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Spark shuffle huge amount of data even read data is not huge

0

Reading few gb say 15gb of parquet skewed data , after few transformation such as data type change for some columns and then doing repartitions (dataframe.repartition(120)) before writing it to s3 in csv gzip format results in huge amount of shuffle writes as can be seen in spark UI though Input data size is 15 gb , shuffle write is 600gbs

Interested to know why its happening ?

Topics
AnalyticsDatabaseStorage
Tags
AWS GlueExtract Transform & Load DataAmazon GameSparksS3 Select
Language
English
Bibhu
asked 23 days ago263 views
1 Answer
0

That number is normally larger (e.g. 2x) because it compresses rows while parquet columnar compression is much more efficient.
Must mean in your data there are many columns with repeated values.

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EXPERT
Gonzalo Herreros
answered 22 days ago
  • Bibhu
    21 days ago

    Can you please explain a bit more. I dont have any repeated values as such but few values are nulls.

    How we can optimise .Because without repartition I tried writing to s3 in csv format ,its 500 GB of data

  • Gonzalo Herreros EXPERT
    21 days ago

    Avoid the shuffle if you can, otherwise don't worry too much about the amount, the transfer is quite fast

  • Bibhu
    21 days ago

    Data is skewed, so using repartition to distribute the data evenly which is resulting in huge shuffle writes. Even without repartition it is taking around 1 hr to complete with G2.x and 60 DPUs

  • Bibhu
    19 days ago

    These parquet data is being read from Glue Catalog Tables directly.

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