Hi Enrico, Thanks for the suggestion, i wanted to know if there are any performance implications of running multi-threaded driver ? If i create multiple Dataframes in parallel, then Spark will schedule those jobs in parallel ?
Thanks Manjunath ________________________________ From: Enrico Minack <m...@enrico.minack.dev> Sent: Thursday, February 27, 2020 8:51 PM To: Manjunath Shetty H <manjunathshe...@live.com>; user@spark.apache.org <user@spark.apache.org> Subject: Re: Convert each partition of RDD to Dataframe Manjunath, You can define your DataFrame in parallel in a multi-threaded driver. Enrico Am 27.02.20 um 15:50 schrieb Manjunath Shetty H: Hi Enrico, In that case how to make effective use of all nodes in the cluster ?. And also whats your opinion on the below * Create 10 Dataframes sequentially in Driver program and transform/write to hdfs one after the other * Or the current approach mentioned in the previous mail What will be the performance implications ? Regards Manjunath ________________________________ From: Enrico Minack <m...@enrico.minack.dev><mailto:m...@enrico.minack.dev> Sent: Thursday, February 27, 2020 7:57 PM To: user@spark.apache.org<mailto:user@spark.apache.org> <user@spark.apache.org><mailto:user@spark.apache.org> Subject: Re: Convert each partition of RDD to Dataframe Hi Manjunath, why not creating 10 DataFrames loading the different tables in the first place? Enrico Am 27.02.20 um 14:53 schrieb Manjunath Shetty H: Hi Vinodh, Thanks for the quick response. Didn't got what you meant exactly, any reference or snippet will be helpful. To explain the problem more, * I have 10 partitions , each partition loads the data from different table and different SQL shard. * Most of the partitions will have different schema. * Before persisting the data i want to do some column level manipulation using data frame. So thats why i want to create 10 (based on partitions ) dataframes that maps to 10 different table/shard from a RDD. Regards Manjunath ________________________________ From: Charles vinodh <mig.flan...@gmail.com><mailto:mig.flan...@gmail.com> Sent: Thursday, February 27, 2020 7:04 PM To: manjunathshe...@live.com<mailto:manjunathshe...@live.com> <manjunathshe...@live.com><mailto:manjunathshe...@live.com> Cc: user <user@spark.apache.org><mailto:user@spark.apache.org> Subject: Re: Convert each partition of RDD to Dataframe Just split the single rdd into multiple individual rdds using a filter operation and then convert each individual rdds to it's respective dataframe.. On Thu, Feb 27, 2020, 7:29 AM Manjunath Shetty H <manjunathshe...@live.com<mailto:manjunathshe...@live.com>> wrote: Hello All, In spark i am creating the custom partitions with Custom RDD, each partition will have different schema. Now in the transformation step we need to get the schema and run some Dataframe SQL queries per partition, because each partition data has different schema. How to get the Dataframe's per partition of a RDD?. As of now i am doing foreachPartition on RDD and converting Iterable<Row> to List and converting that to Dataframe. But the problem is converting Iterable to List will bring all the data to memory and it might crash the process. Is there any known way to do this ? or is there any way to handle Custom Partitions in Dataframes instead of using RDD ? I am using Spark version 1.6.2. Any pointers would be helpful. Thanks in advance
