How to improve spark perf by joining dataframe on a known key -


i have 2 big parquet dataframes , want join them on userid.

what should high performance :

should modify code write files in order :

  • partitionby on userid (very sparse).
  • partitionby on first n char of userid (afaik, if data partitioned on same key, join occur no shuffle)

on read side, better use rdd or dataframe ?

you can perform bucketby operation before save parquet file.

val num_buckets = 20 df1.write.mode(savemode.overwrite).bucketby(num_buckets, "userid").saveastable("bucketed_large_table_1") df2.write.mode(savemode.overwrite).bucketby(num_buckets, "userid").saveastable("bucketed_large_table_2") spark.sql("select * join b on a.num1 = b.num2").collect()

doing way prevent shuffle when join operation performed.

keep in mind in order need enable hive .enablehivesupport, save parquet operation doesn't support bucketby method.


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