[
https://issues.apache.org/jira/browse/SPARK-46706?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joey Pereira updated SPARK-46706:
---------------------------------
Description:
Originally reported in SPARK-40499, I'm opening a new issue as that original
one was closed. It's not entirely clear if this regression exists on HEAD, but
I haven't seen any changes that I would expect to address this. Here's what I
reported in the other ticket.
---
Chiming in, I'm currently hitting a similar issue while doing upgrades from
Spark 2.4 to Spark 3.1.
In our case, we have an application with two queries that take
{{percentile_approx}} of a fairly large dataset and reduce to only a few rows
(reading 37 billion rows and aggregating to ~100 and ~7000 rows, with different
groupBy keys respectively). This roughly what the query looks like:
{code:java}
select
col1,
percentile_approx(col2, 0.95) as q_95,
percentile_approx(col2, 0.99) as q_99,
percentile_approx(col2, 0.99) - percentile_approx(col2, 0.95) as q_diff
from data
group by 1
{code}
Also, I do recognize this could be written more optimally as
{{percentile_approx(col2, array(0.95, 0.99))}} to save on computation, this is
just the snippet as it was when I stumbled on it :)
We've observed that the shuffle write stage is roughly the same time (and size)
but the shuffle read is drastically slower. Note, in this application the
shuffle read is also collecting for a broadcast. The difference for the shuffle
read is from 15 minutes on Spark 2.4 to 5.7 hours on Spark 3.1, and that was
after reducing the {{percentile_approx}} precision from the default (10000) to
100 on Spark 3.1. When left at 10000, the stage was exceeding 15 hours and
hitting some of our application time limits.
*Spark 2.4*
!Screenshot 2024-01-05 at 3.53.10 PM.png!
- Stage 2 (shuffle write): 11 minutes, 67647 tasks, 22 GiB shuffle write size
- Stage 9 (shuffle read): 15 minutes, 396 tasks, 22 GiB shuffle read size
*Spark 3.1*
!Screenshot 2024-01-05 at 3.51.52 PM.png!
- Stage 1 (shuffle write): 7.6 minutes, 67396 tasks, 178.7 GiB input size,
20.3 GiB shuffle write size
- Stage 5 (shuffle read): 5.7 hours, 1331 tasks, 20.3 GiB shuffle read size
Prior to digging further into this, I thought it may have been some AQE issue.
That was incorrect and in fact the post-aggregation task/partition count was
much worse without AQE, at about 150000.
I dug into any relevant changes between Spark 2.4 and 3.1, as well as changes
since 3.1, to see if there's anything significant. I just looked at PRs against
{{QuantileSummaries.scala}} and {{{}ApproximatePercentile.scala{}}}, and
ignored some docs, formatting, refactoring, or very simple behavioural changes.
These are the notable changes with some commentary.
For changes between 2.4 and 3.1:
#
[https://github.com/apache/spark/commit/de360e96d776f51c0fd7c31dcec142feabf3d543]
which re-implements the QuantileSummaries {{merge}} algorithm rather deeply.
This seems potentially relevant, especially since the shuffle reading which is
likely just executing merging is struggling. That being said, I would have
maybe expected this to also impact the partial_percentile_approx calculations
too.
#
[https://github.com/apache/spark/commit/023eb482b23b5d63d2157b3def9926673844e0a3]
-appears to change the behaviour in evaluation for {{case when}} / conditional
inputs. This is possibly relevant, but our SQL code only plumbs a primitive in.
Our actual job is a little more complex and the SQL is over a subquery, but the
subquery is not manipulating the column using for {{{}percentile_approx{}}}.- I
misunderstood this change initially. It looks like an optimization to avoid
reconstructing the data type for some parent plan nodes. Our SQL code does
consume percentiles in some CaseWhen clauses, so I'll need to dig more into
this.
I didn't go far while looking into other code. I did not see relevant changes
in {{{}AggregatingAccumulator{}}}.
For changes between 3.1 onwards to {{ApproximatePercentile}} and
{{{}QuantileSummaries{}}}:
# (in 3.2)
[https://github.com/apache/spark/commit/6f8c62047cea125d52af5dad7fb5ad3eadb7f7d0]
which appears to optimize the {{ApproxPercentiles.getPercentiles}} function.
It seems to only be executed on the final result collection and optimizes for
cases like {{percentile_approx(..., array(0.5, 0.9, 0.95), ...)}} - multiple
value percentile calculations.
