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https://issues.apache.org/jira/browse/SPARK-24095?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-24095.
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Resolution: Incomplete
> Spark Streaming performance drastically drops when when saving dataframes
> with withColumn
> -----------------------------------------------------------------------------------------
>
> Key: SPARK-24095
> URL: https://issues.apache.org/jira/browse/SPARK-24095
> Project: Spark
> Issue Type: Bug
> Components: Structured Streaming
> Affects Versions: 2.3.0
> Reporter: brian wang
> Priority: Major
> Labels: bulk-closed
>
> We have a Spark Streaming application which is streaming data from Kafka and
> ingesting the data in HDFS after a series of transformations. We are using
> Spark SQL to do the transformations and storing the data into HDFS at two
> stages. The ingestion to Spark which we do at the second stage is drastically
> reducing the performance of the application.
> There are close to 40 Million transactions per hour in the incoming data. WE
> have observed a performance bottleneck in the write to hdfs.
> Can you please help us optimize the application performance?
> This is a critical issue since it is holding our deployment to production
> cluster and we are running behind the schedule in production deployment.
>
> Answer: First Stage Save
> test_Transformed_DOW.cache().withColumn("test_class_map", udf(test_class_map,
> StringType())(array(test_class))).write.mode("append").option("header","true").csv("/hive/warehouse/test")
> Second Stage Save
> test_Data_Final=spark.sql("select test1,test2,test3...... when int(seats)>=2
> then 1 when int(seats) < 2 then 0 end as seats from
> test_Data_Unpivoted").write.format("parquet").mode("append").saveAsTable("test_Data_Output")
> It is the first save stage which is slowing our spark application's
> performance if we enable it. If we disable it, the application seems to catch
> up with the incoming data flow.
>
>
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