[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[ https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16991889#comment-16991889 ] Jonathan Hung commented on MAPREDUCE-7208: -- Removing 2.11.0 fix version after branch-2 -> branch-2.10 rename > 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 > Fix For: 3.3.0, 3.1.4, 3.2.2, 2.10.1 > > Attachments: MAPREDUCE-7208-branch-2.10.001.patch, > MAPREDUCE-7208-branch-2.10.002.patch, 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. > > -- This message was sent by Atlassian Jira (v8.3.4#803005) - To unsubscribe, e-mail: mapreduce-issues-unsubscr...@hadoop.apache.org For additional commands, e-mail: mapreduce-issues-h...@hadoop.apache.org
[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[ https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16967913#comment-16967913 ] Ahmed Hussein commented on MAPREDUCE-7208: -- Thanks [~jeagles]. Reviewed 2.10 patch errors. They are unrelated time-out unit tests. > 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 > Fix For: 3.3.0, 3.1.4, 3.2.2, 2.10.1, 2.11.0 > > Attachments: MAPREDUCE-7208-branch-2.10.001.patch, > MAPREDUCE-7208-branch-2.10.002.patch, 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. > > -- This message was sent by Atlassian Jira (v8.3.4#803005) - To unsubscribe, e-mail: mapreduce-issues-unsubscr...@hadoop.apache.org For additional commands, e-mail: mapreduce-issues-h...@hadoop.apache.org
[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[ https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16967872#comment-16967872 ] Hudson commented on MAPREDUCE-7208: --- SUCCESS: Integrated in Jenkins build Hadoop-trunk-Commit #17610 (See [https://builds.apache.org/job/Hadoop-trunk-Commit/17610/]) MAPREDUCE-7208. Tuning TaskRuntimeEstimator. (Ahmed Hussein via jeagles) (jeagles: rev ed302f1fed6d124d682486d24dae958946dba9be) * (edit) hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-app/src/main/java/org/apache/hadoop/mapreduce/v2/app/speculate/DefaultSpeculator.java * (add) hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-app/src/test/java/org/apache/hadoop/mapreduce/v2/app/speculate/forecast/TestSimpleExponentialForecast.java * (edit) hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-app/src/main/java/org/apache/hadoop/mapreduce/v2/app/speculate/DataStatistics.java * (edit) hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/MRJobConfig.java * (edit) hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-jobclient/src/test/java/org/apache/hadoop/mapreduce/v2/TestSpeculativeExecutionWithMRApp.java * (add) hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-app/src/main/java/org/apache/hadoop/mapreduce/v2/app/speculate/forecast/SimpleExponentialSmoothing.java * (edit) hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-app/src/main/java/org/apache/hadoop/mapreduce/v2/app/speculate/StartEndTimesBase.java * (add) hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-jobclient/src/test/java/org/apache/hadoop/mapreduce/v2/TestSpeculativeExecOnCluster.java * (edit) hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-app/src/test/java/org/apache/hadoop/mapreduce/v2/app/TestRuntimeEstimators.java * (add) hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-app/src/main/java/org/apache/hadoop/mapreduce/v2/app/speculate/SimpleExponentialTaskRuntimeEstimator.java * (edit) hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-app/src/main/java/org/apache/hadoop/mapreduce/v2/app/speculate/TaskRuntimeEstimator.java > 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 > Fix For: 3.3.0 > > Attachments: MAPREDUCE-7208-branch-2.10.001.patch, > MAPREDUCE-7208-branch-2.10.002.patch, 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. > > -- This message was sent by Atlassian Jira (v8.3.4#803005) - To unsubscribe, e-mail: mapreduce-issues-unsubscr...@hadoop.apache.org For additional commands, e-mail: mapreduce-issues-h...@hadoop.apache.org
[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[
https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16967819#comment-16967819
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Hadoop QA commented on MAPREDUCE-7208:
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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=16967017#comment-16967017
]
Hadoop QA commented on MAPREDUCE-7208:
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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=16966790#comment-16966790 ] Jonathan Turner Eagles commented on MAPREDUCE-7208: --- +1. I will plan to commit this back to branch-2.10. Thanks for this contribution, [~ahussein]. > 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. > > -- This message was sent by Atlassian Jira (v8.3.4#803005) - To unsubscribe, e-mail: mapreduce-issues-unsubscr...@hadoop.apache.org For additional commands, e-mail: mapreduce-issues-h...@hadoop.apache.org
[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[ https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16966707#comment-16966707 ] Ahmed Hussein commented on MAPREDUCE-7208: -- The failed test case is not related to the patch. > 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. > > -- This message was sent by Atlassian Jira (v8.3.4#803005) - To unsubscribe, e-mail: mapreduce-issues-unsubscr...@hadoop.apache.org For additional commands, e-mail: mapreduce-issues-h...@hadoop.apache.org
[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[
https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=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=16965052#comment-16965052