# (in 3.2)
[https://github.com/apache/spark/commit/0945baf90660a101ae0f86a39d4c91ca74ae5ee3]
which appears to change some plan interfaces. It reads as though only impacted
query compilation performance, rather than execution performance.
Because I'm not seeing any changes that may possible address this performance
issue, I'm led to believe this is still an existing problem in Spark.
I have some follow-up I'll do to dig into this further, namely:
- Test the job with {{percentile}} to see if it performs better, although I'm
a bit dubious we will get this to run with 37 billion input rows
- Test the job on Spark 3.3, which we have production-ready in our environment
- Observe some of the thread dumps / profiling for the stage with poor
performance
- Isolate, or otherwise rewrite, the ApproximatePercentile going into a
CaseWhen in our specific SQL to narrow down if that change is related
Here's an expanded version of our query, with irrelevant columns removed. I
figured I'd include this after realizing the relevant of the CaseWhen parent
node.
{code}
with source_data as (
select col1, col2 -- ... additional columns
from <table>
), percentiles as (
select
col1,
approx_percentile(col2, 0.95) as q_95,
approx_percentile(col2, 0.99) as q_99,
approx_percentile(col2, 0.99) - approx_percentile(col2, 0.95) as q_diff
from source_data
group by 1
), calculations as (
select
col1,
case
when q_diff > 5 then q_95 + 5
else q_99
end as calculated_val
from percentiles
)
select
/*+ BROADCAST (c) */
source_data.*, c.calculated_val
from source_data
left join calculations as c using(col1)
-- ... additional joins / broadcasts
{code}
---
Later on, I found another app of ours with similarly unbearable regressions.
The app previously performed in <2hr run-time prior to upgrading, and is now
>24hr on Spark3. It's a lot simpler, so this is more-or-less the SQL it's doing
(with a number of other internal group-by fields removed for brievity).
{code:sql}
with t as (
select
method,
response_code,
if(response_code >= 200 and response_code < 300, duration_ms, null) as
duration_ms_2xx,
from logs
)
select
method,
count(*) as cnt,
sum(case when response_code >= 200 and response_code < 300 then 1 else 0 end)
as cnt_2xx,
sum(case when response_code >= 300 and response_code < 400 then 1 else 0 end)
as cnt_3xx,
sum(case when response_code >= 400 and response_code < 500 then 1 else 0 end)
as cnt_4xx,
sum(case when response_code >= 500 then 1 else 0 end) as cnt_5xx,
approx_percentile(duration_ms_2xx, 0.5) as latency_p50,
approx_percentile(duration_ms_2xx, 0.9) as latency_p90,
approx_percentile(duration_ms_2xx, 0.99) as latency_p99,
approx_percentile(duration_ms_2xx, 0.999) as latency_p999,
from t
group by 1
{code}
This is simple enough that I'm a bit more convinced it could be the CaseWhen
change given the conditional is a common factor of the two snippets.
was:
Originally reported in SPARK-40499, I'm opening a new issue as that original
one was closed. It's not entirely clear if this regression exists on HEAD, but
I haven't seen any changes that I would expect to address this. Here's what I
reported in the other ticket.
---
Chiming in, I'm currently hitting a similar issue while doing upgrades from
Spark 2.4 to Spark 3.1.
In our case, we have an application with two queries that take
{{percentile_approx}} of a fairly large dataset and reduce to only a few rows
(reading 37 billion rows and aggregating to ~100 and ~7000 rows, with different
groupBy keys respectively). This roughly what the query looks like:
{code:java}
select
col1,
percentile_approx(col2, 0.95) as q_95,
percentile_approx(col2, 0.99) as q_99,
percentile_approx(col2, 0.99) - percentile_approx(col2, 0.95) as q_diff
from data
group by 1
{code}
Also, I do recognize this could be written more optimally as
{{percentile_approx(col2, array(0.95, 0.99))}} to save on computation, this is
just the snippet as it was when I stumbled on it :)
We've observed that the shuffle write stage is roughly the same time (and size)
but the shuffle read is drastically slower. Note, in this application the
shuffle read is also collecting for a broadcast. The difference for the shuffle
read is from 15 minutes on Spark 2.4 to 5.7 hours on Spark 3.1, and that was
after reducing the {{percentile_approx}} precision from the default (10000) to
100 on Spark 3.1. When left at 10000, the stage was exceeding 15 hours and
hitting some of our application time limits.
*Spark 2.4*
!Screenshot 2024-01-05 at 3.53.10 PM.png!
