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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2015/09/10 01:19:46 UTC
[jira] [Commented] (SPARK-10487) MLlib model fitting causes
DataFrame write to break with OutOfMemory exception
[ https://issues.apache.org/jira/browse/SPARK-10487?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14737790#comment-14737790 ]
Joseph K. Bradley commented on SPARK-10487:
-------------------------------------------
Does this failure require there to be an ML model at all? Or can you reproduce it only using dataframes?
Also, can you reproduce it using nothing from ML (not using LabeledPoint or Vector)?
> MLlib model fitting causes DataFrame write to break with OutOfMemory exception
> ------------------------------------------------------------------------------
>
> Key: SPARK-10487
> URL: https://issues.apache.org/jira/browse/SPARK-10487
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.5.0, 1.5.1
> Environment: Tried in a centos-based 1-node YARN in docker and on a real-world CDH5 cluster
> Spark 1.5.0-SNAPSHOT built for Hadoop 2.6.0 (I'm working with the latest nightly build)
> Build flags: -Psparkr -Phadoop-2.6 -Phive -Phive-thriftserver -Pyarn -DzincPort=3034
> I'm using the default resource setup
> 15/09/07 08:49:04 INFO yarn.YarnAllocator: Will request 2 executor containers, each with 1 cores and 1408 MB memory including 384 MB overhead
> 15/09/07 08:49:04 INFO yarn.YarnAllocator: Container request (host: Any, capability: <memory:1408, vCores:1>)
> 15/09/07 08:49:04 INFO yarn.YarnAllocator: Container request (host: Any, capability: <memory:1408, vCores:1>)
> Reporter: Zoltan Toth
>
> After fitting a _spark.ml_ or _mllib model_ in *cluster* deploy mode, no dataframes can be written to hdfs. The driver receives an OutOfMemory exception during the writing. It seems, however, that the file gets written successfully.
> * This happens both in SparkR and pyspark
> * Only happens in cluster deploy mode
> * The write fails regardless the size of the dataframe and whether the dataframe is associated with the ml model.
> REPRO:
> {code}
> from pyspark import SparkContext, SparkConf
> from pyspark.sql import SQLContext
> from pyspark.ml.classification import LogisticRegression
> from pyspark.mllib.regression import LabeledPoint
> from pyspark.mllib.linalg import Vector, Vectors
> conf = SparkConf().setAppName("LogRegTest")
> sc = SparkContext(conf=conf)
> sqlContext = SQLContext(sc)
> sqlContext.setConf("park.sql.parquet.compression.codec", "uncompressed")
> training = sc.parallelize((
> LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
> LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))))
> df = training.toDF()
> reg = LogisticRegression().setMaxIter(10).setRegParam(0.01)
> model = reg.fit(df)
> # Note that this is a brand new dataframe:
> one_df = sc.parallelize((
> LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
> LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5)))).toDF()
> one_df.write.mode("overwrite").parquet("/tmp/df.parquet")
> {code}
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