Flink流处理API大合集：掌握flink流处理API技术，看这一篇就够了

注：本文内容为纯干货，字数较多，建议先点赞收藏慢慢学习研读！

前言

一个Flink应用程序开发的步骤大致为五个步骤：构建执行环境、获取数据源、操作数据源、输出到外部系统、触发程序执行。由这五个模块组成了一个flink任务，接下来围绕着每个模块对应的API进行梳理。
以下所有的代码案例都已收录在本人的Gitee仓库，有需要的同学点击链接直接获取：
Gitee地址：https://gitee.com/xiaoZcode/flink_test

一、构建流执行环境（Environment）

getExecutionEnvironment()

创建一个执行环境，表示当前执行程序的上下文。 如果程序是独立调用的，则此方法返回本地执行环境；如果从命令行客户端调用程序以提交到集群，则此方法返回此集群的执行环境。它会根据查询运行的方式决定返回什么样的运行环境，是最常用的一种创建执行环境的方式。

代码如下：

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

StreamExecutionEnvironment env =StreamExecutionEnvironment.getExecutionEnvironment();

createLocalEnvironment()

返回本地执行环境，需要在调用时指定默认的并行度。

代码如下：

LocalStreamEnvironment env = StreamExecutionEnvironment.createLocalEnvironment(1);

createRemoteEnvironment()

返回集群执行环境，将 Jar 提交到远程服务器。需要在调用时指定 JobManager的 IP 和端口号，并指定要在集群中运行的 Jar 包。

代码如下：

StreamExecutionEnvironment env = 
StreamExecutionEnvironment.createRemoteEnvironment("jobmanage-hostname", 6123, "YOURPATH//xxx.jar");

二、加载数据源（Source）

案例场景：

工业物联网的背景下，收集传感器的温度值，将收集到不同传感器的温度值进行计算分析操作。
注：以下代码都围绕此场景进行编写，获取更完整源代码请移步文章开头部分。

创建传感器对象：SensorReading

public class SensorReading {

    private String id;
    private Long timestamp;
    private Double temperature;

    public SensorReading() {

    }

    public SensorReading(String id, Long timestamp, Double temperature) {
        this.id = id;
        this.timestamp = timestamp;
        this.temperature = temperature;
    }

    public String getId() {
        return id;
    }

    public void setId(String id) {
        this.id = id;
    }

    public Long getTimestamp() {
        return timestamp;
    }

    public void setTimestamp(Long timestamp) {
        this.timestamp = timestamp;
    }

    public Double getTemperature() {
        return temperature;
    }

    public void setTemperature(Double temperature) {
        this.temperature = temperature;
    }

    @Override
    public String toString() {
        return "SensorReading{" +
                "id='" + id + '\'' +
                ", timestamp=" + timestamp +
                ", temperature=" + temperature +
                '}';
    }
}

从集合读取数据

public class SourceTest1_Collection {
    public static void main(String[] args) throws Exception {
        // 创建执行环境
        StreamExecutionEnvironment env=StreamExecutionEnvironment.getExecutionEnvironment();
        //设置并行度为 1
        env.setParallelism(1);

        //从集合中读取数据
        DataStream<SensorReading> dataStream  = env.fromCollection(Arrays.asList(
                new SensorReading("sensor_1", 1547718199L, 35.8),
                new SensorReading("sensor_2", 1547718199L, 35.0),
                new SensorReading("sensor_3", 1547718199L, 38.8),
                new SensorReading("sensor_4", 1547718199L, 39.8)
        ));

        DataStream<Integer> integerDataStream = env.fromElements(1, 2, 3, 4, 5, 789);

        //打印输出
        dataStream.print("data");
        integerDataStream.print("int");

