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From "Wenchen Fan (JIRA)" <>
Subject [jira] [Commented] (SPARK-17788) RangePartitioner results in few very large tasks and many small to empty tasks
Date Mon, 30 Oct 2017 17:03:00 GMT


Wenchen Fan commented on SPARK-17788:

Unfortunately I don't have a reproducible code snippet to prove it has been fixed, but I'm
pretty confident my fix should work for it. cc [] please reopen this
ticket if you still hit this issue, thanks!

> RangePartitioner results in few very large tasks and many small to empty tasks 
> -------------------------------------------------------------------------------
>                 Key: SPARK-17788
>                 URL:
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core, SQL
>    Affects Versions: 2.0.0
>         Environment: Ubuntu 14.04 64bit
> Java 1.8.0_101
>            Reporter: Babak Alipour
>            Assignee: Wenchen Fan
>             Fix For: 2.3.0
> Greetings everyone,
> I was trying to read a single field of a Hive table stored as Parquet in Spark (~140GB
for the entire table, this single field is a Double, ~1.4B records) and look at the sorted
output using the following:
> sql("SELECT " + field + " FROM MY_TABLE ORDER BY " + field + " DESC") 
> ​But this simple line of code gives:
> Caused by: java.lang.IllegalArgumentException: Cannot allocate a page with more than
17179869176 bytes
> Same error for:
> sql("SELECT " + field + " FROM MY_TABLE).sort(field)
> and:
> sql("SELECT " + field + " FROM MY_TABLE).orderBy(field)
> After doing some searching, the issue seems to lie in the RangePartitioner trying to
create equal ranges. [1]
> [1]

>  The Double values I'm trying to sort are mostly in the range [0,1] (~70% of the data
which roughly equates 1 billion records), other numbers in the dataset are as high as 2000.
With the RangePartitioner trying to create equal ranges, some tasks are becoming almost empty
while others are extremely large, due to the heavily skewed distribution. 
> This is either a bug in Apache Spark or a major limitation of the framework. I hope one
of the devs can help solve this issue.
> P.S. Email thread on Spark user mailing list:

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