def test_wordcount(self):
class WordExtractingDoFn(beam.DoFn):
def process(self, element):
text_line = element.strip()
words = text_line.split()
return words
p = beam.Pipeline(
runner=interactive_runner.InteractiveRunner(
direct_runner.DirectRunner()))
# Count the occurrences of each word.
counts = (
p
| beam.Create(['to be or not to be that is the question'])
| 'split' >> beam.ParDo(WordExtractingDoFn())
| 'pair_with_one' >> beam.Map(lambda x: (x, 1))
| 'group' >> beam.GroupByKey()
| 'count' >> beam.Map(lambda wordones: (wordones[0], sum(wordones[1]))))
# Watch the local scope for Interactive Beam so that counts will be cached.
ib.watch(locals())
# This is normally done in the interactive_utils when a transform is
# applied but needs an IPython environment. So we manually run this here.
ie.current_env().track_user_pipelines()
result = p.run()
result.wait_until_finish()
actual = list(result.get(counts))
self.assertSetEqual(
set(actual),
set([
('or', 1),
('that', 1),
('be', 2),
('is', 1),
('question', 1),
('to', 2),
('the', 1),
('not', 1),
]))
# Truncate the precision to millis because the window coder uses millis
# as units then gets upcast to micros.
end_of_window = (GlobalWindow().max_timestamp().micros // 1000) * 1000
df_counts = ib.collect(counts, include_window_info=True, n=10)
df_expected = pd.DataFrame({
0: [e[0] for e in actual],
1: [e[1] for e in actual],
'event_time': [end_of_window for _ in actual],
'windows': [[GlobalWindow()] for _ in actual],
'pane_info': [
PaneInfo(True, True, PaneInfoTiming.ON_TIME, 0, 0) for _ in actual
]
},
columns=[
0, 1, 'event_time', 'windows', 'pane_info'
])
pd.testing.assert_frame_equal(df_expected, df_counts)
actual_reified = result.get(counts, include_window_info=True)
expected_reified = [
WindowedValue(
e,
Timestamp(micros=end_of_window), [GlobalWindow()],
PaneInfo(True, True, PaneInfoTiming.ON_TIME, 0, 0)) for e in actual
]
self.assertEqual(actual_reified, expected_reified)
def test_computed(self):
"""Tests that a PCollection is marked as computed only in a complete state.
Because the background caching job is now long-lived, repeated runs of a
PipelineFragment may yield different results for the same PCollection.
"""
p = beam.Pipeline(InteractiveRunner())
elems = p | beam.Create([0, 1, 2])
ib.watch(locals())
# Create a MockPipelineResult to control the state of a fake run of the
# pipeline.
mock_result = MockPipelineResult()
ie.current_env().track_user_pipelines()
ie.current_env().set_pipeline_result(p, mock_result)
# Create a mock BackgroundCachingJob that will control whether to set the
# PCollections as computed or not.
bcj_mock_result = MockPipelineResult()
background_caching_job = bcj.BackgroundCachingJob(bcj_mock_result, [])
# Create a recording.
recording = Recording(p, [elems],
mock_result,
pi.PipelineInstrument(p),
max_n=10,
max_duration_secs=60)
# The background caching job and the recording isn't done yet so there may
# be more elements to be recorded.
self.assertFalse(recording.is_computed())
self.assertFalse(recording.computed())
self.assertTrue(recording.uncomputed())
# The recording is finished but the background caching job is not. There
# may still be more elements to record, or the intermediate PCollection may
# have stopped caching in an incomplete state, e.g. before a window could
# fire.
mock_result.set_state(PipelineState.DONE)
recording.wait_until_finish()
self.assertFalse(recording.is_computed())
self.assertFalse(recording.computed())
self.assertTrue(recording.uncomputed())
# The background caching job finished before we started a recording which
# is a sure signal that there will be no more elements.
bcj_mock_result.set_state(PipelineState.DONE)
ie.current_env().set_background_caching_job(p, background_caching_job)
recording = Recording(p, [elems],
mock_result,
pi.PipelineInstrument(p),
max_n=10,
max_duration_secs=60)
recording.wait_until_finish()
