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Zoltan Toth edited comment on SPARK-10487 at 9/10/15 5:28 AM: -------------------------------------------------------------- Yes it only happens if you use mllib or ML and you fit a model. You don't need a DataPoint, e.g. if you make a trivial `glm` linear regression on the built in `iris` dataset in SparkR, it also fails. {code} library(SparkR) sc <- sparkR.init() sqlContext <- sparkRSQL.init(sc) training <- createDataFrame(sqlContext, iris) test <- select(training, "Sepal_Length") model <- glm(Sepal_Width ~ Sepal_Length, training, family = "gaussian") prediction <- predict(model, test) SparkR:::saveAsParquetFile(prediction, "/tmp/SparkR-logreg-prediction-data") {code} Again, only in `cluster` master mode. was (Author: zoltanctoth): Yes it only happens if you use mllib or ML and you fit a model. You don't need a DataPoint, e.g. if you make a trivial `glm` linear regression on the built in `iris` dataset in SparkR, it also fails. {code} library(SparkR) sc <- sparkR.init() sqlContext <- sparkRSQL.init(sc) training <- createDataFrame(sqlContext, iris) test <- select(training, "Sepal_Length") model <- glm(Sepal_Width ~ Sepal_Length, training, family = "gaussian") prediction <- predict(model, test) SparkR:::saveAsParquetFile(prediction, "/tmp/SparkR-logreg-prediction-data") {code} Again, onlyi in `cluster` master mode > 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} -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org