        //执行程序
        env.execute();
    }
}

从文件读取数据

从文件中获取数据源的核心代码部分：

DataStream<String> dataStream = env.readTextFile("xxx ");

public class SourceTest2_File {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env=StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //从文件读取数据
        DataStream<String> dataStream = env.readTextFile("sensor.txt");

         dataStream.print();

         env.execute();
    }
}

从Kafka读取数据

首先需要引入Kafka的以来到工程中

<dependency>
 	<groupId>org.apache.flink</groupId>
 	<artifactId>flink-connector-kafka-0.11_2.12</artifactId>
 	<version>1.10.1</version>
</dependency>

public class SourceTest3_Kafka {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env=StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        Properties properties=new Properties();
        properties.setProperty("bootstrap.servers","localhost:9092");
        properties.setProperty("group.id","consumer-group");
        properties.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        properties.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        properties.setProperty("auto.offset.reset","latest");

        DataStream<String> dataStream=env.addSource(new FlinkKafkaConsumer011<String>("sensor",new SimpleStringSchema(),properties));

        dataStream.print();

        env.execute();

    }
}

自定义数据源Source

除了从集合、文件以及Kafka中获取数据外，还给我们提供了一个自定义source的方式，需要传入sourceFunction函数。核心代码如下：

DataStream<SensorReading> dataStream = env.addSource( new MySensor());

public class SourceTest4_UDF {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env=StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //从文件读取数据
        DataStream<SensorReading> dataStream = env.addSource(new MySensorSource());

        dataStream.print();

        env.execute();
    }

    // 实现自定义数据源
    public static class MySensorSource implements SourceFunction<SensorReading>{
        // 定义一个标记位,控制数据产生
        private boolean running = true;

        @Override
        public void run(SourceContext<SensorReading> ctv) throws Exception {
            // 随机数
            Random random=new Random();

            //设置10个初始温度
            HashMap<String, Double> sensorTempMap = new HashMap<>();
            for (int i = 0; i < 10; i++) {
                sensorTempMap.put("sensor_"+(i+1), 60 + random.nextGaussian() * 20); // 正态分布
            }
            while (running){
                for (String sensorId: sensorTempMap.keySet()) {
                    Double newTemp = sensorTempMap.get(sensorId) + random.nextGaussian();
                    sensorTempMap.put(sensorId,newTemp);
                    ctv.collect(new SensorReading(sensorId,System.currentTimeMillis(),newTemp));
                }
                Thread.sleep(1000);
            }
        }

        @Override
        public void cancel() {
            running=false;
        }
    }
}

三、转换算子（Transform）

获取到指定的数据源后，还要对数据源进行分析计算等操作，

基本转换算子：Map、flatMap、Filter

public class TransformTest1_Base {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env=StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //从文件读取数据
        DataStream<String> inputStream = env.readTextFile("sensor.txt");

        // 1. map 把String转换成长度生成
        DataStream<Integer> mapStream = inputStream.map(new MapFunction<String, Integer>() {
            @Override
            public Integer map(String value) throws Exception {
                return value.length();
            }
        });

    //   2. flatmap 按逗号切分字段
        DataStream<String> flatMapStream = inputStream.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String value, Collector<String> out) throws Exception {
                String[] fields=value.split(",");
                for (String field : fields){
                    out.collect(field);
                }
            }
        });

    //    3. filter ,筛选sensor_1 开头对id对应的数据
        DataStream<String> filterStream=inputStream.filter(new FilterFunction<String>() {
            @Override
            public boolean filter(String value) throws Exception {
                return value.startsWith("sensor_1");
            }
        });

    //    打印输出
        mapStream.print("map");
        flatMapStream.print("flatMap");
        filterStream.print("filter");

    // 执行程序
        env.execute();
    }
}

KeyBy、滚动聚合算子【sum()、min()、max()、minBy()、maxBy()】

• KeyBy：DataStream → KeyedStream：逻辑地将一个流拆分成不相交的分区，每个分区包含具有相同 key 的元素，在内部以 hash 的形式实现的。
• 如上算子可以针对 KeyedStream 的每一个支流做聚合。

public class TransformTest2_RollingAggregation {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //从文件读取数据
        DataStream<String> inputStream = env.readTextFile("sensor.txt");

