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Posted to issues@spark.apache.org by "antonkulaga (JIRA)" <ji...@apache.org> on 2019/07/28 09:31:00 UTC
[jira] [Updated] (SPARK-28547) Make it work for wide (> 10K columns
data)
[ https://issues.apache.org/jira/browse/SPARK-28547?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
antonkulaga updated SPARK-28547:
--------------------------------
Description:
Spark is super-slow for all wide data (when there are >15kb columns and >15kb rows). Most of the genomics/transcriptomic data is wide because number of genes is usually >20kb and number of samples ass well. Very popular GTEX dataset is a good example ( see for instance RNA-Seq data at https://storage.googleapis.com/gtex_analysis_v7/rna_seq_data where gct is just a .tsv file with two comments in the beginning). Everything done in wide tables (even simple "describe" functions applied to all the genes-columns) either takes ours or gets frozen (because of lost executors) irrespective of memory and numbers of cores. While the same operations work well with pure pandas (without any spark involved).
f
was:
Spark is super-slow for all wide data (when there are >15kb columns and >15kb rows). Most of the genomics/transcriptomic data is wide because number of genes is usually >20kb and number of samples ass well. Very popular GTEX dataset is a good example ( see for instance RNA-Seq data at https://storage.googleapis.com/gtex_analysis_v7/rna_seq_data where gct is just a .tsv file with two comments in the beginning). Everything done in wide tables either takes ours or gets frozen (because of lost executors) irrespective of memory and numbers of cores. While the same operations work well with pure pandas (without any spark involved).
f
> Make it work for wide (> 10K columns data)
> ------------------------------------------
>
> Key: SPARK-28547
> URL: https://issues.apache.org/jira/browse/SPARK-28547
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 2.4.4, 2.4.3
> Environment: Ubuntu server, Spark 2.4.3 Scala with >64GB RAM per node, 32 cores (tried different configurations of executors)
> Reporter: antonkulaga
> Priority: Critical
>
> Spark is super-slow for all wide data (when there are >15kb columns and >15kb rows). Most of the genomics/transcriptomic data is wide because number of genes is usually >20kb and number of samples ass well. Very popular GTEX dataset is a good example ( see for instance RNA-Seq data at https://storage.googleapis.com/gtex_analysis_v7/rna_seq_data where gct is just a .tsv file with two comments in the beginning). Everything done in wide tables (even simple "describe" functions applied to all the genes-columns) either takes ours or gets frozen (because of lost executors) irrespective of memory and numbers of cores. While the same operations work well with pure pandas (without any spark involved).
> f
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