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https://issues.apache.org/jira/browse/BEAM-7926?focusedWorklogId=332802&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-332802
]
ASF GitHub Bot logged work on BEAM-7926:
----------------------------------------
Author: ASF GitHub Bot
Created on: 23/Oct/19 18:57
Start Date: 23/Oct/19 18:57
Worklog Time Spent: 10m
Work Description: KevinGG commented on pull request #9741: [BEAM-7926]
Visualize PCollection
URL: https://github.com/apache/beam/pull/9741#discussion_r338223025
##########
File path:
sdks/python/apache_beam/runners/interactive/display/pcoll_visualization.py
##########
@@ -0,0 +1,279 @@
+#
+# 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 visualizes PCollection data.
+
+For internal use only; no backwards-compatibility guarantees.
+Only works with Python 3.5+.
+"""
+from __future__ import absolute_import
+
+import base64
+import logging
+from datetime import timedelta
+
+from pandas.io.json import json_normalize
+
+from apache_beam import pvalue
+from apache_beam.runners.interactive import interactive_environment as ie
+from apache_beam.runners.interactive import pipeline_instrument as instr
+
+# jsons doesn't support < Python 3.5. Work around with json for legacy tests.
+# TODO(BEAM-8288): clean up once Py2 is deprecated from Beam.
+try:
+ import jsons
+ _pv_jsons_load = jsons.load
+ _pv_jsons_dump = jsons.dump
+except ImportError:
+ import json
+ _pv_jsons_load = json.load
+ _pv_jsons_dump = json.dump
+
+try:
+ from facets_overview.generic_feature_statistics_generator import
GenericFeatureStatisticsGenerator
+ _facets_gfsg_ready = True
+except ImportError:
+ _facets_gfsg_ready = False
+
+try:
+ from IPython.core.display import HTML
+ from IPython.core.display import Javascript
+ from IPython.core.display import display
+ from IPython.core.display import display_javascript
+ from IPython.core.display import update_display
+ _ipython_ready = True
+except ImportError:
+ _ipython_ready = False
+
+try:
+ from timeloop import Timeloop
+ _tl_ready = True
+except ImportError:
+ _tl_ready = False
+
+# 1-d types that need additional normalization to be compatible with DataFrame.
+_one_dimension_types = (int, float, str, bool, list, tuple)
+
+_DIVE_SCRIPT_TEMPLATE = """
+ document.querySelector("#{display_id}").data = {jsonstr};"""
+_DIVE_HTML_TEMPLATE = """
+ <script
src="https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js";></script>
+ <link rel="import"
href="https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html";>
+ <facets-dive sprite-image-width="{sprite_size}"
sprite-image-height="{sprite_size}" id="{display_id}"
height="600"></facets-dive>
+ <script>
+ document.querySelector("#{display_id}").data = {jsonstr};
+ </script>"""
+_OVERVIEW_SCRIPT_TEMPLATE = """
+ document.querySelector("#{display_id}").protoInput =
"{protostr}";
+ """
+_OVERVIEW_HTML_TEMPLATE = """
+ <script
src="https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js";></script>
+ <link rel="import"
href="https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html";>
+ <facets-overview id="{display_id}"></facets-overview>
+ <script>
+ document.querySelector("#{display_id}").protoInput =
"{protostr}";
+ </script>"""
+_DATAFRAME_PAGINATION_TEMPLATE = """
+ <script
src="https://ajax.googleapis.com/ajax/libs/jquery/2.2.2/jquery.min.js";></script>
+ <script
src="https://cdn.datatables.net/1.10.16/js/jquery.dataTables.js";></script>
+ <link rel="stylesheet"
href="https://cdn.datatables.net/1.10.16/css/jquery.dataTables.css";>
+ {dataframe_html}
+ <script>
+ $("#{table_id}").DataTable();
+ </script>"""
+
+
+def visualize(pcoll, dynamic_plotting_interval=None):
+ """Visualizes the data of a given PCollection. Optionally enables dynamic
+ plotting with interval in seconds if the PCollection is being produced by a
+ running pipeline or the pipeline is streaming indefinitely. The function
+ always returns immediately and is asynchronous when dynamic plotting is on.
+
+ If dynamic plotting enabled, the visualization is updated continuously until
+ the pipeline producing the PCollection is in an end state. The visualization
+ would be anchored to the notebook cell output area. The function
+ asynchronously returns a handle to the visualization job immediately. The
user
+ could manually do::
+
+ # In one notebook cell, enable dynamic plotting every 1 second:
+ handle = visualize(pcoll, dynamic_plotting_interval=1)
+ # Visualization anchored to the cell's output area.
+ # In a different cell:
+ handle.stop()
+ # Will stop the dynamic plotting of the above visualization manually.
+ # Otherwise, dynamic plotting ends when pipeline is not running anymore.
+
+ If dynamic_plotting is not enabled (by default), None is returned.
+ """
+ if not _ipython_ready:
+ return None
+ pv = PCollVisualization(pcoll)
+ pv.display_facets()
+
+ if dynamic_plotting_interval and _tl_ready:
+ # Disables the verbose logging from timeloop.
+ logging.getLogger('timeloop').disabled = True
+ tl = Timeloop()
+
+ def dynamic_plotting(pcoll, pv, tl):
+ @tl.job(interval=timedelta(seconds=dynamic_plotting_interval))
+ def continuous_update_display(): # pylint: disable=unused-variable
+ # Always creates a new PCollVisualization instance when the
+ # PCollection materialization is being updated and dynamic
+ # plotting is in-process.
+ updated_pv = PCollVisualization(pcoll)
+ updated_pv.display_facets(updating_pv=pv)
+ if ie.current_env().is_terminated(pcoll.pipeline):
+ try:
+ tl.stop()
+ except RuntimeError:
+ # The job can only be stopped once. Ignore excessive stops.
+ pass
+
+ tl.start()
+ return tl
+
+ return dynamic_plotting(pcoll, pv, tl)
+ return None
+
+
+class PCollVisualization(object):
Review comment:
Will change to the full name.
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Issue Time Tracking
-------------------
Worklog Id: (was: 332802)
Time Spent: 12h 40m (was: 12.5h)
> Visualize PCollection with Interactive Beam
> -------------------------------------------
>
> Key: BEAM-7926
> URL: https://issues.apache.org/jira/browse/BEAM-7926
> Project: Beam
> Issue Type: New Feature
> Components: runner-py-interactive
> Reporter: Ning Kang
> Assignee: Ning Kang
> Priority: Major
> Time Spent: 12h 40m
> Remaining Estimate: 0h
>
> Support auto plotting / charting of materialized data of a given PCollection
> with Interactive Beam.
> Say an Interactive Beam pipeline defined as
> p = create_pipeline()
> pcoll = p | 'Transform' >> transform()
> The use can call a single function and get auto-magical charting of the data
> as materialized pcoll.
> e.g., visualize(pcoll)
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