        // 转换成SensorReading类型
        DataStream<SensorReading> dataStream=inputStream.map(new MapFunction<String, SensorReading>() {
            @Override
            public SensorReading map(String s) throws Exception {
                String[] fields=s.split(",");
                return new SensorReading(fields[0],new Long(fields[1]),new Double(fields[2]));
            }
        });

        // DataStream<SensorReading> dataStream = inputStream.map(line -> {
        //     String[] fields = line.split(",");
        //     return new SensorReading(fields[0], new Long(fields[1]), new Double(fields[2]));
        // });

        // 分组
        KeyedStream<SensorReading, Tuple> keyedStream = dataStream.keyBy("id");
        // KeyedStream<SensorReading, String> keyedStream1 = dataStream.keyBy(SensorReading::getId);

        //滚动聚合,取当前最大的温度值
        // DataStream<SensorReading> resultStream = keyedStream.maxBy("temperature");
        DataStream<SensorReading> resultStream = keyedStream.maxBy("temperature");

        resultStream.print();

        env.execute();
    }
}

Reduce

KeyedStream → DataStream：一个分组数据流的聚合操作，合并当前的元素和上次聚合的结果，产生一个新的值，返回的流中包含每一次聚合的结果，而不是只返回最后一次聚合的最终结果。

public class TransformTest3_Reduce {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //从文件读取数据
        DataStream<String> inputStream = env.readTextFile("sensor.txt");

        // 转换成SensorReading类型
        DataStream<SensorReading> dataStream=inputStream.map(new MapFunction<String, SensorReading>() {
            @Override
            public SensorReading map(String s) throws Exception {
                String[] fields=s.split(",");
                return new SensorReading(fields[0],new Long(fields[1]),new Double(fields[2]));
            }
        });
        // 分组
        KeyedStream<SensorReading, Tuple> keyedStream = dataStream.keyBy("id");

        // reduce 聚合，取最大的温度，以及当前最新对时间戳
        DataStream<SensorReading> resultStream = keyedStream.reduce(new ReduceFunction<SensorReading>() {
            @Override
            public SensorReading reduce(SensorReading value1, SensorReading value2) throws Exception {
                return new SensorReading(value1.getId(), value2.getTimestamp(), Math.max(value1.getTemperature(), value2.getTemperature()));
            }
        });
        resultStream.print();
        env.execute();
    }
}

分流【Split 、Select】、合流【Connect 、CoMap、union】

Split

DataStream → SplitStream：根据某些特征把一个 DataStream 拆分成两个或者多个 DataStream。

Select

SplitStream→DataStream：从一个 SplitStream 中获取一个或者多个DataStream。

Connect

DataStream,DataStream → ConnectedStreams：连接两个保持他们类型的数据流，两个数据流被 Connect 之后，只是被放在了一个同一个流中，内部依然保持各自的数据和形式不发生任何变化，两个流相互独立。

CoMap、CoFlatMap

ConnectedStreams → DataStream：作用于 ConnectedStreams 上，功能与 map和 flatMap 一样，对 ConnectedStreams 中的每一个 Stream 分别进行 map 和 flatMap处理。

Union

DataStream → DataStream：对两个或者两个以上的 DataStream 进行 union 操作，产生一个包含所有 DataStream 元素的新 DataStream。

DataStream<SensorReading> unionStream = xxxstream.union(xxx);

==Connect 与 Union 区别：==

• Union 之前两个流的类型必须是一样，Connect 可以不一样，在之后的 coMap中再去调整成为一样的。
• Connect 只能操作两个流，Union 可以操作多个。

public class TransformTest4_MultipleStreams {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //从文件读取数据
        DataStream<String> inputStream = env.readTextFile("sensor.txt");

