I have a scenario where the below code overall take more than 10 hours for >2 Billion records. even i tried with 35 instance of the i3 cluster but still the performance was bad. I am looking for an option to replace distinct() with reduceByKey() and to get suggestion to improve the performance...
val df = spark.read.parquet(out)
val df1 = df.
select($"ID", $"col2", $"suffix",
$"date", $"year", $"codes")
val df2 = df1.
repartition(
List(col("ID"), col("col2"), col("suffix"), col("date"),
col("year"), col("codes")): _*
).distinct()
val df3 = df2.withColumn("codes", expr("transform(codes, (c,s) -> (d,s) )"))
df3.createOrReplaceTempView("df3")
val df4 = spark.sql(
"""SELECT
ID, col2, suffix
d.s as seq,
d.c as code,
year,date
FROM
df3
LATERAL VIEW explode(codes) exploded_table as d
""")
df4.
repartition(
600,
List(col("year"), col("date")): _*).
write.
mode("overwrite").
partitionBy("year", "date").
save(OutDir)
I think distinct() is implemented with reduceByKey (reduce), but if you want to implement it by yourself, you could do something
val array=List((1,2),(1,3),(1,5),(1,2),(2,2),(2,2),(3,2),(3,2),(4,1),(1,3))
val pairRDD=session.sparkContext.parallelize(array)
val distinctResult=pairRDD.map(x => (x, null)).reduceByKey((x, _) => x)
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