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https://issues.apache.org/jira/browse/HUDI-1668?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sugamber updated HUDI-1668:
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Attachment: 2nd.png
1st.png
> GlobalSortPartitioner is getting called twice during bulk_insert.
> -----------------------------------------------------------------
>
> Key: HUDI-1668
> URL: https://issues.apache.org/jira/browse/HUDI-1668
> Project: Apache Hudi
> Issue Type: Bug
> Reporter: Sugamber
> Priority: Major
> Attachments: 1st.png, 2nd.png
>
>
> Hi Team,
> I'm using bulk insert option to load close to 2 TB data. The process is
> taking near by 2 hours to get completed. While looking at the job log, it is
> identified that [sortBy at
> GlobalSortPartitioner.java:41|https://gdlcuspc1a3-6.us-central1.us.walmart.net:18481/history/application_1614298633248_1444/1/jobs/job?id=1]
> is running twice.
> It is getting triggered at 1 stage. refer this screenshot.
> Second time it is getting trigged from *HoodieSparkSqlWriter.scala:433*
> *[count at
> HoodieSparkSqlWriter.scala:433|https://gdlcuspc1a3-6.us-central1.us.walmart.net:18481/history/application_1614298633248_1444/1/jobs/job?id=2]*
> step.
> In both cases, same number of job got triggered and running time is close to
> each other.
> Is there any way to run only one time so that data can be loaded faster.
> *Spark and Hudi configurations*
>
> {code:java}
> Spark - 2.3.0
> Scala- 2.11.12
> Hudi - 0.7.0
>
> {code}
>
> Hudi Configuration
> {code:java}
> "hoodie.cleaner.commits.retained" = 2
> "hoodie.bulkinsert.shuffle.parallelism"=2000
> "hoodie.parquet.small.file.limit" = 100000000
> "hoodie.parquet.max.file.size" = 128000000
> "hoodie.index.bloom.num_entries" = 1800000
> "hoodie.bloom.index.filter.type" = "DYNAMIC_V0"
> "hoodie.bloom.index.filter.dynamic.max.entries" = 2500000
> "hoodie.bloom.index.bucketized.checking" = "false"
> "hoodie.datasource.write.operation" = "bulk_insert"
> "hoodie.datasource.write.table.type" = "COPY_ON_WRITE"
> {code}
>
> Spark Configuration -
> {code:java}
> --num-executors 180
> --executor-cores 4
> --executor-memory 16g
> --driver-memory=24g
> --conf spark.rdd.compress=true
> --queue=default
> --conf spark.serializer=org.apache.spark.serializer.KryoSerializer
> --conf spark.executor.memoryOverhead=1600
> --conf spark.driver.memoryOverhead=1200
> --conf spark.driver.maxResultSize=2g
> --conf spark.kryoserializer.buffer.max=512m
> {code}
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