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https://issues.apache.org/jira/browse/SPARK-26204?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Anton Okolnychyi updated SPARK-26204:
-------------------------------------
Description:
The {{InSet}} expression was introduced in SPARK-3711 to avoid O(n) time
complexity in the {{In}} expression. As {{InSet}} relies on Scala
{{immutable.Set}}, it introduces expensive autoboxing. As a consequence, the
performance of {{InSet}} might be significantly slower than {{In}} even on 100+
values.
We need to find an approach how to optimize {{InSet}} expressions and avoid the
cost of autoboxing.
There are a few approaches that we can use:
* Collections for primitive values (e.g., FastUtil, HPPC)
* Type specialization in Scala (e.g., OpenHashSet in Spark)
According to my local benchmarks, {{OpenHashSet}}, which is already available
in Spark and uses type specialization, can significantly reduce the memory
footprint. However, it slows down the computation even compared to the built-in
Scala sets. On the other hand, FastUtil and HPPC did work and gave a
substantial improvement in the performance. So, it makes sense to evaluate
primitive collections.
See the attached screenshot of what I experienced while testing.
was:
The {{InSet}} expression was introduced in SPARK-3711 to avoid O\(n\) time
complexity in the {{In}} expression. As {{InSet}} relies on Scala
{{immutable.Set}}, it introduces expensive autoboxing. As a consequence, the
performance of {{InSet}} might be significantly slower than {{In}} even on 100+
values.
We need to find an approach how to optimize {{InSet}} expressions and avoid the
cost of autoboxing.
There are a few approaches that we can use:
* Collections for primitive values (e.g., FastUtil, HPPC)
* Type specialization in Scala (would it even work for code gen in Spark?)
I tried to use {{OpenHashSet}}, which is already available in Spark and uses
type specialization. However, I did not manage to avoid autoboxing. On the
other hand, FastUtil did work and I saw a substantial improvement in the
performance.
See the attached screenshot of what I experienced while testing.
> Optimize InSet expression
> -------------------------
>
> Key: SPARK-26204
> URL: https://issues.apache.org/jira/browse/SPARK-26204
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Anton Okolnychyi
> Priority: Major
> Attachments: heap size.png
>
>
> The {{InSet}} expression was introduced in SPARK-3711 to avoid O(n) time
> complexity in the {{In}} expression. As {{InSet}} relies on Scala
> {{immutable.Set}}, it introduces expensive autoboxing. As a consequence, the
> performance of {{InSet}} might be significantly slower than {{In}} even on
> 100+ values.
> We need to find an approach how to optimize {{InSet}} expressions and avoid
> the cost of autoboxing.
> There are a few approaches that we can use:
> * Collections for primitive values (e.g., FastUtil, HPPC)
> * Type specialization in Scala (e.g., OpenHashSet in Spark)
> According to my local benchmarks, {{OpenHashSet}}, which is already available
> in Spark and uses type specialization, can significantly reduce the memory
> footprint. However, it slows down the computation even compared to the
> built-in Scala sets. On the other hand, FastUtil and HPPC did work and gave a
> substantial improvement in the performance. So, it makes sense to evaluate
> primitive collections.
> See the attached screenshot of what I experienced while testing.
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