GitHub user YanTangZhai opened a pull request:
https://github.com/apache/spark/pull/2409
[SPARK-3545] Put HadoopRDD.getPartitions forward and put
TaskScheduler.start back to reduce DAGScheduler.JobSubmitted processing time
and shorten cluster resources occupation period
We have two problems:
(1) HadoopRDD.getPartitions is lazyied to process in
DAGScheduler.JobSubmitted. If inputdir is large, getPartitions may spend much
time.
For example, in our cluster, it needs from 0.029s to 766.699s. If one
JobSubmitted event is processing, others should wait. Thus, we
want to put HadoopRDD.getPartitions forward to reduce
DAGScheduler.JobSubmitted processing time. Then other JobSubmitted event don't
need to wait much time. HadoopRDD object could get its partitons when it is
instantiated.
(2) When SparkContext object is instantiated, TaskScheduler is started and
some resources are allocated from cluster. However, these
resources may be not used for the moment. For example,
DAGScheduler.JobSubmitted is processing and so on. These resources are wasted in
this period. Thus, we want to put TaskScheduler.start back to shorten
cluster resources occupation period specially for busy cluster.
TaskScheduler could be started just before running stages.
We could analyse and compare the execution time before and after
optimization.
TaskScheduler.start execution time: [time1__]
DAGScheduler.JobSubmitted (excluding HadoopRDD.getPartitions or
TaskScheduler.start) execution time: [time2_]
HadoopRDD.getPartitions execution time: [time3___]
Stages execution time: [time4_____]
(1) The app has only one job
(a)
The execution time of the job before optimization is
[time1__][time2_][time3___][time4_____].
The execution time of the job after optimization
is....[time3___][time2_][time1__][time4_____].
(b)
The cluster resources occupation period before optimization is
[time2_][time3___][time4_____].
The cluster resources occupation period after optimization
is....[time4_____].
In summary, if the app has only one job, the total execution time is same
before and after optimization while the cluster resources
occupation period after optimization is less than before.
(2) The app has 4 jobs
(a) Before optimization,
job1 execution time is [time2_][time3___][time4_____],
job2 execution time is [time2__________][time3___][time4_____],
job3 execution time
is................................[time2____][time3___][time4_____],
job4 execution time
is................................[time2______________][time3___][time4_____].
After optimization,
job1 execution time is [time3___][time2_][time1__][time4_____],
job2 execution time is [time3___][time2__________][time4_____],
job3 execution time
is................................[time3___][time2_][time4_____],
job4 execution time
is................................[time3___][time2__][time4_____].
In summary, if the app has multiple jobs, average execution time after
optimization is less than before and the cluster resources
occupation period after optimization is less than before.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/YanTangZhai/spark SPARK-3545
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/2409.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #2409
----
commit cdef539abc5d2d42d4661373939bdd52ca8ee8e6
Author: YanTangZhai <[email protected]>
Date: 2014-08-06T13:07:08Z
Merge pull request #1 from apache/master
update
commit cbcba66ad77b96720e58f9d893e87ae5f13b2a95
Author: YanTangZhai <[email protected]>
Date: 2014-08-20T13:14:08Z
Merge pull request #3 from apache/master
Update
commit 8a0010691b669495b4c327cf83124cabb7da1405
Author: YanTangZhai <[email protected]>
Date: 2014-09-12T06:54:58Z
Merge pull request #6 from apache/master
Update
commit 03b62b043ab7fd39300677df61c3d93bb9beb9e3
Author: YanTangZhai <[email protected]>
Date: 2014-09-16T12:03:22Z
Merge pull request #7 from apache/master
Update
commit b88df438033eecbdbe8cad37b2bd4ad3620de6e2
Author: yantangzhai <[email protected]>
Date: 2014-09-16T13:22:12Z
[SPARK-3545] Put HadoopRDD.getPartitions forward and put
TaskScheduler.start back to reduce DAGScheduler.JobSubmitted processing time
and shorten cluster resources occupation period
----
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