为什么 org.apache.hadoop.io.Writable 不能转换为 org.apache.hadoop.io.IntWritable?
Why org.apache.hadoop.io.Writable cannot be cast to org.apache.hadoop.io.IntWritable?
我的 mapreduce 应用程序如下所示。我想对字符串
中的 3 个值求和
public class StockCount {
public static class MapperClass
extends Mapper<Object, Text, Text, IntArrayWritable> {
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
String line[] = value.toString().split(",");
//mgrno,rdate,cusip,shares,sole,shared,no
// [0], [1], [2], [3], [4], [5],[6]
if (line.length > 5){
Text mgrno = new Text(line[0]);
IntWritable[] intArray = new IntWritable[3];
intArray[0] = new IntWritable(Integer.parseInt(line[4]));
intArray[1] = new IntWritable(Integer.parseInt(line[5]));
intArray[2] = new IntWritable(Integer.parseInt(line[6]));
int[] pass = new int[3];
pass[0] = Integer.parseInt(line[4]);
pass[1] = Integer.parseInt(line[5]);
pass[0] = Integer.parseInt(line[6]);
IntArrayWritable array = new IntArrayWritable(intArray);
context.write(mgrno, array);
}
}
}
public static class IntSumReducer
extends Reducer<Text, int[], Text, IntArrayWritable> {
public void reduce(Text key, Iterable<IntArrayWritable> values,
Context context
) throws IOException, InterruptedException {
int sum1 = 0;
int sum2 = 0;
int sum3 = 0;
for (IntArrayWritable val : values) {
IntWritable[] temp = new IntWritable[3];
temp = val.get();
sum1 += temp[0].get();
sum2 += temp[1].get();
sum3 += temp[2].get();
}
IntWritable[] intArray = new IntWritable[3];
intArray[0] = new IntWritable(sum1);
intArray[1] = new IntWritable(sum2);
intArray[2] = new IntWritable(sum3);
IntArrayWritable result = new IntArrayWritable(intArray);
context.write(key, result);
}
}
因为我想对我的 3 个值求和,所以我定义了一个 Class IntArrayWritable 继承自 ArrayWritable。 ArrayWritable 包含 Writable[]-s
import org.apache.hadoop.io.ArrayWritable;
import org.apache.hadoop.io.IntWritable;
public class IntArrayWritable extends ArrayWritable {
public IntArrayWritable(IntWritable[] values) {
super(IntWritable.class, values);
}
public IntArrayWritable() {
super(IntWritable.class);
}
@Override
public IntWritable[] get() {
return (IntWritable[]) super.get();
}
@Override
public String toString() {
IntWritable[] values = get();
return values[0].toString() + ", " + values[1].toString() + ", " +
values[2].toString();
}
}
我真的不明白为什么它不能施放"return (IntWritable[]) super.get();"
17/11/21 04:00:26 WARN mapred.LocalJobRunner: job_local1623924180_0001
java.lang.Exception: java.lang.ClassCastException: [Lorg.apache.hadoop.io.Writable; cannot be cast to [Lorg.apache.hadoop.io.IntWritable;
at org.apache.hadoop.mapred.LocalJobRunner$Job.runTasks(LocalJobRunner.java:462)
at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:529)
Caused by: java.lang.ClassCastException: [Lorg.apache.hadoop.io.Writable; cannot be cast to [Lorg.apache.hadoop.io.IntWritable;
at IntArrayWritable.get(IntArrayWritable.java:15)
at IntArrayWritable.toString(IntArrayWritable.java:22)
at org.apache.hadoop.mapreduce.lib.output.TextOutputFormat$LineRecordWriter.writeObject(TextOutputFormat.java:85)
at org.apache.hadoop.mapreduce.lib.output.TextOutputFormat$LineRecordWriter.write(TextOutputFormat.java:104)
at org.apache.hadoop.mapred.ReduceTask$NewTrackingRecordWriter.write(ReduceTask.java:558)
at org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write(TaskInputOutputContextImpl.java:89)
at org.apache.hadoop.mapreduce.lib.reduce.WrappedReducer$Context.write(WrappedReducer.java:105)
at org.apache.hadoop.mapreduce.Reducer.reduce(Reducer.java:150)
at org.apache.hadoop.mapreduce.Reducer.run(Reducer.java:171)
at org.apache.hadoop.mapred.ReduceTask.runNewReducer(ReduceTask.java:627)
at org.apache.hadoop.mapred.ReduceTask.run(ReduceTask.java:389)
at org.apache.hadoop.mapred.LocalJobRunner$Job$ReduceTaskRunnable.run(LocalJobRunner.java:319)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
非常感谢您的帮助。
比!