]
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
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/Linux |
| Build tool
[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[
https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=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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[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[
https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16963414#comment-16963414
]
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
21s{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
16s{color} | {color:blue} Maven dependency ordering for branch {color} |
| {color:green}+1{color} | {color:green} mvninstall {color} | {color:green} 17m
5s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} compile {color} | {color:green} 1m
42s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} checkstyle {color} | {color:green} 0m
53s{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 24s{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
8s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} javadoc {color} | {color:green} 1m
13s{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
31s{color} | {color:green} the patch passed {color} |
| {color:green}+1{color} | {color:green} compile {color} | {color:green} 1m
35s{color} | {color:green} the patch passed {color} |
| {color:green}+1{color} | {color:green} javac {color} | {color:green} 1m
35s{color} | {color:green} the patch passed {color} |
| {color:orange}-0{color} | {color:orange} checkstyle {color} | {color:orange}
0m 48s{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
27s{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}
12m 30s{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
21s{color} | {color:green} the patch passed {color} |
| {color:green}+1{color} | {color:green} javadoc {color} | {color:green} 1m
3s{color} | {color:green} the patch passed {color} |
|| || || || {color:brown} Other Tests {color} ||
| {color:green}+1{color} | {color:green} unit {color} | {color:green} 5m
8s{color} | {color:green} hadoop-mapreduce-client-core in the patch passed.
{color} |
| {color:green}+1{color} | {color:green} unit {color} | {color:green} 10m
13s{color} | {color:green} hadoop-mapreduce-client-app in the patch passed.
{color} |
| {color:red}-1{color} | {color:red} unit {color} | {color:red}125m 28s{color}
| {color:red} hadoop-mapreduce-client-jobclient in the patch failed. {color} |
| {color:red}-1{color} | {color:red} asflicense {color} | {color:red} 0m
40s{color} | {color:red} The patch generated 1 ASF License warnings. {color} |
| {color:black}{color} | {color:black} {color} | {color:black}202m 0s{color} |
{color:black} {color} |
\\
\\
|| 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 af8bb51752c8 4.15.0-58-generic #64-Ubuntu SMP Tue Aug 6
11:12:41 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux |
| Build tool | maven |
| Personality | /testptch/patchprocess/precommit/personality/provided.sh
[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[
https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16963182#comment-16963182
]
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} patch {color} | {color:red} 0m 8s{color}
| {color:red} MAPREDUCE-7208 does not apply to trunk. Rebase required? Wrong
Branch? See https://wiki.apache.org/hadoop/HowToContribute for help. {color} |
\\
\\
|| Subsystem || Report/Notes ||
| JIRA Issue | MAPREDUCE-7208 |
| JIRA Patch URL |
https://issues.apache.org/jira/secure/attachment/12984384/MAPREDUCE-7208.003.patch
|
| Console output |
https://builds.apache.org/job/PreCommit-MAPREDUCE-Build/7680/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, 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=16962665#comment-16962665
]
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
21s{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 7 new or modified test
files. {color} |
|| || || || {color:brown} trunk Compile Tests {color} ||
| {color:blue}0{color} | {color:blue} mvndep {color} | {color:blue} 1m
13s{color} | {color:blue} Maven dependency ordering for branch {color} |
| {color:green}+1{color} | {color:green} mvninstall {color} | {color:green} 17m
46s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} compile {color} | {color:green} 16m
7s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} checkstyle {color} | {color:green} 2m
53s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} mvnsite {color} | {color:green} 6m
58s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} shadedclient {color} | {color:green}
12m 11s{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} 9m
44s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} javadoc {color} | {color:green} 7m
2s{color} | {color:green} trunk passed {color} |
|| || || || {color:brown} Patch Compile Tests {color} ||
| {color:blue}0{color} | {color:blue} mvndep {color} | {color:blue} 0m
33s{color} | {color:blue} Maven dependency ordering for patch {color} |
| {color:green}+1{color} | {color:green} mvninstall {color} | {color:green} 4m
50s{color} | {color:green} the patch passed {color} |
| {color:green}+1{color} | {color:green} compile {color} | {color:green} 14m
40s{color} | {color:green} the patch passed {color} |
| {color:green}+1{color} | {color:green} javac {color} | {color:green} 14m
40s{color} | {color:green} the patch passed {color} |
| {color:orange}-0{color} | {color:orange} checkstyle {color} | {color:orange}
3m 0s{color} | {color:orange} root: The patch generated 19 new + 1195
unchanged - 8 fixed = 1214 total (was 1203) {color} |
