Flink：流处理Api

创建执行环境

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，也就是说，getExecutionEnvironment 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。

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

如果没有设置并行度，会以 flink-conf.yaml 中的配置为准，默认是 1。

createLocalEnvironment

返回本地执行环境，需要在调用时指定默认的并行度。

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。

public static StreamExecutionEnvironment createRemoteEnvironment(String host, int port, String... jarFiles)

source

从集合读取数据

java实体类：

/**
 * @author wen.jie
 * @date 2021/9/1 16:45
 * 传感器温度读数的数据类型
 */
public class SensorReading {

    //id
    private String id;

    //时间戳
    private Long timestamp;

    //温度
    private Double temperature;
	//toString、getter、setter、有参无参构造省略
}

测试：

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStream<SensorReading> sensorDataStream = env.fromCollection(Arrays.asList(
                new SensorReading("sensor_1", 1547718199L, 35.8),
                new SensorReading("sensor_6", 1547718201L, 15.4),
                new SensorReading("sensor_7", 1547718202L, 6.7),
                new SensorReading("sensor_10", 1547718205L, 38.1)
        ));

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

        sensorDataStream.print();
        integerDataStream.print().setParallelism(1);

        env.execute();
    }

从文件读取数据

sensor.txt：

sensor_1 1547718199L 35.8
sensor_6 1547718201L 15.4
sensor_7 1547718202L 6.7
sensor_10 1547718205L 38.1

测试代码：

public static void main(String[] args) throws Exception {
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    DataStream<String> sensorDataStream = env.readTextFile("D:\project\flink-demo\src\main\resources\sensor.txt");
    sensorDataStream.print();
    env.execute();
}

从kafka读取数据

新增flink链接kafka的依赖：

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

kafka安装包，具体安装过程这里不演示：https://archive.apache.org/dist/kafka/2.1.0/kafka_2.11-2.1.0.tgz

代码：

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

        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", "192.168.1.77:9092");
        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> sensorDataStream = env.addSource(new FlinkKafkaConsumer011<>("sensor", new SimpleStringSchema(), properties));
        sensorDataStream.print();
        env.execute();
    }

./bin/kafka-console-producer.sh --broker-list 192.168.1.77:9092 --topic sensor

效果如下：

自定义source

除了以上的 source 数据来源，我们还可以自定义 source。需要做的，只是传入一个 SourceFunction 就可以。具体调用如下：

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

我们希望可以随机生成传感器数据，MySensorSource 具体的代码实现如下：

    public static class MySensor implements SourceFunction<SensorReading> {

        private boolean running = true;

        @Override
        public void run(SourceContext<SensorReading> ctx) throws Exception {
            Random random = new Random();
            HashMap<String, Double> sensorTempMap = new HashMap<>();
            for( int i = 0; i < 10; i++ ){
                sensorTempMap.put("sensor_" + (i + 1), 60 + random.nextGaussian() * 20);
            }
            while (running) {
                for(String sensorId: sensorTempMap.keySet() ){
                    Double newTemp = sensorTempMap.get(sensorId) + random.nextGaussian();
                    sensorTempMap.put(sensorId, newTemp);
                    ctx.collect( new SensorReading(sensorId, System.currentTimeMillis(),
                            newTemp));
                }
                Thread.sleep(1000L);
            }
        }

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

测试方法：

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //添加自定义数据源
        DataStreamSource<SensorReading> dataStreamSource = env.addSource(new MySensor());
        dataStreamSource.print();
        env.execute();
    }

测试结果：

123414351435t2435

Transform

基本转换操作

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        DataStream<String> inputStream = env.readTextFile("D:\project\flink-demo\src\main\resources\sensor.txt");
        //map
        DataStream<Integer> mapStream = inputStream.map(String::length);
        //flatmap
        DataStream<String> flatMapStream = inputStream.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String value, Collector<String> out) throws Exception {
                String[] strs = value.split(" ");
                for (String field : strs) {
                    out.collect(field);
                }
            }
        });
        //filter
        DataStream<String> filterStream = inputStream.filter((str) -> str.startsWith("sensor_1"));
        mapStream.print("map");
        flatMapStream.print("flatMap");
        filterStream.print("filter");

        env.execute();
    }

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。

滚动聚合算子（Rolling Aggregation）

这些算子可以针对 KeyedStream 的每一个支流做聚合。

sum(),min(),max(),minBy(),maxBy()

sensor.txt

sensor_1 1547718199 35.8
sensor_6 1547718201 15.4
sensor_7 1547718202 6.7
sensor_10 1547718205 38.1
sensor_10 1547718204 39.1
sensor_1 1547748199 32.8
sensor_7 1547718234 6.1

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        DataStream<String> inputStream = env.readTextFile("D:\project\flink-demo\src\main\resources\sensor.txt");

