[jira] [Commented] (FLINK-33940) Update the auto-derivation rule of max parallelism for enlarged upscaling space
[
https://issues.apache.org/jira/browse/FLINK-33940?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17802080#comment-17802080
]
Zhanghao Chen commented on FLINK-33940:
---
Thanks for the input, [~fanrui]. I'll take a look on the benchmark.
> Update the auto-derivation rule of max parallelism for enlarged upscaling
> space
> ---
>
> Key: FLINK-33940
> URL: https://issues.apache.org/jira/browse/FLINK-33940
> Project: Flink
> Issue Type: Improvement
> Components: API / Core
>Reporter: Zhanghao Chen
>Assignee: Zhanghao Chen
>Priority: Major
> Attachments: image-2024-01-03-10-52-05-861.png
>
>
> *Background*
> The choice of the max parallelism of an stateful operator is important as it
> limits the upper bound of the parallelism of the opeartor while it can also
> add extra overhead when being set too large. Currently, the max parallelism
> of an opeartor is either fixed to a value specified by API core / pipeline
> option or auto-derived with the following rules:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * 1.5), 128), 32767)}}
> *Problem*
> Recently, the elasticity of Flink jobs is becoming more and more valued by
> users. The current auto-derived max parallelism was introduced a time time
> ago and only allows the operator parallelism to be roughly doubled, which is
> not desired for elasticity. Setting an max parallelism manually may not be
> desired as well: users may not have the sufficient expertise to select a good
> max-parallelism value.
> *Proposal*
> Update the auto-derivation rule of max parallelism to derive larger max
> parallelism for better elasticity experience out of the box. A candidate is
> as follows:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), {*}1024{*}),
> 32767)}}
> Looking forward to your opinions on this.
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[jira] [Commented] (FLINK-33940) Update the auto-derivation rule of max parallelism for enlarged upscaling space
[
https://issues.apache.org/jira/browse/FLINK-33940?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17801992#comment-17801992
]
Rui Fan commented on FLINK-33940:
-
Hi [~Zhanghao Chen] , IIUC flink-benchmarks[1] is checking the performance
related to state, and we can see the performance change in this web UI[2].
Such as: the valueGet.HEAP[3][4] means valueState.get for hashmap state backend.
!image-2024-01-03-10-52-05-861.png|width=949,height=359!
[1] [https://github.com/apache/flink-benchmarks]
[2][http://flink-speed.xyz|http://flink-speed.xyz/]
[3][https://github.com/apache/flink-benchmarks/blob/master/src/main/java/org/apache/flink/state/benchmark/ValueStateBenchmark.java]
[4]http://flink-speed.xyz/timeline/?ben=valueGet.HEAP=3
> Update the auto-derivation rule of max parallelism for enlarged upscaling
> space
> ---
>
> Key: FLINK-33940
> URL: https://issues.apache.org/jira/browse/FLINK-33940
> Project: Flink
> Issue Type: Improvement
> Components: API / Core
>Reporter: Zhanghao Chen
>Assignee: Zhanghao Chen
>Priority: Major
> Attachments: image-2024-01-03-10-52-05-861.png
>
>
> *Background*
> The choice of the max parallelism of an stateful operator is important as it
> limits the upper bound of the parallelism of the opeartor while it can also
> add extra overhead when being set too large. Currently, the max parallelism
> of an opeartor is either fixed to a value specified by API core / pipeline
> option or auto-derived with the following rules:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * 1.5), 128), 32767)}}
> *Problem*
> Recently, the elasticity of Flink jobs is becoming more and more valued by
> users. The current auto-derived max parallelism was introduced a time time
> ago and only allows the operator parallelism to be roughly doubled, which is
> not desired for elasticity. Setting an max parallelism manually may not be
> desired as well: users may not have the sufficient expertise to select a good
> max-parallelism value.
> *Proposal*
> Update the auto-derivation rule of max parallelism to derive larger max
> parallelism for better elasticity experience out of the box. A candidate is
> as follows:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), {*}1024{*}),
> 32767)}}
> Looking forward to your opinions on this.
