[
https://issues.apache.org/jira/browse/SPARK-27940?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Josh Rosen updated SPARK-27940:
-------------------------------
Description:
{{SubtractedRDD}}, which is used to implement {{RDD.subtract()}} and
{{PairRDDFunctions.subtractByKey()}}, currently buffers one partition in memory
and does not support spilling:
[https://github.com/apache/spark/blob/v2.4.3/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala#L42]
In principle, we could implement {{subtractByKey}} as a left-outer join
followed by a filter (e.g. as an antijoin), but the Scaladoc explains why this
approach wasn't taken:
{code:java}
* It is possible to implement this operation with just `cogroup`, but
* that is less efficient because all of the entries from `rdd2`, for
* both matching and non-matching values in `rdd1`, are kept in the
* JHashMap until the end.{code}
For example, if we have {{left.subtractByKey(right)}} and {{right}} has
hundreds of occurrences of a key then we'd end up buffering hundreds of tuples.
Instead, maybe we could implement a sort-merge join where we build an
{{ExternalAppendOnlyMap}} of unique {{right}} keys, use an {{ExternalSorter}}
to sort the {{left}}| input, then iterate over both sorted iterators and
perform a merge.
Note that this problem only impacts the RDD API.
Here are some existing workarounds for this OOM-proneness:
* Use more partitions: e.g. {{left.subtractByKey(right, 2000)}} (or pass in a
custom partitioner). This may not help if you have heavily skewed keys, though.
* Use a left join followed by filter:
{code:java}
left
.leftOuterJoin(right)
.collect { case (k, (lv, None)) => (k, lv) }{code}
If you wanted to further optimize, you could replace {{right}} values with
dummy placeholders to avoid having to shuffle them:
{code:java}
left
.leftOuterJoin(right.map { case (k, v) => (k, 0) })
.collect { case (k, (lv, None)) => (k, lv) }{code}
* Use DataFrames / Datasets instead of RDDs.
was:
{{SubtractedRDD}}, which is used to implement {{RDD.subtract()}} and
{{PairRDDFunctions.subtractByKey()}}, currently buffers one partition in memory
and does not support spilling:
[https://github.com/apache/spark/blob/v2.4.3/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala#L42]
In principle, we could implement {{subtractByKey}} as a left-outer join
followed by a filter (e.g. as an antijoin), but the Scaladoc explains why this
approach wasn't taken:
{code:java}
* It is possible to implement this operation with just `cogroup`, but
* that is less efficient because all of the entries from `rdd2`, for
* both matching and non-matching values in `rdd1`, are kept in the
* JHashMap until the end.{code}
For example, if we have {{left.subtractByKey(right)}} and {{right}} has
hundreds of occurrences of a key then we'd end up buffering hundreds of tuples.
Instead, maybe we could implement a sort-merge join where we build an
{{ExternalAppendOnlyMap}} of unique {{right}} keys, use an {{ExternalSorter}}
to sort the {{left}}| input, then iterate over both sorted iterators and
perform a merge.
Note that this problem only impacts the RDD API.
Here are some existing workarounds for this OOM-proneness:
* Use more partitions: e.g. {{left.subtractByKey(right, 2000)}} (or pass in a
custom partitioner).
* Use a left join followed by filter:
{code:java}
left
.leftOuterJoin(right)
.collect { case (k, (lv, None)) => (k, lv) }{code}
If you wanted to further optimize, you could replace {{right}} values with
dummy placeholders to avoid having to shuffle them:
{code:java}
left
.leftOuterJoin(right.map { case (k, v) => (k, 0) })
.collect { case (k, (lv, None)) => (k, lv) }{code}
* Use DataFrames / Datasets instead of RDDs.
> SubtractedRDD is OOM-prone because it does not support spilling
> ---------------------------------------------------------------
>
> Key: SPARK-27940
> URL: https://issues.apache.org/jira/browse/SPARK-27940
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.4.0
> Reporter: Josh Rosen
> Priority: Minor
>
> {{SubtractedRDD}}, which is used to implement {{RDD.subtract()}} and
> {{PairRDDFunctions.subtractByKey()}}, currently buffers one partition in
> memory and does not support spilling:
> [https://github.com/apache/spark/blob/v2.4.3/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala#L42]
> In principle, we could implement {{subtractByKey}} as a left-outer join
> followed by a filter (e.g. as an antijoin), but the Scaladoc explains why
> this approach wasn't taken:
> {code:java}
> * It is possible to implement this operation with just `cogroup`, but
> * that is less efficient because all of the entries from `rdd2`, for
> * both matching and non-matching values in `rdd1`, are kept in the
> * JHashMap until the end.{code}
> For example, if we have {{left.subtractByKey(right)}} and {{right}} has
> hundreds of occurrences of a key then we'd end up buffering hundreds of
> tuples.
> Instead, maybe we could implement a sort-merge join where we build an
> {{ExternalAppendOnlyMap}} of unique {{right}} keys, use an {{ExternalSorter}}
> to sort the {{left}}| input, then iterate over both sorted iterators and
> perform a merge.
> Note that this problem only impacts the RDD API.
> Here are some existing workarounds for this OOM-proneness:
> * Use more partitions: e.g. {{left.subtractByKey(right, 2000)}} (or pass in
> a custom partitioner). This may not help if you have heavily skewed keys,
> though.
> * Use a left join followed by filter:
> {code:java}
> left
> .leftOuterJoin(right)
> .collect { case (k, (lv, None)) => (k, lv) }{code}
> If you wanted to further optimize, you could replace {{right}} values with
> dummy placeholders to avoid having to shuffle them:
> {code:java}
> left
> .leftOuterJoin(right.map { case (k, v) => (k, 0) })
> .collect { case (k, (lv, None)) => (k, lv) }{code}
> * Use DataFrames / Datasets instead of RDDs.
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