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<!-- TOC -->
## Outline

- TsFile-Hadoop-Connector User Guide
	- About TsFile-Hadoop-Connector
	- System Requirements
	- Data Type Correspondence
	- TSFInputFormat Explanation
	- Examples
		- Read Example: calculate the sum
		- Write Example: write the average into Tsfile

<!-- /TOC -->
# TsFile-Hadoop-Connector User Guide

## About TsFile-Hadoop-Connector

TsFile-Hadoop-Connector implements the support of Hadoop for external data sources of Tsfile type. This enables users to read, write and query Tsfile by Hadoop.

With this connector, you can
* load a single TsFile, from either the local file system or hdfs, into Hadoop
* load all files in a specific directory, from either the local file system or hdfs, into hadoop
* write data from Hadoop into TsFile

## System Requirements

|Hadoop Version | Java Version | TsFile Version|
|-------------  | ------------ |------------ |
| `2.7.3`       | `1.8`        | `0.8.0-SNAPSHOT`|

> Note: For more information about how to download and use TsFile, please see the following link: https://github.com/apache/incubator-iotdb/tree/master/tsfile.

## Data Type Correspondence

| TsFile data type | Hadoop writable |
| ---------------- | --------------- |
| BOOLEAN          | BooleanWritable |
| INT32            | IntWritable     |
| INT64       	   | LongWritable    |
| FLOAT       	   | FloatWritable   |
| DOUBLE      	   | DoubleWritable  |
| TEXT      	   | Text            |

## TSFInputFormat Explanation

TSFInputFormat extract data from tsfile and format them into records of `MapWritable`.

Supposing that we want to extract data of the device named `d1` which has three sensors named `s1`, `s2`, `s3`.

`s1`'s type is `BOOLEAN`, `s2`'s type is `DOUBLE`, `s3`'s type is `TEXT`.

The `MapWritable` struct will be like:
```
{
    "time_stamp": 10000000,
    "device_id":  d1,
    "s1":         true,
    "s2":         3.14,
    "s3":         "middle"
}
```

In the Map job of Hadoop, you can get any value you want by key as following:

`mapwritable.get(new Text("s1"))`
> Note: All the keys in `MapWritable` have type of `Text`.

## Examples

### Read Example: calculate the sum

First of all, we should tell InputFormat what kind of data we want from tsfile.

```
    // 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);
```

And then,the output key and value of mapper and reducer should be specified

```
    // 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);
```

Then, the `mapper` and `reducer` class is how you deal with the `MapWritable` produced by `TSFInputFormat` class.

```
  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));
    }
  }
```

> Note: For the complete code, please see the following link: https://github.com/apache/incubator-iotdb/blob/master/example/hadoop/src/main/java/org/apache/iotdb/tsfile/hadoop/TSFMRReadExample.java


### Write Example: write the average into Tsfile

Except for the `OutputFormatClass`, the rest of configuration code for hadoop map-reduce job is almost same as above.

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

Then, the `mapper` and `reducer` class is how you deal with the `MapWritable` produced by `TSFInputFormat` class.

```
    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);
        }
    }
```
> Note: For the complete code, please see the following link: https://github.com/apache/incubator-iotdb/blob/master/example/hadoop/src/main/java/org/apache/iotdb/tsfile/hadoop/TSMRWriteExample.java