[
https://issues.apache.org/jira/browse/BEAM-8016?focusedWorklogId=345740&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-345740
]
ASF GitHub Bot logged work on BEAM-8016:
----------------------------------------
Author: ASF GitHub Bot
Created on: 19/Nov/19 01:16
Start Date: 19/Nov/19 01:16
Worklog Time Spent: 10m
Work Description: KevinGG commented on pull request #10132: [BEAM-8016]
Pipeline Graph
URL: https://github.com/apache/beam/pull/10132#discussion_r347031607
##########
File path:
sdks/python/apache_beam/runners/interactive/display/pipeline_graph.py
##########
@@ -136,14 +159,50 @@ def _generate_graph_dicts(self):
vertex_dict[invisible_leaf] = {'style': 'invis'}
self._edge_to_vertex_pairs[pcoll_id].append(
(transform.unique_name, invisible_leaf))
- edge_dict[(transform.unique_name, invisible_leaf)] = {}
+ if self._pin:
+ edge_label = {'label':
+ self._pin.cacheable_var_by_pcoll_id(pcoll_id)}
+ edge_dict[(transform.unique_name, invisible_leaf)] = edge_label
+ else:
+ edge_dict[(transform.unique_name, invisible_leaf)] = {}
+ # For PCollections with more than one consuming PTransform, we also add
+ # an invisible dummy node to diverge the edge in the middle as the
+ # single output is used by multiple down stream PTransforms as inputs
+ # instead of emitting multiple edges.
+ elif len(self._consumers[pcoll_id]) > 1:
+ intermediate_dummy = 'diverge{}'.format(
+ hash(pcoll_id) % 10000)
+ vertex_dict[intermediate_dummy] = {'shape': 'point',
+ 'width': '0'}
+ for consumer in self._consumers[pcoll_id]:
+ producer_name = transform.unique_name
+ consumer_name = transforms[consumer].unique_name
+ self._edge_to_vertex_pairs[pcoll_id].append(
+ (producer_name, intermediate_dummy))
+ if self._pin:
+ edge_dict[(producer_name, intermediate_dummy)] = {
+ 'arrowhead': 'none',
+ 'label':
+ self._pin.cacheable_var_by_pcoll_id(pcoll_id)}
+ else:
+ edge_dict[(producer_name, intermediate_dummy)] = {
+ 'arrowhead': 'none'}
+ self._edge_to_vertex_pairs[pcoll_id].append(
+ (intermediate_dummy, consumer_name))
+ edge_dict[(intermediate_dummy, consumer_name)] = {}
else:
for consumer in self._consumers[pcoll_id]:
producer_name = transform.unique_name
consumer_name = transforms[consumer].unique_name
self._edge_to_vertex_pairs[pcoll_id].append(
(producer_name, consumer_name))
- edge_dict[(producer_name, consumer_name)] = {}
+ if self._pin:
+ edge_dict[(producer_name, consumer_name)] = {
+ 'label':
+ self._pin.cacheable_var_by_pcoll_id(pcoll_id)
+ }
+ else:
+ edge_dict[(producer_name, consumer_name)] = {}
Review comment:
The difference introduced:
Before:
After (edge diverge for single output, user variable names labelled
PCollections):
In the future:
With data-centric user flow, notebook cell execution metadata and new Beam
pipeline graph proposal:
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Issue Time Tracking
-------------------
Worklog Id: (was: 345740)
Time Spent: 3h 20m (was: 3h 10m)
> Render Beam Pipeline as DOT with Interactive Beam
> ---------------------------------------------------
>
> Key: BEAM-8016
> URL: https://issues.apache.org/jira/browse/BEAM-8016
> Project: Beam
> Issue Type: Improvement
> Components: runner-py-interactive
> Reporter: Ning Kang
> Assignee: Ning Kang
> Priority: Major
> Time Spent: 3h 20m
> Remaining Estimate: 0h
>
> With work in https://issues.apache.org/jira/browse/BEAM-7760, Beam pipeline
> converted to DOT then rendered should mark user defined variables on edges.
> With work in https://issues.apache.org/jira/browse/BEAM-7926, it might be
> redundant or confusing to render arbitrary random sample PCollection data on
> edges.
> We'll also make sure edges in the graph corresponds to output -> input
> relationship in the user defined pipeline. Each edge is one output. If
> multiple down stream inputs take the same output, it should be rendered as
> one edge diverging into two instead of two edges.
> For advanced interactivity highlight where each execution highlights the part
> of the pipeline really executed from the original pipeline, we'll also
> provide the support in beta.
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