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## Flink-IoTDB-Connector
IoTDB integration for [Apache Flink](https://flink.apache.org/). This module includes the IoTDB sink that allows a flink job to write events into timeseries, and the IoTDB source allowing reading data from IoTDB.
### IoTDBSink
To use the `IoTDBSink`, you need construct an instance of it by specifying `IoTDBSinkOptions` and `IoTSerializationSchema` instances.
The `IoTDBSink` send only one event after another by default, but you can change to batch by invoking `withBatchSize(int)`.
#### Example
This example shows a case that sends data to a IoTDB server from a Flink job:
- A simulated Source `SensorSource` generates data points per 1 second.
- Flink uses `IoTDBSink` to consume the generated data points and write the data into IoTDB.
It is noteworthy that to use IoTDBSink, schema auto-creation in IoTDB should be enabled.
```java
import org.apache.iotdb.flink.options.IoTDBSinkOptions;
import org.apache.iotdb.tsfile.file.metadata.enums.CompressionType;
import org.apache.iotdb.tsfile.file.metadata.enums.TSDataType;
import org.apache.iotdb.tsfile.file.metadata.enums.TSEncoding;
import com.google.common.collect.Lists;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import java.security.SecureRandom;
import java.util.HashMap;
import java.util.Map;
import java.util.Random;
public class FlinkIoTDBSink {
public static void main(String[] args) throws Exception {
// run the flink job on local mini cluster
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
IoTDBSinkOptions options = new IoTDBSinkOptions();
options.setHost("127.0.0.1");
options.setPort(6667);
options.setUser("root");
options.setPassword("root");
// If the server enables auto_create_schema, then we do not need to register all timeseries
// here.
options.setTimeseriesOptionList(
Lists.newArrayList(
new IoTDBSinkOptions.TimeseriesOption(
"root.sg.d1.s1", TSDataType.DOUBLE, TSEncoding.GORILLA, CompressionType.SNAPPY)));
IoTSerializationSchema serializationSchema = new DefaultIoTSerializationSchema();
IoTDBSink ioTDBSink =
new IoTDBSink(options, serializationSchema)
// enable batching
.withBatchSize(10)
// how many connections to the server will be created for each parallelism
.withSessionPoolSize(3);
env.addSource(new SensorSource())
.name("sensor-source")
.setParallelism(1)
.addSink(ioTDBSink)
.name("iotdb-sink");
env.execute("iotdb-flink-example");
}
private static class SensorSource implements SourceFunction<Map<String, String>> {
boolean running = true;
Random random = new SecureRandom();
@Override
public void run(SourceContext context) throws Exception {
while (running) {
Map<String, String> tuple = new HashMap();
tuple.put("device", "root.sg.d1");
tuple.put("timestamp", String.valueOf(System.currentTimeMillis()));
tuple.put("measurements", "s1");
tuple.put("types", "DOUBLE");
tuple.put("values", String.valueOf(random.nextDouble()));
context.collect(tuple);
Thread.sleep(1000);
}
}
@Override
public void cancel() {
running = false;
}
}
}
```
#### Usage
* Launch the IoTDB server.
* Run `org.apache.iotdb.flink.FlinkIoTDBSink.java` to run the flink job on local mini cluster.
### IoTDBSource
To use the `IoTDBSource`, you need to construct an instance of `IoTDBSource` by specifying `IoTDBSourceOptions`
and implementing the abstract method `convert()` in `IoTDBSource`. The `convert` methods defines how
you want the row data to be transformed.
#### Example
This example shows a case where data are read from IoTDB.
```java
import org.apache.iotdb.flink.options.IoTDBSourceOptions;
import org.apache.iotdb.rpc.IoTDBConnectionException;
import org.apache.iotdb.rpc.StatementExecutionException;
import org.apache.iotdb.rpc.TSStatusCode;
import org.apache.iotdb.session.Session;
import org.apache.iotdb.tsfile.file.metadata.enums.CompressionType;
import org.apache.iotdb.tsfile.file.metadata.enums.TSDataType;
import org.apache.iotdb.tsfile.file.metadata.enums.TSEncoding;
import org.apache.iotdb.tsfile.read.common.RowRecord;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import java.util.ArrayList;
import java.util.List;
public class FlinkIoTDBSource {
static final String LOCAL_HOST = "127.0.0.1";
static final String ROOT_SG1_D1_S1 = "root.sg1.d1.s1";
static final String ROOT_SG1_D1 = "root.sg1.d1";
public static void main(String[] args) throws Exception {
prepareData();
// run the flink job on local mini cluster
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
IoTDBSourceOptions ioTDBSourceOptions =
new IoTDBSourceOptions("127.0.0.1", 6667, "root", "root",
"select s1 from " + ROOT_SG1_D1 + " align by device");
env.addSource(
new IoTDBSource<RowRecord>(ioTDBSourceOptions) {
@Override
public RowRecord convert(RowRecord rowRecord) {
return rowRecord;
}
})
.name("sensor-source")
.print()
.setParallelism(2);
env.execute();
}
/**
* Write some data to IoTDB
*/
private static void prepareData() throws IoTDBConnectionException, StatementExecutionException {
Session session = new Session(LOCAL_HOST, 6667, "root", "root");
session.open(false);
try {
session.setStorageGroup("root.sg1");
if (!session.checkTimeseriesExists(ROOT_SG1_D1_S1)) {
session.createTimeseries(
ROOT_SG1_D1_S1, TSDataType.INT64, TSEncoding.RLE, CompressionType.SNAPPY);
List<String> measurements = new ArrayList<>();
List<TSDataType> types = new ArrayList<>();
measurements.add("s1");
measurements.add("s2");
measurements.add("s3");
types.add(TSDataType.INT64);
types.add(TSDataType.INT64);
types.add(TSDataType.INT64);
for (long time = 0; time < 100; time++) {
List<Object> values = new ArrayList<>();
values.add(1L);
values.add(2L);
values.add(3L);
session.insertRecord(ROOT_SG1_D1, time, measurements, types, values);
}
}
} catch (StatementExecutionException e) {
if (e.getStatusCode() != TSStatusCode.PATH_ALREADY_EXIST_ERROR.getStatusCode()) {
throw e;
}
}
}
}
```
#### Usage
Launch the IoTDB server.
Run org.apache.iotdb.flink.FlinkIoTDBSource.java to run the flink job on local mini cluster.