docs/zh/UserGuide/Ecosystem Integration/MapReduce TsFile.md

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## Hadoop-TsFile


TsFile的Hadoop连接器实现了对Hadoop读取外部Tsfile类型的文件格式的支持。让用户可以使用Hadoop的map、reduce等操作对Tsfile文件进行读取、写入和查询。

有了这个连接器，用户可以
* 将单个Tsfile文件加载进Hadoop，不论文件是存储在本地文件系统或者是HDFS中
* 将某个特定目录下的所有文件加载进Hadoop，不论文件是存储在本地文件系统或者是HDFS中
* 将Hadoop处理完后的结果以Tsfile的格式保存

### 系统环境要求

|Hadoop 版本     | Java 版本     | TsFile 版本 |
|-------------  | ------------ |------------ |
| `2.7.3`       | `1.8`        | `0.10.0+`|

>注意：关于如何下载和使用Tsfile, 请参考以下链接: https://github.com/apache/iotdb/tree/master/tsfile.

### 数据类型对应关系

| TsFile 数据类型    | Hadoop writable |
| ---------------- | --------------- |
| BOOLEAN          | BooleanWritable |
| INT32            | IntWritable     |
| INT64            | LongWritable    |
| FLOAT            | FloatWritable   |
| DOUBLE           | DoubleWritable  |
| TEXT             | Text            |

### 关于TSFInputFormat的说明

TSFInputFormat继承了Hadoop中FileInputFormat类，重写了其中切片的方法。

目前的切片方法是根据每个ChunkGroup的中点的offset是否属于Hadoop所切片的startOffset和endOffset之间，来判断是否将该ChunkGroup放入此切片。

TSFInputFormat将tsfile中的数据以多个`MapWritable`记录的形式返回给用户。

假设我们想要从Tsfile中获得名为`d1`的设备的数据，该设备有三个传感器，名称分别为`s1`, `s2`, `s3`。

`s1`的类型是`BOOLEAN`, `s2`的类型是 `DOUBLE`, `s3`的类型是`TEXT`.

`MapWritable`的结构如下所示：
```
{
    "time_stamp": 10000000,
    "device_id":  d1,
    "s1":         true,
    "s2":         3.14,
    "s3":         "middle"
}
```

在Hadoop的Map job中，你可以采用如下方法获得你想要的任何值

`mapwritable.get(new Text("s1"))`
> 注意: `MapWritable`中所有的键值类型都是`Text`。

### 使用示例

#### 读示例: 求和

首先，我们需要在TSFInputFormat中配置我们需要哪些数据

```
// configure reading time enable
TSFInputFormat.setReadTime(job, true); 
// configure reading deviceId enable
TSFInputFormat.setReadDeviceId(job, true); 
// configure reading which deltaObjectIds
String[] deviceIds = {"device_1"};
TSFInputFormat.setReadDeviceIds(job, deltaObjectIds);
// configure reading which measurementIds
String[] measurementIds = {"sensor_1", "sensor_2", "sensor_3"};
TSFInputFormat.setReadMeasurementIds(job, measurementIds);
```

然后，必须指定mapper和reducer输出的键和值类型

```
// set inputformat and outputformat
job.setInputFormatClass(TSFInputFormat.class);
// set mapper output key and value
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(DoubleWritable.class);
// set reducer output key and value
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(DoubleWritable.class);
```
接着，就可以编写包含具体的处理数据逻辑的`mapper`和`reducer`类了。

```
public static class TSMapper extends Mapper<NullWritable, MapWritable, Text, DoubleWritable> {

	@Override
	protected void map(NullWritable key, MapWritable value,
	    Mapper<NullWritable, MapWritable, Text, DoubleWritable>.Context context)
	    throws IOException, InterruptedException {
	
	  Text deltaObjectId = (Text) value.get(new Text("device_id"));
	  context.write(deltaObjectId, (DoubleWritable) value.get(new Text("sensor_3")));
	}
}

public static class TSReducer extends Reducer<Text, DoubleWritable, Text, DoubleWritable> {

	@Override
	protected void reduce(Text key, Iterable<DoubleWritable> values,
	    Reducer<Text, DoubleWritable, Text, DoubleWritable>.Context context)
	    throws IOException, InterruptedException {
	
	  double sum = 0;
	  for (DoubleWritable value : values) {
	    sum = sum + value.get();
	  }
	  context.write(key, new DoubleWritable(sum));
	}
}
```

> 注意: 完整的代码示例可以在如下链接中找到：https://github.com/apache/iotdb/blob/master/example/hadoop/src/main/java/org/apache/iotdb/hadoop/tsfile/TSFMRReadExample.java


### 写示例: 计算平均数并写入Tsfile中

除了`OutputFormatClass`，剩下的配置代码跟上面的读示例是一样的

```
job.setOutputFormatClass(TSFOutputFormat.class);
// set reducer output key and value
job.setOutputKeyClass(NullWritable.class);
job.setOutputValueClass(HDFSTSRecord.class);
```

然后，是包含具体的处理数据逻辑的`mapper`和`reducer`类。

```
public static class TSMapper extends Mapper<NullWritable, MapWritable, Text, MapWritable> {

    @Override
    protected void map(NullWritable key, MapWritable value,
                       Mapper<NullWritable, MapWritable, Text, MapWritable>.Context context)
            throws IOException, InterruptedException {

        Text deltaObjectId = (Text) value.get(new Text("device_id"));
        long timestamp = ((LongWritable)value.get(new Text("timestamp"))).get();
        if (timestamp % 100000 == 0) {
            context.write(deltaObjectId, new MapWritable(value));
        }
    }
}

/**
 * This reducer calculate the average value.
 */
public static class TSReducer extends Reducer<Text, MapWritable, NullWritable, HDFSTSRecord> {

    @Override
    protected void reduce(Text key, Iterable<MapWritable> values,
                          Reducer<Text, MapWritable, NullWritable, HDFSTSRecord>.Context context) throws IOException, InterruptedException {
        long sensor1_value_sum = 0;
        long sensor2_value_sum = 0;
        double sensor3_value_sum = 0;
        long num = 0;
        for (MapWritable value : values) {
            num++;
            sensor1_value_sum += ((LongWritable)value.get(new Text("sensor_1"))).get();
            sensor2_value_sum += ((LongWritable)value.get(new Text("sensor_2"))).get();
            sensor3_value_sum += ((DoubleWritable)value.get(new Text("sensor_3"))).get();
        }
        HDFSTSRecord tsRecord = new HDFSTSRecord(1L, key.toString());
        DataPoint dPoint1 = new LongDataPoint("sensor_1", sensor1_value_sum / num);
        DataPoint dPoint2 = new LongDataPoint("sensor_2", sensor2_value_sum / num);
        DataPoint dPoint3 = new DoubleDataPoint("sensor_3", sensor3_value_sum / num);
        tsRecord.addTuple(dPoint1);
        tsRecord.addTuple(dPoint2);
        tsRecord.addTuple(dPoint3);
        context.write(NullWritable.get(), tsRecord);
    }
}
```
> 注意: 完整的代码示例可以在如下链接中找到：https://github.com/apache/iotdb/blob/master/example/hadoop/src/main/java/org/apache/iotdb/hadoop/tsfile/TSMRWriteExample.java