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https://issues.apache.org/jira/browse/TINKERPOP-3133?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17927024#comment-17927024
]
ASF GitHub Bot commented on TINKERPOP-3133:
-------------------------------------------
ministat commented on code in PR #3026:
URL: https://github.com/apache/tinkerpop/pull/3026#discussion_r1955477627
##########
spark-gremlin/src/main/java/org/apache/tinkerpop/gremlin/spark/structure/io/PersistedOutputRDD.java:
##########
@@ -73,15 +74,29 @@ public <K, V> Iterator<KeyValue<K, V>> writeMemoryRDD(final
Configuration config
throw new IllegalArgumentException("There is no provided " +
Constants.GREMLIN_HADOOP_OUTPUT_LOCATION + " to write the persisted RDD to");
final String memoryRDDName =
Constants.getMemoryLocation(configuration.getString(Constants.GREMLIN_HADOOP_OUTPUT_LOCATION),
memoryKey);
Spark.removeRDD(memoryRDDName);
-
memoryRDD.setName(memoryRDDName).persist(StorageLevel.fromString(configuration.getString(Constants.GREMLIN_SPARK_PERSIST_STORAGE_LEVEL,
"MEMORY_ONLY")))
+ final JavaPairRDD<K, V> javaPairRDD =
repartitionJavaPairRDD(configuration, memoryRDD);
+
javaPairRDD.setName(memoryRDDName).persist(StorageLevel.fromString(configuration.getString(Constants.GREMLIN_SPARK_PERSIST_STORAGE_LEVEL,
"MEMORY_ONLY")))
// call action to eager store rdd
.count();
Spark.refresh(); // necessary to do really fast so the Spark GC
doesn't clear out the RDD
- return IteratorUtils.map(memoryRDD.collect().iterator(), tuple -> new
KeyValue<>(tuple._1(), tuple._2()));
+ return IteratorUtils.map(javaPairRDD.collect().iterator(), tuple ->
new KeyValue<>(tuple._1(), tuple._2()));
}
@Override
public boolean supportsResultGraphPersistCombination(final
GraphComputer.ResultGraph resultGraph, final GraphComputer.Persist persist) {
return persist.equals(GraphComputer.Persist.NOTHING) ||
resultGraph.equals(GraphComputer.ResultGraph.NEW);
}
+
+ /**
+ * Allow users to customize the RDD partitions to reduce HDFS small files
+ */
+ private static <K, V> JavaPairRDD<K, V> repartitionJavaPairRDD(final
Configuration configuration, JavaPairRDD<K, V> graphRDD) {
Review Comment:
ok
> Customize the file count by repartition the OutputRDD in Spark to reduce HDFS
> small files
> -----------------------------------------------------------------------------------------
>
> Key: TINKERPOP-3133
> URL: https://issues.apache.org/jira/browse/TINKERPOP-3133
> Project: TinkerPop
> Issue Type: Improvement
> Components: hadoop
> Affects Versions: 3.7.3
> Reporter: Redriver
> Priority: Major
>
> The Graph export to HDFS through OutputRDD, but we often saw there are many
> small files in production environment. For example, there are more than
> 50,000 files and each is about 17 MB, which will trigger HDFS small files
> alerts. So, it is better allow customize the output file numbers by
> repartition the OutputRDD.
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