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https://issues.apache.org/jira/browse/SPARK-34369?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17279126#comment-17279126
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Apache Spark commented on SPARK-34369:
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User 'sririshindra' has created a pull request for this issue:
https://github.com/apache/spark/pull/31477
> Track number of pairs processed out of Join
> -------------------------------------------
>
> Key: SPARK-34369
> URL: https://issues.apache.org/jira/browse/SPARK-34369
> Project: Spark
> Issue Type: New Feature
> Components: Web UI
> Affects Versions: 3.2.0
> Reporter: Srinivas Rishindra Pothireddi
> Priority: Major
>
> Often users face a scenario where even a modest skew in a join can lead to
> tasks appearing to be stuck, due to the O(n^2) nature of a join considering
> all pairs of rows with matching keys. When this happens users think that
> spark has gotten deadlocked. If there is a bound condition, the "number of
> output rows" metric may look typical. Other metrics may look very modest (eg:
> shuffle read). In those cases, it is very hard to understand what the problem
> is. There is no conclusive proof without getting a heap dump and looking at
> some internal data structures.
> It would be much better if spark had a metric(which we propose be titled
> “number of matched pairs” as a companion to “number of output rows”) which
> showed the user how many pairs were being processed in the join. This would
> get updated in the live UI (when metrics get collected during heartbeats), so
> the user could easily see what was going on.
> This would even help in cases where there was some other cause of a stuck
> executor (eg. network issues) just to disprove this theory. For example, you
> may have 100k records with the same key on each side of a join. That probably
> won't really show up as extreme skew in task input data. But it'll become 10B
> join pairs that spark works through, in one task.
>
> To further demonstrate the usefulness of this metric please follow the steps
> below.
>
> _val df1 = spark.range(0, 200000).map \{ x => (x % 20, 20) }.toDF("b",
> "c")_
> _val df2 = spark.range(0, 300000).map \{ x => (77, 20) }.toDF("b", "c")_
>
> _val df3 = spark.range(0, 200000).map(x => (x + 1, x + 2)).toDF("b", "c")_
> _val df4 = spark.range(0, 300000).map(x => (77, x + 2)).toDF("b", "c")_
>
> _val df5 = df1.union(df2)_
> _val df6 = df3.union(df4)_
>
> _df5.createOrReplaceTempView("table1")_
> _df6.createOrReplaceTempView("table2")_
> h3. InnerJoin
> _sql("select p.**,* f.* from table2 p join table1 f on f.b = p.b and f.c >
> p.c").count_
> _number of output rows: 5,580,000_
> _number of matched pairs: 90,000,490,000_
> h3. FullOuterJoin
> _spark.sql("select p.**,* f.* from table2 p full outer join table1 f on f.b =
> p.b and f.c > p.c").count_
> _number of output rows: 6,099,964_
> _number of matched pairs: 90,000,490,000_
> h3. LeftOuterJoin
> _sql("select p.**,* f.* from table2 p left outer join table1 f on f.b = p.b
> and f.c > p.c").count_
> _number of output rows: 6,079,964_
> _number of matched pairs: 90,000,490,000_
> h3. RightOuterJoin
> _spark.sql("select p.**,* f.* from table2 p right outer join table1 f on f.b
> = p.b and f.c > p.c").count_
> _number of output rows: 5,600,000_
> _number of matched pairs: 90,000,490,000_
> h3. LeftSemiJoin
> _spark.sql("select * from table2 p left semi join table1 f on f.b = p.b and
> f.c > p.c").count_
> _number of output rows: 36_
> _number of matched pairs: 89,994,910,036_
> h3. CrossJoin
> _spark.sql("select p.*, f.* from table2 p cross join table1 f on f.b = p.b
> and f.c > p.c").count_
> _number of output rows: 5,580,000_
> _number of matched pairs: 90,000,490,000_
> h3. LeftAntiJoin
> _spark.sql("select * from table2 p anti join table1 f on f.b = p.b and f.c >
> p.c").count_
> number of output rows: 499,964
> number of matched pairs: 89,994,910,036
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