Re: Enum values in custom objects mess up RDD operations
Thanks a lot Igor,
the following hashCode function is stable:
@Override
public int hashCode() {
int hash = 5;
hash = 41 * hash + this.myEnum.ordinal();
return hash;
}
For anyone having the same problem:
http://tech.technoflirt.com/2014/08/21/issues-with-java-enum-hashcode/
Cheers,
Sebastian
Igor Berman igor.ber...@gmail.com schrieb am Do., 6. Aug. 2015 um
10:59 Uhr:
enums hashcode is jvm instance specific(ie. different jvms will give you
different values), so you can use ordinal in hashCode computation or use
hashCode on enums ordinal as part of hashCode computation
On 6 August 2015 at 11:41, Warfish sebastian.ka...@gmail.com wrote:
Hi everyone,
I was working with Spark for a little while now and have encountered a
very
strange behaviour that caused me a lot of headaches:
I have written my own POJOs to encapsulate my data and this data is held
in
some JavaRDDs. Part of these POJOs is a member variable of a custom enum
type. Whenever I do some operations on these RDDs such as subtract,
groupByKey, reduce or similar things, the results are inconsistent and
non-sensical. However, this happens only when the application runs in
standalone cluster mode (10 nodes). When running locally on my developer
machine, the code executes just fine. If you want to reproduce this
behaviour, here
http://apache-spark-user-list.1001560.n3.nabble.com/file/n24149/SparkTest.zip
is the complete Maven project that you can run out of the box. I am
running
Spark 1.4.0 and submitting the application using
/usr/local/spark-1.4.0-bin-hadoop2.4/bin/spark-submit --class
de.spark.test.Main ./SparkTest-1.0-SNAPSHOT.jar
Consider the following code for my custom object:
package de.spark.test;
import java.io.Serializable;
import java.util.Objects;
public class MyObject implements Serializable {
private MyEnum myEnum;
public MyObject(MyEnum myEnum) {
this.myEnum = myEnum;
}
public MyEnum getMyEnum() {
return myEnum;
}
public void setMyEnum(MyEnum myEnum) {
this.myEnum = myEnum;
}
@Override
public int hashCode() {
int hash = 5;
hash = 41 * hash + Objects.hashCode(this.myEnum);
return hash;
}
@Override
public boolean equals(Object obj) {
if (obj == null) {
return false;
}
if (getClass() != obj.getClass()) {
return false;
}
final MyObject other = (MyObject) obj;
if (this.myEnum != other.myEnum) {
return false;
}
return true;
}
@Override
public String toString() {
return MyObject{ + myEnum= + myEnum + '}';
}
}
As you can see, I have overriden equals() and hashCode() (both are
auto-generated). The enum is given as follows:
package de.spark.test;
import java.io.Serializable;
public enum MyEnum implements Serializable {
VALUE1, VALUE2
}
The main() method is defined by:
package de.spark.test;
import java.util.ArrayList;
import java.util.List;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
public class Main {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName(Spark Test)
.setMaster(myMaster);
JavaSparkContext jsc = new JavaSparkContext(conf);
System.out.println(///
Object generation);
ListMyObject l1 = new ArrayList();
for(int i = 0; i 1000; i++) {
l1.add(new MyObject(MyEnum.VALUE1));
}
JavaRDDMyObject myObjectRDD1 = jsc.parallelize(l1);
JavaRDDMyObject myObjectRDD2 = jsc.parallelize(l1);
System.out.println(myObjectRDD1 count = +
myObjectRDD1.count());
System.out.println(myObjectRDD2 count = +
myObjectRDD2.count());
System.out.println(///
Distinct);
JavaRDDMyObject myObjectRDD1Distinct = myObjectRDD1.distinct();
JavaRDDMyObject myObjectRDD2Distinct = myObjectRDD2.distinct();
System.out.println(myObjectRDD1Distinct count = +
myObjectRDD1Distinct.count());
System.out.println(myObjectRDD2Distinct count = +
myObjectRDD2Distinct.count());
System.out.println(///
Subtract);
JavaRDDMyObject myObjectRDD1Minus1 =
myObjectRDD1.subtract(myObjectRDD1);
JavaRDDMyObject myObjectRDD1Minus2 =
myObjectRDD1.subtract(myObjectRDD2);
JavaRDDMyObject myObjectRDD2Minus1 =
myObjectRDD2.subtract(myObjectRDD1);
System.out.println(myObjectRDD1Minus1 count= +
myObjectRDD1Minus1.count());
System.out.println(myObjectRDD1Minus2 count= +
myObjectRDD1Minus2.count());
System.out.println(myObjectRDD2Minus1 count= +
myObjectRDD2Minus1.count());
Re: Issues with JavaRDD.subtract(JavaRDD) method in local vs. cluster mode
Thanks for the quick reply. I will be unable to collect more data until
Monday though, but I will update the thread accordingly.
