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https://issues.apache.org/jira/browse/SPARK-4644?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14937033#comment-14937033
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vidmantas zemleris edited comment on SPARK-4644 at 9/30/15 3:53 PM:
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IMHO a good start could be implementing special case of left-join with NULL
values in join conditions (and would be much simpler and more efficient than
proposed implementation generic skew join [1]).
currently we're only interested in this special case of NULL values, consider:
{code:sql}
SELECT * FROM t1
LEFT JOIN t2
ON (t1.nullableCol = t2.nullableCol AND t1.portal = t2.portal)
{code}
The problem is that if the join key contains a column with many NULL values
(and it's the only join column, or arrity of other join columns is low), it'll
get shuffled to one or few tasks - causing straggler tasks, or failing
altogether.
A naive solution could be along these lines:
{code:scala}
def nullAwareLeftJoin(left: DataFrame, right: DataFrame, joinConditions:
Column) = {
val rowsWithNulls = left.filter(joinConditions.containsNullValues)
val safeRows = left
.filter(!joinConditions.containsNullValues)
.join(right, joinConditions)
safeRows.unionAll(
rowsWithNulls.addNullsForMissingColumns(safeRows.columns)
)
}
{code}
likely this could be more efficient if implemented internally...
What do you think guys?
--
[1] https://github.com/tresata/spark-skewjoin
> A skew join is just like a normal join except that keys with large amounts of
> values are not processed by a single task but instead spread out across many
> tasks. This is achieved by replicating key-value pairs for one side of the
> join in such way that they go to multiple tasks...
was (Author: vidma):
IMHO a good start could be implementing special case of left-join with NULL
values in join conditions (and would be much simpler and more efficient than
proposed implementation generic skew join [1]).
currently we're only interested in this special case of NULL values, consider:
{code:sql}
SELECT * FROM t1
LEFT JOIN t2
ON (t1.nullableCol = t2.nullableCol AND t1.portal = t2.portal)
{code}
The problem is that if the join key contains a column with many NULL values
(and it's the only join column, or arrity of other join columns is low), it'll
get shuffled to one or few tasks - causing straggler tasks, or failing
altogether.
A naive solution could be along these lines:
```scala
def nullAwareLeftJoin(left: DataFrame, right: DataFrame, joinConditions:
Column) = {
val rowsWithNulls = left.filter(joinConditions.containsNullValues)
val safeRows = left
.filter(!joinConditions.containsNullValues)
.join(right, joinConditions)
safeRows.unionAll(
rowsWithNulls.addNullsForMissingColumns(safeRows.columns)
)
}
```
likely this could be more efficient if implemented internally...
What do you think guys?
--
[1] https://github.com/tresata/spark-skewjoin
> A skew join is just like a normal join except that keys with large amounts of
> values are not processed by a single task but instead spread out across many
> tasks. This is achieved by replicating key-value pairs for one side of the
> join in such way that they go to multiple tasks...
> Implement skewed join
> ---------------------
>
> Key: SPARK-4644
> URL: https://issues.apache.org/jira/browse/SPARK-4644
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Reporter: Shixiong Zhu
> Attachments: Skewed Join Design Doc.pdf
>
>
> Skewed data is not rare. For example, a book recommendation site may have
> several books which are liked by most of the users. Running ALS on such
> skewed data will raise a OutOfMemory error, if some book has too many users
> which cannot be fit into memory. To solve it, we propose a skewed join
> implementation.
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