Bump
On Tuesday, August 5, 2014, Chengi Liu <[email protected]> wrote:
> Hi,
> I am doing some basic preprocessing in pyspark (local mode as follows):
>
> files = [ input files]
> def read(filename,sc):
> #process file
> return rdd
>
> if __name__ =="__main__":
> conf = SparkConf()
> conf.setMaster('local')
> sc = SparkContext(conf =conf)
> sc.setCheckpointDir(root+"temp/")
>
> data = sc.parallelize([])
>
> for i,f in enumerate(files):
>
> data = data.union(read(f,sc))
> if i ==20:
> data.checkpoint()
> data.count()
> if i == 500:break
> #print data.count()
> #rdd_1 = read(files[0],sc)
> data.saveAsTextFile(root+"output/")
>
>
> But I see this error:
> keyed._jrdd.map(self.ctx._jvm.BytesToString()).saveAsTextFile(path)
> File
> "/Users/ping/Desktop/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py",
> line 538, in __call__
> File
> "/Users/ping/Desktop/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py",
> line 300, in get_return_value
> py4j.protocol.Py4JJavaError: An error occurred while calling
> o9564.saveAsTextFile.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task
> serialization failed: java.lang.StackOverflowError
> java.io.Bits.putInt(Bits.java:93)
>
> java.io.ObjectOutputStream$BlockDataOutputStream.writeInt(ObjectOutputStream.java:1927)
>
