[jira] [Created] (SPARK-19007) Speedup and optimize the GradientBoostedTrees in the "data>memory" scene

["zhangdenghui (JIRA)" <ji...@apache.org>]

zhangdenghui created SPARK-19007:
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             Summary: Speedup and optimize the GradientBoostedTrees in the "data>memory" scene
                 Key: SPARK-19007
                 URL: https://issues.apache.org/jira/browse/SPARK-19007
             Project: Spark
          Issue Type: Improvement
          Components: ML, MLlib
    Affects Versions: 2.1.0, 2.0.2, 2.0.1, 2.0.0, 1.6.3, 1.6.2, 1.6.1, 1.6.0, 1.5.2, 1.5.1, 1.5.0
         Environment: A CDH cluster consists of 3 redhat server ,(120G memory、40 cores、43TB disk per server).

            Reporter: zhangdenghui
             Fix For: 2.1.0


Test data：80G CTR training data from criteolabs(http://criteolabs.wpengine.com/downloads/download-terabyte-click-logs/),I used 1 of the 24 days' data.Some  features needed to be repalced by new generated continuous features，the way to generate the new features refers to the way mentioned in the xgboost's paper.

Recource allocated: spark on yarn, 20 executors, 8G memory and 2 cores per executor.

I tested the GradientBoostedTrees algorithm in mllib  using 80G CTR data mentioned above.
It totally costs 1.5 hour, and i found many task failures after 6 or 7 GBT rounds later.Without these task failures and task retry it can be much faster ,which can save about half the time. I think it's caused by the RDD named predError in the while loop of  the boost method at GradientBoostedTrees.scala,because the lineage of the RDD named predError is growing after every GBT round, and then it caused failures like this :
(ExecutorLostFailure (executor 6 exited caused by one of the running tasks) Reason: Container killed by YARN for exceeding memory limits. 10.2 GB of 10 GB physical memory used. Consider boosting spark.yarn.executor.memoryOverhead.).  

I tried to boosting spark.yarn.executor.memoryOverhead  but the meomry it needed is too much (even increase half the memory  can't solve the problem) so i think it's not a proper method. 

Although it can set the predCheckpoint  Interval  smaller  to cut the line of the lineage  but it increases IO cost a lot. 

I tried  another way to solve this problem.I persisted the RDD named predError every round  and use  pre_predError to record the previous RDD  and unpersist it  because it's useless anymore.

Finally it costs about 45 min after i tried my method and no task failure occured and no more memeory added. 

So when the data is much larger than memory, my little improvement can speedup  the  GradientBoostedTrees  1~2 times.




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