# There are no more elements and the recording finished, meaning that the
# intermediate PCollections are in a complete state. They can now be marked
# as computed.
self.assertTrue(recording.is_computed())
self.assertTrue(recording.computed())
self.assertFalse(recording.uncomputed())
def test_streaming_wordcount(self):
class WordExtractingDoFn(beam.DoFn):
def process(self, element):
text_line = element.strip()
words = text_line.split()
return words
# Add the TestStream so that it can be cached.
ib.options.capturable_sources.add(TestStream)
p = beam.Pipeline(
runner=interactive_runner.InteractiveRunner(),
options=StandardOptions(streaming=True))
data = (
p
| TestStream()
.advance_watermark_to(0)
.advance_processing_time(1)
.add_elements(['to', 'be', 'or', 'not', 'to', 'be'])
.advance_watermark_to(20)
.advance_processing_time(1)
.add_elements(['that', 'is', 'the', 'question'])
.advance_watermark_to(30)
.advance_processing_time(1)
.advance_watermark_to(40)
.advance_processing_time(1)
.advance_watermark_to(50)
.advance_processing_time(1)
| beam.WindowInto(beam.window.FixedWindows(10))) # yapf: disable
counts = (
data
| 'split' >> beam.ParDo(WordExtractingDoFn())
| 'pair_with_one' >> beam.Map(lambda x: (x, 1))
| 'group' >> beam.GroupByKey()
| 'count' >> beam.Map(lambda wordones: (wordones[0], sum(wordones[1]))))
# Watch the local scope for Interactive Beam so that referenced PCollections
# will be cached.
ib.watch(locals())
# This is normally done in the interactive_utils when a transform is
# applied but needs an IPython environment. So we manually run this here.
ie.current_env().track_user_pipelines()
# This tests that the data was correctly cached.
pane_info = PaneInfo(True, True, PaneInfoTiming.UNKNOWN, 0, 0)
expected_data_df = pd.DataFrame([
('to', 0, [IntervalWindow(0, 10)], pane_info),
('be', 0, [IntervalWindow(0, 10)], pane_info),
('or', 0, [IntervalWindow(0, 10)], pane_info),
('not', 0, [IntervalWindow(0, 10)], pane_info),
('to', 0, [IntervalWindow(0, 10)], pane_info),
('be', 0, [IntervalWindow(0, 10)], pane_info),
('that', 20000000, [IntervalWindow(20, 30)], pane_info),
('is', 20000000, [IntervalWindow(20, 30)], pane_info),
('the', 20000000, [IntervalWindow(20, 30)], pane_info),
('question', 20000000, [IntervalWindow(20, 30)], pane_info)
], columns=[0, 'event_time', 'windows', 'pane_info']) # yapf: disable
data_df = ib.collect(data, n=10, include_window_info=True)
pd.testing.assert_frame_equal(expected_data_df, data_df)
# This tests that the windowing was passed correctly so that all the data
# is aggregated also correctly.
pane_info = PaneInfo(True, False, PaneInfoTiming.ON_TIME, 0, 0)
expected_counts_df = pd.DataFrame([
('be', 2, 9999999, [IntervalWindow(0, 10)], pane_info),
('not', 1, 9999999, [IntervalWindow(0, 10)], pane_info),
('or', 1, 9999999, [IntervalWindow(0, 10)], pane_info),
('to', 2, 9999999, [IntervalWindow(0, 10)], pane_info),
('is', 1, 29999999, [IntervalWindow(20, 30)], pane_info),
('question', 1, 29999999, [IntervalWindow(20, 30)], pane_info),
('that', 1, 29999999, [IntervalWindow(20, 30)], pane_info),
('the', 1, 29999999, [IntervalWindow(20, 30)], pane_info),
], columns=[0, 1, 'event_time', 'windows', 'pane_info']) # yapf: disable
counts_df = ib.collect(counts, n=8, include_window_info=True)
# The group by key has no guarantee of order. So we post-process the DF by
# sorting so we can test equality.
sorted_counts_df = (counts_df
.sort_values(['event_time', 0], ascending=True)
.reset_index(drop=True)) # yapf: disable
pd.testing.assert_frame_equal(expected_counts_df, sorted_counts_df)
def test_instrument_example_unbounded_pipeline_to_read_cache_not_cached(
self):
"""Tests that the instrumenter works when the PCollection is not cached.
