Re: SortMergeJoinExec: Utilizing child partitioning when joining
“Where can I find information on how to run standard performance
tests/benchmarks?“
The grand standard is spark-sql-perf and in particular the tpc-ds benchmark.
Most of the big optimization teams are using this as the primary benchmark.
One word of warning is that most groups have also extended this to add entirely
new types of benchmarks which are not in open source but the core tpc-ds
benchmark will get you most of the way there.
https://aws.amazon.com/blogs/big-data/amazon-emr-introduces-emr-runtime-for-apache-spark/
https://github.com/databricks/spark-sql-perf
http://www.tpc.org/tpcds/
“Are performance degradations to existing queries that are fixable by new
equivalent queries not allowed for a new major spark version”
The general rule of thumb for my group (which is NOT databricks) is, as long as
the geomean of tpcds increases you’re fine as long as you don’t break any
existing queries. For example regressing a couple queries by 5% is fine BUT
causing a query that would have previously run to crash is not ok. Additionally
we have a sample of user queries +etl processes that we try not to break either.
Cheers Andrew
From: Brett Marcott
Date: Tuesday, January 7, 2020 at 12:00 AM
To: "Long, Andrew"
Cc: "dev@spark.apache.org"
Subject: Re: SortMergeJoinExec: Utilizing child partitioning when joining
1. Where can I find information on how to run standard performance
tests/benchmarks?
2. Are performance degradations to existing queries that are fixable by new
equivalent queries not allowed for a new major spark version?
On Thu, Jan 2, 2020 at 3:05 PM Brett Marcott
mailto:brett.marc...@gmail.com>> wrote:
Thanks for the response Andrew.
1. The approach
The approach I mentioned will not introduce any new skew, so it should only be
worsen performance if the user was relying on the shuffle to fix skew they had
before.
The user can address this by either not introducing their own skewed partition
in the first place, or repartitioning with less skew again before the join.
Today the user cannot change partitioning without changing the join condition
in a hacky way:joinkey1 >= joinkey2 && joinkey1 <= joinkey2
The condition I mentioned below ensures that the same keys on left and right
formed their respective subsets:
left and right partition expressions have the same subset (with regards
to indices) of their respective join keys
I don't believe EnsureRequirements will require any changes, just what the
Exec's are saying is required.
2. output partitionings
Yea I got as far as you mentioned, but I didn't at first get why for outer
joins only one side is used.
Now however, I think it makes sense because for outer joins you may be
introducing nulls for at least one side, which makes that sides partitioning
invalid right?
Warn New Year Regards,
Brett
On Thu, Jan 2, 2020 at 2:28 PM Long, Andrew
mailto:loand...@amazon.com>> wrote:
“Thoughts on this approach?“
Just to warn you this is a hazardous optimization without cardinality
information. Removing columns from the hash exchange reduces entropy
potentially resulting in skew. Also keep in mind that if you reduce the number
of columns on one side of the join you need todo it on the other. This will
require you to rewrite EnsureRequirements or add a special case to detect this.
As a word of warning there’s a whole bunch of subtle things that
EnsureRequirements is doing and its really easy to unintentionally create
performance regressions while making improvements in other areas.
“Could someone help explain why the different join types have different output
partitionings“
Long story short when a join happens the join exec zips together the partitions
of the left and right side so that one partition of the join has the elements
of the left and right. In the case of an inner join this means that that the
resulting RDD is now partitioned by both the left join keys and the right join
keys. I’d suggest taking a look at the join execs and take a look at how they
build the result RDD from the partitions of the left and right RDDs.(see
doExecute(…)) left/right outer does look surprising though.
You should see something like…
left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
Cheers Andrew
From: Brett Marcott mailto:brett.marc...@gmail.com>>
Date: Tuesday, December 31, 2019 at 11:49 PM
To: "dev@spark.apache.org<mailto:dev@spark.apache.org>"
mailto:dev@spark.apache.org>>
Subject: SortMergeJoinExec: Utilizing child partitioning when joining
Hi all,
I found this jira for an issue I ran into recently:
https://issues.apache.org/jira/browse/SPARK-28771
My initial idea for a fix is to change SortMergeJoinExec's (and
ShuffledHashJoinExec) requiredChildDistribution.
At least if all below conditions are met, we could only require a subset of
keys for partitioning:
left and right children's output partiti
Re: SortMergeJoinExec: Utilizing child partitioning when joining
1. Where can I find information on how to run standard performance
tests/benchmarks?
2. Are performance degradations to existing queries that are fixable by new
equivalent queries not allowed for a new major spark version?
On Thu, Jan 2, 2020 at 3:05 PM Brett Marcott
wrote:
> Thanks for the response Andrew.
