介绍
Spark Streaming架构图
the micro-batch architecture of Spark Streaming
Execution of Spark Streaming within Spark’s components
JAVA代码示例
执行方式
1：修改log4j的日志级别为error,不然会打印太多的日志
2：将如下两个类导出一个jar
3: nc -lk ip port
3: 使用spark-submit提交任务
spark-submit --class com.spark.streaming.SimpleDemo test.jarJAVA代码
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.net.ConnectException;
import java.net.Socket;
import org.apache.spark.storage.StorageLevel;
import org.apache.spark.streaming.receiver.Receiver;
/**
* DateTime: 2015年6月18日 下午5:08:20
*
*/
public class JavaCustomReceiver extends Receiver<String> {
String host = null;
int port = -1;
Socket socket = null;
BufferedReader reader = null;
public JavaCustomReceiver(String host_, int port_) {
super(StorageLevel.MEMORY_ONLY());
host = host_;
port = port_;
}
public void onStart() {
// Start the thread that receives data over a connection
new Thread() {
@Override
public void run() {
receive();
}
}.start();
}
public void onStop() {
try {
if (socket != null) {
socket.close();
}
if (reader != null) {
reader.close();
}
}
catch (IOException e) {
e.printStackTrace();
}
}
/** Create a socket connection and receive data until receiver is stopped */
private void receive() {
String userInput = null;
try {
// connect to the server
socket = new Socket(host, port);
reader = new BufferedReader(new InputStreamReader(socket.getInputStream()));
// Until stopped or connection broken continue reading
while (!isStopped() && (userInput = reader.readLine()) != null) {
System.out.println("Received data '" + userInput + "'");
store(userInput);
}
reader.close();
socket.close();
// Restart in an attempt to connect again when server is active
// again
restart("Trying to connect again");
}
catch (ConnectException ce) {
// restart if could not connect to server
restart("Could not connect", ce);
}
catch (Throwable t) {
// restart if there is any other error
restart("Error receiving data", t);
}
}
}