--
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[jira] [Commented] (FLINK-33940) Update the auto-derivation rule of max parallelism for enlarged upscaling space
[
https://issues.apache.org/jira/browse/FLINK-33940?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17801987#comment-17801987
]
Zhanghao Chen commented on FLINK-33940:
---
[~mxm] thanks for the quick response, I'll update with some Nextmark benchmark
result later on the performance impact of bumping the num of keygroups for an
informed decision. As for the factor, 10 sounds good for me, maybe let's also
see what [~fanrui] & [~gyfora] think about it.
> Update the auto-derivation rule of max parallelism for enlarged upscaling
> space
> ---
>
> Key: FLINK-33940
> URL: https://issues.apache.org/jira/browse/FLINK-33940
> Project: Flink
> Issue Type: Improvement
> Components: API / Core
>Reporter: Zhanghao Chen
>Assignee: Zhanghao Chen
>Priority: Major
>
> *Background*
> The choice of the max parallelism of an stateful operator is important as it
> limits the upper bound of the parallelism of the opeartor while it can also
> add extra overhead when being set too large. Currently, the max parallelism
> of an opeartor is either fixed to a value specified by API core / pipeline
> option or auto-derived with the following rules:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * 1.5), 128), 32767)}}
> *Problem*
> Recently, the elasticity of Flink jobs is becoming more and more valued by
> users. The current auto-derived max parallelism was introduced a time time
> ago and only allows the operator parallelism to be roughly doubled, which is
> not desired for elasticity. Setting an max parallelism manually may not be
> desired as well: users may not have the sufficient expertise to select a good
> max-parallelism value.
> *Proposal*
> Update the auto-derivation rule of max parallelism to derive larger max
> parallelism for better elasticity experience out of the box. A candidate is
> as follows:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), {*}1024{*}),
> 32767)}}
> Looking forward to your opinions on this.
--
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[jira] [Commented] (FLINK-33940) Update the auto-derivation rule of max parallelism for enlarged upscaling space
[
https://issues.apache.org/jira/browse/FLINK-33940?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17801843#comment-17801843
]
Maximilian Michels commented on FLINK-33940:
[~Zhanghao Chen] Even though the factor only affects high parallelism operators
> 840, I wonder whether we need to leave more room for scaleup. But I don't
have a strong opinion.
{quote}
IIUC, when the parallelism of one job is very small(it's 1 or 2) and the max
parallelism is 1024, one subtask will have 1024 keyGroups. From state backend
side, too many key groups may effect the performance. (This is my concern to
change it by default in Flink Community.)
{quote}
[~fanrui] I think we need to find out how big the performance impact actually
is when jumping from 128 to 840 key groups. But 128 may just have been a very
conservative number.
> Update the auto-derivation rule of max parallelism for enlarged upscaling
> space
> ---
>
> Key: FLINK-33940
> URL: https://issues.apache.org/jira/browse/FLINK-33940
> Project: Flink
> Issue Type: Improvement
> Components: API / Core
>Reporter: Zhanghao Chen
>Assignee: Zhanghao Chen
>Priority: Major
>
> *Background*
> The choice of the max parallelism of an stateful operator is important as it
> limits the upper bound of the parallelism of the opeartor while it can also
> add extra overhead when being set too large. Currently, the max parallelism
> of an opeartor is either fixed to a value specified by API core / pipeline
> option or auto-derived with the following rules:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * 1.5), 128), 32767)}}
> *Problem*
> Recently, the elasticity of Flink jobs is becoming more and more valued by
> users. The current auto-derived max parallelism was introduced a time time
> ago and only allows the operator parallelism to be roughly doubled, which is
> not desired for elasticity. Setting an max parallelism manually may not be
> desired as well: users may not have the sufficient expertise to select a good
> max-parallelism value.
> *Proposal*
> Update the auto-derivation rule of max parallelism to derive larger max
> parallelism for better elasticity experience out of the box. A candidate is
> as follows:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), {*}1024{*}),
> 32767)}}
> Looking forward to your opinions on this.