"""
# Create the pipeline that will be instrumented.
from apache_beam.options.pipeline_options import StandardOptions
options = StandardOptions(streaming=True)
p_original = beam.Pipeline(interactive_runner.InteractiveRunner(),
options)
ie.current_env().set_cache_manager(StreamingCache(cache_dir=None),
p_original)
source_1 = p_original | 'source1' >> beam.io.ReadFromPubSub(
subscription='projects/fake-project/subscriptions/fake_sub')
# pylint: disable=possibly-unused-variable
pcoll_1 = source_1 | 'square1' >> beam.Map(lambda x: x * x)
# Watch but do not cache the PCollections.
ib.watch(locals())
# Instrument the original pipeline to create the pipeline the user will see.
p_copy = beam.Pipeline.from_runner_api(
p_original.to_runner_api(),
runner=interactive_runner.InteractiveRunner(),
options=options)
instrumenter = instr.build_pipeline_instrument(p_copy)
actual_pipeline = beam.Pipeline.from_runner_api(
proto=instrumenter.instrumented_pipeline_proto(),
runner=interactive_runner.InteractiveRunner(),
options=options)
# Now, build the expected pipeline which replaces the unbounded source with
# a TestStream.
source_1_cache_key = self.cache_key_of('source_1', source_1)
p_expected = beam.Pipeline()
ie.current_env().set_cache_manager(StreamingCache(cache_dir=None),
p_expected)
test_stream = (p_expected
| TestStream(output_tags=[source_1_cache_key]))
# pylint: disable=expression-not-assigned
(test_stream[source_1_cache_key]
| 'square1' >> beam.Map(lambda x: x * x)
| 'reify' >> beam.Map(lambda _: _)
| cache.WriteCache(ie.current_env().get_cache_manager(p_expected),
'unused'))
# Test that the TestStream is outputting to the correct PCollection.
class TestStreamVisitor(PipelineVisitor):
def __init__(self):
self.output_tags = set()
def enter_composite_transform(self, transform_node):
self.visit_transform(transform_node)
def visit_transform(self, transform_node):
transform = transform_node.transform
if isinstance(transform, TestStream):
self.output_tags = transform.output_tags
v = TestStreamVisitor()
actual_pipeline.visit(v)
expected_output_tags = set([source_1_cache_key])
actual_output_tags = v.output_tags
self.assertSetEqual(expected_output_tags, actual_output_tags)
# Test that the pipeline is as expected.
assert_pipeline_proto_equal(self, p_expected.to_runner_api(),
instrumenter.instrumented_pipeline_proto())
def test_instrument_example_unbounded_pipeline_to_multiple_read_cache(
self):
"""Tests that the instrumenter works for multiple unbounded sources.
"""
# Create the pipeline that will be instrumented.
p_original = beam.Pipeline(interactive_runner.InteractiveRunner())
ie.current_env().set_cache_manager(StreamingCache(cache_dir=None),
p_original)
source_1 = p_original | 'source1' >> beam.io.ReadFromPubSub(
subscription='projects/fake-project/subscriptions/fake_sub')
source_2 = p_original | 'source2' >> beam.io.ReadFromPubSub(
subscription='projects/fake-project/subscriptions/fake_sub')
# pylint: disable=possibly-unused-variable
pcoll_1 = source_1 | 'square1' >> beam.Map(lambda x: x * x)
# pylint: disable=possibly-unused-variable
pcoll_2 = source_2 | 'square2' >> beam.Map(lambda x: x * x)
# Mock as if cacheable PCollections are cached.
ib.watch(locals())
for name, pcoll in locals().items():
if not isinstance(pcoll, beam.pvalue.PCollection):
continue
cache_key = self.cache_key_of(name, pcoll)
self._mock_write_cache(p_original, [b''], cache_key)
