Sean

 

Yes I know that I can use persist() to persist to disk, but it is still a big extra cost of persist a huge RDD to disk. I hope that I can do one pass to get the count as well as rdd.saveAsObjectFile(file2), but I don’t know how.

 

May be use accumulator to count the total ?

 

Ningjun

 

From: Mark Hamstra [mailto:mark@clearstorydata.com]
Sent: Thursday, March 26, 2015 12:37 PM
To: Sean Owen
Cc: Wang, Ningjun (LNG-NPV); user@spark.apache.org
Subject: Re: How to get rdd count() without double evaluation of the RDD?

 

You can also always take the more extreme approach of using SparkContext#runJob (or submitJob) to write a custom Action that does what you want in one pass.  Usually that's not worth the extra effort.

 

On Thu, Mar 26, 2015 at 9:27 AM, Sean Owen <sowen@cloudera.com> wrote:

To avoid computing twice you need to persist the RDD but that need not be in memory. You can persist to disk with persist().

On Mar 26, 2015 4:11 PM, "Wang, Ningjun (LNG-NPV)" <ningjun.wang@lexisnexis.com> wrote:

I have a rdd that is expensive to compute. I want to save it as object file and also print the count. How can I avoid double computation of the RDD?

 

val rdd = sc.textFile(someFile).map(line => expensiveCalculation(line))

 

val count = rdd.count()  // this force computation of the rdd

println(count)

rdd.saveAsObjectFile(file2) // this compute the RDD again

 

I can avoid double computation by using cache

 

val rdd = sc.textFile(someFile).map(line => expensiveCalculation(line))

rdd.cache()

val count = rdd.count() 

println(count)

rdd.saveAsObjectFile(file2) // this compute the RDD again

 

This only compute rdd once. However the rdd has millions of items and will cause out of memory.

 

Question: how can I avoid double computation without using cache?

 

 

Ningjun