Brad Willard created SPARK-5075: ----------------------------------- Summary: Memory Leak when repartitioning SchemaRDD from JSON Key: SPARK-5075 URL: https://issues.apache.org/jira/browse/SPARK-5075 Project: Spark Issue Type: Bug Components: PySpark, Spark Core Affects Versions: 1.2.0 Environment: spark-ec2 launched 10 node cluster of type c3.8xlarge Reporter: Brad Willard
I'm trying to repartition a json dataset for better cpu optimization and save in parquet format for better performance. The Json dataset is about 200gb from pyspark.sql import SQLContext sql_context = SQLContext(sc) rdd = sql_context.jsonFile('s3c://some_path') rdd = rdd.repartition(256) rdd.saveAsParquetFile('hdfs://some_path') In ganglia when the dataset first loads it's about 200G in memory which is expected. However once it attempts the repartition, it balloons over 2.5x in memory which is never returned making any subsequent operations fail from memory errors. https://s3.amazonaws.com/f.cl.ly/items/3k2n2n3j35273i2v1Y3t/Screen%20Shot%202015-01-04%20at%201.20.29%20PM.png -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org