--
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[jira] [Commented] (FLINK-33940) Update the auto-derivation rule of max parallelism for enlarged upscaling space
[
https://issues.apache.org/jira/browse/FLINK-33940?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17801815#comment-17801815
]
Zhanghao Chen commented on FLINK-33940:
---
[~mxm] There's no concrete rationale for selecting the factor 5, it's just a
magic number that most users are satified with in our production env. I'd be
good with 10 as well. Do you have any perference on the selection of the factor?
> Update the auto-derivation rule of max parallelism for enlarged upscaling
> space
> ---
>
> Key: FLINK-33940
> URL: https://issues.apache.org/jira/browse/FLINK-33940
> Project: Flink
> Issue Type: Improvement
> Components: API / Core
>Reporter: Zhanghao Chen
>Assignee: Zhanghao Chen
>Priority: Major
>
> *Background*
> The choice of the max parallelism of an stateful operator is important as it
> limits the upper bound of the parallelism of the opeartor while it can also
> add extra overhead when being set too large. Currently, the max parallelism
> of an opeartor is either fixed to a value specified by API core / pipeline
> option or auto-derived with the following rules:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * 1.5), 128), 32767)}}
> *Problem*
> Recently, the elasticity of Flink jobs is becoming more and more valued by
> users. The current auto-derived max parallelism was introduced a time time
> ago and only allows the operator parallelism to be roughly doubled, which is
> not desired for elasticity. Setting an max parallelism manually may not be
> desired as well: users may not have the sufficient expertise to select a good
> max-parallelism value.
> *Proposal*
> Update the auto-derivation rule of max parallelism to derive larger max
> parallelism for better elasticity experience out of the box. A candidate is
> as follows:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), {*}1024{*}),
> 32767)}}
> Looking forward to your opinions on this.
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[jira] [Commented] (FLINK-33940) Update the auto-derivation rule of max parallelism for enlarged upscaling space
[
https://issues.apache.org/jira/browse/FLINK-33940?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17801790#comment-17801790
]
Maximilian Michels commented on FLINK-33940:
[~Zhanghao Chen] Thank you for the proposal. I agree with using highly
composite numbers, as this will provide more flexibility to the autoscaler. I'm
not sure about the {{operatorParallelism * 5}}. What is the rational for
selecting this factor? Why not {{*10}}?
> Update the auto-derivation rule of max parallelism for enlarged upscaling
> space
> ---
>
> Key: FLINK-33940
> URL: https://issues.apache.org/jira/browse/FLINK-33940
> Project: Flink
> Issue Type: Improvement
> Components: API / Core
>Reporter: Zhanghao Chen
>Assignee: Zhanghao Chen
>Priority: Major
>
> *Background*
> The choice of the max parallelism of an stateful operator is important as it
> limits the upper bound of the parallelism of the opeartor while it can also
> add extra overhead when being set too large. Currently, the max parallelism
> of an opeartor is either fixed to a value specified by API core / pipeline
> option or auto-derived with the following rules:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * 1.5), 128), 32767)}}
> *Problem*
> Recently, the elasticity of Flink jobs is becoming more and more valued by
> users. The current auto-derived max parallelism was introduced a time time
> ago and only allows the operator parallelism to be roughly doubled, which is
> not desired for elasticity. Setting an max parallelism manually may not be
> desired as well: users may not have the sufficient expertise to select a good
> max-parallelism value.
> *Proposal*
> Update the auto-derivation rule of max parallelism to derive larger max
> parallelism for better elasticity experience out of the box. A candidate is
> as follows:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), {*}1024{*}),
> 32767)}}
> Looking forward to your opinions on this.
