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https://issues.apache.org/jira/browse/BEAM-4783?focusedWorklogId=142120&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-142120
]
ASF GitHub Bot logged work on BEAM-4783:
----------------------------------------
Author: ASF GitHub Bot
Created on: 07/Sep/18 10:14
Start Date: 07/Sep/18 10:14
Worklog Time Spent: 10m
Work Description: iemejia commented on a change in pull request #6181:
[BEAM-4783] Add bundleSize for splitting BoundedSources.
URL: https://github.com/apache/beam/pull/6181#discussion_r215893873
##########
File path:
runners/spark/src/main/java/org/apache/beam/runners/spark/io/SourceRDD.java
##########
@@ -80,35 +81,16 @@ public Bounded(
super(sc, NIL, JavaSparkContext$.MODULE$.fakeClassTag());
this.source = source;
this.options = options;
- // the input parallelism is determined by Spark's scheduler backend.
- // when running on YARN/SparkDeploy it's the result of max(totalCores,
2).
- // when running on Mesos it's 8.
- // when running local it's the total number of cores (local = 1,
local[N] = N,
- // local[*] = estimation of the machine's cores).
- // ** the configuration "spark.default.parallelism" takes precedence
over all of the above **
- this.numPartitions = sc.defaultParallelism();
- checkArgument(this.numPartitions > 0, "Number of partitions must be
greater than zero.");
+ this.bundleSize =
options.get().as(SparkPipelineOptions.class).getBundleSize();
+ checkArgument(this.bundleSize > 0, "Bundle size must be greater than
zero.");
this.stepName = stepName;
this.metricsAccum = MetricsAccumulator.getInstance();
}
- private static final long DEFAULT_BUNDLE_SIZE = 64L * 1024L * 1024L;
-
@Override
public Partition[] getPartitions() {
- long desiredSizeBytes = DEFAULT_BUNDLE_SIZE;
- try {
- desiredSizeBytes = source.getEstimatedSizeBytes(options.get()) /
numPartitions;
- } catch (Exception e) {
- LOG.warn(
- "Failed to get estimated bundle size for source {}, using default
bundle "
- + "size of {} bytes.",
- source,
- DEFAULT_BUNDLE_SIZE);
- }
try {
- List<? extends Source<T>> partitionedSources =
- source.split(desiredSizeBytes, options.get());
+ List<? extends Source<T>> partitionedSources =
source.split(bundleSize, options.get());
Review comment:
What is the argument for changing the default approach for a more knob based
one ? Maybe is better to preserve the default and only overwrite it if this is
parametrized.
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Issue Time Tracking
-------------------
Worklog Id: (was: 142120)
Time Spent: 1h (was: 50m)
> Spark SourceRDD Not Designed With Dynamic Allocation In Mind
> ------------------------------------------------------------
>
> Key: BEAM-4783
> URL: https://issues.apache.org/jira/browse/BEAM-4783
> Project: Beam
> Issue Type: Improvement
> Components: runner-spark
> Affects Versions: 2.5.0
> Reporter: Kyle Winkelman
> Assignee: Jean-Baptiste Onofré
> Priority: Major
> Labels: newbie
> Time Spent: 1h
> Remaining Estimate: 0h
>
> When the spark-runner is used along with the configuration
> spark.dynamicAllocation.enabled=true the SourceRDD does not detect this. It
> then falls back to the value calculated in this description:
> // when running on YARN/SparkDeploy it's the result of max(totalCores,
> 2).
> // when running on Mesos it's 8.
> // when running local it's the total number of cores (local = 1,
> local[N] = N,
> // local[*] = estimation of the machine's cores).
> // ** the configuration "spark.default.parallelism" takes precedence
> over all of the above **
> So in most cases this default is quite small. This is an issue when using a
> very large input file as it will only get split in half.
> I believe that when Dynamic Allocation is enable the SourceRDD should use the
> DEFAULT_BUNDLE_SIZE and possibly expose a SparkPipelineOptions that allows
> you to change this DEFAULT_BUNDLE_SIZE.
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