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https://issues.apache.org/jira/browse/SPARK-34843?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17308343#comment-17308343
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Jason Yarbrough edited comment on SPARK-34843 at 3/25/21, 4:21 AM:
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I've modified the formula slightly (reflected in the description). I've also
added logic to determine the extra amount per stride that is lost, take that,
cut it in half, and then add that many strides to the starting value of the
first partition. That will more evenly distribute the first and last
partitions, and bring the middle of the partitions closer to the midpoint of
the lower and upper bounds.
I'll get a PR in soon. Need to check the other unit tests and make sure nothing
regressed.
was (Author: hanover-fiste):
I've modified the formula slightly (reflected in the description). I've also
added logic to determine the extra amount per stride that is lost, take that,
cut it in half (round down), and then add that many strides to the starting
value of the first partition. That will more evenly distribute the first and
last partitions, and bring the middle of the partitions closer to the midpoint
of the lower and upper bounds.
I'll get a PR in soon. Need to check the unit tests and make sure nothing
regressed.
> JDBCRelation columnPartition function improperly determines stride size
> ---
>
> Key: SPARK-34843
> URL: https://issues.apache.org/jira/browse/SPARK-34843
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 3.0.0
>Reporter: Jason Yarbrough
>Priority: Minor
> Attachments: SPARK-34843.patch
>
>
> Currently, in JDBCRelation (line 123), the stride size is calculated as
> follows:
> val stride: Long = upperBound / numPartitions - lowerBound / numPartitions
>
> Due to truncation happening on both divisions, the stride size can fall short
> of what it should be. This can lead to a big difference between the provided
> upper bound and the actual start of the last partition.
> I propose this formula, as it is much more accurate and leads to better
> distribution:
> val stride = (upperBound / numPartitions.toFloat - lowerBound /
> numPartitions.toFloat).toLong
>
> An example (using a date column):
> Say you're creating 1,000 partitions. If you provide a lower bound of
> 1927-04-05 (this gets translated to -15611), and an upper bound of 2020-10-27
> (translated to 18563), Spark determines the stride size as follows:
>
> (18563L / 1000L) - (-15611 / 1000L) = 33
> Starting from the lower bound, doing 998 strides of 33, you end up at
> 2017-06-05 (currently, it will actually do 2017-07-08 due to adding the first
> stride into the lower partition). This is over 3 years of extra data that
> will go into the last partition, and depending on the shape of the data could
> cause a very long running task at the end of a job.
>
> Using the formula I'm proposing, you'd get:
> ((18563L / 1000F) - (-15611 / 1000F)).toLong = 34
> This would put the upper bound at 2020-02-28 (currently, it will actually do
> 2020-04-02 due to adding the first stride into the lower partition), which is
> much closer to the original supplied upper bound. This is the best you can do
> to get as close as possible to the upper bound (without adjusting the number
> of partitions). For example, a stride size of 35 would go well past the
> supplied upper bound (over 2 years, 2022-11-22).
>
> In the above example, there is only a difference of 1 between the stride size
> using the current formula and the stride size using the proposed formula, but
> with greater distance between the lower and upper bounds, or a lower number
> of partitions, the difference can be much greater.
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