| {color:green}+1{color} | {color:green} mvnsite {color} | {color:green} 7m
30s{color} | {color:green} the patch passed {color} |
| {color:green}+1{color} | {color:green} shellcheck {color} | {color:green} 0m
0s{color} | {color:green} There were no new shellcheck issues. {color} |
| {color:green}+1{color} | {color:green} shelldocs {color} | {color:green} 0m
39s{color} | {color:green} There were no new shelldocs issues. {color} |
| {color:red}-1{color} | {color:red} whitespace {color} | {color:red} 0m
1s{color} | {color:red} The patch has 1 line(s) that end in whitespace. Use git
apply --whitespace=fix <>. Refer https://git-scm.com/docs/git-apply
{color} |
| {color:green}+1{color} | {color:green} xml {color} | {color:green} 0m
2s{color} | {color:green} The patch has no ill-formed XML file. {color} |
| {color:green}+1{color} | {color:green} shadedclient {color} | {color:green}
12m 15s{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} 10m
44s{color} | {color:green} the patch passed {color} |
| {color:green}+1{color} | {color:green} javadoc {color} | {color:green} 7m
1s{color} | {color:green} the patch passed {color} |
|| || || || {color:brown} Other Tests {color} ||
| {color:red}-1{color} | {color:red} unit {color} | {color:red} 8m 37s{color}
| {color:red} hadoop-common in the patch failed. {color} |
| {color:red}-1{color} | {color:red} unit {color} | {color:red} 84m 59s{color}
| {color:red} hadoop-hdfs in the patch failed. {color} |
| {color:red}-1{color} | {color:red} unit {color} | {color:red} 82m 4s{color}
| {color:red} hadoop-yarn-server-resourcemanager in the patch failed. {color} |
| {color:green}+1{color} | {color:green} unit {color} | {color:green} 5m
37s{color} | {color:green} hadoop-mapreduce-client-core in the patch passed.
{color} |
| {color:green}+1{color} | {color:green} unit {color} | {color:green} 10m
25s{color} | {color:green} hadoop-mapreduce-client-app in the patch passed.
{color} |
| {color:red}-1{color} | {color:red} unit {color} | {color:red}118m 32s{color}
|
[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[
https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16962437#comment-16962437
]
Ahmed Hussein commented on MAPREDUCE-7208:
--
Thanks [~jeagles]. I looked at the test cases:
* {{hadoop.mapreduce.v2.TestSpeculativeExecutionWithMRApp}} is a related test
case and It was failing because I changed the threshold of the estimate that
triggers a new speculative task. I fixed that default behavior in the new patch.
* {{hadoop.mapred.TestLocalMRNotification}} and
{{hadoop.mapreduce.v2.TestMROldApiJobs}} seem to be a random failure. They pass
successfully on local machine.
> 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,
> 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=16961092#comment-16961092 ] Jonathan Turner Eagles commented on MAPREDUCE-7208: --- [~ahussein], could you take a look at the test failures? Also, some of the checkstyle seem relevant, but others not. > 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, 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. > > -- This message was sent by Atlassian Jira (v8.3.4#803005) - To unsubscribe, e-mail: mapreduce-issues-unsubscr...@hadoop.apache.org For additional commands, e-mail: mapreduce-issues-h...@hadoop.apache.org
[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[
https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16960231#comment-16960231
]
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
23s{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 3 new or modified test
files. {color} |
|| || || || {color:brown} trunk Compile Tests {color} ||
| {color:blue}0{color} | {color:blue} mvndep {color} | {color:blue} 0m
59s{color} | {color:blue} Maven dependency ordering for branch {color} |
| {color:green}+1{color} | {color:green} mvninstall {color} | {color:green} 20m
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18s{color} | {color:green} trunk passed {color} |
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17m 5s{color} | {color:green} branch has no errors when building and testing
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27s{color} | {color:green} trunk passed {color} |
| {color:green}+1{color} | {color:green} javadoc {color} | {color:green} 1m
7s{color} | {color:green} trunk passed {color} |
|| || || || {color:brown} Patch Compile Tests {color} ||
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14s{color} | {color:blue} Maven dependency ordering for patch {color} |
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48s{color} | {color:green} the patch passed {color} |
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52s{color} | {color:green} the patch passed {color} |
| {color:green}+1{color} | {color:green} javac {color} | {color:green} 1m
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| {color:orange}-0{color} | {color:orange} checkstyle {color} | {color:orange}
0m 53s{color} | {color:orange}
hadoop-mapreduce-project/hadoop-mapreduce-client: The patch generated 101 new +
686 unchanged - 6 fixed = 787 total (was 692) {color} |
| {color:green}+1{color} | {color:green} mvnsite {color} | {color:green} 1m
36s{color} | {color:green} the patch passed {color} |
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| {color:green}+1{color} | {color:green} javadoc {color} | {color:green} 1m
2s{color} | {color:green} the patch passed {color} |
|| || || || {color:brown} Other Tests {color} ||
| {color:green}+1{color} | {color:green} unit {color} | {color:green} 5m
43s{color} | {color:green} hadoop-mapreduce-client-core in the patch passed.
