【spark】sortByKey实现二次排序

2023年5月30日 201次阅读 来源: 小飞_侠_kobe

最近在项目中遇到二次排序的需求，和平常开发spark的application一样，开始查看API，编码，调试，验证结果。由于之前对spark的API使用过，知道API中的sortByKey()可以自定义排序规则，通过实现自定义的排序规则来实现二次排序。
这里为了说明问题，举了一个简单的例子,key是由两部分组成的，我们这里按key的第一部分的降序排，key的第二部分升序排，具体如下：

JavaSparkContext javaSparkContext = new JavaSparkContext(sparkConf);

List<Integer> data = Arrays.asList(5, 1, 1, 4, 4, 2, 2);

JavaRDD<Integer> javaRDD = javaSparkContext.parallelize(data);

final Random random = new Random(100);

JavaPairRDD javaPairRDD = javaRDD.mapToPair(new PairFunction<Integer, String, Integer>() {    
        @Override    
        public Tuple2<String, Integer> call(Integer integer) throws Exception {        
          return new Tuple2<String, Integer>(Integer.toString(integer) + " " + random.nextInt(10),random.nextInt(10));   
     }
});

JavaPairRDD<String,Integer> sortByKeyRDD = javaPairRDD.sortByKey(new Comparator<String>() {    
    @Override    
    public int compare(String o1, String o2) {        
        String []o1s = o1.split(" ");        
        String []o2s = o2.split(" ");       
        if(o1s[0].compareTo(o2s[0]) == 0)            
              return o1s[1].compareTo(o2s[1]);        
        else            
              return -o1s[0].compareTo(o2s[0]);    
  }
});
System.out.println("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~" + sortByKeyRDD.collect());

上面编码从语法上没有什么问题，可是运行下报了如下错误：

java.lang.reflect.InvocationTargetException
    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 org.apache.spark.serializer.SerializationDebugger$ObjectStreamClassMethods$.getObjFieldValues$extension(SerializationDebugger.scala:248)
    at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visitSerializable(SerializationDebugger.scala:158)
    at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visit(SerializationDebugger.scala:107)
    at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visitSerializable(SerializationDebugger.scala:166)
    at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visit(SerializationDebugger.scala:107)
    at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visitSerializable(SerializationDebugger.scala:166)
    at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visit(SerializationDebugger.scala:107)
    at org.apache.spark.serializer.SerializationDebugger$.find(SerializationDebugger.scala:66)
    at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:41)
    at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:47)
    at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:81)
    at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:312)
    at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:305)
    at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:132)
    at org.apache.spark.SparkContext.clean(SparkContext.scala:1891)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1764)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1779)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:885)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:148)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:109)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:286)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:884)
    at org.apache.spark.api.java.JavaRDDLike$class.collect(JavaRDDLike.scala:335)
    at org.apache.spark.api.java.AbstractJavaRDDLike.collect(JavaRDDLike.scala:47)

因此，我再次去查看相应的spark Java API文档，但是我没有发现任何指明错误的地方。好吧，那只能扒下源码吧，在javaPairRDD中

def sortByKey(comp: Comparator[K], ascending: Boolean): JavaPairRDD[K, V] = {  
    implicit val ordering = comp // Allow implicit conversion of Comparator to Ordering.  
    fromRDD(new OrderedRDDFunctions[K, V, (K, V)](rdd).sortByKey(ascending))
}

其实在OrderedRDDFunctions类中有个变量ordering它是隐形的：private val ordering = implicitly[Ordering[K]]。他就是默认的排序规则，我们自己重写的comp就修改了默认的排序规则。到这里还是没有发现问题，但是发现类OrderedRDDFunctions extends Logging with Serializable，又回到上面的报错信息，扫描到“serializable”！！！因此，返回上述代码，查看Comparator interface实现，发现原来是它没有extend Serializable，故只需创建一个 serializable的comparator就可以：public interface SerializableComparator<T> extends Comparator<T>, Serializable { }
具体如下：

private static class Comp implements Comparator<String>,Serializable{    
    @Override    
    public int compare(String o1, String o2) {            
          String []o1s = o1.split(" ");            
          String []o2s = o2.split(" ");            
          if(o1s[0].compareTo(o2s[0]) == 0)                
              return o1s[1].compareTo(o2s[1]);
           else
                return -o1s[0].compareTo(o2s[0]);    
  }
}
JavaPairRDD<String,Integer> sortByKeyRDD = javaPairRDD.sortByKey(new Comp());

总结下，在spark的Java API中，如果需要使用Comparator接口，须注意是否需要序列化，如sortByKey(),repartitionAndSortWithinPartitions()等都是需要序列化的。

    原文作者：小飞_侠_kobe
    原文地址: https://www.jianshu.com/p/37231b87de81
    本文转自网络文章，转载此文章仅为分享知识，如有侵权，请联系博主进行删除。
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