        // 转换成SensorReading类型
        DataStream<SensorReading> dataStream=inputStream.map(new MapFunction<String, SensorReading>() {
            @Override
            public SensorReading map(String s) throws Exception {
                String[] fields=s.split(",");
                return new SensorReading(fields[0],new Long(fields[1]),new Double(fields[2]));
            }
        });

        // 1。分流 按照温度值30度为界进行分流
        SplitStream<SensorReading> splitStream = dataStream.split(new OutputSelector<SensorReading>() {
            @Override
            public Iterable<String> select(SensorReading value) {
                return (value.getTemperature() > 30) ? Collections.singletonList("high") : Collections.singletonList("low");
            }
        });
        // 通过条件选择对应流数据
        DataStream<SensorReading> highTempStream = splitStream.select("high");
        DataStream<SensorReading> lowTempStream = splitStream.select("low");
        DataStream<SensorReading> allTempStream = splitStream.select("high","low");

        highTempStream.print("high");
        lowTempStream.print("low");
        allTempStream.print("all");

        // 2。合流 connect，先将高温流转换为二元组，与低温流合并后，输出状态信息。
        DataStream<Tuple2<String, Double>> warningStream = highTempStream.map(new MapFunction<SensorReading, Tuple2<String, Double>>() {
            @Override
            public Tuple2<String, Double> map(SensorReading value) throws Exception {
                return new Tuple2<>(value.getId(), value.getTemperature());
            }
        });

        // 只能是两条流进行合并，但是两条流的数据类型可以不一致
        ConnectedStreams<Tuple2<String, Double>, SensorReading> connectStream = warningStream.connect(lowTempStream);
        DataStream<Object> resultStream = connectStream.map(new CoMapFunction<Tuple2<String, Double>, SensorReading, Object>() {
            @Override
            public Object map1(Tuple2<String, Double> value) throws Exception {
                return new Tuple3<>(value.f0, value.f1, "high temp warning");
            }

            @Override
            public Object map2(SensorReading value) throws Exception {
                return new Tuple2<>(value.getId(), "normal");
            }
        });

        resultStream.print();

        // 3。union联合多条流 限制就是每条流数据类型必须一致
        DataStream<SensorReading> union = highTempStream.union(lowTempStream, allTempStream);
        union.print("union stream");

        env.execute();
    }
}

四、数据输出（Sink）

Flink官方提供了一部分框架的Sink，用户也可以自定义实现Sink。flink将任务进行输出的操作核心代码：stream.addSink(new MySink(xxxx))。

Kafka
引入Kafka依赖：

<dependency>
 <groupId>org.apache.flink</groupId>
 <artifactId>flink-connector-kafka-0.11_2.12</artifactId>
 <version>1.10.1</version>
</dependency>

public class SinkTest1_Kafka {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //从文件读取数据
        DataStream<String> inputStream = env.readTextFile("/Volumes/Update/flink/flink_test/src/main/resources/sensor.txt");

        // 转换成SensorReading类型
        DataStream<String> dataStream=inputStream.map(new MapFunction<String, String>() {
            @Override
            public String map(String s) throws Exception {
                String[] fields=s.split(",");
                return new SensorReading(fields[0],new Long(fields[1]),new Double(fields[2])).toString();
            }
        });

        //输出到外部系统
        dataStream.addSink(new FlinkKafkaProducer011<String>("localhost:9092","sinktest",new SimpleStringSchema()));

        env.execute();
    }
}

Redis
引入Redis依赖：

<dependency>
 <groupId>org.apache.bahir</groupId>
 <artifactId>flink-connector-redis_2.11</artifactId>
 <version>1.0</version>
</dependency>

public class SinkTest2_Redis {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //从文件读取数据
        DataStream<String> inputStream = env.readTextFile("/Volumes/Update/flink/flink_test/src/main/resources/sensor.txt");