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[jira] [Commented] (FLINK-33940) Update the auto-derivation rule of max parallelism for enlarged upscaling space
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https://issues.apache.org/jira/browse/FLINK-33940?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17800444#comment-17800444
]
Zhanghao Chen commented on FLINK-33940:
---
Thanks [~gyfora] for pointing that. We also observe some data skew introduced
by imbalanced key group assignment in our production use, using highly
composite numbers should be useful. How about using the following formula:
{{{}min(max({*}roundUpToHighlyCompositeNum{*}(operatorParallelism * {*}5{*}),
{*}840{*}), {*}45360{*}){}}}, where the highly composite number series is
defined in [A002182 - OEIS|https://oeis.org/A002182]?
> Update the auto-derivation rule of max parallelism for enlarged upscaling
> space
> ---
>
> Key: FLINK-33940
> URL: https://issues.apache.org/jira/browse/FLINK-33940
> Project: Flink
> Issue Type: Improvement
> Components: API / Core
>Reporter: Zhanghao Chen
>Assignee: Zhanghao Chen
>Priority: Major
>
> *Background*
> The choice of the max parallelism of an stateful operator is important as it
> limits the upper bound of the parallelism of the opeartor while it can also
> add extra overhead when being set too large. Currently, the max parallelism
> of an opeartor is either fixed to a value specified by API core / pipeline
> option or auto-derived with the following rules:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * 1.5), 128), 32767)}}
> *Problem*
> Recently, the elasticity of Flink jobs is becoming more and more valued by
> users. The current auto-derived max parallelism was introduced a time time
> ago and only allows the operator parallelism to be roughly doubled, which is
> not desired for elasticity. Setting an max parallelism manually may not be
> desired as well: users may not have the sufficient expertise to select a good
> max-parallelism value.
> *Proposal*
> Update the auto-derivation rule of max parallelism to derive larger max
> parallelism for better elasticity experience out of the box. A candidate is
> as follows:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), {*}1024{*}),
> 32767)}}
> Looking forward to your opinions on this.
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[jira] [Commented] (FLINK-33940) Update the auto-derivation rule of max parallelism for enlarged upscaling space
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]
Rui Fan commented on FLINK-33940:
-
Thanks [~gyfora] for the reminder! Too many divisors is useful for avoid data
skew, 840 makes sense to me. (y)
> Update the auto-derivation rule of max parallelism for enlarged upscaling
> space
> ---
>
> Key: FLINK-33940
> URL: https://issues.apache.org/jira/browse/FLINK-33940
> Project: Flink
> Issue Type: Improvement
> Components: API / Core
>Reporter: Zhanghao Chen
>Assignee: Zhanghao Chen
>Priority: Major
>
> *Background*
> The choice of the max parallelism of an stateful operator is important as it
> limits the upper bound of the parallelism of the opeartor while it can also
> add extra overhead when being set too large. Currently, the max parallelism
> of an opeartor is either fixed to a value specified by API core / pipeline
> option or auto-derived with the following rules:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * 1.5), 128), 32767)}}
> *Problem*
> Recently, the elasticity of Flink jobs is becoming more and more valued by
> users. The current auto-derived max parallelism was introduced a time time
> ago and only allows the operator parallelism to be roughly doubled, which is
> not desired for elasticity. Setting an max parallelism manually may not be
> desired as well: users may not have the sufficient expertise to select a good
> max-parallelism value.
> *Proposal*
> Update the auto-derivation rule of max parallelism to derive larger max
> parallelism for better elasticity experience out of the box. A candidate is
> as follows:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), {*}1024{*}),
> 32767)}}
> Looking forward to your opinions on this.