# Instrument the original pipeline to create the pipeline the user will see.
instrumenter = instr.build_pipeline_instrument(p_original)
actual_pipeline = beam.Pipeline.from_runner_api(
proto=instrumenter.instrumented_pipeline_proto(),
runner=interactive_runner.InteractiveRunner(),
options=None)
# Now, build the expected pipeline which replaces the unbounded source with
# a TestStream.
source_1_cache_key = self.cache_key_of('source_1', source_1)
source_2_cache_key = self.cache_key_of('source_2', source_2)
p_expected = beam.Pipeline()
test_stream = (p_expected
| TestStream(output_tags=[
self.cache_key_of('source_1', source_1),
self.cache_key_of('source_2', source_2)
]))
# pylint: disable=expression-not-assigned
test_stream[source_1_cache_key] | 'square1' >> beam.Map(
lambda x: x * x)
# pylint: disable=expression-not-assigned
test_stream[source_2_cache_key] | 'square2' >> beam.Map(
lambda x: x * x)
# Test that the TestStream is outputting to the correct PCollection.
class TestStreamVisitor(PipelineVisitor):
def __init__(self):
self.output_tags = set()
def enter_composite_transform(self, transform_node):
self.visit_transform(transform_node)
def visit_transform(self, transform_node):
transform = transform_node.transform
if isinstance(transform, TestStream):
self.output_tags = transform.output_tags
v = TestStreamVisitor()
actual_pipeline.visit(v)
expected_output_tags = set([source_1_cache_key, source_2_cache_key])
actual_output_tags = v.output_tags
self.assertSetEqual(expected_output_tags, actual_output_tags)
# Test that the pipeline is as expected.
assert_pipeline_proto_equal(self, p_expected.to_runner_api(),
instrumenter.instrumented_pipeline_proto())
def test_able_to_cache_intermediate_unbounded_source_pcollection(self):
"""Tests being able to cache an intermediate source PCollection.
In the following pipeline, the source doesn't have a reference and so is
not automatically cached in the watch() command. This tests that this case
is taken care of.
"""
# Create the pipeline that will be instrumented.
from apache_beam.options.pipeline_options import StandardOptions
options = StandardOptions(streaming=True)
streaming_cache_manager = StreamingCache(cache_dir=None)
p_original = beam.Pipeline(interactive_runner.InteractiveRunner(),
options)
ie.current_env().set_cache_manager(streaming_cache_manager, p_original)
# pylint: disable=possibly-unused-variable
source_1 = (
p_original
| 'source1' >> beam.io.ReadFromPubSub(
subscription='projects/fake-project/subscriptions/fake_sub')
| beam.Map(lambda e: e))
# Watch but do not cache the PCollections.
ib.watch(locals())
# Make sure that sources without a user reference are still cached.
instr.watch_sources(p_original)
intermediate_source_pcoll = None
for watching in ie.current_env().watching():
watching = list(watching)
for var, watchable in watching:
if 'synthetic' in var:
intermediate_source_pcoll = watchable
break
# Instrument the original pipeline to create the pipeline the user will see.
p_copy = beam.Pipeline.from_runner_api(
p_original.to_runner_api(),
runner=interactive_runner.InteractiveRunner(),
options=options)
instrumenter = instr.build_pipeline_instrument(p_copy)
actual_pipeline = beam.Pipeline.from_runner_api(
proto=instrumenter.instrumented_pipeline_proto(),
runner=interactive_runner.InteractiveRunner(),
options=options)
# Now, build the expected pipeline which replaces the unbounded source with
# a TestStream.
intermediate_source_pcoll_cache_key = \
self.cache_key_of('synthetic_var_' + str(id(intermediate_source_pcoll)),
intermediate_source_pcoll)
p_expected = beam.Pipeline()
ie.current_env().set_cache_manager(streaming_cache_manager, p_expected)
test_stream = (
p_expected
| TestStream(output_tags=[intermediate_source_pcoll_cache_key]))
# pylint: disable=expression-not-assigned
(test_stream[intermediate_source_pcoll_cache_key]
| 'square1' >> beam.Map(lambda e: e)
| 'reify' >> beam.Map(lambda _: _)
| cache.WriteCache(ie.current_env().get_cache_manager(p_expected),
'unused'))
