In Pyspark HiveContext what is the equivalent of S

问题:

Or a more specific question would be how can I process large amounts of data that do not fit into memory at once? With OFFSET I was trying to do hiveContext.sql("select ... limit 10 offset 10") while incrementing offset to get all the data but offset doesn't seem to be valid within hiveContext. What is the alternative usually used to achieve this goal?

For some context the pyspark code starts with

from pyspark.sql import HiveContext
hiveContext = HiveContext(sc)
hiveContext.sql("select ... limit 10 offset 10").show()

回答1:

You code will look like

  from pyspark.sql import HiveContext
hiveContext = HiveContext(sc)
hiveContext.sql("    with result as
 (   SELECT colunm1 ,column2,column3, ROW_NUMBER() OVER (ORDER BY columnname) AS RowNum FROM tablename )
select colunm1 ,column2,column3 from result where RowNum >= OFFSEtvalue and  RowNum < (OFFSEtvalue +limtvalue ").show()

Note: Update below variables according your requirement tcolunm1 , tablename, OFFSEtvalue, limtvalue