Java 在arraylist中缓存iterable以在reducer中迭代两次不会';行不通
我的MR程序有一些奇怪的问题,不知道为什么会这样。 也许你能给我一个提示它有什么问题 这就是我的映射器函数的外观:Java 在arraylist中缓存iterable以在reducer中迭代两次不会';行不通,java,hadoop,arraylist,mapreduce,iterable,Java,Hadoop,Arraylist,Mapreduce,Iterable,我的MR程序有一些奇怪的问题,不知道为什么会这样。 也许你能给我一个提示它有什么问题 这就是我的映射器函数的外观: Integer Click_ID = 0; public void map(LongWritable key, Text value, Context context) throws IOException , InterruptedException { String line = value.toString();
Integer Click_ID = 0;
public void map(LongWritable key, Text value, Context context)
throws IOException , InterruptedException
{
String line = value.toString();
String []lineArr = line.split("\t");
String nm_uv_id = lineArr[0];
String session_id = lineArr[1];
String time_stamp = lineArr[2];
String click_counter = lineArr[3];
String is_robot = lineArr[4];
Click_ID++;
String full_line = Click_ID + "\t"+ nm_uv_id +"\t"+ session_id+"\t"+time_stamp+"\t"+click_counter+"\t"+ is_robot;
context.write(new Text(session_id), new Text(full_line));
}
到目前为止,一切都很好——当我将还原数设置为0时,我的映射器生成预期的输出
这是我的减速机的样子。我想做的是,对我的每个键迭代两次。为此,我尝试将Iterable的每个值缓存在单独的ArrayList中:
public void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
List<Text> cache = new ArrayList<Text>();
// first iterable
for (Text value : values) {
cache.add(value); }
//second iterable
for (Text entity : cache) {
context.write(key, entity); }
}
但是,我的输出文件如下所示:
nm_uv_id_1 session_id_2 1234567891 1 is_robot_no
nm_uv_id_1 session_id_2 1234567892 2 is_robot_no
nm_uv_id_1 session_id_2 1234567893 3 is_robot_no
nm_uv_id_1 session_id_2 1234567894 3 is_robot_no
nm_uv_id_1 session_id_1 1234567895 1 is_robot_no
nm_uv_id_1 session_id_1 1234567896 2 is_robot_no
nm_uv_id_1 session_id_1 1234567897 3 is_robot_no
nm_uv_id_1 session_id_1 1234567898 4 is_robot_no
nm_uv_id_1 session_id_1 1234567899 5 is_robot_no
nm_uv_id_1 session_id_1 1234567888 6 is_robot_no
nm_uv_id_1 session_id_1 1234567890 7 is_robot_no
nm_uv_id_1 session_id_1 1234567890 8 is_robot_no
nm_uv_id_1 session_id_1 1234567890 9 is_robot_no
nm_uv_id_1 session_id_1 1234567890 10 is_robot_no
nm_uv_id_1 session_id_3 1234567890 1 is_robot_no
nm_uv_id_2 session_id_4 1234587890 1 is_robot_no
nm_uv_id_2 session_id_4 1234587890 2 is_robot_no
nm_uv_id_2 session_id_4 1234587890 3 is_robot_no
nm_uv_id_2 session_id_4 1234587890 4 is_robot_no
nm_uv_id_2 session_id_4 1234587890 5 is_robot_no
nm_uv_id_2 session_id_4 1234587890 6 is_robot_no
nm_uv_id_2 session_id_4 1234587890 7 is_robot_no
nm_uv_id_2 session_id_4 1234587890 8 is_robot_no
nm_uv_id_2 session_id_4 1234587890 9 is_robot_no
nm_uv_id_2 session_id_5 1234587890 1 is_robot_no
nm_uv_id_2 session_id_5 1234587890 2 is_robot_no
nm_uv_id_2 session_id_5 1234587890 3 is_robot_yes
nm_uv_id_2 session_id_5 1234587890 4 is_robot_yes
nm_uv_id_2 session_id_5 1234587890 5 is_robot_no
nm_uv_id_2 session_id_5 123457890 6 is_robot_no
session_id_1 13 nm_uv_id_1 session_id_1 1234567890 9 is_robot_no
session_id_1 13 nm_uv_id_1 session_id_1 1234567890 9 is_robot_no
session_id_1 13 nm_uv_id_1 session_id_1 1234567890 9 is_robot_no
session_id_1 13 nm_uv_id_1 session_id_1 1234567890 9 is_robot_no
session_id_1 13 nm_uv_id_1 session_id_1 1234567890 9 is_robot_no
session_id_1 13 nm_uv_id_1 session_id_1 1234567890 9 is_robot_no
session_id_1 13 nm_uv_id_1 session_id_1 1234567890 9 is_robot_no
session_id_1 13 nm_uv_id_1 session_id_1 1234567890 9 is_robot_no
session_id_1 13 nm_uv_id_1 session_id_1 1234567890 9 is_robot_no
session_id_1 13 nm_uv_id_1 session_id_1 1234567890 9 is_robot_no
session_id_2 2 nm_uv_id_1 session_id_2 1234567892 2 is_robot_no
session_id_2 2 nm_uv_id_1 session_id_2 1234567892 2 is_robot_no
session_id_2 2 nm_uv_id_1 session_id_2 1234567892 2 is_robot_no
session_id_2 2 nm_uv_id_1 session_id_2 1234567892 2 is_robot_no
session_id_3 15 nm_uv_id_1 session_id_3 1234567890 1 is_robot_no
session_id_4 24 nm_uv_id_2 session_id_4 1234587890 9 is_robot_no
session_id_4 24 nm_uv_id_2 session_id_4 1234587890 9 is_robot_no
session_id_4 24 nm_uv_id_2 session_id_4 1234587890 9 is_robot_no
session_id_4 24 nm_uv_id_2 session_id_4 1234587890 9 is_robot_no
session_id_4 24 nm_uv_id_2 session_id_4 1234587890 9 is_robot_no
session_id_4 24 nm_uv_id_2 session_id_4 1234587890 9 is_robot_no
session_id_4 24 nm_uv_id_2 session_id_4 1234587890 9 is_robot_no
session_id_4 24 nm_uv_id_2 session_id_4 1234587890 9 is_robot_no
session_id_4 24 nm_uv_id_2 session_id_4 1234587890 9 is_robot_no
session_id_5 30 nm_uv_id_2 session_id_5 123457890 6 is_robot_no
session_id_5 30 nm_uv_id_2 session_id_5 123457890 6 is_robot_no
session_id_5 30 nm_uv_id_2 session_id_5 123457890 6 is_robot_no
session_id_5 30 nm_uv_id_2 session_id_5 123457890 6 is_robot_no
session_id_5 30 nm_uv_id_2 session_id_5 123457890 6 is_robot_no
session_id_5 30 nm_uv_id_2 session_id_5 123457890 6 is_robot_no
我不理解为什么减缩器总是为一个特定的键写入相同的键值对。我尝试了几种方法,似乎第一个for循环(我在其中进行缓存)可以很好地工作。当我写context.write(key,value)时,我得到了预期的输出。
然而,第二次,当我想在第二个for循环中使用缓存时,程序为我写了一些奇怪的东西
有人能帮忙吗?这是重复使用相同的
文本
缓冲区作为优化。因此,您需要手动克隆以缓存它
我只想更改您的缓存循环:
for (Text value : values) { cache.add(new Text(value)); }