# Test that the TestStream is outputting to the correct PCollection.
class TestStreamVisitor(PipelineVisitor):
def __init__(self):
self.output_tags = set()
def enter_composite_transform(self, transform_node):
self.visit_transform(transform_node)
def visit_transform(self, transform_node):
transform = transform_node.transform
if isinstance(transform, TestStream):
self.output_tags = transform.output_tags
v = TestStreamVisitor()
actual_pipeline.visit(v)
expected_output_tags = set([intermediate_source_pcoll_cache_key])
actual_output_tags = v.output_tags
self.assertSetEqual(expected_output_tags, actual_output_tags)
# Test that the pipeline is as expected.
assert_pipeline_proto_equal(self, p_expected.to_runner_api(),
instrumenter.instrumented_pipeline_proto())
def test_instrument_mixed_streaming_batch(self):
"""Tests caching for both batch and streaming sources in the same pipeline.
This ensures that cached bounded and unbounded sources are read from the
TestStream.
"""
# Create the pipeline that will be instrumented.
from apache_beam.options.pipeline_options import StandardOptions
options = StandardOptions(streaming=True)
p_original = beam.Pipeline(interactive_runner.InteractiveRunner(),
options)
streaming_cache_manager = StreamingCache(cache_dir=None)
ie.current_env().set_cache_manager(streaming_cache_manager, p_original)
source_1 = p_original | 'source1' >> beam.io.ReadFromPubSub(
subscription='projects/fake-project/subscriptions/fake_sub')
source_2 = p_original | 'source2' >> beam.Create([1, 2, 3, 4, 5])
# pylint: disable=possibly-unused-variable
pcoll_1 = ((source_1, source_2)
| beam.Flatten()
| 'square1' >> beam.Map(lambda x: x * x))
# Watch but do not cache the PCollections.
ib.watch(locals())
self._mock_write_cache(p_original, [b''],
self.cache_key_of('source_2', source_2))
ie.current_env().mark_pcollection_computed([source_2])
# Instrument the original pipeline to create the pipeline the user will see.
p_copy = beam.Pipeline.from_runner_api(
p_original.to_runner_api(),
runner=interactive_runner.InteractiveRunner(),
options=options)
ie.current_env().add_derived_pipeline(p_original, p_copy)
instrumenter = instr.build_pipeline_instrument(p_copy)
actual_pipeline = beam.Pipeline.from_runner_api(
proto=instrumenter.instrumented_pipeline_proto(),
runner=interactive_runner.InteractiveRunner(),
options=options)
# Now, build the expected pipeline which replaces the unbounded source with
# a TestStream.
source_1_cache_key = self.cache_key_of('source_1', source_1)
source_2_cache_key = self.cache_key_of('source_2', source_2)
p_expected = beam.Pipeline()
ie.current_env().set_cache_manager(streaming_cache_manager, p_expected)
test_stream = (
p_expected
| TestStream(output_tags=[source_1_cache_key, source_2_cache_key]))
# pylint: disable=expression-not-assigned
((test_stream[self.cache_key_of('source_1', source_1)],
test_stream[self.cache_key_of('source_2', source_2)])
| beam.Flatten()
| 'square1' >> beam.Map(lambda x: x * x)
| 'reify' >> beam.Map(lambda _: _)
| cache.WriteCache(ie.current_env().get_cache_manager(p_expected),
'unused'))
# Test that the TestStream is outputting to the correct PCollection.
class TestStreamVisitor(PipelineVisitor):
def __init__(self):
self.output_tags = set()
def enter_composite_transform(self, transform_node):
self.visit_transform(transform_node)
def visit_transform(self, transform_node):
transform = transform_node.transform
if isinstance(transform, TestStream):
self.output_tags = transform.output_tags
v = TestStreamVisitor()
actual_pipeline.visit(v)
expected_output_tags = set([source_1_cache_key, source_2_cache_key])
actual_output_tags = v.output_tags
self.assertSetEqual(expected_output_tags, actual_output_tags)
# Test that the pipeline is as expected.
assert_pipeline_proto_equal(self, p_expected.to_runner_api(),
instrumenter.instrumented_pipeline_proto())
def test_watch_locals(self):
# test_env serves as local var too.
test_env = ie.InteractiveEnvironment()
ib.watch(locals())
test_env.watch(locals())
self.assertEqual(ie.current_env().watching(), test_env.watching())
def test_instrument_example_unbounded_pipeline_direct_from_source(self):
"""Tests that the it caches PCollections from a source.