- Stage 2 (shuffle write): 11 minutes, 67647 tasks, 22 GiB shuffle write size
- Stage 9 (shuffle read): 15 minutes, 396 tasks, 22 GiB shuffle read size
*Spark 3.1*
!Screenshot 2024-01-05 at 3.51.52 PM.png!
- Stage 1 (shuffle write): 7.6 minutes, 67396 tasks, 178.7 GiB input size,
20.3 GiB shuffle write size
- Stage 5 (shuffle read): 5.7 hours, 1331 tasks, 20.3 GiB shuffle read size
Prior to digging further into this, I thought it may have been some AQE issue.
That was incorrect and in fact the post-aggregation task/partition count was
much worse without AQE, at about 150000.
I dug into any relevant changes between Spark 2.4 and 3.1, as well as changes
since 3.1, to see if there's anything significant. I just looked at PRs against
{{QuantileSummaries.scala}} and {{{}ApproximatePercentile.scala{}}}, and
ignored some docs, formatting, refactoring, or very simple behavioural changes.
These are the notable changes with some commentary.
For changes between 2.4 and 3.1:
#
[https://github.com/apache/spark/commit/de360e96d776f51c0fd7c31dcec142feabf3d543]
which re-implements the QuantileSummaries {{merge}} algorithm rather deeply.
This seems potentially relevant, especially since the shuffle reading which is
likely just executing merging is struggling. That being said, I would have
maybe expected this to also impact the partial_percentile_approx calculations
too.
#
[https://github.com/apache/spark/commit/023eb482b23b5d63d2157b3def9926673844e0a3]
-appears to change the behaviour in evaluation for {{case when}} / conditional
inputs. This is possibly relevant, but our SQL code only plumbs a primitive in.
Our actual job is a little more complex and the SQL is over a subquery, but the
subquery is not manipulating the column using for {{{}percentile_approx{}}}.- I
misunderstood this change initially. It looks like an optimization to avoid
reconstructing the data type for some parent plan nodes. Our SQL code does
consume percentiles in some CaseWhen clauses, so I'll need to dig more into
this.
I didn't go far while looking into other code. I did not see relevant changes
in {{{}AggregatingAccumulator{}}}.
For changes between 3.1 onwards to {{ApproximatePercentile}} and
{{{}QuantileSummaries{}}}:
# (in 3.2)
[https://github.com/apache/spark/commit/6f8c62047cea125d52af5dad7fb5ad3eadb7f7d0]
which appears to optimize the {{ApproxPercentiles.getPercentiles}} function.
It seems to only be executed on the final result collection and optimizes for
cases like {{percentile_approx(..., array(0.5, 0.9, 0.95), ...)}} - multiple
value percentile calculations.
# (in 3.2)
[https://github.com/apache/spark/commit/0945baf90660a101ae0f86a39d4c91ca74ae5ee3]
which appears to change some plan interfaces. It reads as though only impacted
query compilation performance, rather than execution performance.
Because I'm not seeing any changes that may possible address this performance
issue, I'm led to believe this is still an existing problem in Spark.
I have some follow-up I'll do to dig into this further, namely:
- Test the job with {{percentile}} to see if it performs better, although I'm
a bit dubious we will get this to run with 37 billion input rows
- Test the job on Spark 3.3, which we have production-ready in our environment
- Observe some of the thread dumps / profiling for the stage with poor
performance
- Isolate, or otherwise rewrite, the ApproximatePercentile going into a
CaseWhen in our specific SQL to narrow down if that change is related
Here's an expanded version of our query, with irrelevant columns removed. I
figured I'd include this after realizing the relevant of the CaseWhen parent
node.
{code}
with source_data as (
select col1, col2 -- ... additional columns
from <table>
), percentiles as (
select
col1,
approx_percentile(col2, 0.95) as q_95,
approx_percentile(col2, 0.99) as q_99,
approx_percentile(col2, 0.99) - approx_percentile(col2, 0.95) as q_diff
from source_data
group by 1
), calculations as (
select
col1,
case
when q_diff > 5 then q_95 + 5
else q_99
end as calculated_val
from percentiles
)
select
/*+ BROADCAST (c) */
source_data.*, c.calculated_val
from source_data
left join calculations as c using(col1)
-- ... additional joins / broadcasts
{code}
---
Later on, I found another app of ours with similarly unbearable regressions.
The app previously performed in <2hr run-time prior to upgrading, and is now
>24hr on Spark3. It's a lot simpler, so this is more-or-less the SQL it's doing
(with a number of other internal group-by fields removed for brievity).
