How are 'println' commands in spark containers returned to the console on master?
Here is a sketch of some Scala 2.10/Spark 1.6.3 code that I am running:
def processText(df: DataFrame): DataFrame = {
df.select(col(firstCol), col(secondCol))
.as[(TypeA, TypeB)]
.mapPartitions(partitionFunc)
.map { case (a: TypeA, c: TypeC) => OutputCaseClass(a, b) }
.toDF().toDF(colNames: _*)
}
def partitionFunc(rows: Iterator[(TypeA, TypeB)]): Iterator[(TypeA, TypeC)] = {
def rowFunc(a: TypeA, b: TypeB): Option[(TypeA, TypeC)] = {
try {
val returnC = functionality(b)
Option(a, returnC)
} catch {
case e: Exception => println("Error Message")
None
}
}
rows.map((returnFunc _).tupled)
.flatten
}
partitionFunc is mapped to all the partitions. Each row in a partition is processed (its NLP). If there is some error, nothing is returned, but an error message is generated.
As a placeholder, I just put a println into my code. To my surprise, the stuff sent to println ended up on the console when I executed this job using spark-submit.
I want to know how this is happening. The code is executed on a JVM on a processing node. How does the message sent to STDOUT on that node make its way back to the master and onto the console?
The execution test is done with
spark-submit --class com.package.mainclass --master local[4] path/to/jarfile.jar
scala apache-spark stdout
asked Jan 18 at 16:14
kingledionkingledion
789719
add a comment |
Here is a sketch of some Scala 2.10/Spark 1.6.3 code that I am running:
def processText(df: DataFrame): DataFrame = {
df.select(col(firstCol), col(secondCol))
.as[(TypeA, TypeB)]
.mapPartitions(partitionFunc)
.map { case (a: TypeA, c: TypeC) => OutputCaseClass(a, b) }
.toDF().toDF(colNames: _*)
}
def partitionFunc(rows: Iterator[(TypeA, TypeB)]): Iterator[(TypeA, TypeC)] = {
def rowFunc(a: TypeA, b: TypeB): Option[(TypeA, TypeC)] = {
try {
val returnC = functionality(b)
Option(a, returnC)
} catch {
case e: Exception => println("Error Message")
None
}
}
rows.map((returnFunc _).tupled)
.flatten
}
partitionFunc is mapped to all the partitions. Each row in a partition is processed (its NLP). If there is some error, nothing is returned, but an error message is generated.
As a placeholder, I just put a println into my code. To my surprise, the stuff sent to println ended up on the console when I executed this job using spark-submit.
I want to know how this is happening. The code is executed on a JVM on a processing node. How does the message sent to STDOUT on that node make its way back to the master and onto the console?
The execution test is done with
spark-submit --class com.package.mainclass --master local[4] path/to/jarfile.jar
scala apache-spark stdout
asked Jan 18 at 16:14
kingledionkingledion
789719
what is your configuration? e.g.spark.master?
– Raphael Roth
Jan 19 at 12:18
add a comment |
Here is a sketch of some Scala 2.10/Spark 1.6.3 code that I am running:
def processText(df: DataFrame): DataFrame = {
df.select(col(firstCol), col(secondCol))
.as[(TypeA, TypeB)]
.mapPartitions(partitionFunc)
.map { case (a: TypeA, c: TypeC) => OutputCaseClass(a, b) }
.toDF().toDF(colNames: _*)
}
def partitionFunc(rows: Iterator[(TypeA, TypeB)]): Iterator[(TypeA, TypeC)] = {
def rowFunc(a: TypeA, b: TypeB): Option[(TypeA, TypeC)] = {
try {
val returnC = functionality(b)
Option(a, returnC)
} catch {
case e: Exception => println("Error Message")
None
}
}
rows.map((returnFunc _).tupled)
.flatten
}
partitionFunc is mapped to all the partitions. Each row in a partition is processed (its NLP). If there is some error, nothing is returned, but an error message is generated.
As a placeholder, I just put a println into my code. To my surprise, the stuff sent to println ended up on the console when I executed this job using spark-submit.
I want to know how this is happening. The code is executed on a JVM on a processing node. How does the message sent to STDOUT on that node make its way back to the master and onto the console?
The execution test is done with
spark-submit --class com.package.mainclass --master local[4] path/to/jarfile.jar
scala apache-spark stdout
asked Jan 18 at 16:14
kingledionkingledion
789719
Here is a sketch of some Scala 2.10/Spark 1.6.3 code that I am running:
def processText(df: DataFrame): DataFrame = {
df.select(col(firstCol), col(secondCol))
.as[(TypeA, TypeB)]
.mapPartitions(partitionFunc)
.map { case (a: TypeA, c: TypeC) => OutputCaseClass(a, b) }
.toDF().toDF(colNames: _*)
}
def partitionFunc(rows: Iterator[(TypeA, TypeB)]): Iterator[(TypeA, TypeC)] = {
def rowFunc(a: TypeA, b: TypeB): Option[(TypeA, TypeC)] = {
try {
val returnC = functionality(b)
Option(a, returnC)
} catch {
case e: Exception => println("Error Message")
None
}
}
rows.map((returnFunc _).tupled)
.flatten
}
partitionFunc is mapped to all the partitions. Each row in a partition is processed (its NLP). If there is some error, nothing is returned, but an error message is generated.
As a placeholder, I just put a println into my code. To my surprise, the stuff sent to println ended up on the console when I executed this job using spark-submit.
I want to know how this is happening. The code is executed on a JVM on a processing node. How does the message sent to STDOUT on that node make its way back to the master and onto the console?
The execution test is done with
spark-submit --class com.package.mainclass --master local[4] path/to/jarfile.jar
scala apache-spark stdout
scala apache-spark stdout
asked Jan 18 at 16:14
kingledionkingledion
789719
asked Jan 18 at 16:14
kingledionkingledion
789719
edited Jan 19 at 13:29
kingledion
asked Jan 18 at 16:14
kingledionkingledion
789719
asked Jan 18 at 16:14
kingledionkingledion
789719
asked Jan 18 at 16:14
kingledionkingledion
789719
789719
what is your configuration? e.g.spark.master?
– Raphael Roth
Jan 19 at 12:18
add a comment |
what is your configuration? e.g.spark.master?
– Raphael Roth
Jan 19 at 12:18
what is your configuration? e.g.
spark.master?– Raphael Roth
Jan 19 at 12:18
what is your configuration? e.g.
spark.master?– Raphael Roth
Jan 19 at 12:18
add a comment |
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what is your configuration? e.g.
spark.master?– Raphael Roth
Jan 19 at 12:18