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https://issues.apache.org/jira/browse/SPARK-34843?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Jason Yarbrough updated SPARK-34843:
------------------------------------
Description:
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 strides of 33, you'll end up at
2017-07-08. 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 2020-04-02, 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.
was:
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.
> 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 strides of 33, you'll end up at
> 2017-07-08. 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 2020-04-02, 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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