TsFile-Flink-Connector 对Tsfile类型的外部数据源实现 Flink 的支持。 这使用户可以通过 Flink DataStream/DataSet 进行读取，写入和查询。

使用此连接器，您可以

  • 从本地文件系统或 hdfs 加载单个或多个 TsFile （只支持以DataSet的形式）到 Flink 。
  • 将本地文件系统或 hdfs 中特定目录中的所有文件加载到 Flink 中。

快速开始

TsFileInputFormat 示例

  1. 使用默认的 RowRowRecordParser 创建 TsFileInputFormat 。
1. String[] filedNames = {
2. QueryConstant.RESERVED_TIME,
3. "device_1.sensor_1",
4. "device_1.sensor_2",
5. "device_1.sensor_3",
6. "device_2.sensor_1",
7. "device_2.sensor_2",
8. "device_2.sensor_3"
9. };
10. TypeInformation[] typeInformations = new TypeInformation[] {
11. Types.LONG,
12. Types.FLOAT,
13. Types.INT,
14. Types.INT,
15. Types.FLOAT,
16. Types.INT,
17. Types.INT
18. };
19. List<Path> paths = Arrays.stream(filedNames)
20. .filter(s -> !s.equals(QueryConstant.RESERVED_TIME))
21. .map(Path::new)
22. .collect(Collectors.toList());
23. RowTypeInfo rowTypeInfo = new RowTypeInfo(typeInformations, filedNames);
24. QueryExpression queryExpression = QueryExpression.create(paths, null);
25. RowRowRecordParser parser = RowRowRecordParser.create(rowTypeInfo, queryExpression.getSelectedSeries());
26. TsFileInputFormat inputFormat = new TsFileInputFormat<>(queryExpression, parser);
  1. 从输入格式读取数据并打印到标准输出 stdout：

DataStream：

1. StreamExecutionEnvironment senv = StreamExecutionEnvironment.getExecutionEnvironment();
2. inputFormat.setFilePath("source.tsfile");
3. DataStream<Row> source = senv.createInput(inputFormat);
4. DataStream<String> rowString = source.map(Row::toString);
5. Iterator<String> result = DataStreamUtils.collect(rowString);
6. while (result.hasNext()) {
7. System.out.println(result.next());
8. }

DataSet：

1. ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
2. inputFormat.setFilePath("source.tsfile");
3. DataSet<Row> source = env.createInput(inputFormat);
4. List<String> result = source.map(Row::toString).collect();
5. for (String s : result) {
6. System.out.println(s);
7. }

TSRecordOutputFormat 示例

  1. 使用默认的 RowTSRecordConverter 创建 TSRecordOutputFormat 。
1. String[] filedNames = {
2. QueryConstant.RESERVED_TIME,
3. "device_1.sensor_1",
4. "device_1.sensor_2",
5. "device_1.sensor_3",
6. "device_2.sensor_1",
7. "device_2.sensor_2",
8. "device_2.sensor_3"
9. };
10. TypeInformation[] typeInformations = new TypeInformation[] {
11. Types.LONG,
12. Types.LONG,
13. Types.LONG,
14. Types.LONG,
15. Types.LONG,
16. Types.LONG,
17. Types.LONG
18. };
19. RowTypeInfo rowTypeInfo = new RowTypeInfo(typeInformations, filedNames);
20. Schema schema = new Schema();
21. schema.extendTemplate("template", new MeasurementSchema("sensor_1", TSDataType.INT64, TSEncoding.TS_2DIFF));
22. schema.extendTemplate("template", new MeasurementSchema("sensor_2", TSDataType.INT64, TSEncoding.TS_2DIFF));
23. schema.extendTemplate("template", new MeasurementSchema("sensor_3", TSDataType.INT64, TSEncoding.TS_2DIFF));
24. RowTSRecordConverter converter = new RowTSRecordConverter(rowTypeInfo);
25. TSRecordOutputFormat<Row> outputFormat = new TSRecordOutputFormat<>(schema, converter);
  1. 通过输出格式写数据：

DataStream:

1. StreamExecutionEnvironment senv = StreamExecutionEnvironment.getExecutionEnvironment();
2. senv.setParallelism(1);
3. List<Tuple7> data = new ArrayList<>(7);
4. data.add(new Tuple7(1L, 2L, 3L, 4L, 5L, 6L, 7L));
5. data.add(new Tuple7(2L, 3L, 4L, 5L, 6L, 7L, 8L));
6. data.add(new Tuple7(3L, 4L, 5L, 6L, 7L, 8L, 9L));
7. data.add(new Tuple7(4L, 5L, 6L, 7L, 8L, 9L, 10L));
8. data.add(new Tuple7(6L, 6L, 7L, 8L, 9L, 10L, 11L));
9. data.add(new Tuple7(7L, 7L, 8L, 9L, 10L, 11L, 12L));
10. data.add(new Tuple7(8L, 8L, 9L, 10L, 11L, 12L, 13L));
11. outputFormat.setOutputFilePath(new org.apache.flink.core.fs.Path(path));
12. DataStream<Tuple7> source = senv.fromCollection(
13. data, Types.TUPLE(Types.LONG, Types.LONG, Types.LONG, Types.LONG, Types.LONG, Types.LONG, Types.LONG));
14. source.map(t -> {
15. Row row = new Row(7);
16. for (int i = 0; i < 7; i++) {
17. row.setField(i, t.getField(i));
18. }
19. return row;
20. }).returns(rowTypeInfo).writeUsingOutputFormat(outputFormat);
21. senv.execute();

DataSet:

1. ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
2. env.setParallelism(1);
3. List<Tuple7> data = new ArrayList<>(7);
4. data.add(new Tuple7(1L, 2L, 3L, 4L, 5L, 6L, 7L));
5. data.add(new Tuple7(2L, 3L, 4L, 5L, 6L, 7L, 8L));
6. data.add(new Tuple7(3L, 4L, 5L, 6L, 7L, 8L, 9L));
7. data.add(new Tuple7(4L, 5L, 6L, 7L, 8L, 9L, 10L));
8. data.add(new Tuple7(6L, 6L, 7L, 8L, 9L, 10L, 11L));
9. data.add(new Tuple7(7L, 7L, 8L, 9L, 10L, 11L, 12L));
10. data.add(new Tuple7(8L, 8L, 9L, 10L, 11L, 12L, 13L));
11. DataSet<Tuple7> source = env.fromCollection(
12. data, Types.TUPLE(Types.LONG, Types.LONG, Types.LONG, Types.LONG, Types.LONG, Types.LONG, Types.LONG));
13. source.map(t -> {
14. Row row = new Row(7);
15. for (int i = 0; i < 7; i++) {
16. row.setField(i, t.getField(i));
17. }
18. return row;
19. }).returns(rowTypeInfo).write(outputFormat, path);
20. env.execute();