>
> *1. The approach*
> The approach I mentioned will not introduce any new skew, so it should
> only be worsen performance if the user was relying on the shuffle to fix
> skew they had before.
> The user can address this by either not introducing their own skewed
> partition in the first place, or repartitioning with less skew again before
> the join.
> Today the user cannot change partitioning without changing the join
> condition in a hacky way:joinkey1 >= joinkey2 && joinkey1 <= joinkey2
>
> The condition I mentioned below ensures that the *same* keys on left and
> right formed their respective subsets:
> left and right partition expressions have the same subset (with
> regards to indices) of their respective join keys
>
> I don't believe EnsureRequirements will require any changes, just what the
> Exec's are saying is required.
>
> *2. output partitionings*
> Yea I got as far as you mentioned, but I didn't at first get why for outer
> joins only one side is used.
> Now however, I think it makes sense because for outer joins you may be
> introducing nulls for at least one side, which makes that sides
> partitioning invalid right?
>
> Warn New Year Regards,
> Brett
>
> On Thu, Jan 2, 2020 at 2:28 PM Long, Andrew wrote:
>
>> “Thoughts on this approach?“
>>
>>
>>
>> Just to warn you this is a hazardous optimization without cardinality
>> information. Removing columns from the hash exchange reduces entropy
>> potentially resulting in skew. Also keep in mind that if you reduce the
>> number of columns on one side of the join you need todo it on the other.
>> This will require you to rewrite EnsureRequirements or add a special case
>> to detect this.
>>
>>
>>
>> As a word of warning there’s a whole bunch of subtle things that
>> EnsureRequirements is doing and its really easy to unintentionally create
>> performance regressions while making improvements in other areas.
>>
>>
>>
>> “Could someone help explain why the different join types have different
>> output partitionings“
>>
>>
>>
>> Long story short when a join happens the join exec zips together the
>> partitions of the left and right side so that one partition of the join has
>> the elements of the left and right. In the case of an inner join this
>> means that that the resulting RDD is now partitioned by both the left join
>> keys and the right join keys. I’d suggest taking a look at the join execs
>> and take a look at how they build the result RDD from the partitions of the
>> left and right RDDs.(see doExecute(…)) left/right outer does look
>> surprising though.
>>
>>
>>
>> You should see something like…
>>
>>
>>
>> left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
>>
>>
>>
>>
>>
>> Cheers Andrew
>>
>>
>>
>> *From: *Brett Marcott
>> *Date: *Tuesday, December 31, 2019 at 11:49 PM
>> *To: *"dev@spark.apache.org"
>> *Subject: *SortMergeJoinExec: Utilizing child partitioning when joining
>>
>>
>>
>> Hi all,
>>
>>
>>
>> I found this jira for an issue I ran into recently:
>>
>> https://issues.apache.org/jira/browse/SPARK-28771
>>
>>
>>
>> My initial idea for a fix is to change SortMergeJoinExec's (and
>> ShuffledHashJoinExec) requiredChildDistribution.
>>
>>
>>
>> At least if all below conditions are met, we could only require a subset
>> of keys for partitioning:
>>
>> left and right children's output partitionings are hashpartitioning with
>> same numpartitions
>>
>> left and right partition expressions have the same subset (with regards
>> to indices) of their respective join keys
>>
>>
>>
>> If that subset of keys is returned by requiredChildDistribution,
>> then EnsureRequirements.ensureDistributionAndOrdering would not add a
>> shuffle stage, hence reusing the children's partitioning.
>>
>>
>>
>> 1.Thoughts on this approach?
>>
>>
>>
>> 2. Could someone help explain why the different join types have different
>> output partitionings in SortMergeJoinExec.outputPartitioning
>>
>> ?
>>
>>
>>
>> Thanks,
>>
>> Brett
>>
>>
>>
>>
>>
>
Re: SortMergeJoinExec: Utilizing child partitioning when joining
Thanks for the response Andrew.
*1. The approach*
The approach I mentioned will not introduce any new skew, so it should only
be worsen performance if the user was relying on the shuffle to fix skew
they had before.
The user can address this by either not introducing their own skewed
partition in the first place, or repartitioning with less skew again before
the join.
Today the user cannot change partitioning without changing the join
condition in a hacky way:joinkey1 >= joinkey2 && joinkey1 <= joinkey2
The condition I mentioned below ensures that the *same* keys on left and
right formed their respective subsets:
left and right partition expressions have the same subset (with
regards to indices) of their respective join keys
I don't believe EnsureRequirements will require any changes, just what the
Exec's are saying is required.
*2. output partitionings*
Yea I got as far as you mentioned, but I didn't at first get why for outer
joins only one side is used.