{color} |
| {color:green}+1{color} | {color:green} unit {color} | {color:green} 10m
51s{color} | {color:green} hadoop-mapreduce-client-app in the patch passed.
{color} |
| {color:red}-1{color} | {color:red} unit {color} | {color:red}139m 57s{color}
| {color:red} hadoop-mapreduce-client-jobclient in the patch failed. {color} |
| {color:red}-1{color} | {color:red} asflicense {color} | {color:red} 0m
44s{color} | {color:red} The patch generated 1 ASF License warnings. {color} |
| {color:black}{color} | {color:black} {color} | {color:black}229m 44s{color} |
{color:black} {color} |
\\
\\
|| Reason || Tests ||
| Failed junit tests | hadoop.mapreduce.v2.TestMROldApiJobs |
| | hadoop.mapred.TestLocalMRNotification |
| | hadoop.mapreduce.v2.TestSpeculativeExecutionWithMRApp |
\\
\\
|| 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/12970329/MAPREDUCE-7208.001.patch
|
| Optional Tests | dupname asflicense compile javac javadoc mvninstall
mvnsite unit shadedclient findbugs checkstyle |
| uname | Linux
[jira] [Commented] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
[
https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16852022#comment-16852022
]
Ahmed Hussein commented on MAPREDUCE-7208:
--
[~jeagles], [~tgraves], [~vinodkv], [~nroberts]
I had some issues using {{ExponentiallySmoothedTaskRuntimeEstimator}}. I made
some investigation and implemented a new estimator that addresses some issues
with the existing smoothing factor estimator. Do you mind taking a look at the
suggested fixes and implementations?
*{{SimpleExponentialTaskRuntimeEstimator}} (new) Vs
{{ExponentiallySmoothedTaskRuntimeEstimator}} (old)*
# New estimator follows Basic Exponential Smooth.
# New estimator does not return an estimate for the first few cycles. This
increases the accuracy of estimation; especially for long running tasks
# New Estimator detects tasks that are slowing down. Old estimator fails to
detect such scenarios.
# New Estimator detects stalled tasks. Old estimator will not launch any
speculative attempts when an attempt has a sharp slow down.
*Is the default speculator affected?*
* The speculator is still using the {{LegacyTaskRuntimeEstimator}} by default.
* The existing implementation uses the statistics.mean to get an
{{estimatedNewAttemptRuntime()}}. This causes frequent speculation as the
smallest difference between the {{estimatedRuntime}} and the mean will create a
new speculativeAttempt. I changed the implementation of
{{estimatedNewAttemptRuntime()}} so that it uses (mean + a small delta)
* I created a n JUnit {{TestSpeculativeExecOnCluster}} that verifies the
speculator running on {{MiniMRYarnCluster}}. The test case can be used for the
old estimators.
*Tuning parameters:*
* {{job.task.estimator.simple.exponential.smooth.lambda-ms}}: The lambda value
in the smoothing function of the task estimator
* {{job.task.estimator.simple.exponential.smooth.stagnated-ms}}: The window
length in the simple exponential smoothing that considers the task attempt is
stagnated. This allows the speculator to detect stalled progress.
* {{job.task.estimator.simple.exponential.smooth.skip-initials}}: The number
of initial readings that the estimator ignores before giving a prediction. A
simple smoothing needs several iterations before adjusting and returning good
estimates. The skip-initials parameter instructs the estimator to return
"no-information" progress updates did not reach that value.
> 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, 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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