I am using Spark 1.4.0. Were there any related issues reported? I wasn't
able to find any, but I may have overlooked something. I have also updated
the original question to include the relevant Java files, maybe the issue
is hidden there somewhere.
Ted Yu yuzhih...@gmail.com schrieb am Fr., 31. Juli 2015 um 18:09 Uhr:
Can you call collect() and log the output to get more clue what is left ?
Which Spark release are you using ?
Cheers
On Fri, Jul 31, 2015 at 9:01 AM, Warfish sebastian.ka...@gmail.com
wrote:
Hi everyone,
I work with Spark for a little while now and have encountered a strange
problem that gives me headaches, which has to do with the JavaRDD.subtract
method. Consider the following piece of code:
public static void main(String[] args) {
//context is of type JavaSparkContext; FILE is the filepath to my
input file
JavaRDDString rawTestSet = context.textFile(FILE);
JavaRDDString rawTestSet2 = context.textFile(FILE);
//Gives 0 everytime - Correct
System.out.println(rawTestSetMinusRawTestSet2= +
rawTestSet.subtract(rawTestSet2).count());
//SearchData is a custom POJO that holds my data
JavaRDDSearchData testSet = convert(rawTestSet);
JavaRDDSearchData testSet2= convert(rawTestSet);
JavaRDDSearchData testSet3= convert(rawTestSet2);
//These calls give numbers !=0 on cluster mode - Incorrect
System.out.println(testSetMinuesTestSet2 = +
testSet.subtract(testSet2).count());
System.out.println(testSetMinuesTestSet3 = +
testSet.subtract(testSet3).count());
System.out.println(testSet2MinuesTestSet3 = +
testSet2.subtract(testSet3).count());
}
private static JavaRDDSearchData convert(JavaRDDString input) {
return input.filter(new Matches(myRegex))
.map(new DoSomething())
.map(new Split(mySplitParam))
.map(new ToMap())
.map(new Clean())
.map(new ToSearchData());
}
In this code, I read a file (usually from HDFS, but applies to disk as
well)
and then convert the Strings into custom objects to hold the data using a
chain of filter- and map-operations. These objects are simple POJOs with
overriden hashCode() and equal() functions. I then apply the subtract
method
to several JavaRDDs that contain exact equal data.
Note: I have omitted the POJO code and the filter- and map-functions to
make
the code more concise, but I can post it later if the need arises.
In the main method shown above are several calls of the subtract method,
all
of which should give empty RDDs as results because the data in all RDDs
should be exactly the same. This works for Spark in local mode, however
when
executing the code on a cluster the second block of subtract calls does
not
result in empty sets, which tells me that it is a more complicated issue.
The input data on local and cluster mode was exactly the same.
Can someone shed some light on this issue? I feel like I'm overlooking
something rather obvious.
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/Issues-with-JavaRDD-subtract-JavaRDD-method-in-local-vs-cluster-mode-tp24099.html
Sent from the Apache Spark User List mailing list archive at Nabble.com.
-
To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
For additional commands, e-mail: user-h...@spark.apache.org
