com.mongodb.MongoSocketOpenException: Exception opening socket at com.mongodb.internal.connection.SocketStream.open (SocketStream.java:70) at com.mongodb.internal.connection.InternalStreamConnection.open(InternalStreamConnection.java com.mongodb.internal.connection.SocketStreamHelper.initialize(SocketStreamHelper.java:64) at com.mongodb.internal.connection.SocketStream.initializeSocket (SocketStream.java:79) at com.mongodb.internal.connection.SocketStream.open(SocketStream.java:65) .
继承Stream,针对Socket的读写,本质上就是调用了Socket的读写方法 class SocketStream : public Stream { public: typedef std::shared_ptr<SocketStream> ptr; // owner表示是否直接接管sock,负责它的close SocketStream(Socket: :ptr sock, bool owner = true); ~SocketStream(); virtual int read(void* buffer, size_t length return * @retval >0 返回实际接收到的数据长度 * @retval =0 socket被远端关闭 * @retval <0 socket错误 */ int SocketStream return * @retval >0 返回实际接收到的数据长度 * @retval =0 socket被远端关闭 * @retval <0 socket错误 */ int SocketStream
wordCounts.print() //5.启动流式处理 ssc.start() ssc.awaitTermination() 18.4 流式数据输入 (1)内置数据源(StreamingContext) socketStream
instantiate abstract class # IStream with abstract methods read, write 抽象类的目的就是让别的类继承它并实现特定的抽象方法: class SocketStream
3.3 Uri 对string uri的解析,具体请参看源码 3.4 HttpSession 继承自SocketStream,实现了在套接字流上读取HTTP请求与发送HTTP响应的功能,在读取HTTP请求时需要借助 class HttpSession : public SocketStream { public: typedef std::shared_ptr<HttpSession> ptr; 发送响应 session->sendResponse(rsp); }while(m_isKeepalive); } 3.6 HttpConnection HTTP客户端,继承自SocketStream class HttpConnection : public SocketStream{ public: HttpResponse::ptr recvResponse(); // 接收响应 int sendRequest(HttpRequest::ptr req); // 发送请求 // ... }; 为了方便使用,封装了几个发送请求的方法 class HttpConnection : public SocketStream
env.setParallelism(1); // env.getConfig().setAutoWatermarkInterval(100); DataStream<String> socketStream = env.socketTextStream("localhost", 9999); DataStream<Tuple2<String, Long>> resultStream = socketStream env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); env.setParallelism(1); DataStream<String> socketStream SingleOutputStreamOperator<Tuple2<String, Long>> resultStream = socketStream // Time.seconds
spark.streaming.batchDuration = 5 spark.app.name = "seatunnel" spark.ui.port = 13000 } input { socketStream delimiter = "," } } output { stdout {} } Flink env { execution.parallelism = 1 } source { SocketStream
MessageWorker–>…–>joinTopology:629, ClientImpl–>sendJoinRequest, ClientImpl) @Nullable private T3<SocketStream
stream.Close(); }); // 得到客户端的流,然后给我们的SafeWriteStream,然后我们就可以进行转发了 var socketStream await Task.WhenAll( stream.CopyToAsync(socketStream, context.RequestAborted), socketStream.CopyToAsync(stream, context.RequestAborted) ); } catch (Exception
spark.streaming.batchDuration = 5 spark.app.name = "seatunnel" spark.ui.port = 13000 } input { socketStream } } output { stdout {} } Flink env { execution.parallelism = 1 } source { SocketStream
下面我们一步步将其改造成spark Streaming的 SocketStream。 在伪异步模式,我们是客户端通过TCP链接到服务端。
下面浪尖带领一步步将其改造成spark Streaming的 SocketStream。 在伪异步模式,我们是客户端通过TCP链接到服务端。
StorageLevel.MEMORY_AND_DISK_SER_2 ): ReceiverInputDStream[String] = withNamedScope("socket text stream") { socketStream [String](hostname, port, SocketReceiver.bytesToLines, storageLevel) } def socketStream[T: ClassTag](
Socket Stream SocketStream 是一个有趣的框架,专注于客户端和服务端数据的快速同步,它致力于前后端数据的实时更新。 SocketStream framework 在最近几月获得了很好的发展,未来一片光明。
StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment // 创建 SocketSource val socketStream node01",9999) // 对 stream 进行处理并按 key 聚合 val keyByStream: KeyedStream[(String, String), Tuple] = socketStream.map
Amazon Web Services, Elastic Beanstalk, Firebase, AngularJS, Coffeescript, Kafka, Simple Queue System, SocketStream
storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2 ): ReceiverInputDStream[String] = { socketStream } 1、StoageLevel是StorageLevel.MEMORY_AND_DISK_SER_2 2、使用SocketReceiver的bytesToLines把输入流转换成可遍历的数据 继续看socketStream
* @tparam T Type of the objects received (after converting bytes to objects) */ def socketStream
这里我利用mapWithState实现了一个计数的demo:程序的数据源是9999端口的socketStream,我向端口写入数据:随后输出状态数据:我是同时写入的前六行数据,所以这六条数据都在同一个RDD
Email √ Hbase √ √ Kafka √ Console √ Kudu √ √ Redis √ √ Stream FakeStream √ KafkaStream √ SocketSTream