If you only need the group by in the same hierarchy logic, then you can group
by at the lowest level, and cache it, then use the cached DF to derive to the
higher level, so Spark will only scan the originally table once, and reuse the
cache in the following.
val df_base = sqlContext.sql("select col1,col2,col3,col4,col5, count(*) from
table groupby col1,col2,col3,col4,col5").cache
df_base.registerTempTable("df_base")
val df1 = sqlContext.sql("select col1, col2, count(*) from df_base group by
col1, col2")
val df2 = // similar logic
Yong
________________________________
From: Patrick <titlibat...@gmail.com>
Sent: Saturday, February 18, 2017 4:23 PM
To: user
Subject: Efficient Spark-Sql queries when only nth Column changes
Hi,
I have read 5 columns from parquet into data frame. My queries on the parquet
table is of below type:
val df1 = sqlContext.sql(select col1,col2,count(*) from table groupby col1,col2)
val df2 = sqlContext.sql(select col1,col3,count(*) from table groupby
col1,col3)
val df3 = sqlContext.sql(select col1,col4,count(*) from table groupby
col1,col4)
val df4 = sqlContext.sql(select col1,col5,count(*) from table groupby
col1,col5)
And then i require to union the results from df1 to df4 into a single df.
So basically, only the second column is changing, Is there any efficient way to
write the above queries in Spark-Sql instead of writing 4 different queries(OR
in loop) and doing union to get the result.
Thanks