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[jira] [Commented] (FLINK-33940) Update the auto-derivation rule of max parallelism for enlarged upscaling space
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]
Gyula Fora commented on FLINK-33940:
Consider 840 in instead of 1024, 840 has 32 divisors compared to the 11
divisors of 1024 and it’s a smaller number… it’s a much better max parallelism
setting in my opinion
please see my previous comment regarding highly composite numbers
> Update the auto-derivation rule of max parallelism for enlarged upscaling
> space
> ---
>
> Key: FLINK-33940
> URL: https://issues.apache.org/jira/browse/FLINK-33940
> Project: Flink
> Issue Type: Improvement
> Components: API / Core
>Reporter: Zhanghao Chen
>Assignee: Zhanghao Chen
>Priority: Major
>
> *Background*
> The choice of the max parallelism of an stateful operator is important as it
> limits the upper bound of the parallelism of the opeartor while it can also
> add extra overhead when being set too large. Currently, the max parallelism
> of an opeartor is either fixed to a value specified by API core / pipeline
> option or auto-derived with the following rules:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * 1.5), 128), 32767)}}
> *Problem*
> Recently, the elasticity of Flink jobs is becoming more and more valued by
> users. The current auto-derived max parallelism was introduced a time time
> ago and only allows the operator parallelism to be roughly doubled, which is
> not desired for elasticity. Setting an max parallelism manually may not be
> desired as well: users may not have the sufficient expertise to select a good
> max-parallelism value.
> *Proposal*
> Update the auto-derivation rule of max parallelism to derive larger max
> parallelism for better elasticity experience out of the box. A candidate is
> as follows:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), {*}1024{*}),
> 32767)}}
> Looking forward to your opinions on this.
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[jira] [Commented] (FLINK-33940) Update the auto-derivation rule of max parallelism for enlarged upscaling space
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]
Rui Fan commented on FLINK-33940:
-
Thanks [~Zhanghao Chen] for driving this improvement and for the ping!:)
1024 as the min value of max parallelism makes sense to me, and our internal
flink version also uses 1024 instead of 128. And it's fine for most of jobs.
IIUC, when the parallelism of one job is very small(it's 1 or 2) and the max
parallelism is 1024, one subtask will have 1024 keyGroups. From state backend
side, too many key groups may effect the performance. (This is my concern to
change it by default in Flink Community.)
Note: this performance drop may be insignificant in a real production
environment.
> Update the auto-derivation rule of max parallelism for enlarged upscaling
> space
> ---
>
> Key: FLINK-33940
> URL: https://issues.apache.org/jira/browse/FLINK-33940
> Project: Flink
> Issue Type: Improvement
> Components: API / Core
>Reporter: Zhanghao Chen
>Priority: Major
>
> *Background*
> The choice of the max parallelism of an stateful operator is important as it
> limits the upper bound of the parallelism of the opeartor while it can also
> add extra overhead when being set too large. Currently, the max parallelism
> of an opeartor is either fixed to a value specified by API core / pipeline
> option or auto-derived with the following rules:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * 1.5), 128), 32767)}}
> *Problem*
> Recently, the elasticity of Flink jobs is becoming more and more valued by
> users. The current auto-derived max parallelism was introduced a time time
> ago and only allows the operator parallelism to be roughly doubled, which is
> not desired for elasticity. Setting an max parallelism manually may not be
> desired as well: users may not have the sufficient expertise to select a good
> max-parallelism value.
> *Proposal*
> Update the auto-derivation rule of max parallelism to derive larger max
> parallelism for better elasticity experience out of the box. A candidate is
> as follows:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), {*}1024{*}),
> 32767)}}
> Looking forward to your opinions on this.
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[jira] [Commented] (FLINK-33940) Update the auto-derivation rule of max parallelism for enlarged upscaling space
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]
Zhanghao Chen commented on FLINK-33940:
---
Here I'm proposing to roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), so we
would allow at least a 5-fold increase of parallelism
> Update the auto-derivation rule of max parallelism for enlarged upscaling
> space
> ---
>
> Key: FLINK-33940
> URL: https://issues.apache.org/jira/browse/FLINK-33940
> Project: Flink
> Issue Type: Improvement
> Components: API / Core
>Reporter: Zhanghao Chen
>Priority: Major
>
> *Background*
> The choice of the max parallelism of an stateful operator is important as it
> limits the upper bound of the parallelism of the opeartor while it can also
> add extra overhead when being set too large. Currently, the max parallelism
> of an opeartor is either fixed to a value specified by API core / pipeline
> option or auto-derived with the following rules:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * 1.5), 128), 32767)}}
> *Problem*
> Recently, the elasticity of Flink jobs is becoming more and more valued by
> users. The current auto-derived max parallelism was introduced a time time
> ago and only allows the operator parallelism to be roughly doubled, which is
> not desired for elasticity. Setting an max parallelism manually may not be
> desired as well: users may not have the sufficient expertise to select a good
> max-parallelism value.
