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| ## Flink-TsFile-Connector | |
| ### About Flink-TsFile-Connector | |
| Flink-TsFile-Connector implements the support of Flink for external data sources of Tsfile type. | |
| This enables users to read and write Tsfile by Flink via DataStream/DataSet API. | |
| With this connector, you can | |
| * load a single TsFile or multiple TsFiles(only for DataSet), from either the local file system or hdfs, into Flink | |
| * load all files in a specific directory, from either the local file system or hdfs, into Flink | |
| ### Quick Start | |
| #### TsFileInputFormat Example | |
| 1. create TsFileInputFormat with default RowRowRecordParser. | |
| ```java | |
| String[] filedNames = { | |
| QueryConstant.RESERVED_TIME, | |
| "device_1.sensor_1", | |
| "device_1.sensor_2", | |
| "device_1.sensor_3", | |
| "device_2.sensor_1", | |
| "device_2.sensor_2", | |
| "device_2.sensor_3" | |
| }; | |
| TypeInformation[] typeInformations = new TypeInformation[] { | |
| Types.LONG, | |
| Types.FLOAT, | |
| Types.INT, | |
| Types.INT, | |
| Types.FLOAT, | |
| Types.INT, | |
| Types.INT | |
| }; | |
| List<Path> paths = Arrays.stream(filedNames) | |
| .filter(s -> !s.equals(QueryConstant.RESERVED_TIME)) | |
| .map(Path::new) | |
| .collect(Collectors.toList()); | |
| RowTypeInfo rowTypeInfo = new RowTypeInfo(typeInformations, filedNames); | |
| QueryExpression queryExpression = QueryExpression.create(paths, null); | |
| RowRowRecordParser parser = RowRowRecordParser.create(rowTypeInfo, queryExpression.getSelectedSeries()); | |
| TsFileInputFormat inputFormat = new TsFileInputFormat<>(queryExpression, parser); | |
| ``` | |
| 2. Read data from the input format and print to stdout: | |
| DataStream: | |
| ```java | |
| StreamExecutionEnvironment senv = StreamExecutionEnvironment.getExecutionEnvironment(); | |
| inputFormat.setFilePath("source.tsfile"); | |
| DataStream<Row> source = senv.createInput(inputFormat); | |
| DataStream<String> rowString = source.map(Row::toString); | |
| Iterator<String> result = DataStreamUtils.collect(rowString); | |
| while (result.hasNext()) { | |
| System.out.println(result.next()); | |
| } | |
| ``` | |
| DataSet: | |
| ```java | |
| ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); | |
| inputFormat.setFilePath("source.tsfile"); | |
| DataSet<Row> source = env.createInput(inputFormat); | |
| List<String> result = source.map(Row::toString).collect(); | |
| for (String s : result) { | |
| System.out.println(s); | |
| } | |
| ``` | |
| #### Example of TSRecordOutputFormat | |
| 1. create TSRecordOutputFormat with default RowTSRecordConverter. | |
| ```java | |
| String[] filedNames = { | |
| QueryConstant.RESERVED_TIME, | |
| "device_1.sensor_1", | |
| "device_1.sensor_2", | |
| "device_1.sensor_3", | |
| "device_2.sensor_1", | |
| "device_2.sensor_2", | |
| "device_2.sensor_3" | |
| }; | |
| TypeInformation[] typeInformations = new TypeInformation[] { | |
| Types.LONG, | |
| Types.LONG, | |
| Types.LONG, | |
| Types.LONG, | |
| Types.LONG, | |
| Types.LONG, | |
| Types.LONG | |
| }; | |
| RowTypeInfo rowTypeInfo = new RowTypeInfo(typeInformations, filedNames); | |
| Schema schema = new Schema(); | |
| schema.extendTemplate("template", new MeasurementSchema("sensor_1", TSDataType.INT64, TSEncoding.TS_2DIFF)); | |
| schema.extendTemplate("template", new MeasurementSchema("sensor_2", TSDataType.INT64, TSEncoding.TS_2DIFF)); | |
| schema.extendTemplate("template", new MeasurementSchema("sensor_3", TSDataType.INT64, TSEncoding.TS_2DIFF)); | |
| RowTSRecordConverter converter = new RowTSRecordConverter(rowTypeInfo); | |
| TSRecordOutputFormat<Row> outputFormat = new TSRecordOutputFormat<>(schema, converter); | |
| ``` | |
| 2. write data via the output format: | |
| DataStream: | |
| ```java | |
| StreamExecutionEnvironment senv = StreamExecutionEnvironment.getExecutionEnvironment(); | |
| senv.setParallelism(1); | |
| List<Tuple7> data = new ArrayList<>(7); | |
| data.add(new Tuple7(1L, 2L, 3L, 4L, 5L, 6L, 7L)); | |
| data.add(new Tuple7(2L, 3L, 4L, 5L, 6L, 7L, 8L)); | |
| data.add(new Tuple7(3L, 4L, 5L, 6L, 7L, 8L, 9L)); | |
| data.add(new Tuple7(4L, 5L, 6L, 7L, 8L, 9L, 10L)); | |
| data.add(new Tuple7(6L, 6L, 7L, 8L, 9L, 10L, 11L)); | |
| data.add(new Tuple7(7L, 7L, 8L, 9L, 10L, 11L, 12L)); | |
| data.add(new Tuple7(8L, 8L, 9L, 10L, 11L, 12L, 13L)); | |
| outputFormat.setOutputFilePath(new org.apache.flink.core.fs.Path(path)); | |
| DataStream<Tuple7> source = senv.fromCollection( | |
| data, Types.TUPLE(Types.LONG, Types.LONG, Types.LONG, Types.LONG, Types.LONG, Types.LONG, Types.LONG)); | |
| source.map(t -> { | |
| Row row = new Row(7); | |
| for (int i = 0; i < 7; i++) { | |
| row.setField(i, t.getField(i)); | |
| } | |
| return row; | |
| }).returns(rowTypeInfo).writeUsingOutputFormat(outputFormat); | |
| senv.execute(); | |
| ``` | |
| DataSet: | |
| ```java | |
| ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); | |
| env.setParallelism(1); | |
| List<Tuple7> data = new ArrayList<>(7); | |
| data.add(new Tuple7(1L, 2L, 3L, 4L, 5L, 6L, 7L)); | |
| data.add(new Tuple7(2L, 3L, 4L, 5L, 6L, 7L, 8L)); | |
| data.add(new Tuple7(3L, 4L, 5L, 6L, 7L, 8L, 9L)); | |
| data.add(new Tuple7(4L, 5L, 6L, 7L, 8L, 9L, 10L)); | |
| data.add(new Tuple7(6L, 6L, 7L, 8L, 9L, 10L, 11L)); | |
| data.add(new Tuple7(7L, 7L, 8L, 9L, 10L, 11L, 12L)); | |
| data.add(new Tuple7(8L, 8L, 9L, 10L, 11L, 12L, 13L)); | |
| DataSet<Tuple7> source = env.fromCollection( | |
| data, Types.TUPLE(Types.LONG, Types.LONG, Types.LONG, Types.LONG, Types.LONG, Types.LONG, Types.LONG)); | |
| source.map(t -> { | |
| Row row = new Row(7); | |
| for (int i = 0; i < 7; i++) { | |
| row.setField(i, t.getField(i)); | |
| } | |
| return row; | |
| }).returns(rowTypeInfo).write(outputFormat, path); | |
| env.execute(); | |
| ``` | |