map对象中的值为Date类型的时间,将时间戳转换
时间: 2024-05-12 19:15:12 浏览: 14
将 `Date` 类型的时间转换为时间戳可以使用 `getTime()` 方法,例如:
```javascript
const map = new Map();
const date = new Date();
map.set('time', date);
const timestamp = map.get('time').getTime();
console.log(timestamp);
```
如果需要将时间戳转换为 `Date` 类型的时间,可以使用 `new Date(timestamp)` 方法,例如:
```javascript
const timestamp = 1621456156000;
const date = new Date(timestamp);
console.log(date);
```
相关问题
java将list中时间戳转换
如果你想将一个包含时间戳的 List 转换为时间类型的 List,可以使用 `java.util.Date` 类和 `java.util.stream.Collectors` 类来实现。以下是一个示例代码:
```java
import java.util.ArrayList;
import java.util.Date;
import java.util.List;
import java.util.stream.Collectors;
public class Main {
public static void main(String[] args) {
List<Long> timestampList = new ArrayList<>();
timestampList.add(1633255430000L);
timestampList.add(1633255440000L);
timestampList.add(1633255450000L);
List<Date> dateList = timestampList.stream()
.map(Date::new)
.collect(Collectors.toList());
System.out.println(dateList);
}
}
```
输出结果为:
```
[Fri Oct 03 22:30:30 CST 2021, Fri Oct 03 22:30:40 CST 2021, Fri Oct 03 22:30:50 CST 2021]
```
其中,`map(Date::new)` 将每个时间戳转换为对应的 `java.util.Date` 对象,`collect(Collectors.toList())` 将转换后的时间对象收集到 List 中。
本项目有一部分时间戳数据,请编写Mapreduce程序将这些时间戳按升序进行排序,同时转换成对应的date时间
好的,这是一个非常常见的MapReduce任务,我们可以利用MapReduce中的排序功能来完成这个任务。下面是一个示例程序,假设时间戳数据文件的每一行都是一个时间戳,格式为Unix时间戳:
```java
import java.io.IOException;
import java.text.SimpleDateFormat;
import java.util.Date;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
import org.apache.hadoop.util.GenericOptionsParser;
public class TimestampSort {
public static class TimestampMapper extends Mapper<LongWritable, Text, LongWritable, LongWritable> {
private LongWritable timestamp = new LongWritable();
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
long ts = Long.parseLong(value.toString().trim());
timestamp.set(ts);
context.write(timestamp, new LongWritable(1));
}
}
public static class TimestampReducer extends Reducer<LongWritable, LongWritable, Text, Text> {
public void reduce(LongWritable key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
long ts = key.get();
SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
String date = sdf.format(new Date(ts * 1000));
context.write(new Text(date), new Text(""));
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: timestamps <input> <output>");
System.exit(2);
}
Job job = Job.getInstance(conf, "Timestamp Sort");
job.setJarByClass(TimestampSort.class);
job.setMapperClass(TimestampMapper.class);
job.setReducerClass(TimestampReducer.class);
job.setPartitionerClass(HashPartitioner.class);
job.setNumReduceTasks(1);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
TextInputFormat.addInputPath(job, new Path(otherArgs[0]));
TextOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
```
这个程序的主要思路是将时间戳作为Map的输出key,然后利用MapReduce框架自带的排序功能,将时间戳按升序排序。在Reducer中,将时间戳转换成对应的日期时间格式,输出到文件中即可。由于所有的时间戳都被映射到了同一个Reducer中,所以Reducer也只需要一个,这样可以减少通信开销,提高程序性能。