Mixing
Spark Java Heap Space
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I have one problem with spark , when i tried to generate the model i get one exception with java heap space that i can't solve .
i tried to put this values on the VM options -Xmx4g but nothing happens.
i tried too adding this parameters to spark config but again nothing happend .
Java version : 7
Spark Version : 2.1.0
SparkConf conf = newSparkConf().setAppName("myAPP").setMaster("local");
conf = (conf.setMaster("local[*]"));
SparkContext sc = new SparkContext(conf);
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
// Split initial RDD into two... [60% training data, 40% testing data].
JavaRDD<LabeledPoint> splits =
data.randomSplit(new double{0.6, 0.4}, 11L);
JavaRDD<LabeledPoint> training = splits[0].cache();
JavaRDD<LabeledPoint> test = splits[1];
// Run training algorithm to build the model.
final LogisticRegressionModel model = new LogisticRegressionWithLBFGS()
.setNumClasses(2)
.run(training.rdd());
// Clear the prediction threshold so the model will return probabilities
model.clearThreshold();
// Compute raw scores on the test set.
JavaRDD<Tuple2<Object, Object>> predictionAndLabels = test.map(
new Function<LabeledPoint, Tuple2<Object, Object>>() {
@Override
public Tuple2<Object, Object> call(LabeledPoint p) {
Double prediction = model.predict(p.features());
return new Tuple2<Object, Object>(prediction, p.label());
}
}
);
// Get evaluation metrics.
BinaryClassificationMetrics metrics =
new BinaryClassificationMetrics(predictionAndLabels.rdd());
Error
18/05/02 13:06:49 INFO DAGScheduler: Job 1 finished: first at GeneralizedLinearAlgorithm.scala:206, took 0,038806 s
Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
at org.apache.spark.mllib.linalg.Vectors$.zeros(Vectors.scala:340)
at org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.run(GeneralizedLinearAlgorithm.scala:222)
at Principal.main(Principal.java:114)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:134)
java apache-spark heap space
edited Dec 27 '18 at 9:58
Nikhil
399614
asked May 2 '18 at 13:34
HallionHallion
161
add a comment |
I have one problem with spark , when i tried to generate the model i get one exception with java heap space that i can't solve .
i tried to put this values on the VM options -Xmx4g but nothing happens.
i tried too adding this parameters to spark config but again nothing happend .
Java version : 7
Spark Version : 2.1.0
SparkConf conf = newSparkConf().setAppName("myAPP").setMaster("local");
conf = (conf.setMaster("local[*]"));
SparkContext sc = new SparkContext(conf);
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
// Split initial RDD into two... [60% training data, 40% testing data].
JavaRDD<LabeledPoint> splits =
data.randomSplit(new double{0.6, 0.4}, 11L);
JavaRDD<LabeledPoint> training = splits[0].cache();
JavaRDD<LabeledPoint> test = splits[1];
// Run training algorithm to build the model.
final LogisticRegressionModel model = new LogisticRegressionWithLBFGS()
.setNumClasses(2)
.run(training.rdd());
// Clear the prediction threshold so the model will return probabilities
model.clearThreshold();
// Compute raw scores on the test set.
JavaRDD<Tuple2<Object, Object>> predictionAndLabels = test.map(
new Function<LabeledPoint, Tuple2<Object, Object>>() {
@Override
public Tuple2<Object, Object> call(LabeledPoint p) {
Double prediction = model.predict(p.features());
return new Tuple2<Object, Object>(prediction, p.label());
}
}
);
// Get evaluation metrics.
BinaryClassificationMetrics metrics =
new BinaryClassificationMetrics(predictionAndLabels.rdd());
Error
18/05/02 13:06:49 INFO DAGScheduler: Job 1 finished: first at GeneralizedLinearAlgorithm.scala:206, took 0,038806 s
Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
at org.apache.spark.mllib.linalg.Vectors$.zeros(Vectors.scala:340)
at org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.run(GeneralizedLinearAlgorithm.scala:222)
at Principal.main(Principal.java:114)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:134)
java apache-spark heap space
edited Dec 27 '18 at 9:58
Nikhil
399614
asked May 2 '18 at 13:34
HallionHallion
161
add a comment |
I have one problem with spark , when i tried to generate the model i get one exception with java heap space that i can't solve .
i tried to put this values on the VM options -Xmx4g but nothing happens.
i tried too adding this parameters to spark config but again nothing happend .
Java version : 7
Spark Version : 2.1.0
SparkConf conf = newSparkConf().setAppName("myAPP").setMaster("local");
conf = (conf.setMaster("local[*]"));
SparkContext sc = new SparkContext(conf);
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
// Split initial RDD into two... [60% training data, 40% testing data].