        // 转换成SensorReading类型
        DataStream<SensorReading> dataStream=inputStream.map(new MapFunction<String, SensorReading>() {
            @Override
            public SensorReading map(String s) throws Exception {
                String[] fields=s.split(",");
                return new SensorReading(fields[0],new Long(fields[1]),new Double(fields[2]));
            }
        });
        // jedis配置
        FlinkJedisPoolConfig config = new FlinkJedisPoolConfig.Builder()
                .setHost("localhost")
                .setPort(6379)
                .build();
        dataStream.addSink(new RedisSink<>(config,new MyRedisMapper()));


        env.execute();
    }
    // 自定义RedisMapper
    public static class MyRedisMapper implements RedisMapper<SensorReading>{
        //自定义保存数据到Redis的命令，存成hash表Hset
        @Override
        public RedisCommandDescription getCommandDescription() {
            return new RedisCommandDescription(RedisCommand.HSET,"sensor_temp");
        }

        @Override
        public String getKeyFromData(SensorReading data) {
            return data.getId();
        }

        @Override
        public String getValueFromData(SensorReading data) {
            return data.getTemperature().toString();
        }
    }

}

Elasticsearch
引入依赖：

<dependency>
 <groupId>org.apache.flink</groupId>
 <artifactId>flink-connector-elasticsearch6_2.12</artifactId>
 <version>1.10.1</version>
</dependency>

public class SinkTest3_ES {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env;
        env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //从文件读取数据
        DataStream<String> inputStream = env.readTextFile("/Volumes/Update/flink/flink_test/src/main/resources/sensor.txt");

        // 转换成SensorReading类型
        DataStream<SensorReading> dataStream=inputStream.map(new MapFunction<String, SensorReading>() {
            public SensorReading map(String s) throws Exception {
                String[] fields=s.split(",");
                return new SensorReading(fields[0],new Long(fields[1]),new Double(fields[2]));
            }
        });
        // 定义ES的链接配置
        ArrayList<HttpHost> httpHosts = new ArrayList<>();
        httpHosts.add(new HttpHost("localhost",9200));

        dataStream.addSink(new ElasticsearchSink.Builder<SensorReading>(httpHosts,new MyEsSinkFunction()).build());
        
        env.execute();
    }

    //实现自定义的ES写入操作
    public static class MyEsSinkFunction implements ElasticsearchSinkFunction<SensorReading> {
        @Override
        public void process(SensorReading element, RuntimeContext ctx, RequestIndexer indexer) {
            // 定义写入的数据source
            HashMap<String, String> dataSource = new HashMap<>();
            dataSource.put("id",element.getId());
            dataSource.put("temp",element.getTemperature().toString());
            dataSource.put("ts",element.getTimestamp().toString());

            // 创建请求作为向ES发起的写入命令
            IndexRequest indexRequest = Requests.indexRequest()
                    .index("sensor")
                    .type("readingdata")
                    .source(dataSource);

            // 用indexer发送请求
            indexer.add(indexRequest);
        }
    }
}

自定义Sink（JDBC）
引入依赖：

<dependency>
 <groupId>mysql</groupId>
 <artifactId>mysql-connector-java</artifactId>
 <version>5.1.44</version>
</dependency>

public class SinkTest4_JDBC {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //从文件读取数据
        DataStream<String> inputStream = env.readTextFile("sensor.txt");

        // 转换成SensorReading类型
        DataStream<SensorReading> dataStream=inputStream.map(new MapFunction<String, SensorReading>() {
            @Override
            public SensorReading map(String s) throws Exception {
                String[] fields=s.split(",");
                return new SensorReading(fields[0],new Long(fields[1]),new Double(fields[2]));
            }
        });

        dataStream.addSink(new MyJDBCSink());
        env.execute();
    }

    // 实现自定义SinkFunction
    public static class MyJDBCSink extends RichSinkFunction<SensorReading> {
        //声明连接和预编译
        Connection connection=null;
        PreparedStatement insert=null;
        PreparedStatement update=null;
        @Override
        public void open(Configuration parameters) throws Exception {
            connection= DriverManager.getConnection("jdbc:mysql://localhost:3306/test","root","123456");
            insert=connection.prepareStatement("insert into sensor_temp (id,temp) values (?,?)");
            update=connection.prepareStatement("update sensor_temp set temp = ? where id = ? ");
        }

