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https://issues.apache.org/jira/browse/SPARK-17788?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15696321#comment-15696321
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Herman van Hovell edited comment on SPARK-17788 at 11/25/16 5:09 PM:
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That is fair. The solution is not that straightforward TBH:
- Always add some kind of tie breaking value to the range. This could be
random, but I'd rather add something like monotonically_increasing_id(). This
always incurs some cost.
- Only add a tie-breaker when the you have (suspect) skew. Here we need to add
some heavy hitter algorithm, which is potentially much more resource intensive
than reservoir sampling. The other thing is that when we suspect skew, we would
need to scan the data again (which would make the total of scans 3).
So I would be slightly in favor of option 1 and a flag to disable it.
was (Author: hvanhovell):
That is fair. The solution is not that straightforward TBH:
- Always add some kind of tie breaking value to the range. This could be
random, but I'd rather add something like monotonically_increasing_id(). This
always incurs some cost.
- Only add a tie-breaker when the you have (suspect) skew. Here we need to add
some heavy hitter algorithm, which is potentially much more resource intensive
than reservoir sampling. The other thing is that when we suspect skew, we would
need to scan the data again (which would make the total of scans 3).
So I would be slightly in favor of option 1 and a flag to disable it.
> RangePartitioner results in few very large tasks and many small to empty
> tasks
> -------------------------------------------------------------------------------
>
> Key: SPARK-17788
> URL: https://issues.apache.org/jira/browse/SPARK-17788
> 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
>
> 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]
> https://spark.apache.org/docs/2.0.0/api/java/org/apache/spark/RangePartitioner.html
>
> 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:
> http://mail-archives.apache.org/mod_mbox/spark-user/201610.mbox/%3CCA%2B_of14hTVYTUHXC%3DmS9Kqd6qegVvkoF-ry3Yj2%2BRT%2BWSBNzhg%40mail.gmail.com%3E
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