Thanks Yong for your response. Let me see if I can understand what you're suggesting, so the whole data set, when I load them into Spark(I'm using custom Hadoop InputFormat), I will add an extra field to each element in RDD, like bucket_id.
For example Key: 1 - 10, bucket_id=1 11-20, bucket_id=2 ... 90-100, butcket_id =10 then I can re-partition the RDD with a partitioner that will put all records with the same bucket_id in the same partition, after I get DataFrame from the RDD, the partition is still preserved(is it correct?) then reset of work is only issue SQL query like SELECT * from XXX where bucket_id=1 SELECT * from XXX where bucket_id=2 .. Am I right? Thanks Anfernee On Thu, Oct 29, 2015 at 11:07 AM, java8964 <java8...@hotmail.com> wrote: > Won't you be able to use case statement to generate a virtual column (like > partition_num), then use analytic SQL partition by this virtual column? > > In this case, the full dataset will be just scanned once. > > Yong > > ------------------------------ > Date: Thu, 29 Oct 2015 10:51:53 -0700 > Subject: RDD's filter() or using 'where' condition in SparkSQL > From: anfernee...@gmail.com > To: user@spark.apache.org > > > Hi, > > I have a pretty large data set(2M entities) in my RDD, the data has > already been partitioned by a specific key, the key has a range(type in > long), now I want to create a bunch of key buckets, for example, the key > has range > > 1 -> 100, > > I will break the whole range into below buckets > > 1 -> 10 > 11 -> 20 > ... > 90 -> 100 > > I want to run some analytic SQL functions over the data that owned by > each key range, so I come up with 2 approaches, > > 1) run RDD's filter() on the full data set RDD, the filter will create the > RDD corresponding to each key bucket, and with each RDD, I can create > DataFrame and run the sql. > > > 2) create a DataFrame for the whole RDD, and using a buch of SQL's to do > my job. > > SELECT * from XXXX where key>=key1 AND key <key2 > > So my question is which one is better from performance perspective? > > Thanks > > -- > --Anfernee > -- --Anfernee