        // 每来一条数据，调用链接，执行sql
        @Override
        public void invoke(SensorReading value, Context context) throws Exception {
           // 直接执行更新
            update.setDouble(1,value.getTemperature());
            update.setString(2,value.getId());
            update.execute();
            if (update.getUpdateCount() == 0){
                insert.setString(1,value.getId());
                insert.setDouble(2,value.getTemperature());
                insert.execute();
            }
        }

        // 关闭连接流
        @Override
        public void close() throws Exception {
            connection.close();
            insert.close();
            update.close();
        }
    }
}

五、数据类型、UDF 函数、富函数

Flink支持的数据类型

• Flink 支持所有的 Java 和 Scala 基础数据类型，Int, Double, Long, String等

DataStream<Integer> numberStream = env.fromElements(1, 2, 3, 4);

• Java 和 Scala 元组（Tuples）

DataStream<Tuple2<String, Integer>> personStream = env.fromElements(
 new Tuple2("Adam", 17),
 new Tuple2("Sarah", 23) );
personStream.filter(p -> p.f1 > 18);

• Flink 对 Java 和 Scala 中的一些特殊目的的类型也都是支持的，比如 Java 的
  ArrayList，HashMap，Enum 等等

UDF 函数

Flink 暴露了所有 udf 函数的接口(实现方式为接口或者抽象类)。例如MapFunction, FilterFunction, ProcessFunction 等等。

富函数（Rich Functions）

“富函数”是 DataStream API 提供的一个函数类的接口，所有 Flink 函数类都有其 Rich 版本。它与常规函数的不同在于，可以获取运行环境的上下文，并拥有一些生命周期方法，所以可以实现更复杂的功能。RichMapFunction、RichFlatMapFunction、RichFilterFunction

==Rich Function 有一个生命周期的概念。典型的生命周期方法有：==

• open()方法是 rich function 的初始化方法，当一个算子例如 map 或者 filter 被调用之前open()会被调用。
• close()方法是生命周期中的最后一个调用的方法，做一些清理工作。
• getRuntimeContext()方法提供了函数的 RuntimeContext 的一些信息，例如函 数执行的并行度，任务的名字，以及state 状态。

public class TransformTest5_RichFunction {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(4);

        //从文件读取数据
        DataStream<String> inputStream = env.readTextFile("sensor.txt");

        // 转换成SensorReading类型
        DataStream<SensorReading> dataStream=inputStream.map(new MapFunction<String, SensorReading>() {
            @Override
            public SensorReading map(String s) throws Exception {
                String[] fields=s.split(",");
                return new SensorReading(fields[0],new Long(fields[1]),new Double(fields[2]));
            }
        });

        DataStream<Tuple2<String,Integer>> resultStream=dataStream.map(new MyMapper());
        resultStream.print();

        env.execute();
    }

    public static class MyMapper0 implements MapFunction<SensorReading,Tuple2<String,Integer>>{
        @Override
        public Tuple2<String, Integer> map(SensorReading value) throws Exception {
            return new Tuple2<>(value.getId(),value.getId().length());
        }
    }

    // 继承富函数
    public static class MyMapper extends RichMapFunction<SensorReading,Tuple2<String,Integer>>{
        @Override
        public Tuple2<String, Integer> map(SensorReading value) throws Exception {
            // getRuntimeContext().getState()
            return new Tuple2<String,Integer>(value.getId(),getRuntimeContext().getIndexOfThisSubtask());
        }

        @Override
        public void open(Configuration parameters) throws Exception {
            // 初始化工作，一般是定义状态，或者创建数据库链接
            System.out.println("open");
            // super.open(parameters);
        }

        @Override
        public void close() throws Exception {
            // 关闭链接，收尾状态
            System.out.println("close");
            // super.close();
        }
    }
}