[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[
https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16965057#comment-16965057
]
Ahmed Hussein commented on MAPREDUCE-7208:
--
{{TestJobSplitWriterWithEC}} seems not related to the patch. I will do further
investigation before confirming that it is a flaky test.
> Tuning TaskRuntimeEstimator
>
>
> Key: MAPREDUCE-7208
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-7208
> Project: Hadoop Map/Reduce
> Issue Type: Improvement
>Reporter: Ahmed Hussein
>Assignee: Ahmed Hussein
>Priority: Minor
> Attachments: MAPREDUCE-7208.001.patch, MAPREDUCE-7208.002.patch,
> MAPREDUCE-7208.003.patch, MAPREDUCE-7208.004.patch, smoothing-exponential.md
>
>
> By default, MR uses LegacyTaskRuntimeEstimator to get an estimate of the
> runtime. The estimator does not adjust dynamically to the progress rate of
> the tasks. On the other hand, the existing alternative
> "ExponentiallySmoothedTaskRuntimeEstimator" behavior in unpredictable.
>
> There are several dimensions to improve the exponential implementation:
> # Exponential shooting needs a warmup period. Otherwise, the estimate will
> be affected by the initial values.
> # Using a single smoothing factor (Lambda) does not work well for all the
> tasks. To increase the level of smoothing across the majority of tasks, we
> need to give a range of flexibility to dynamically adjust the smoothing
> factor based on the history of the task progress.
> # Design wise, it is better to separate between the statistical model and
> the MR interface. We need to have a way to evaluate estimators statistically,
> without the need to run MR. For example, an estimator can be evaluated as a
> black box by using a stream of raw data as input and testing the accuracy of
> the generated stream of estimates.
> # The exponential estimator speculates frequently and fails to detect
> slowing tasks. It does not detect slowing tasks. As a result, a taskAttempt
> that does not do any progress won't trigger a new speculation.
>
> The file [^smoothing-exponential.md] describes how Simple Exponential
> smoothing factor works.
>
>
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[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[
https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16965052#comment-16965052
]
Hadoop QA commented on MAPREDUCE-7208:
--
| (x) *{color:red}-1 overall{color}* |
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\\
|| Vote || Subsystem || Runtime || Comment ||
| {color:blue}0{color} | {color:blue} reexec {color} | {color:blue} 0m
20s{color} | {color:blue} Docker mode activated. {color} |
|| || || || {color:brown} Prechecks {color} ||
| {color:green}+1{color} | {color:green} @author {color} | {color:green} 0m
0s{color} | {color:green} The patch does not contain any @author tags. {color} |
| {color:green}+1{color} | {color:green} test4tests {color} | {color:green} 0m
0s{color} | {color:green} The patch appears to include 4 new or modified test
files. {color} |
|| || || || {color:brown} trunk Compile Tests {color} ||
| {color:blue}0{color} | {color:blue} mvndep {color} | {color:blue} 0m
32s{color} | {color:blue} Maven dependency ordering for branch {color} |
| {color:green}+1{color} | {color:green} mvninstall {color} | {color:green} 18m
56s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} compile {color} | {color:green} 1m
49s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} checkstyle {color} | {color:green} 0m
54s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} mvnsite {color} | {color:green} 1m
46s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} shadedclient {color} | {color:green}
14m 58s{color} | {color:green} branch has no errors when building and testing
our client artifacts. {color} |
| {color:green}+1{color} | {color:green} findbugs {color} | {color:green} 2m
18s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} javadoc {color} | {color:green} 1m
16s{color} | {color:green} trunk passed {color} |
|| || || || {color:brown} Patch Compile Tests {color} ||
| {color:blue}0{color} | {color:blue} mvndep {color} | {color:blue} 0m
14s{color} | {color:blue} Maven dependency ordering for patch {color} |
| {color:green}+1{color} | {color:green} mvninstall {color} | {color:green} 1m
38s{color} | {color:green} the patch passed {color} |
| {color:green}+1{color} | {color:green} compile {color} | {color:green} 1m
43s{color} | {color:green} the patch passed {color} |
| {color:green}+1{color} | {color:green} javac {color} | {color:green} 1m
43s{color} | {color:green} the patch passed {color} |
| {color:orange}-0{color} | {color:orange} checkstyle {color} | {color:orange}
0m 52s{color} | {color:orange}
hadoop-mapreduce-project/hadoop-mapreduce-client: The patch generated 2 new +
702 unchanged - 2 fixed = 704 total (was 704) {color} |