首先,Reducer<Text, int[],
应该有一个 Writable 类型而不是 int[]
但是,您可以只使用映射器中逗号分隔的 Text Writable 值。
编写自己的 Writable class 仅用于传递数组并没有明显的好处。
你可以从reducer解析和求和
我只是在处理实现 TextArrayWritable
class 的同一件事。对我来说似乎不够优雅,但遍历数组并强制转换每个元素就可以了。
public class TextArrayWritable extends ArrayWritable{
public Text[] get() {
Writable[] temp = super.get();
if (temp != null) {
int n = temp.length;
Text[] items = new Text[n];
for (int i = 0; i < temp.length; i++) {
items[i] = (Text)temp[i];
}
return items;
} else {
return null;
}
}
我的 mapreduce 应用程序如下所示。我想对字符串
中的 3 个值求和public class StockCount {
public static class MapperClass
extends Mapper<Object, Text, Text, IntArrayWritable> {
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
String line[] = value.toString().split(",");
//mgrno,rdate,cusip,shares,sole,shared,no
// [0], [1], [2], [3], [4], [5],[6]
if (line.length > 5){
Text mgrno = new Text(line[0]);
IntWritable[] intArray = new IntWritable[3];
intArray[0] = new IntWritable(Integer.parseInt(line[4]));
intArray[1] = new IntWritable(Integer.parseInt(line[5]));
intArray[2] = new IntWritable(Integer.parseInt(line[6]));
int[] pass = new int[3];
pass[0] = Integer.parseInt(line[4]);
pass[1] = Integer.parseInt(line[5]);
pass[0] = Integer.parseInt(line[6]);
IntArrayWritable array = new IntArrayWritable(intArray);
context.write(mgrno, array);
}
}
}
public static class IntSumReducer
extends Reducer<Text, int[], Text, IntArrayWritable> {
public void reduce(Text key, Iterable<IntArrayWritable> values,
Context context
) throws IOException, InterruptedException {
int sum1 = 0;
int sum2 = 0;
int sum3 = 0;
for (IntArrayWritable val : values) {
IntWritable[] temp = new IntWritable[3];
temp = val.get();
sum1 += temp[0].get();
sum2 += temp[1].get();
sum3 += temp[2].get();
}
IntWritable[] intArray = new IntWritable[3];
intArray[0] = new IntWritable(sum1);
intArray[1] = new IntWritable(sum2);
intArray[2] = new IntWritable(sum3);
IntArrayWritable result = new IntArrayWritable(intArray);
context.write(key, result);
}
}
因为我想对我的 3 个值求和,所以我定义了一个 Class IntArrayWritable 继承自 ArrayWritable。 ArrayWritable 包含 Writable[]-s
import org.apache.hadoop.io.ArrayWritable;
import org.apache.hadoop.io.IntWritable;
public class IntArrayWritable extends ArrayWritable {
public IntArrayWritable(IntWritable[] values) {
super(IntWritable.class, values);
}
public IntArrayWritable() {
super(IntWritable.class);
}
@Override
public IntWritable[] get() {
return (IntWritable[]) super.get();
}
@Override
public String toString() {
IntWritable[] values = get();
return values[0].toString() + ", " + values[1].toString() + ", " +
values[2].toString();
}
}
我真的不明白为什么它不能施放"return (IntWritable[]) super.get();"
17/11/21 04:00:26 WARN mapred.LocalJobRunner: job_local1623924180_0001
java.lang.Exception: java.lang.ClassCastException: [Lorg.apache.hadoop.io.Writable; cannot be cast to [Lorg.apache.hadoop.io.IntWritable;
at org.apache.hadoop.mapred.LocalJobRunner$Job.runTasks(LocalJobRunner.java:462)
at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:529)
Caused by: java.lang.ClassCastException: [Lorg.apache.hadoop.io.Writable; cannot be cast to [Lorg.apache.hadoop.io.IntWritable;
at IntArrayWritable.get(IntArrayWritable.java:15)
at IntArrayWritable.toString(IntArrayWritable.java:22)
at org.apache.hadoop.mapreduce.lib.output.TextOutputFormat$LineRecordWriter.writeObject(TextOutputFormat.java:85)
at org.apache.hadoop.mapreduce.lib.output.TextOutputFormat$LineRecordWriter.write(TextOutputFormat.java:104)
at org.apache.hadoop.mapred.ReduceTask$NewTrackingRecordWriter.write(ReduceTask.java:558)
at org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write(TaskInputOutputContextImpl.java:89)
at org.apache.hadoop.mapreduce.lib.reduce.WrappedReducer$Context.write(WrappedReducer.java:105)
at org.apache.hadoop.mapreduce.Reducer.reduce(Reducer.java:150)
at org.apache.hadoop.mapreduce.Reducer.run(Reducer.java:171)
at org.apache.hadoop.mapred.ReduceTask.runNewReducer(ReduceTask.java:627)
at org.apache.hadoop.mapred.ReduceTask.run(ReduceTask.java:389)
at org.apache.hadoop.mapred.LocalJobRunner$Job$ReduceTaskRunnable.run(LocalJobRunner.java:319)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
非常感谢您的帮助。
比!
首先,Reducer<Text, int[],
应该有一个 Writable 类型而不是 int[]
但是,您可以只使用映射器中逗号分隔的 Text Writable 值。
编写自己的 Writable class 仅用于传递数组并没有明显的好处。
你可以从reducer解析和求和
我只是在处理实现 TextArrayWritable
class 的同一件事。对我来说似乎不够优雅,但遍历数组并强制转换每个元素就可以了。
public class TextArrayWritable extends ArrayWritable{
public Text[] get() {
Writable[] temp = super.get();
if (temp != null) {
int n = temp.length;
Text[] items = new Text[n];
for (int i = 0; i < temp.length; i++) {
items[i] = (Text)temp[i];
}
return items;
} else {
return null;
}
}