"""
# Create the pipeline that will be instrumented.
from apache_beam.options.pipeline_options import StandardOptions
options = StandardOptions(streaming=True)
p_original_direct_source = beam.Pipeline(
interactive_runner.InteractiveRunner(), options)
ie.current_env().set_cache_manager(StreamingCache(cache_dir=None),
p_original_direct_source)
source_1 = p_original_direct_source | 'source1' >> beam.io.ReadFromPubSub(
subscription='projects/fake-project/subscriptions/fake_sub')
# pylint: disable=possibly-unused-variable
p_expected = beam.Pipeline()
# pylint: disable=unused-variable
test_stream = (
p_expected
|
TestStream(output_tags=[self.cache_key_of('source_1', source_1)]))
# Watch but do not cache the PCollections.
ib.watch(locals())
# This should be noop.
utils.watch_sources(p_original_direct_source)
# Instrument the original pipeline to create the pipeline the user will see.
p_copy = beam.Pipeline.from_runner_api(
p_original_direct_source.to_runner_api(),
runner=interactive_runner.InteractiveRunner(),
options=options)
ie.current_env().add_derived_pipeline(p_original_direct_source, p_copy)
instrumenter = instr.build_pipeline_instrument(p_copy)
actual_pipeline = beam.Pipeline.from_runner_api(
proto=instrumenter.instrumented_pipeline_proto(),
runner=interactive_runner.InteractiveRunner(),
options=options)
ie.current_env().add_derived_pipeline(p_original_direct_source,
actual_pipeline)
# Now, build the expected pipeline which replaces the unbounded source with
# a TestStream.
source_1_cache_key = self.cache_key_of('source_1', source_1)
# Test that the TestStream is outputting to the correct PCollection.
class TestStreamVisitor(PipelineVisitor):
def __init__(self):
self.output_tags = set()
def enter_composite_transform(self, transform_node):
self.visit_transform(transform_node)
def visit_transform(self, transform_node):
transform = transform_node.transform
if isinstance(transform, TestStream):
self.output_tags = transform.output_tags
v = TestStreamVisitor()
actual_pipeline.visit(v)
expected_output_tags = set([source_1_cache_key])
actual_output_tags = v.output_tags
self.assertSetEqual(expected_output_tags, actual_output_tags)
# Test that the pipeline is as expected.
assert_pipeline_proto_equal(self, p_expected.to_runner_api(),
instrumenter.instrumented_pipeline_proto())
def test_watch_a_module_by_name(self):
test_env = ie.InteractiveEnvironment()
ib.watch(_module_name)
test_env.watch(_module_name)
self.assertEqual(ie.current_env().watching(), test_env.watching())
def test_watch_a_module_by_module_object(self):
test_env = ie.InteractiveEnvironment()
module = importlib.import_module(_module_name)
ib.watch(module)
test_env.watch(module)
self.assertEqual(ie.current_env().watching(), test_env.watching())
def test_recordings_record(self):
"""Tests that recording pipeline succeeds."""
# Add the TestStream so that it can be cached.
ib.options.recordable_sources.add(TestStream)
# Create a pipeline with an arbitrary amonunt of elements.
p = beam.Pipeline(ir.InteractiveRunner(),
options=PipelineOptions(streaming=True))
# pylint: disable=unused-variable
_ = (p
| TestStream()
.advance_watermark_to(0)
.advance_processing_time(1)
.add_elements(list(range(10)))
.advance_processing_time(1)) # yapf: disable
ib.watch(locals())
ie.current_env().track_user_pipelines()
# Assert that the pipeline starts in a good state.
self.assertEqual(
ib.recordings.describe(p)['state'], PipelineState.STOPPED)
self.assertEqual(ib.recordings.describe(p)['size'], 0)
