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https://issues.apache.org/jira/browse/BEAM-7760?focusedWorklogId=290630&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-290630
]
ASF GitHub Bot logged work on BEAM-7760:
----------------------------------------
Author: ASF GitHub Bot
Created on: 07/Aug/19 17:58
Start Date: 07/Aug/19 17:58
Worklog Time Spent: 10m
Work Description: KevinGG commented on pull request #9278: [BEAM-7760]
Added iBeam module
URL: https://github.com/apache/beam/pull/9278#discussion_r311655571
##########
File path: sdks/python/apache_beam/runners/interactive/interactive_beam.py
##########
@@ -0,0 +1,199 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Module of the current iBeam (interactive Beam) environment.
+
+The purpose of the module is to reduce the learning curve of iBeam users,
+provide a single place for importing and add sugar syntax for all iBeam
+components. It gives users capability to manipulate existing environment for
+interactive beam, TODO(ningk) run interactive pipeline on selected runner as
+normal pipeline, create pipeline with interactive runner and visualize
+PCollections as bounded dataset.
+
+Note: iBeam works the same as normal Beam with DirectRunner when not in an
+interactively environment such as Jupyter lab or Jupyter Notebook. You can also
+run pipeline created by iBeam as normal Beam pipeline by run_pipeline() with
+desired runners.
+"""
+
+import importlib
+
+import apache_beam as beam
+from apache_beam.runners.interactive import interactive_runner
+
+_ibeam_env = None
+
+
+def watch(watchable):
+ """Watches a watchable so that iBeam can understand your pipeline.
Review comment:
R: @rosetn
PTAL
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Issue Time Tracking
-------------------
Worklog Id: (was: 290630)
Time Spent: 1h (was: 50m)
> Interactive Beam Caching PCollections bound to user defined vars in notebook
> ----------------------------------------------------------------------------
>
> Key: BEAM-7760
> URL: https://issues.apache.org/jira/browse/BEAM-7760
> Project: Beam
> Issue Type: New Feature
> Components: examples-python
> Reporter: Ning Kang
> Priority: Major
> Time Spent: 1h
> Remaining Estimate: 0h
>
> Cache only PCollections bound to user defined variables in a pipeline when
> running pipeline with interactive runner in jupyter notebooks.
> [Interactive
> Beam|[https://github.com/apache/beam/tree/master/sdks/python/apache_beam/runners/interactive]]
> has been caching and using caches of "leaf" PCollections for interactive
> execution in jupyter notebooks.
> The interactive execution is currently supported so that when appending new
> transforms to existing pipeline for a new run, executed part of the pipeline
> doesn't need to be re-executed.
> A PCollection is "leaf" when it is never used as input in any PTransform in
> the pipeline.
> The problem with building caches and pipeline to execute around "leaf" is
> that when a PCollection is consumed by a sink with no output, the pipeline to
> execute built will miss the subgraph generating and consuming that
> PCollection.
> An example, "ReadFromPubSub --> WirteToPubSub" will result in an empty
> pipeline.
> Caching around PCollections bound to user defined variables and replacing
> transforms with source and sink of caches could resolve the pipeline to
> execute properly under the interactive execution scenario. Also, cached
> PCollection now can trace back to user code and can be used for user data
> visualization if user wants to do it.
> E.g.,
> {code:java}
> // ...
> p = beam.Pipeline(interactive_runner.InteractiveRunner(),
> options=pipeline_options)
> messages = p | "Read" >> beam.io.ReadFromPubSub(subscription='...')
> messages | "Write" >> beam.io.WriteToPubSub(topic_path)
> result = p.run()
> // ...
> visualize(messages){code}
> The interactive runner automatically figures out that PCollection
> {code:java}
> messages{code}
> created by
> {code:java}
> p | "Read" >> beam.io.ReadFromPubSub(subscription='...'){code}
> should be cached and reused if the notebook user appends more transforms.
> And once the pipeline gets executed, the user could use any
> visualize(PCollection) module to visualize the data statically (batch) or
> dynamically (stream)
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