JavaRDD<LabeledPoint> splits =
data.randomSplit(new double{0.6, 0.4}, 11L);
JavaRDD<LabeledPoint> training = splits[0].cache();
JavaRDD<LabeledPoint> test = splits[1];
// Run training algorithm to build the model.
final LogisticRegressionModel model = new LogisticRegressionWithLBFGS()
.setNumClasses(2)
.run(training.rdd());
// Clear the prediction threshold so the model will return probabilities
model.clearThreshold();
// Compute raw scores on the test set.
JavaRDD<Tuple2<Object, Object>> predictionAndLabels = test.map(
new Function<LabeledPoint, Tuple2<Object, Object>>() {
@Override
public Tuple2<Object, Object> call(LabeledPoint p) {
Double prediction = model.predict(p.features());
return new Tuple2<Object, Object>(prediction, p.label());
}
}
);
// Get evaluation metrics.
BinaryClassificationMetrics metrics =
new BinaryClassificationMetrics(predictionAndLabels.rdd());
Error
18/05/02 13:06:49 INFO DAGScheduler: Job 1 finished: first at GeneralizedLinearAlgorithm.scala:206, took 0,038806 s
Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
at org.apache.spark.mllib.linalg.Vectors$.zeros(Vectors.scala:340)
at org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.run(GeneralizedLinearAlgorithm.scala:222)
at Principal.main(Principal.java:114)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:134)
java apache-spark heap space
edited Dec 27 '18 at 9:58
Nikhil
399614
asked May 2 '18 at 13:34
HallionHallion
161
I have one problem with spark , when i tried to generate the model i get one exception with java heap space that i can't solve .
i tried to put this values on the VM options -Xmx4g but nothing happens.
i tried too adding this parameters to spark config but again nothing happend .
Java version : 7
Spark Version : 2.1.0
SparkConf conf = newSparkConf().setAppName("myAPP").setMaster("local");
conf = (conf.setMaster("local[*]"));
SparkContext sc = new SparkContext(conf);
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
// Split initial RDD into two... [60% training data, 40% testing data].
JavaRDD<LabeledPoint> splits =
data.randomSplit(new double{0.6, 0.4}, 11L);
JavaRDD<LabeledPoint> training = splits[0].cache();
JavaRDD<LabeledPoint> test = splits[1];
// Run training algorithm to build the model.
final LogisticRegressionModel model = new LogisticRegressionWithLBFGS()
.setNumClasses(2)
.run(training.rdd());
// Clear the prediction threshold so the model will return probabilities
model.clearThreshold();
// Compute raw scores on the test set.
JavaRDD<Tuple2<Object, Object>> predictionAndLabels = test.map(
new Function<LabeledPoint, Tuple2<Object, Object>>() {
@Override
public Tuple2<Object, Object> call(LabeledPoint p) {
Double prediction = model.predict(p.features());
return new Tuple2<Object, Object>(prediction, p.label());
}
}
);
// Get evaluation metrics.
BinaryClassificationMetrics metrics =
new BinaryClassificationMetrics(predictionAndLabels.rdd());
Error
18/05/02 13:06:49 INFO DAGScheduler: Job 1 finished: first at GeneralizedLinearAlgorithm.scala:206, took 0,038806 s
Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
at org.apache.spark.mllib.linalg.Vectors$.zeros(Vectors.scala:340)
at org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.run(GeneralizedLinearAlgorithm.scala:222)
at Principal.main(Principal.java:114)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at com.intellij.rt.execution.application.AppMain.main(AppMain.java:134)
java apache-spark heap space
java apache-spark heap space
edited Dec 27 '18 at 9:58
Nikhil
399614
asked May 2 '18 at 13:34
HallionHallion
161
edited Dec 27 '18 at 9:58
Nikhil
399614
asked May 2 '18 at 13:34
HallionHallion
161
edited Dec 27 '18 at 9:58
Nikhil
399614
edited Dec 27 '18 at 9:58
Nikhil
399614
edited Dec 27 '18 at 9:58
Nikhil
399614
399614
asked May 2 '18 at 13:34
HallionHallion
161
asked May 2 '18 at 13:34
HallionHallion
161
asked May 2 '18 at 13:34
HallionHallion
161
161
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
I suffered from this issue a lot, we use dynamic resource allocation and I thought it will utilize my cluster resources to best fit the application.
But the truth is, the dynamic resource allocation doesn't set the driver memory and it keeps it to its default value which is 1g.
I have resolved it by setting spark.driver.memory to a number that suits my driver's memory (for 32gb ram I set it to 18gb)
you can set it using spark submit command as follows:
spark-submit --conf spark.driver.memory=18gb ....cont
Very important note, this property will not be taken into consideration if you set it from code, according to spark documentation:
Spark properties mainly can be divided into two kinds: one is related
to deploy, like “spark.driver.memory”, “spark.executor.instances”,
this kind of properties may not be affected when setting
programmatically through SparkConf in runtime, or the behavior is
depending on which cluster manager and deploy mode you choose, so it
would be suggested to set through configuration file or spark-submit
command line options; another is mainly related to Spark runtime
control, like “spark.task.maxFailures”, this kind of properties can be
set in either way.
answered Jan 3 at 13:09
Abdulhafeth SartawiAbdulhafeth Sartawi
405314
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
I suffered from this issue a lot, we use dynamic resource allocation and I thought it will utilize my cluster resources to best fit the application.
