Why pre-partition will benefit spark job because o

Question

Many tutorials mention that pre-partition of RDD will optimize data shuffling of spark jobs. What I'm confused is that, for my understanding pre-partition will also lead to shuffling, why shuffling in advance here will benefit some operation? Especially spark it self will do the optimization for a set of transformations.

For example:

If I want to join two dataset country (id, country) and income (id, (income, month, year)), what's the difference between this two kind of operation? (I use PySpark schema)

1. pre-partition by id

   country = country.partitionBy(10).persist()
   income = income.partitionBy(10).persist()
   income.join(country)
   

2. directly join without pre-partition:

   income.join(country)
   

If I only need to calculate this join once, is it still useful to use pre-partition before join? I think partitionBy also requires shuffling right? And if my further computation after join is all base on using country as key (previous key id used for join will be useless and be eliminated from RDD), what should I do to optimize the calculation?

Answer 1

parititionBy does not shuffle the data, if that's what you are asking.

By applying partitionBy preemptively you don't avoid the shuffle. You just push it in another place. This can be a good idea if partitioned RDD is reused multiple times, but you gain nothing for a one-off join.

Answer 2

If I only need to calculate this join once, is it still useful to use pre-partition before join? I think partitionBy also requires shuffling right?

You're perfectly right. Preemptive partitioning makes sense only if partitioned data will be reused for multiple DAG paths. If you join only once it just shuffles in a different place.