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https://issues.apache.org/jira/browse/SPARK-28547?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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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 hours or gets frozen (because of lost executors) irrespective of memory
and numbers of cores. While the same operations work fast (minutes) and 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
(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
> 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 hours or gets frozen (because of lost executors) irrespective of
> memory and numbers of cores. While the same operations work fast (minutes)
> and well with pure pandas (without any spark involved).
> f
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