# Create a lmiter that stops the background caching job when something is
# written to cache. This is used to make ensure that the pipeline is
# functioning properly and that there are no data races with the test.
class SizeLimiter(Limiter):
def __init__(self, pipeline):
self.pipeline = pipeline
self.should_trigger = False
def is_triggered(self):
return (ib.recordings.describe(self.pipeline)['size'] > 0
and self.should_trigger)
limiter = SizeLimiter(p)
ib.options.capture_control.set_limiters_for_test([limiter])
# Assert that a recording can be started only once.
self.assertTrue(ib.recordings.record(p))
self.assertFalse(ib.recordings.record(p))
self.assertEqual(
ib.recordings.describe(p)['state'], PipelineState.RUNNING)
# Wait for the pipeline to start and write something to cache.
limiter.should_trigger = True
for _ in range(60):
if limiter.is_triggered():
break
time.sleep(1)
self.assertTrue(
limiter.is_triggered(),
'Test timed out waiting for limiter to be triggered. This indicates '
'that the BackgroundCachingJob did not cache anything.')
# Assert that a recording can be stopped and can't be started again until
# after the cache is cleared.
ib.recordings.stop(p)
self.assertEqual(
ib.recordings.describe(p)['state'], PipelineState.STOPPED)
self.assertFalse(ib.recordings.record(p))
ib.recordings.clear(p)
self.assertTrue(ib.recordings.record(p))
ib.recordings.stop(p)
def test_watch_class_instance(self):
test_env = ie.InteractiveEnvironment()
ib.watch(self)
test_env.watch(self)
self.assertEqual(ie.current_env().watching(), test_env.watching())
def test_dataframe_caching(self, cell):
# Create a pipeline that exercises the DataFrame API. This will also use
# caching in the background.
with cell: # Cell 1
p = beam.Pipeline(interactive_runner.InteractiveRunner())
ib.watch({'p': p})
with cell: # Cell 2
data = p | beam.Create([
1, 2, 3
]) | beam.Map(lambda x: beam.Row(square=x * x, cube=x * x * x))
with beam.dataframe.allow_non_parallel_operations():
df = to_dataframe(data).reset_index(drop=True)
ib.collect(df)
with cell: # Cell 3
df['output'] = df['square'] * df['cube']
ib.collect(df)
with cell: # Cell 4
df['output'] = 0
ib.collect(df)
# We use a trace through the graph to perform an isomorphism test. The end
# output should look like a linear graph. This indicates that the dataframe
# transform was correctly broken into separate pieces to cache. If caching
# isn't enabled, all the dataframe computation nodes are connected to a
# single shared node.
trace = []
# Only look at the top-level transforms for the isomorphism. The test
# doesn't care about the transform implementations, just the overall shape.
class TopLevelTracer(beam.pipeline.PipelineVisitor):
def _find_root_producer(self,
node: beam.pipeline.AppliedPTransform):
if node is None or not node.full_label:
return None
parent = self._find_root_producer(node.parent)
if parent is None:
return node
return parent
def _add_to_trace(self, node, trace):
if '/' not in str(node):
if node.inputs:
producer = self._find_root_producer(
node.inputs[0].producer)
producer_name = producer.full_label if producer else ''
trace.append((producer_name, node.full_label))
def visit_transform(self, node: beam.pipeline.AppliedPTransform):
self._add_to_trace(node, trace)
def enter_composite_transform(
self, node: beam.pipeline.AppliedPTransform):
self._add_to_trace(node, trace)
p.visit(TopLevelTracer())
# Do the isomorphism test which states that the topological sort of the
# graph yields a linear graph.
trace_string = '\n'.join(str(t) for t in trace)
prev_producer = ''
for producer, consumer in trace:
self.assertEqual(producer, prev_producer, trace_string)
prev_producer = consumer
def _build_an_empty_stream_pipeline():
pipeline_options = PipelineOptions(streaming=True)
p = beam.Pipeline(
interactive_runner.InteractiveRunner(), options=pipeline_options)
ib.watch({'pipeline': p})
return p
def test_streaming_wordcount(self):
class WordExtractingDoFn(beam.DoFn):
def process(self, element):
text_line = element.strip()
words = text_line.split()
