max_ts - min_ts) * (t - min_ts) + min_scale
model._interactions_array["scale"][i] = new_scale
# Solve and unmap to the solution
try:
sampleset = solve_fn(solver, model, prev_sampleset, sorted_elements, incoming, outgoing)
except Exception as e:
yield f"Failed to solve: {e}\n{traceback.format_exc()}"
return
# Clear the BagState so we can hold only the latest state, and
# Register new sampleset to the state
sampleset_state.clear()
sampleset_state.add(sampleset)
try:
yield unmap_fn(sampleset, sorted_elements, incoming, outgoing)
except Exception as e:
yield f"Failed to unmap: {e}\n{traceback.format_exc()}"
def test_equality(self):
self.assertEquals(coders.PickleCoder(), coders.PickleCoder())
self.assertEquals(coders.Base64PickleCoder(),
coders.Base64PickleCoder())
self.assertNotEquals(coders.Base64PickleCoder(), coders.PickleCoder())
self.assertNotEquals(coders.Base64PickleCoder(), object())
