[
https://issues.apache.org/jira/browse/BEAM-7131?focusedWorklogId=241939&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-241939
]
ASF GitHub Bot logged work on BEAM-7131:
----------------------------------------
Author: ASF GitHub Bot
Created on: 14/May/19 18:29
Start Date: 14/May/19 18:29
Worklog Time Spent: 10m
Work Description: ibzib 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_r283905278
##########
File path:
runners/spark/src/test/java/org/apache/beam/runners/spark/SparkPortableExecutionTest.java
##########
@@ -163,4 +168,75 @@ public void process(ProcessContext context) {
Thread.sleep(1000);
}
}
+
+ /**
+ * Verifies that each executable stage runs exactly once, even if that
executable stage has
+ * multiple outputs. While re-computation may be necessary in the event of
failure, re-computation
+ * of a whole executable stage is expensive and can cause unexpected
behavior when the executable
+ * stage has side effects (BEAM-7131).
+ */
+ @Test(timeout = 120_000)
+ public void testParDoWithSideEffects() throws Exception {
+ PipelineOptions options = PipelineOptionsFactory.create();
+ options.setRunner(CrashingRunner.class);
+ options
+ .as(PortablePipelineOptions.class)
+ .setDefaultEnvironmentType(Environments.ENVIRONMENT_EMBEDDED);
+ Pipeline pipeline = Pipeline.create(options);
+ Path path = FileSystems.getDefault().getPath(TEST_DIR_PREFIX,
UUID.randomUUID().toString());
+ File dir = new File(path.toString());
+ PCollection<String> a =
+ pipeline
+ .apply("impulse", Impulse.create())
+ .apply(
+ "A",
+ ParDo.of(
+ new DoFn<byte[], String>() {
+ @ProcessElement
+ public void process(ProcessContext context) throws
Exception {
+ context.output("A");
+ // ParDos A, B, and C will all be fused together into
the same executable
+ // stage. This check verifies that stage is not run
more than once by
+ // enacting a side effect via the local file system.
+ Assert.assertFalse("ParDo A should only have been run
once.", dir.exists());
+ dir.mkdirs();
+ }
+ }));
+ PCollection<KV<String, String>> b =
+ a.apply(
+ "B",
+ ParDo.of(
+ new DoFn<String, KV<String, String>>() {
+ @ProcessElement
+ public void process(ProcessContext context) throws Exception
{
+ context.output(KV.of(context.element(), "B"));
+ }
+ }));
+ PCollection<KV<String, String>> c =
+ a.apply(
+ "C",
+ ParDo.of(
+ new DoFn<String, KV<String, String>>() {
+ @ProcessElement
+ public void process(ProcessContext context) throws Exception
{
+ context.output(KV.of(context.element(), "C"));
+ }
+ }));
+ // Use GBKs to force re-computation of executable stage unless cached.
+ b.apply(GroupByKey.create());
+ c.apply(GroupByKey.create());
+ RunnerApi.Pipeline pipelineProto = PipelineTranslation.toProto(pipeline);
+ JobInvocation jobInvocation =
+ SparkJobInvoker.createJobInvocation(
+ "testParDoWithSideEffects",
+ "testParDoWithSideEffectsRetrievalToken",
+ sparkJobExecutor,
+ pipelineProto,
+ options.as(SparkPipelineOptions.class));
+ jobInvocation.start();
+ while (jobInvocation.getState() != Enum.DONE) {
+ Thread.sleep(1000);
Review comment:
Looks like `sameThreadExecutor` is deprecated and `newDirectExecutorService`
is preferred now, so I will use that
https://google.github.io/guava/releases/20.0/api/docs/com/google/common/util/concurrent/MoreExecutors.html#sameThreadExecutor--
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Issue Time Tracking
-------------------
Worklog Id: (was: 241939)
Time Spent: 50m (was: 40m)
> 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: 50m
> 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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