return words
# Add the TestStream so that it can be cached.
ib.options.capturable_sources.add(TestStream)
ib.options.capture_duration = timedelta(seconds=5)
p = beam.Pipeline(runner=interactive_runner.InteractiveRunner(),
options=StandardOptions(streaming=True))
data = (
p
| TestStream()
.advance_watermark_to(0)
.advance_processing_time(1)
.add_elements(['to', 'be', 'or', 'not', 'to', 'be'])
.advance_watermark_to(20)
.advance_processing_time(1)
.add_elements(['that', 'is', 'the', 'question'])
| beam.WindowInto(beam.window.FixedWindows(10))) # yapf: disable
counts = (data
| 'split' >> beam.ParDo(WordExtractingDoFn())
| 'pair_with_one' >> beam.Map(lambda x: (x, 1))
| 'group' >> beam.GroupByKey()
| 'count' >> beam.Map(lambda wordones:
(wordones[0], sum(wordones[1]))))
# Watch the local scope for Interactive Beam so that referenced PCollections
# will be cached.
ib.watch(locals())
# This is normally done in the interactive_utils when a transform is
# applied but needs an IPython environment. So we manually run this here.
ie.current_env().track_user_pipelines()
# Create a fake limiter that cancels the BCJ once the main job receives the
# expected amount of results.
class FakeLimiter:
def __init__(self, p, pcoll):
self.p = p
self.pcoll = pcoll
def is_triggered(self):
result = ie.current_env().pipeline_result(self.p)
if result:
try:
results = result.get(self.pcoll)
except ValueError:
return False
return len(results) >= 10
return False
# This sets the limiters to stop reading when the test receives 10 elements
# or after 5 seconds have elapsed (to eliminate the possibility of hanging).
ie.current_env().options.capture_control.set_limiters_for_test(
[FakeLimiter(p, data),
DurationLimiter(timedelta(seconds=5))])
# This tests that the data was correctly cached.
pane_info = PaneInfo(True, True, PaneInfoTiming.UNKNOWN, 0, 0)
expected_data_df = pd.DataFrame([
('to', 0, [IntervalWindow(0, 10)], pane_info),
('be', 0, [IntervalWindow(0, 10)], pane_info),
('or', 0, [IntervalWindow(0, 10)], pane_info),
('not', 0, [IntervalWindow(0, 10)], pane_info),
('to', 0, [IntervalWindow(0, 10)], pane_info),
('be', 0, [IntervalWindow(0, 10)], pane_info),
('that', 20000000, [IntervalWindow(20, 30)], pane_info),
('is', 20000000, [IntervalWindow(20, 30)], pane_info),
('the', 20000000, [IntervalWindow(20, 30)], pane_info),
('question', 20000000, [IntervalWindow(20, 30)], pane_info)
], columns=[0, 'event_time', 'windows', 'pane_info']) # yapf: disable
data_df = ib.collect(data, include_window_info=True)
pd.testing.assert_frame_equal(expected_data_df, data_df)
# This tests that the windowing was passed correctly so that all the data
# is aggregated also correctly.
pane_info = PaneInfo(True, False, PaneInfoTiming.ON_TIME, 0, 0)
expected_counts_df = pd.DataFrame([
('be', 2, 9999999, [IntervalWindow(0, 10)], pane_info),
('not', 1, 9999999, [IntervalWindow(0, 10)], pane_info),
('or', 1, 9999999, [IntervalWindow(0, 10)], pane_info),
('to', 2, 9999999, [IntervalWindow(0, 10)], pane_info),
('is', 1, 29999999, [IntervalWindow(20, 30)], pane_info),
('question', 1, 29999999, [IntervalWindow(20, 30)], pane_info),
('that', 1, 29999999, [IntervalWindow(20, 30)], pane_info),
('the', 1, 29999999, [IntervalWindow(20, 30)], pane_info),
], columns=[0, 1, 'event_time', 'windows', 'pane_info']) # yapf: disable
counts_df = ib.collect(counts, include_window_info=True)
# The group by key has no guarantee of order. So we post-process the DF by
# sorting so we can test equality.
sorted_counts_df = (counts_df
.sort_values(['event_time', 0], ascending=True)
.reset_index(drop=True)) # yapf: disable
pd.testing.assert_frame_equal(expected_counts_df, sorted_counts_df)
