TsFile-Hadoop-Connector User Guide

关于 TsFile-Hadoop-Connector

TsFile-Hadoop-Connector 实现了 Hadoop 对 Tsfile 类型的外部数据源的支持。这使用户能够通过Hadoop读取，写入和查询Tsfile。

使用此连接器，咱们就可以：

• 将单个 TsFile 从本地文件系统或 hdfs 加载到 Hadoop 中
• 将特定目录中的所有文件（从本地文件系统或HDFS加载到Hadoop中）
• 将数据从 Hadoop 写入 TsFile

系统要求

数据类型对应

TSFInput格式说明

TSFInputFormat 从 tsfile 中提取数据，并将其格式化为 .MapWritable

假设我们要提取名为该设备的数据，该设备具有三个名为 、、 的传感器。d1s1s2s3

s1的类型为 ，的类型为 ，的类型为 。BOOLEANs2DOUBLEs3TEXT

结构将如下所示：MapWritable

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

在 Hadoop 的 Map 作业中，你可以按键获取任何你想要的值，如下所示：

mapwritable.get(new Text("s1"))

注意：中的所有键的类型均为 。MapWritableText

例子

阅读示例：计算总和

首先，我们应该告诉 InputFormat 我们想要从 tsfile 获得什么样的数据。

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，应指定映射器和化简器的输出键和值

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);

然后，和类是你如何处理类产生的。mapperreducerMapWritableTSFInputFormat

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：有关完整的代码，请参阅以下链接：https://github.com/apache/incubator-iotdb/blob/master/example/hadoop/src/main/java/org/apache/iotdb//hadoop/tsfile/TSFMRReadExample.java

写入示例：将平均值写入 Tsfile

除了 ，Hadoop-map-reduce 作业的其余配置代码几乎与上面相同。OutputFormatClass

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

然后，和类是你如何处理类产生的。mapperreducerMapWritableTSFInputFormat

1. public static class TSMapper extends Mapper<NullWritable, MapWritable, Text, MapWritable> {
2. @Override
3. protected void map(NullWritable key, MapWritable value,
4. Mapper<NullWritable, MapWritable, Text, MapWritable>.Context context)
5. throws IOException, InterruptedException {
7. Text deltaObjectId = (Text) value.get(new Text("device_id"));
8. long timestamp = ((LongWritable)value.get(new Text("timestamp"))).get();
9. if (timestamp % 100000 == 0) {
10. context.write(deltaObjectId, new MapWritable(value));
11. }
12. }
13. }
15. /**
16. * This reducer calculate the average value.
17. */
18. public static class TSReducer extends Reducer<Text, MapWritable, NullWritable, HDFSTSRecord> {
20. @Override
21. protected void reduce(Text key, Iterable<MapWritable> values,
22. Reducer<Text, MapWritable, NullWritable, HDFSTSRecord>.Context context) throws IOException, InterruptedException {
23. long sensor1_value_sum = 0;
24. long sensor2_value_sum = 0;
25. double sensor3_value_sum = 0;
26. long num = 0;
27. for (MapWritable value : values) {
28. num++;
29. sensor1_value_sum += ((LongWritable)value.get(new Text("sensor_1"))).get();
30. sensor2_value_sum += ((LongWritable)value.get(new Text("sensor_2"))).get();
31. sensor3_value_sum += ((DoubleWritable)value.get(new Text("sensor_3"))).get();
32. }
33. HDFSTSRecord tsRecord = new HDFSTSRecord(1L, key.toString());
34. DataPoint dPoint1 = new LongDataPoint("sensor_1", sensor1_value_sum / num);
35. DataPoint dPoint2 = new LongDataPoint("sensor_2", sensor2_value_sum / num);
36. DataPoint dPoint3 = new DoubleDataPoint("sensor_3", sensor3_value_sum / num);
37. tsRecord.addTuple(dPoint1);
38. tsRecord.addTuple(dPoint2);
39. tsRecord.addTuple(dPoint3);
40. context.write(NullWritable.get(), tsRecord);
41. }
42. }

注意：有关完整的代码，请参阅以下链接：https://github.com/apache/incubator-iotdb/blob/master/example/hadoop/src/main/java/org/apache/iotdb//hadoop/tsfile/TSMRWriteExample.java