But the truth is, the dynamic resource allocation doesn't set the driver memory and it keeps it to its default value which is 1g.
I have resolved it by setting spark.driver.memory to a number that suits my driver's memory (for 32gb ram I set it to 18gb)
you can set it using spark submit command as follows:
spark-submit --conf spark.driver.memory=18gb ....cont
Very important note, this property will not be taken into consideration if you set it from code, according to spark documentation:
Spark properties mainly can be divided into two kinds: one is related
to deploy, like “spark.driver.memory”, “spark.executor.instances”,
this kind of properties may not be affected when setting
programmatically through SparkConf in runtime, or the behavior is
depending on which cluster manager and deploy mode you choose, so it
would be suggested to set through configuration file or spark-submit
command line options; another is mainly related to Spark runtime
control, like “spark.task.maxFailures”, this kind of properties can be
set in either way.
answered Jan 3 at 13:09
Abdulhafeth SartawiAbdulhafeth Sartawi
405314
add a comment |
I suffered from this issue a lot, we use dynamic resource allocation and I thought it will utilize my cluster resources to best fit the application.
But the truth is, the dynamic resource allocation doesn't set the driver memory and it keeps it to its default value which is 1g.
I have resolved it by setting spark.driver.memory to a number that suits my driver's memory (for 32gb ram I set it to 18gb)
you can set it using spark submit command as follows:
spark-submit --conf spark.driver.memory=18gb ....cont
Very important note, this property will not be taken into consideration if you set it from code, according to spark documentation:
Spark properties mainly can be divided into two kinds: one is related
to deploy, like “spark.driver.memory”, “spark.executor.instances”,
this kind of properties may not be affected when setting
programmatically through SparkConf in runtime, or the behavior is
depending on which cluster manager and deploy mode you choose, so it
would be suggested to set through configuration file or spark-submit
command line options; another is mainly related to Spark runtime
control, like “spark.task.maxFailures”, this kind of properties can be
set in either way.
answered Jan 3 at 13:09
Abdulhafeth SartawiAbdulhafeth Sartawi
405314
add a comment |
I suffered from this issue a lot, we use dynamic resource allocation and I thought it will utilize my cluster resources to best fit the application.
But the truth is, the dynamic resource allocation doesn't set the driver memory and it keeps it to its default value which is 1g.
I have resolved it by setting spark.driver.memory to a number that suits my driver's memory (for 32gb ram I set it to 18gb)
you can set it using spark submit command as follows:
spark-submit --conf spark.driver.memory=18gb ....cont
Very important note, this property will not be taken into consideration if you set it from code, according to spark documentation:
Spark properties mainly can be divided into two kinds: one is related
to deploy, like “spark.driver.memory”, “spark.executor.instances”,
this kind of properties may not be affected when setting
programmatically through SparkConf in runtime, or the behavior is
depending on which cluster manager and deploy mode you choose, so it
would be suggested to set through configuration file or spark-submit
command line options; another is mainly related to Spark runtime
control, like “spark.task.maxFailures”, this kind of properties can be
set in either way.
answered Jan 3 at 13:09
Abdulhafeth SartawiAbdulhafeth Sartawi
405314
I suffered from this issue a lot, we use dynamic resource allocation and I thought it will utilize my cluster resources to best fit the application.
But the truth is, the dynamic resource allocation doesn't set the driver memory and it keeps it to its default value which is 1g.
I have resolved it by setting spark.driver.memory to a number that suits my driver's memory (for 32gb ram I set it to 18gb)
you can set it using spark submit command as follows:
spark-submit --conf spark.driver.memory=18gb ....cont
Very important note, this property will not be taken into consideration if you set it from code, according to spark documentation:
Spark properties mainly can be divided into two kinds: one is related
to deploy, like “spark.driver.memory”, “spark.executor.instances”,
this kind of properties may not be affected when setting
programmatically through SparkConf in runtime, or the behavior is
depending on which cluster manager and deploy mode you choose, so it
would be suggested to set through configuration file or spark-submit
command line options; another is mainly related to Spark runtime
control, like “spark.task.maxFailures”, this kind of properties can be
set in either way.
answered Jan 3 at 13:09
Abdulhafeth SartawiAbdulhafeth Sartawi
405314
answered Jan 3 at 13:09
Abdulhafeth SartawiAbdulhafeth Sartawi
405314
answered Jan 3 at 13:09
Abdulhafeth SartawiAbdulhafeth Sartawi
405314
answered Jan 3 at 13:09
Abdulhafeth SartawiAbdulhafeth Sartawi
405314
405314
add a comment |
add a comment |
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