Hi,
Can you try to using save method instead of write?
ex: out_df.save("path","parquet")
b0c1
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Skype: boci13, Hangout: boci.b...@gmail.com
On Mon, Sep 7, 2015 at 3:35 PM, Zoltán Tóth <zoltanct...@gmail.com> wrote:
> Aaand, the error! :)
>
> Exception in thread "org.apache.hadoop.hdfs.PeerCache@4e000abf"
> Exception: java.lang.OutOfMemoryError thrown from the
> UncaughtExceptionHandler in thread "org.apache.hadoop.hdfs.PeerCache@4e000abf"
> Exception in thread "Thread-7"
> Exception: java.lang.OutOfMemoryError thrown from the
> UncaughtExceptionHandler in thread "Thread-7"
> Exception in thread "LeaseRenewer:r...@docker.rapidminer.com:8020"
> Exception: java.lang.OutOfMemoryError thrown from the
> UncaughtExceptionHandler in thread
> "LeaseRenewer:r...@docker.rapidminer.com:8020"
> Exception in thread "Reporter"
> Exception: java.lang.OutOfMemoryError thrown from the
> UncaughtExceptionHandler in thread "Reporter"
> Exception in thread "qtp2115718813-47"
> Exception: java.lang.OutOfMemoryError thrown from the
> UncaughtExceptionHandler in thread "qtp2115718813-47"
>
> Exception: java.lang.OutOfMemoryError thrown from the
> UncaughtExceptionHandler in thread "sparkDriver-scheduler-1"
>
> Log Type: stdout
>
> Log Upload Time: Mon Sep 07 09:03:01 -0400 2015
>
> Log Length: 986
>
> Traceback (most recent call last):
> File "spark-ml.py", line 33, in <module>
> out_df.write.parquet("/tmp/logparquet")
> File
> "/var/lib/hadoop-yarn/cache/yarn/nm-local-dir/usercache/root/appcache/application_1441224592929_0022/container_1441224592929_0022_01_000001/pyspark.zip/pyspark/sql/readwriter.py",
> line 422, in parquet
> File
> "/var/lib/hadoop-yarn/cache/yarn/nm-local-dir/usercache/root/appcache/application_1441224592929_0022/container_1441224592929_0022_01_000001/py4j-0.8.2.1-src.zip/py4j/java_gateway.py",
> line 538, in __call__
> File
> "/var/lib/hadoop-yarn/cache/yarn/nm-local-dir/usercache/root/appcache/application_1441224592929_0022/container_1441224592929_0022_01_000001/pyspark.zip/pyspark/sql/utils.py",
> line 36, in deco
> File
> "/var/lib/hadoop-yarn/cache/yarn/nm-local-dir/usercache/root/appcache/application_1441224592929_0022/container_1441224592929_0022_01_000001/py4j-0.8.2.1-src.zip/py4j/protocol.py",
> line 300, in get_return_value
> py4j.protocol.Py4JJavaError
>
>
>
> On Mon, Sep 7, 2015 at 3:27 PM, Zoltán Tóth <zoltanct...@gmail.com> wrote:
>
>> Hi,
>>
>> When I execute the Spark ML Logisitc Regression example in pyspark I run
>> into an OutOfMemory exception. I'm wondering if any of you experienced the
>> same or has a hint about how to fix this.
>>
>> The interesting bit is that I only get the exception when I try to write
>> the result DataFrame into a file. If I only "print" any of the results, it
>> all works fine.
>>
>> My Setup:
>> 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>)
>>
>> The script I'm executing:
>> from pyspark import SparkContext, SparkConf
>> from pyspark.sql import SQLContext
>>
>> conf = SparkConf().setAppName("pysparktest")
>> sc = SparkContext(conf=conf)
>> sqlContext = SQLContext(sc)
>>
>> from pyspark.mllib.regression import LabeledPoint
>> from pyspark.mllib.linalg import Vector, Vectors
>>
>> training = sc.parallelize((
>> LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
>> LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
>> LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
>> LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))))
>>
>> training_df = training.toDF()
>>
>> from pyspark.ml.classification import LogisticRegression
>>
>> reg = LogisticRegression()
>>
>> reg.setMaxIter(10).setRegParam(0.01)
>> model = reg.fit(training.toDF())
>>
>> test = sc.parallelize((
>> LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
>> LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
>> LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))))
>>
>> out_df = model.transform(test.toDF())
>>
>> out_df.write.parquet("/tmp/logparquet")
>>
>> And the command:
>> spark-submit --master yarn --deploy-mode cluster spark-ml.py
>>
>> Thanks,
>> z
>>
>
>