> *Proposal*
> Update the auto-derivation rule of max parallelism to derive larger max
> parallelism for better elasticity experience out of the box. A candidate is
> as follows:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), {*}1024{*}),
> 32767)}}
> Looking forward to your opinions on this.
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[jira] [Commented] (FLINK-33940) Update the auto-derivation rule of max parallelism for enlarged upscaling space
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]
Gyula Fora commented on FLINK-33940:
If we are going to change this, powers of 2 are not really good as they only
allow you to double the parallelism.
it’s better to consider factorial or highly composite numbers
([https://mathworld.wolfram.com/HighlyCompositeNumber.html)] for practical use.
> Update the auto-derivation rule of max parallelism for enlarged upscaling
> space
> ---
>
> Key: FLINK-33940
> URL: https://issues.apache.org/jira/browse/FLINK-33940
> Project: Flink
> Issue Type: Improvement
> Components: API / Core
>Reporter: Zhanghao Chen
>Priority: Major
>
> *Background*
> The choice of the max parallelism of an stateful operator is important as it
> limits the upper bound of the parallelism of the opeartor while it can also
> add extra overhead when being set too large. Currently, the max parallelism
> of an opeartor is either fixed to a value specified by API core / pipeline
> option or auto-derived with the following rules:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * 1.5), 128), 32767)}}
> *Problem*
> Recently, the elasticity of Flink jobs is becoming more and more valued by
> users. The current auto-derived max parallelism was introduced a time time
> ago and only allows the operator parallelism to be roughly doubled, which is
> not desired for elasticity. Setting an max parallelism manually may not be
> desired as well: users may not have the sufficient expertise to select a good
> max-parallelism value.
> *Proposal*
> Update the auto-derivation rule of max parallelism to derive larger max
> parallelism for better elasticity experience out of the box. A candidate is
> as follows:
> {{min(max(roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), {*}1024{*}),
> 32767)}}
> Looking forward to your opinions on this.
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[jira] [Commented] (FLINK-33940) Update the auto-derivation rule of max parallelism for enlarged upscaling space
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https://issues.apache.org/jira/browse/FLINK-33940?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17800413#comment-17800413
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Zhanghao Chen commented on FLINK-33940:
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cc [~mxm] [~fanrui] Looking forward to the opinions from you, active
contributors on autoscaling
> Update the auto-derivation rule of max parallelism for enlarged upscaling
> space
> ---
>
> Key: FLINK-33940
> URL: https://issues.apache.org/jira/browse/FLINK-33940
> Project: Flink
> Issue Type: Improvement
> Components: API / Core
>Reporter: Zhanghao Chen
>Priority: Major
>
> *Background*
> The choice of the max parallelism of an stateful operator is important as it
> limits the upper bound of the parallelism of the opeartor while it can also
> add extra overhead when being set too large. Currently, the max parallelism
> of an opeartor is either fixed to a value specified by API core / pipeline
> option or auto-derived with the following rules:
> `min(max(roundUpToPowerOfTwo(operatorParallelism * 1.5), 128), 32767)`
> *Problem*
> Recently, the elasticity of Flink jobs is becoming more and more valued by
> users. The current auto-derived max parallelism was introduced a time time
> ago and only allows the operator parallelism to be roughly doubled, which is
> not desired for elasticity. Setting an max parallelism manually may not be
> desired as well: users may not have the sufficient expertise to select a good
> max-parallelism value.
> *Proposal*
> Update the auto-derivation rule of max parallelism to derive larger max
> parallelism for better elasticity experience out of the box. A candidate is
> as follows:
> `min(max(roundUpToPowerOfTwo(operatorParallelism * {*}5{*}), {*}1024{*}),
> 32767)`
> Looking forward to your opinions on this.
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