I have pyspark app loading a large-ish (100GB) dataframe from JSON files and it turns out there are a number of duplicate JSON objects in the source data. I am trying to find the best way to remove these duplicates before using the dataframe.
With both df.dropDuplicates() and df.sqlContext.sql(‘’’SELECT DISTINCT *…’’’) the application is not able to complete a shuffle stage due to lost executors. Is there a more efficient way to remove these duplicate rows? If not, what settings can I tweak to help this succeed? I have tried both increasing and decreasing the number of default shuffle partitions (to 100 and 500, respectively) but neither changes the behavior. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
