def has_source_to_cache(user_pipeline):
"""Determines if a user-defined pipeline contains any source that need to be
cached. If so, also immediately wrap current cache manager held by current
interactive environment into a streaming cache if this has not been done.
The wrapping doesn't invalidate existing cache in any way.
This can help determining if a background source recording job is needed to
write cache for sources and if a test stream service is needed to serve the
cache.
Throughout the check, if source-to-cache has changed from the last check, it
also cleans up the invalidated cache early on.
"""
# TODO(BEAM-8335): we temporarily only cache replaceable unbounded sources.
# Add logic for other cacheable sources here when they are available.
has_cache = utils.has_unbounded_sources(user_pipeline)
if has_cache:
if not isinstance(
ie.current_env().get_cache_manager(user_pipeline,
create_if_absent=True),
streaming_cache.StreamingCache):
file_based_cm = ie.current_env().get_cache_manager(user_pipeline)
ie.current_env().set_cache_manager(
streaming_cache.StreamingCache(
file_based_cm._cache_dir,
is_cache_complete=is_cache_complete,
sample_resolution_sec=1.0,
saved_pcoders=file_based_cm._saved_pcoders), user_pipeline)
return has_cache
def has_source_to_cache(user_pipeline):
"""Determines if a user-defined pipeline contains any source that need to be
cached. If so, also immediately wrap current cache manager held by current
interactive environment into a streaming cache if this has not been done.
The wrapping doesn't invalidate existing cache in any way.
This can help determining if a background caching job is needed to write cache
for sources and if a test stream service is needed to serve the cache.
Throughout the check, if source-to-cache has changed from the last check, it
also cleans up the invalidated cache early on.
"""
from apache_beam.runners.interactive import pipeline_instrument as instr
# TODO(BEAM-8335): we temporarily only cache replaceable unbounded sources.
# Add logic for other cacheable sources here when they are available.
has_cache = instr.has_unbounded_sources(user_pipeline)
if has_cache:
if not isinstance(ie.current_env().cache_manager(),
streaming_cache.StreamingCache):
# Wrap the cache manager into a streaming cache manager. Note this
# does not invalidate the current cache manager.
def is_cache_complete():
job = ie.current_env().get_background_caching_job(user_pipeline)
is_done = job and job.is_done()
cache_changed = is_source_to_cache_changed(
user_pipeline, update_cached_source_signature=False)
return is_done and not cache_changed
ie.current_env().set_cache_manager(
streaming_cache.StreamingCache(
ie.current_env().cache_manager()._cache_dir,
is_cache_complete=is_cache_complete,
sample_resolution_sec=1.0))
return has_cache
def test_background_caching_pipeline_proto(self):
ie.new_env(cache_manager=streaming_cache.StreamingCache(cache_dir=None))
p = beam.Pipeline(interactive_runner.InteractiveRunner())
# Test that the two ReadFromPubSub are correctly cut out.
a = p | 'ReadUnboundedSourceA' >> beam.io.ReadFromPubSub(
subscription='projects/fake-project/subscriptions/fake_sub')
b = p | 'ReadUnboundedSourceB' >> beam.io.ReadFromPubSub(
subscription='projects/fake-project/subscriptions/fake_sub')
# Add some extra PTransform afterwards to make sure that only the unbounded
# sources remain.
c = (a, b) | beam.CoGroupByKey()
_ = c | beam.Map(lambda x: x)
ib.watch(locals())
instrumenter = instr.build_pipeline_instrument(p)
actual_pipeline = instrumenter.background_caching_pipeline_proto()
# Now recreate the expected pipeline, which should only have the unbounded
# sources.
p = beam.Pipeline(interactive_runner.InteractiveRunner())
a = p | 'ReadUnboundedSourceA' >> beam.io.ReadFromPubSub(
subscription='projects/fake-project/subscriptions/fake_sub')
_ = (
a
| 'reify a' >> beam.Map(lambda _: _)
| 'a' >> cache.WriteCache(ie.current_env().cache_manager(), ''))
b = p | 'ReadUnboundedSourceB' >> beam.io.ReadFromPubSub(
subscription='projects/fake-project/subscriptions/fake_sub')
_ = (
b
| 'reify b' >> beam.Map(lambda _: _)
| 'b' >> cache.WriteCache(ie.current_env().cache_manager(), ''))
expected_pipeline = p.to_runner_api(
return_context=False, use_fake_coders=True)
assert_pipeline_proto_equal(self, expected_pipeline, actual_pipeline)
def test_instrument_example_unbounded_pipeline_to_multiple_read_cache(
self):
"""Tests that the instrumenter works for multiple unbounded sources.
"""
# Create a new interactive environment to make the test idempotent.
ie.new_env(cache_manager=streaming_cache.StreamingCache(
cache_dir=None))
# Create the pipeline that will be instrumented.
p_original = beam.Pipeline(interactive_runner.InteractiveRunner())
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())
def cache_key_of(name, pcoll):
return name + '_' + str(id(pcoll)) + '_' + str(id(pcoll.producer))
for name, pcoll in locals().items():
if not isinstance(pcoll, beam.pvalue.PCollection):
continue
cache_key = cache_key_of(name, pcoll)
self._mock_write_cache([TestStreamPayload()], 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 = cache_key_of('source_1', source_1)
source_2_cache_key = cache_key_of('source_2', source_2)
p_expected = beam.Pipeline()
test_stream = (p_expected
| TestStream(output_tags=[
cache_key_of('source_1', source_1),
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_instrument_example_unbounded_pipeline_to_read_cache_not_cached(
self):
"""Tests that the instrumenter works when the PCollection is not cached.
"""
# Create a new interactive environment to make the test idempotent.
ie.new_env(cache_manager=streaming_cache.StreamingCache(
cache_dir=None))
# 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)
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())
def cache_key_of(name, pcoll):
return name + '_' + str(id(pcoll)) + '_' + str(id(pcoll.producer))
# 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 = cache_key_of('source_1', source_1)
p_expected = beam.Pipeline()
test_stream = (
p_expected
| TestStream(output_tags=[cache_key_of('source_1', source_1)]))
# pylint: disable=expression-not-assigned
(test_stream
| 'square1' >> beam.Map(lambda x: x * x)
| 'reify' >> beam.Map(lambda _: _)
| cache.WriteCache(ie.current_env().cache_manager(), '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(use_fake_coders=True),
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 a new interactive environment to make the test idempotent.
ie.new_env(cache_manager=streaming_cache.StreamingCache(
cache_dir=None))
# 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)
# 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())
def cache_key_of(name, pcoll):
return name + '_' + str(id(pcoll)) + '_' + str(id(pcoll.producer))
# 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 = \
cache_key_of('synthetic_var_' + str(id(intermediate_source_pcoll)),
intermediate_source_pcoll)
p_expected = beam.Pipeline()
test_stream = (
p_expected
| TestStream(output_tags=[intermediate_source_pcoll_cache_key]))
# pylint: disable=expression-not-assigned
(test_stream
| 'square1' >> beam.Map(lambda e: e)
| 'reify' >> beam.Map(lambda _: _)
| cache.WriteCache(ie.current_env().cache_manager(), '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(use_fake_coders=True),
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 a new interactive environment to make the test idempotent.
ie.new_env(cache_manager=streaming_cache.StreamingCache(cache_dir=None))
# 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)
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([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)
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()
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().cache_manager(), '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(use_fake_coders=True),
instrumenter.instrumented_pipeline_proto())