Few ideas on top of my head for how to go about solving the problem
1. Try with subsets: Try reproducing the issue with smaller subsets of your data to pinpoint the specific operation causing the memory problems. 2. Explode or Flatten Nested Structures: If your DataFrame schema involves deep nesting, consider using techniques like explode or flattening to transform it into a less nested structure. This can reduce memory usage during operations like withColumn. 3. Lazy Evaluation: Use select before withColumn: this ensures lazy evaluation, meaning Spark only materializes the data when necessary. This can improve memory usage compared to directly calling withColumn on the entire DataFrame. 4. spark.sql.shuffle.partitions: Setting this configuration to a value close to the number of executors can improve shuffle performance and potentially reduce memory usage. 5. Spark UI Monitoring: Utilize the Spark UI to monitor memory usage throughout your job execution and identify potential memory bottlenecks. HTH Mich Talebzadeh, Technologist | Architect | Data Engineer | Generative AI | FinCrime London United Kingdom view my Linkedin profile <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> https://en.everybodywiki.com/Mich_Talebzadeh *Disclaimer:* The information provided is correct to the best of my knowledge but of course cannot be guaranteed . It is essential to note that, as with any advice, quote "one test result is worth one-thousand expert opinions (Werner <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)". On Mon, 27 May 2024 at 12:50, Gaurav Madan <gauravma...@urbancompany.com.invalid> wrote: > Dear Community, > > I'm reaching out to seek your assistance with a memory issue we've been > facing while processing certain large and nested DataFrames using Apache > Spark. We have encountered a scenario where the driver runs out of memory > when applying the `withColumn` method on specific DataFrames in Spark > 3.4.1. However, the same DataFrames are processed successfully in Spark > 2.4.0. > > > *Problem Summary:*For certain DataFrames, applying the `withColumn` > method in Spark 3.4.1 causes the driver to choke and run out of memory. > However, the same DataFrames are processed successfully in Spark 2.4.0. > > > *Heap Dump Analysis:*We performed a heap dump analysis after enabling > heap dump on out-of-memory errors, and the analysis revealed the following > significant frames and local variables: > > ``` > > org.apache.spark.sql.Dataset.withPlan(Lorg/apache/spark/sql/catalyst/plans/logical/LogicalPlan;)Lorg/apache/spark/sql/Dataset; > (Dataset.scala:4273) > > org.apache.spark.sql.Dataset @ 0x680190b10 retains 4,307,840,192 (80.38%) > bytes > > org.apache.spark.sql.Dataset.select(Lscala/collection/Seq;)Lorg/apache/spark/sql/Dataset; > (Dataset.scala:1622) > > org.apache.spark.sql.Dataset @ 0x680190b10 retains 4,307,840,192 (80.38%) > bytes > > org.apache.spark.sql.Dataset.withColumns(Lscala/collection/Seq;Lscala/collection/Seq;)Lorg/apache/spark/sql/Dataset; > (Dataset.scala:2820) > > org.apache.spark.sql.Dataset @ 0x680190b10 retains 4,307,840,192 (80.38%) > bytes > > org.apache.spark.sql.Dataset.withColumn(Ljava/lang/String;Lorg/apache/spark/sql/Column;)Lorg/apache/spark/sql/Dataset; > (Dataset.scala:2759) > > org.apache.spark.sql.Dataset @ 0x680190b10 retains 4,307,840,192 (80.38%) > bytes > > com.urbanclap.dp.eventpersistence.utils.DataPersistenceUtil$.addStartTimeInPartitionColumn(Lorg/apache/spark/sql/Dataset;Lcom/urbanclap/dp/factory/output/Event;Lcom/urbanclap/dp/eventpersistence/JobMetadata;)Lorg/apache/spark/sql/Dataset; > (DataPersistenceUtil.scala:88) > > org.apache.spark.sql.Dataset @ 0x680190b10 retains 4,307,840,192 (80.38%) > bytes > > com.urbanclap.dp.eventpersistence.utils.DataPersistenceUtil$.writeRawData(Lorg/apache/spark/sql/Dataset;Lcom/urbanclap/dp/factory/output/Event;Ljava/lang/String;Lcom/urbanclap/dp/eventpersistence/JobMetadata;)V > (DataPersistenceUtil.scala:19) > > org.apache.spark.sql.Dataset @ 0x680190b10 retains 4,307,840,192 (80.38%) > bytes > > com.urbanclap.dp.eventpersistence.step.bronze.BronzeStep$.persistRecords(Lorg/apache/spark/sql/Dataset;Lcom/urbanclap/dp/factory/output/Event;Lcom/urbanclap/dp/eventpersistence/JobMetadata;)V > (BronzeStep.scala:23) > > org.apache.spark.sql.Dataset @ 0x680190b10 retains 4,307,840,192 (80.38%) > bytes > > com.urbanclap.dp.eventpersistence.MainJob$.processRecords(Lorg/apache/spark/sql/SparkSession;Lcom/urbanclap/dp/factory/output/Event;Lcom/urbanclap/dp/eventpersistence/JobMetadata;Lorg/apache/spark/sql/Dataset;)V > (MainJob.scala:78) > > org.apache.spark.sql.Dataset @ 0x680190b10 retains 4,307,840,192 (80.38%) > bytes > > com.urbanclap.dp.eventpersistence.MainJob$.processBatchCB(Lorg/apache/spark/sql/SparkSession;Lcom/urbanclap/dp/eventpersistence/JobMetadata;Lcom/urbanclap/dp/factory/output/Event;Lorg/apache/spark/sql/Dataset;)V > (MainJob.scala:66) > > org.apache.spark.sql.Dataset @ 0x680190b10 retains 4,307,840,192 (80.38%) > bytes > > ``` > > > *Driver Configuration:*1. Driver instance: c6g.xlarge with 4 vCPUs and 8 > GB RAM. > 2. `spark.driver.memory` and `spark.driver.memoryOverhead` are set to > default values. > > > *Observations:*- The DataFrame schema is very nested and large, which > might be contributing to the memory issue. > - Despite similar configurations, Spark 2.4.0 processes the DataFrame > without issues, while Spark 3.4.1 does not. > > > *Tried Solutions:*We have tried several solutions, including disabling > Adaptive Query Execution, setting the driver max result size, increasing > driver cores, and enabling specific optimizer rules. However, the issue > persisted until we increased the driver memory to 48 GB and memory overhead > to 5 GB, which allowed the driver to schedule the tasks successfully. > > > *Request for Suggestions:*Are there any additional configurations or > optimizations that could help mitigate this memory issue without always > resorting to a larger machine? We would greatly appreciate any insights or > recommendations from the community on how to resolve this issue effectively. > > I have attached the DataFrame schema and the complete stack trace from the > heap dump analysis for your reference. > > Doc explaining the issue > <https://docs.google.com/document/d/1FL6Zeim6IN1riLH1Hp7Jw4acsBoSyWbSN13mCYnjo60/edit?usp=sharing> > DataFrame Schema > <https://drive.google.com/file/d/1wgFB0_WvdQdGoEMGFePhZwLR7aQZ5fPn/view?usp=sharing> > > Thank you in advance for your assistance. > > Best regards, > Gaurav Madan > LinkedIn <https://www.linkedin.com/in/gaurav-madan-210b62177/> > *Personal Mail: *gauravmadan...@gmail.com > *Work Mail:* gauravma...@urbancompany.com >