Now however, I think it makes sense because for outer joins you may be
introducing nulls for at least one side, which makes that sides
partitioning invalid right?
Warn New Year Regards,
Brett
On Thu, Jan 2, 2020 at 2:28 PM Long, Andrew wrote:
> “Thoughts on this approach?“
>
>
>
> Just to warn you this is a hazardous optimization without cardinality
> information. Removing columns from the hash exchange reduces entropy
> potentially resulting in skew. Also keep in mind that if you reduce the
> number of columns on one side of the join you need todo it on the other.
> This will require you to rewrite EnsureRequirements or add a special case
> to detect this.
>
>
>
> As a word of warning there’s a whole bunch of subtle things that
> EnsureRequirements is doing and its really easy to unintentionally create
> performance regressions while making improvements in other areas.
>
>
>
> “Could someone help explain why the different join types have different
> output partitionings“
>
>
>
> Long story short when a join happens the join exec zips together the
> partitions of the left and right side so that one partition of the join has
> the elements of the left and right. In the case of an inner join this
> means that that the resulting RDD is now partitioned by both the left join
> keys and the right join keys. I’d suggest taking a look at the join execs
> and take a look at how they build the result RDD from the partitions of the
> left and right RDDs.(see doExecute(…)) left/right outer does look
> surprising though.
>
>
>
> You should see something like…
>
>
>
> left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
>
>
>
>
>
> Cheers Andrew
>
>
>
> *From: *Brett Marcott
> *Date: *Tuesday, December 31, 2019 at 11:49 PM
> *To: *"dev@spark.apache.org"
> *Subject: *SortMergeJoinExec: Utilizing child partitioning when joining
>
>
>
> Hi all,
>
>
>
> I found this jira for an issue I ran into recently:
>
> https://issues.apache.org/jira/browse/SPARK-28771
>
>
>
> My initial idea for a fix is to change SortMergeJoinExec's (and
> ShuffledHashJoinExec) requiredChildDistribution.
>
>
>
> At least if all below conditions are met, we could only require a subset
> of keys for partitioning:
>
> left and right children's output partitionings are hashpartitioning with
> same numpartitions
>
> left and right partition expressions have the same subset (with regards to
> indices) of their respective join keys
>
>
>
> If that subset of keys is returned by requiredChildDistribution,
> then EnsureRequirements.ensureDistributionAndOrdering would not add a
> shuffle stage, hence reusing the children's partitioning.
>
>
>
> 1.Thoughts on this approach?
>
>
>
> 2. Could someone help explain why the different join types have different
> output partitionings in SortMergeJoinExec.outputPartitioning
>
> ?
>
>
>
> Thanks,
>
> Brett
>
>
>
>
>
Re: SortMergeJoinExec: Utilizing child partitioning when joining
“Thoughts on this approach?“
Just to warn you this is a hazardous optimization without cardinality
information. Removing columns from the hash exchange reduces entropy
potentially resulting in skew. Also keep in mind that if you reduce the number
of columns on one side of the join you need todo it on the other. This will
require you to rewrite EnsureRequirements or add a special case to detect this.
As a word of warning there’s a whole bunch of subtle things that
EnsureRequirements is doing and its really easy to unintentionally create
performance regressions while making improvements in other areas.
“Could someone help explain why the different join types have different output
partitionings“
Long story short when a join happens the join exec zips together the partitions
of the left and right side so that one partition of the join has the elements
of the left and right. In the case of an inner join this means that that the
resulting RDD is now partitioned by both the left join keys and the right join
keys. I’d suggest taking a look at the join execs and take a look at how they
build the result RDD from the partitions of the left and right RDDs.(see
doExecute(…)) left/right outer does look surprising though.
You should see something like…
left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
Cheers Andrew
From: Brett Marcott
Date: Tuesday, December 31, 2019 at 11:49 PM
To: "dev@spark.apache.org"
Subject: SortMergeJoinExec: Utilizing child partitioning when joining
Hi all,
I found this jira for an issue I ran into recently:
https://issues.apache.org/jira/browse/SPARK-28771
My initial idea for a fix is to change SortMergeJoinExec's (and
ShuffledHashJoinExec) requiredChildDistribution.
At least if all below conditions are met, we could only require a subset of
keys for partitioning:
left and right children's output partitionings are hashpartitioning with same
numpartitions
left and right partition expressions have the same subset (with regards to
indices) of their respective join keys
If that subset of keys is returned by requiredChildDistribution, then
EnsureRequirements.ensureDistributionAndOrdering would not add a shuffle stage,
hence reusing the children's partitioning.
1.Thoughts on this approach?
2. Could someone help explain why the different join types have different
output partitionings in
SortMergeJoinExec.outputPartitioning?
Thanks,
Brett
