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https://issues.apache.org/jira/browse/BEAM-7131?focusedWorklogId=247340&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-247340
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ASF GitHub Bot logged work on BEAM-7131:
----------------------------------------
Author: ASF GitHub Bot
Created on: 23/May/19 08:33
Start Date: 23/May/19 08:33
Worklog Time Spent: 10m
Work Description: robertwb commented on pull request #8558: [BEAM-7131]
Spark: cache executable stage output to prevent re-computation
URL: https://github.com/apache/beam/pull/8558#discussion_r286832034
##########
File path:
runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkBatchPortablePipelineTranslator.java
##########
@@ -111,6 +112,25 @@ public void translate(final RunnerApi.Pipeline pipeline,
SparkTranslationContext
QueryablePipeline p =
QueryablePipeline.forTransforms(
pipeline.getRootTransformIdsList(), pipeline.getComponents());
+ for (PipelineNode.PTransformNode transformNode :
p.getTopologicallyOrderedTransforms()) {
+ // Pre-scan pipeline to count which pCollections are consumed as inputs
more than once so
+ // their corresponding RDDs can later be cached.
+ for (String inputId :
transformNode.getTransform().getInputsMap().values()) {
+ context.incrementConsumptionCountBy(inputId, 1);
Review comment:
I was thinking that rather than putting consumption counts on the context,
we would just compute them locally and then inform the context of the full set
of collections that need caching (together with their coders). But we can keep
it this way if you'd prefer.
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Issue Time Tracking
-------------------
Worklog Id: (was: 247340)
Time Spent: 4h 40m (was: 4.5h)
> Spark portable runner appears to be repeating work (in TFX example)
> -------------------------------------------------------------------
>
> Key: BEAM-7131
> URL: https://issues.apache.org/jira/browse/BEAM-7131
> Project: Beam
> Issue Type: Bug
> Components: runner-spark
> Reporter: Kyle Weaver
> Assignee: Kyle Weaver
> Priority: Major
> Time Spent: 4h 40m
> Remaining Estimate: 0h
>
> I've been trying to run the TFX Chicago taxi example [1] on the Spark
> portable runner. TFDV works fine, but the preprocess step
> (preprocess_flink.sh [2]) fails with the following error:
> RuntimeError: AlreadyExistsError: file already exists [while running
> 'WriteTransformFn/WriteTransformFn']
> Assets are being written multiple times to different temp directories, which
> is okay, but the error occurs when they are copied to the same permanent
> output directory. Specifically, the copy tree operation in transform_fn_io.py
> [3] is run twice with the same output directory. The error doesn't occur when
> that code is modified to allow overwriting existing files, but that's only a
> shallow fix. While the TF transform should probably be made idempotent, this
> is also an issue with the Spark runner, which shouldn't be repeating work
> like this regularly (in the absence of a failure condition).
> [1] [https://github.com/tensorflow/tfx/tree/master/tfx/examples/chicago_taxi]
> [2]
> [https://github.com/tensorflow/tfx/blob/master/tfx/examples/chicago_taxi/preprocess_flink.sh]
> [3]
> [https://github.com/tensorflow/transform/blob/master/tensorflow_transform/beam/tft_beam_io/transform_fn_io.py#L33-L45]
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