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

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

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：

1. {
2. "time_stamp": 10000000,
3. "device_id":  d1,
4. "s1":         true,
5. "s2":         3.14,
6. "s3":         "middle"
7. }

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

1. // configure reading time enable
2. TSFInputFormat.setReadTime(job, true);
3. // configure reading deviceId enable
4. TSFInputFormat.setReadDeviceId(job, true);
5. // configure reading which deltaObjectIds
6. String[] deviceIds = {"device_1"};
7. TSFInputFormat.setReadDeviceIds(job, deltaObjectIds);
8. // configure reading which measurementIds
9. String[] measurementIds = {"sensor_1", "sensor_2", "sensor_3"};
10. TSFInputFormat.setReadMeasurementIds(job, measurementIds);

然后，必须指定mapper和reducer输出的键和值类型

1. // set inputformat and outputformat
2. job.setInputFormatClass(TSFInputFormat.class);
3. // set mapper output key and value
4. job.setMapOutputKeyClass(Text.class);
5. job.setMapOutputValueClass(DoubleWritable.class);
6. // set reducer output key and value
7. job.setOutputKeyClass(Text.class);
8. 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。

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

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

8. Text deltaObjectId = (Text) value.get(new Text("device_id"));
9. context.write(deltaObjectId, (DoubleWritable) value.get(new Text("sensor_3")));
10. }
11. }

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

15. @Override
16. protected void reduce(Text key, Iterable<DoubleWritable> values,
17. Reducer<Text, DoubleWritable, Text, DoubleWritable>.Context context)
18. throws IOException, InterruptedException {

20. double sum = 0;
21. for (DoubleWritable value : values) {
22. sum = sum + value.get();
23. }
24. context.write(key, new DoubleWritable(sum));
25. }
26. }

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

1. job.setOutputFormatClass(TSFOutputFormat.class);
2. // set reducer output key and value
3. job.setOutputKeyClass(NullWritable.class);
4. job.setOutputValueClass(HDFSTSRecord.class);

然后，是包含具体的处理数据逻辑的mapper和reducer类。

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

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

8. Text deltaObjectId = (Text) value.get(new Text("device_id"));
9. long timestamp = ((LongWritable)value.get(new Text("timestamp"))).get();
10. if (timestamp % 100000 == 0) {
11. context.write(deltaObjectId, new MapWritable(value));
12. }
13. }
14. }

16. /**
17. * This reducer calculate the average value.
18. */
19. public static class TSReducer extends Reducer<Text, MapWritable, NullWritable, HDFSTSRecord> {

21. @Override
22. protected void reduce(Text key, Iterable<MapWritable> values,
23. Reducer<Text, MapWritable, NullWritable, HDFSTSRecord>.Context context) throws IOException, InterruptedException {
24. long sensor1_value_sum = 0;
25. long sensor2_value_sum = 0;
26. double sensor3_value_sum = 0;
27. long num = 0;
28. for (MapWritable value : values) {
29. num++;
30. sensor1_value_sum += ((LongWritable)value.get(new Text("sensor_1"))).get();
31. sensor2_value_sum += ((LongWritable)value.get(new Text("sensor_2"))).get();
32. sensor3_value_sum += ((DoubleWritable)value.get(new Text("sensor_3"))).get();
33. }
34. HDFSTSRecord tsRecord = new HDFSTSRecord(1L, key.toString());
35. DataPoint dPoint1 = new LongDataPoint("sensor_1", sensor1_value_sum / num);
36. DataPoint dPoint2 = new LongDataPoint("sensor_2", sensor2_value_sum / num);
37. DataPoint dPoint3 = new DoubleDataPoint("sensor_3", sensor3_value_sum / num);
38. tsRecord.addTuple(dPoint1);
39. tsRecord.addTuple(dPoint2);
40. tsRecord.addTuple(dPoint3);
41. context.write(NullWritable.get(), tsRecord);
42. }
43. }

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>