{code:sql}
with t as (
select
method,
response_code,
if(response_code >= 200 and response_code < 300, duration_ms, null) as
duration_ms_2xx,
from logs
)
select
method,
count(*) as cnt,
sum(case when response_code >= 200 and response_code < 300 then 1 else 0 end)
as cnt_2xx,
sum(case when response_code >= 300 and response_code < 400 then 1 else 0 end)
as cnt_3xx,
sum(case when response_code >= 400 and response_code < 500 then 1 else 0 end)
as cnt_4xx,
sum(case when response_code >= 500 then 1 else 0 end) as cnt_5xx,
approx_percentile(duration_ms_2xx, 0.5) as latency_p50,
approx_percentile(duration_ms_2xx, 0.9) as latency_p90,
approx_percentile(duration_ms_2xx, 0.99) as latency_p99,
approx_percentile(duration_ms_2xx, 0.999) as latency_p999,
from t
group by 1
{code}
This is simple enough that I'm a bit more convinced it could be the CaseWhen
change given the conditional is a common factor of the two snippets.
> percentile_approx regression between at least Spark 3.1
> -------------------------------------------------------
>
> Key: SPARK-46706
> URL: https://issues.apache.org/jira/browse/SPARK-46706
> Project: Spark
> Issue Type: Bug
> Components: Shuffle, SQL
> Affects Versions: 3.1.3
> Reporter: Joey Pereira
> Priority: Major
> Attachments: Screenshot 2024-01-05 at 3.51.52 PM.png, Screenshot
> 2024-01-05 at 3.53.10 PM.png
>
>
> Originally reported in SPARK-40499, I'm opening a new issue as that original
> one was closed. It's not entirely clear if this regression exists on HEAD,
> but I haven't seen any changes that I would expect to address this. Here's
> what I reported in the other ticket.
> ---
> Chiming in, I'm currently hitting a similar issue while doing upgrades from
> Spark 2.4 to Spark 3.1.
> In our case, we have an application with two queries that take
> {{percentile_approx}} of a fairly large dataset and reduce to only a few rows
> (reading 37 billion rows and aggregating to ~100 and ~7000 rows, with
> different groupBy keys respectively). This roughly what the query looks like:
> {code:java}
> select
> col1,
> percentile_approx(col2, 0.95) as q_95,
> percentile_approx(col2, 0.99) as q_99,
> percentile_approx(col2, 0.99) - percentile_approx(col2, 0.95) as q_diff
> from data
> group by 1
> {code}
> Also, I do recognize this could be written more optimally as
> {{percentile_approx(col2, array(0.95, 0.99))}} to save on computation, this
> is just the snippet as it was when I stumbled on it :)
> We've observed that the shuffle write stage is roughly the same time (and
> size) but the shuffle read is drastically slower. Note, in this application
> the shuffle read is also collecting for a broadcast. The difference for the
> shuffle read is from 15 minutes on Spark 2.4 to 5.7 hours on Spark 3.1, and
> that was after reducing the {{percentile_approx}} precision from the default
> (10000) to 100 on Spark 3.1. When left at 10000, the stage was exceeding 15
> hours and hitting some of our application time limits.
> *Spark 2.4*
> !Screenshot 2024-01-05 at 3.53.10 PM.png!
> - Stage 2 (shuffle write): 11 minutes, 67647 tasks, 22 GiB shuffle write size
> - Stage 9 (shuffle read): 15 minutes, 396 tasks, 22 GiB shuffle read size
> *Spark 3.1*
> !Screenshot 2024-01-05 at 3.51.52 PM.png!
> - Stage 1 (shuffle write): 7.6 minutes, 67396 tasks, 178.7 GiB input size,
> 20.3 GiB shuffle write size
> - Stage 5 (shuffle read): 5.7 hours, 1331 tasks, 20.3 GiB shuffle read size
> Prior to digging further into this, I thought it may have been some AQE
> issue. That was incorrect and in fact the post-aggregation task/partition
> count was much worse without AQE, at about 150000.
> I dug into any relevant changes between Spark 2.4 and 3.1, as well as changes
> since 3.1, to see if there's anything significant. I just looked at PRs
> against {{QuantileSummaries.scala}} and {{{}ApproximatePercentile.scala{}}},
> and ignored some docs, formatting, refactoring, or very simple behavioural
> changes. These are the notable changes with some commentary.
> For changes between 2.4 and 3.1:
> #
> [https://github.com/apache/spark/commit/de360e96d776f51c0fd7c31dcec142feabf3d543]
> which re-implements the QuantileSummaries {{merge}} algorithm rather deeply.
