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https://issues.apache.org/jira/browse/BEAM-7926?focusedWorklogId=335833&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-335833
]
ASF GitHub Bot logged work on BEAM-7926:
----------------------------------------
Author: ASF GitHub Bot
Created on: 30/Oct/19 00:13
Start Date: 30/Oct/19 00:13
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_r340383572
##########
File path:
sdks/python/apache_beam/runners/interactive/display/pcoll_visualization.py
##########
@@ -0,0 +1,269 @@
+#
+# 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
+
+try:
+ import jsons # pylint: disable=import-error
+ from IPython import get_ipython # pylint: disable=import-error
+ from IPython.core.display import HTML # pylint: disable=import-error
+ from IPython.core.display import Javascript # pylint: disable=import-error
+ from IPython.core.display import display # pylint: disable=import-error
+ from IPython.core.display import display_javascript # pylint:
disable=import-error
+ from IPython.core.display import update_display # pylint:
disable=import-error
+ from facets_overview.generic_feature_statistics_generator import
GenericFeatureStatisticsGenerator # pylint: disable=import-error
+ from timeloop import Timeloop # pylint: disable=import-error
+
+ if get_ipython():
+ _pcoll_visualization_ready = True
+ else:
+ _pcoll_visualization_ready = False
+except ImportError:
+ _pcoll_visualization_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):
Review comment:
Yes, I'll mark it as experimental! Actually the whole module is marked as
`For internal use only; no backwards-compatibility guarantees.`
For renaming, we only need to touch the `visualize()` function that has not
been implemented in module `interactive_beam`. Since a notebook user should
only use APIs provided in that module.
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Issue Time Tracking
-------------------
Worklog Id: (was: 335833)
Time Spent: 17h 50m (was: 17h 40m)
> 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: 17h 50m
> 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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