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https://issues.apache.org/jira/browse/BEAM-8335?focusedWorklogId=401623&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-401623
]
ASF GitHub Bot logged work on BEAM-8335:
----------------------------------------
Author: ASF GitHub Bot
Created on: 11/Mar/20 17:56
Start Date: 11/Mar/20 17:56
Worklog Time Spent: 10m
Work Description: pabloem commented on pull request #11005: [BEAM-8335]
Modify the StreamingCache to subclass the CacheManager
URL: https://github.com/apache/beam/pull/11005#discussion_r391156960
##########
File path:
sdks/python/apache_beam/runners/interactive/caching/streaming_cache.py
##########
@@ -19,15 +19,298 @@
from __future__ import absolute_import
+import itertools
+import os
+import shutil
+import tempfile
+import time
+
+import apache_beam as beam
+from apache_beam.portability.api.beam_interactive_api_pb2 import
TestStreamFileHeader
+from apache_beam.portability.api.beam_interactive_api_pb2 import
TestStreamFileRecord
from apache_beam.portability.api.beam_runner_api_pb2 import TestStreamPayload
+from apache_beam.runners.interactive.cache_manager import CacheManager
+from apache_beam.runners.interactive.cache_manager import
SafeFastPrimitivesCoder
+from apache_beam.testing.test_stream import ReverseTestStream
from apache_beam.utils import timestamp
-class StreamingCache(object):
+class StreamingCacheSink(beam.PTransform):
+ """A PTransform that writes TestStreamFile(Header|Records)s to file.
+
+ This transform takes in an arbitrary element stream and writes the
best-effort
+ list of TestStream events (as TestStreamFileRecords) to file.
+
+ Note that this PTransform is assumed to be only run on a single machine where
+ the following assumptions are correct: elements come in ordered, no two
+ transforms are writing to the same file. This PTransform is assumed to only
+ run correctly with the DirectRunner.
+ """
+ def __init__(
+ self,
+ cache_dir,
+ filename,
+ sample_resolution_sec,
+ coder=SafeFastPrimitivesCoder()):
+ self._cache_dir = cache_dir
+ self._filename = filename
+ self._sample_resolution_sec = sample_resolution_sec
+ self._coder = coder
+ self._path = os.path.join(self._cache_dir, self._filename)
+
+ @property
+ def path(self):
+ """Returns the path the sink leads to."""
+ return self._path
+
+ def expand(self, pcoll):
+ class StreamingWriteToText(beam.DoFn):
+ """DoFn that performs the writing.
+
+ Note that the other file writing methods cannot be used in streaming
+ contexts.
+ """
+ def __init__(self, full_path, coder=SafeFastPrimitivesCoder()):
+ self._full_path = full_path
+ self._coder = coder
+
+ # Try and make the given path.
+ os.makedirs(os.path.dirname(full_path), exist_ok=True)
+
+ def start_bundle(self):
+ # Open the file for 'append-mode' and writing 'bytes'.
+ self._fh = open(self._full_path, 'ab')
+
+ def finish_bundle(self):
+ self._fh.close()
+
+ def process(self, e):
+ """Appends the given element to the file.
+ """
+ self._fh.write(self._coder.encode(e))
+ self._fh.write(b'\n')
+
+ return (
+ pcoll
+ | ReverseTestStream(
+ output_tag=self._filename,
+ sample_resolution_sec=self._sample_resolution_sec,
+ output_format=ReverseTestStream.Format.
+ SERIALIZED_TEST_STREAM_FILE_RECORDS,
+ coder=self._coder)
+ | beam.ParDo(
+ StreamingWriteToText(full_path=self._path, coder=self._coder)))
+
+
+class StreamingCacheSource:
+ """A class that reads and parses TestStreamFile(Header|Reader)s.
+
+ This source operates in the following way:
+
+ 1. Wait for up to `timeout_secs` for the file to be available.
+ 2. Read, parse, and emit the entire contents of the file
+ 3. Wait for more events to come or until `is_cache_complete` returns True
+ 4. If there are more events, then go to 2
+ 5. Otherwise, stop emitting.
+
+ This class is used to read from file and send its to the TestStream via the
+ StreamingCacheManager.Reader.
+ """
+ def __init__(
+ self,
+ cache_dir,
+ labels,
+ is_cache_complete=None,
+ coder=SafeFastPrimitivesCoder()):
+ self._cache_dir = cache_dir
+ self._coder = coder
+ self._labels = labels
+ self._is_cache_complete = (
+ is_cache_complete if is_cache_complete else lambda: True)
+
+ def _wait_until_file_exists(self, timeout_secs=30):
+ """Blocks until the file exists for a maximum of timeout_secs.
+ """
+ f = None
+ now_secs = time.time()
+ timeout_timestamp_secs = now_secs + timeout_secs
+
+ # Wait for up to `timeout_secs` for the file to be available.
+ while f is None and now_secs < timeout_timestamp_secs:
+ now_secs = time.time()
+ try:
+ path = os.path.join(self._cache_dir, *self._labels)
+ f = open(path, mode='r')
+ except EnvironmentError as e:
+ # For Python2 and Python3 compatibility, this checks the
+ # EnvironmentError to see if the file exists.
+ # TODO: Change this to a FileNotFoundError when Python3 migration is
+ # complete.
+ import errno
+ if e.errno != errno.ENOENT:
+ # Raise the exception if it is not a FileNotFoundError.
+ raise
+ time.sleep(1)
+ if now_secs >= timeout_timestamp_secs:
+ raise RuntimeError(
+ "Timed out waiting for file '{}' to be available".format(path))
+ return f
+
+ def _emit_from_file(self, fh, tail):
+ """Emits the TestStreamFile(Header|Record)s from file.
+
+ This returns a generator to be able to read all lines from the given file.
+ If `tail` is True, then it will wait until the cache is complete to exit.
+ Otherwise, it will read the file only once.
+ """
+ # Always read at least once to read the whole file.
+ while True:
+ pos = fh.tell()
+ line = fh.readline()
Review comment:
We've discussed this. Sam will add the fix by re-reading from the initial
position.
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Issue Time Tracking
-------------------
Worklog Id: (was: 401623)
Time Spent: 105h 50m (was: 105h 40m)
> Add streaming support to Interactive Beam
> -----------------------------------------
>
> Key: BEAM-8335
> URL: https://issues.apache.org/jira/browse/BEAM-8335
> Project: Beam
> Issue Type: Improvement
> Components: runner-py-interactive
> Reporter: Sam Rohde
> Assignee: Sam Rohde
> Priority: Major
> Time Spent: 105h 50m
> Remaining Estimate: 0h
>
> This issue tracks the work items to introduce streaming support to the
> Interactive Beam experience. This will allow users to:
> * Write and run a streaming job in IPython
> * Automatically cache records from unbounded sources
> * Add a replay experience that replays all cached records to simulate the
> original pipeline execution
> * Add controls to play/pause/stop/step individual elements from the cached
> records
> * Add ability to inspect/visualize unbounded PCollections
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