> This seems potentially relevant, especially since the shuffle reading which
> is likely just executing merging is struggling. That being said, I would have
> maybe expected this to also impact the partial_percentile_approx calculations
> too.
> #
> [https://github.com/apache/spark/commit/023eb482b23b5d63d2157b3def9926673844e0a3]
> -appears to change the behaviour in evaluation for {{case when}} /
> conditional inputs. This is possibly relevant, but our SQL code only plumbs a
> primitive in. Our actual job is a little more complex and the SQL is over a
> subquery, but the subquery is not manipulating the column using for
> {{{}percentile_approx{}}}.- I misunderstood this change initially. It looks
> like an optimization to avoid reconstructing the data type for some parent
> plan nodes. Our SQL code does consume percentiles in some CaseWhen clauses,
> so I'll need to dig more into this.
> I didn't go far while looking into other code. I did not see relevant changes
> in {{{}AggregatingAccumulator{}}}.
> For changes between 3.1 onwards to {{ApproximatePercentile}} and
> {{{}QuantileSummaries{}}}:
> # (in 3.2)
> [https://github.com/apache/spark/commit/6f8c62047cea125d52af5dad7fb5ad3eadb7f7d0]
> which appears to optimize the {{ApproxPercentiles.getPercentiles}} function.
> It seems to only be executed on the final result collection and optimizes for
> cases like {{percentile_approx(..., array(0.5, 0.9, 0.95), ...)}} - multiple
> value percentile calculations.
> # (in 3.2)
> [https://github.com/apache/spark/commit/0945baf90660a101ae0f86a39d4c91ca74ae5ee3]
> which appears to change some plan interfaces. It reads as though only
> impacted query compilation performance, rather than execution performance.
> Because I'm not seeing any changes that may possible address this performance
> issue, I'm led to believe this is still an existing problem in Spark.
> I have some follow-up I'll do to dig into this further, namely:
> - Test the job with {{percentile}} to see if it performs better, although
> I'm a bit dubious we will get this to run with 37 billion input rows
> - Test the job on Spark 3.3, which we have production-ready in our
> environment
> - Observe some of the thread dumps / profiling for the stage with poor
> performance
> - Isolate, or otherwise rewrite, the ApproximatePercentile going into a
> CaseWhen in our specific SQL to narrow down if that change is related
> Here's an expanded version of our query, with irrelevant columns removed. I
> figured I'd include this after realizing the relevant of the CaseWhen parent
> node.
> {code}
> with source_data as (
> select col1, col2 -- ... additional columns
> from <table>
> ), percentiles as (
> select
> col1,
> approx_percentile(col2, 0.95) as q_95,
> approx_percentile(col2, 0.99) as q_99,
> approx_percentile(col2, 0.99) - approx_percentile(col2, 0.95) as q_diff
> from source_data
> group by 1
> ), calculations as (
> select
> col1,
> case
> when q_diff > 5 then q_95 + 5
> else q_99
> end as calculated_val
> from percentiles
> )
> select
> /*+ BROADCAST (c) */
> source_data.*, c.calculated_val
> from source_data
> left join calculations as c using(col1)
> -- ... additional joins / broadcasts
> {code}
> ---
> Later on, I found another app of ours with similarly unbearable regressions.
> The app previously performed in <2hr run-time prior to upgrading, and is now
> >24hr on Spark3. It's a lot simpler, so this is more-or-less the SQL it's
> doing (with a number of other internal group-by fields removed for brievity).
> {code:sql}
> with t as (
> select
> method,
> response_code,
> if(response_code >= 200 and response_code < 300, duration_ms, null) as
> duration_ms_2xx,
> from logs
> )
> select
> method,
> count(*) as cnt,
> sum(case when response_code >= 200 and response_code < 300 then 1 else 0
> end) as cnt_2xx,
> sum(case when response_code >= 300 and response_code < 400 then 1 else 0
> end) as cnt_3xx,
> sum(case when response_code >= 400 and response_code < 500 then 1 else 0
> end) as cnt_4xx,
> sum(case when response_code >= 500 then 1 else 0 end) as cnt_5xx,
> approx_percentile(duration_ms_2xx, 0.5) as latency_p50,
> approx_percentile(duration_ms_2xx, 0.9) as latency_p90,
> approx_percentile(duration_ms_2xx, 0.99) as latency_p99,
> approx_percentile(duration_ms_2xx, 0.999) as latency_p999,
> from t
> group by 1
> {code}
> This is simple enough that I'm a bit more convinced it could be the CaseWhen
> change given the conditional is a common factor of the two snippets.
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