| {color:green}+1{color} | {color:green} mvnsite {color} | {color:green} 1m
35s{color} | {color:green} the patch passed {color} |
| {color:green}+1{color} | {color:green} whitespace {color} | {color:green} 0m
0s{color} | {color:green} The patch has no whitespace issues. {color} |
| {color:green}+1{color} | {color:green} shadedclient {color} | {color:green}
13m 49s{color} | {color:green} patch has no errors when building and testing
our client artifacts. {color} |
| {color:green}+1{color} | {color:green} findbugs {color} | {color:green} 2m
33s{color} | {color:green} the patch passed {color} |
| {color:green}+1{color} | {color:green} javadoc {color} | {color:green} 0m
58s{color} | {color:green} the patch passed {color} |
|| || || || {color:brown} Other Tests {color} ||
| {color:red}-1{color} | {color:red} unit {color} | {color:red} 20m 13s{color}
| {color:red} hadoop-mapreduce-client-core in the patch failed. {color} |
| {color:red}-1{color} | {color:red} unit {color} | {color:red} 10m 32s{color}
| {color:red} hadoop-mapreduce-client-app in the patch failed. {color} |
| {color:red}-1{color} | {color:red} unit {color} | {color:red}126m 55s{color}
| {color:red} hadoop-mapreduce-client-jobclient in the patch failed. {color} |
| {color:red}-1{color} | {color:red} asflicense {color} | {color:red} 0m
45s{color} | {color:red} The patch generated 1 ASF License warnings. {color} |
| {color:black}{color} | {color:black} {color} | {color:black}223m 46s{color} |
{color:black} {color} |
\\
\\
|| Reason || Tests ||
| Failed junit tests | hadoop.mapreduce.split.TestJobSplitWriterWithEC |
\\
\\
|| Subsystem || Report/Notes ||
| Docker | Client=19.03.4 Server=19.03.4 Image:yetus/hadoop:104ccca9169 |
| JIRA Issue | MAPREDUCE-7208 |
| JIRA Patch URL |
https://issues.apache.org/jira/secure/attachment/12984392/MAPREDUCE-7208.004.patch
|
| Optional Tests | dupname asflicense compile javac javadoc mvninstall
mvnsite unit shadedclient findbugs checkstyle |
| uname | Linux d3013037aefe 4.15.0-58-generic #64-Ubuntu SMP Tue Aug 6
11:12:41 UTC 2019 x86_64 x86_64 x86_64 GNU/Linu
[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[
https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16964937#comment-16964937
]
Hadoop QA commented on MAPREDUCE-7208:
--
| (x) *{color:red}-1 overall{color}* |
\\
\\
|| Vote || Subsystem || Runtime || Comment ||
| {color:blue}0{color} | {color:blue} reexec {color} | {color:blue} 0m
0s{color} | {color:blue} Docker mode activated. {color} |
| {color:red}-1{color} | {color:red} docker {color} | {color:red} 7m
3s{color} | {color:red} Docker failed to build yetus/hadoop:104ccca9169.
{color} |
\\
\\
|| Subsystem || Report/Notes ||
| JIRA Issue | MAPREDUCE-7208 |
| JIRA Patch URL |
https://issues.apache.org/jira/secure/attachment/12984392/MAPREDUCE-7208.004.patch
|
| Console output |
https://builds.apache.org/job/PreCommit-MAPREDUCE-Build/7683/console |
| Powered by | Apache Yetus 0.8.0 http://yetus.apache.org |
This message was automatically generated.
> Tuning TaskRuntimeEstimator
>
>
> Key: MAPREDUCE-7208
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-7208
> Project: Hadoop Map/Reduce
> Issue Type: Improvement
>Reporter: Ahmed Hussein
>Assignee: Ahmed Hussein
>Priority: Minor
> Attachments: MAPREDUCE-7208.001.patch, MAPREDUCE-7208.002.patch,
> MAPREDUCE-7208.003.patch, MAPREDUCE-7208.004.patch, smoothing-exponential.md
>
>
> By default, MR uses LegacyTaskRuntimeEstimator to get an estimate of the
> runtime. The estimator does not adjust dynamically to the progress rate of
> the tasks. On the other hand, the existing alternative
> "ExponentiallySmoothedTaskRuntimeEstimator" behavior in unpredictable.
>
> There are several dimensions to improve the exponential implementation:
> # Exponential shooting needs a warmup period. Otherwise, the estimate will
> be affected by the initial values.
> # Using a single smoothing factor (Lambda) does not work well for all the
> tasks. To increase the level of smoothing across the majority of tasks, we
> need to give a range of flexibility to dynamically adjust the smoothing
> factor based on the history of the task progress.
> # Design wise, it is better to separate between the statistical model and
> the MR interface. We need to have a way to evaluate estimators statistically,
> without the need to run MR. For example, an estimator can be evaluated as a
> black box by using a stream of raw data as input and testing the accuracy of
> the generated stream of estimates.
> # The exponential estimator speculates frequently and fails to detect
> slowing tasks. It does not detect slowing tasks. As a result, a taskAttempt
> that does not do any progress won't trigger a new speculation.
>
> The file [^smoothing-exponential.md] describes how Simple Exponential
> smoothing factor works.
>
>
--
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