        DataStream<SensorReading> mapStream = inputStream.map((str) -> {
            String[] split = str.split(" ");
            return new SensorReading(split[0], Long.parseLong(split[1]), Double.parseDouble(split[2]));
        });

        //分组
        KeyedStream<SensorReading, Tuple> keyByStream = mapStream.keyBy("id");

        //滚动聚合,取当前最大值(来一条数据取一个最大值)
        DataStream<SensorReading> maxStream = keyByStream.maxBy("temperature");

        keyByStream.print("keyByStream");
        maxStream.print("maxStream");

        env.execute();
    }

运行结果：

reduce聚合

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。

需求：取最大温度值以及当前最新的时间戳

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        DataStream<String> inputStream = env.readTextFile("D:\project\flink-demo\src\main\resources\sensor.txt");
        DataStream<SensorReading> mapStream = inputStream.map((str) -> {
            String[] split = str.split(" ");
            return new SensorReading(split[0], Long.parseLong(split[1]), Double.parseDouble(split[2]));
        });

        KeyedStream<SensorReading, Tuple> keyedStream = mapStream.keyBy("id");

        keyedStream.reduce((v1, v2) -> new SensorReading(v1.getId(), v2.getTimestamp(), Math.max(v1.getTemperature(), v2.getTemperature())))
                .print();

        env.execute();
    }

分流：Split 和 Select

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。

需求：传感器数据按照温度高低（以 30 度为界），拆分成两个流，并取出高温的数据。

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        DataStream<String> inputStream = env.readTextFile("D:\project\flink-demo\src\main\resources\sensor.txt");
        DataStream<SensorReading> mapStream = inputStream.map((str) -> {
            String[] split = str.split(" ");
            return new SensorReading(split[0], Long.parseLong(split[1]), Double.parseDouble(split[2]));
        });

        SplitStream<SensorReading> splitStream = mapStream.split((value -> value.getTemperature() > 30 ? Collections.singletonList("high") : Collections.singletonList("low")));
        splitStream.select("high").print();
        env.execute();

合流：Connect和CoMap

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。

测试代码：

		//前面代码与上面分流代码一样        
		SplitStream<SensorReading> splitStream = mapStream.split((value -> value.getTemperature() > 30 ? Collections.singletonList("high") : Collections.singletonList("low")));
        DataStream<SensorReading> highStream = splitStream.select("high");
        DataStream<SensorReading> lowStream = splitStream.select("low");
        DataStream<SensorReading> allStream = splitStream.select("high", "low");

        //合流
        //先将高温六转换成二元组类型,与低温流连接合并之后,输出状态信息
        DataStream<Tuple2<String, Double>> warningStream = highStream.map(new MapFunction<SensorReading, Tuple2<String, Double>>() {
            @Override
            public Tuple2<String, Double> map(SensorReading value) throws Exception {
                return new Tuple2<>(value.getId(), value.getTemperature());
            }
        });
        //连接两个流
        ConnectedStreams<Tuple2<String, Double>, SensorReading> connectedStreams
                = warningStream.connect(lowStream);

        SingleOutputStreamOperator<Object> resultStream = connectedStreams.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, "warning");
            }

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

        resultStream.print();

        env.execute();

union合流

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。

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。

        DataStream<SensorReading> unionStream = highStream.union(lowStream);
        unionStream.print();

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

测试：

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(4);
        DataStream<String> inputStream = env.readTextFile("D:\project\flink-demo\src\main\resources\sensor.txt");
        DataStream<SensorReading> mapStream = inputStream.map((str) -> {
            String[] split = str.split(" ");
            return new SensorReading(split[0], Long.parseLong(split[1]), Double.parseDouble(split[2]));
        });

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

        resultStream.print();
        env.execute();

    }

    // 实现自定义富函数类
    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<>(value.getId(), getRuntimeContext().getIndexOfThisSubtask());
        }

        @Override
        public void open(Configuration parameters) throws Exception {
            // 初始化工作，一般是定义状态，或者建立数据库连接
            System.out.println("open");
        }

        @Override
        public void close() throws Exception {
            // 一般是关闭连接和清空状态的收尾操作
            System.out.println("close");
        }
    }

sink

Flink 没有类似于 spark 中 foreach 方法，让用户进行迭代的操作。虽有对外的输出操作都要利用 Sink 完成。最后通过类似如下方式完成整个任务最终输出操作。

stream.addSink(new MySink(xxxx))

官方提供了一部分的框架的 sink。除此以外，需要用户自定义实现 sink。

Kafka

代码测试：

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(4);
        DataStream<String> inputStream = env.readTextFile("D:\project\flink-demo\src\main\resources\sensor.txt");
        DataStream<String> mapStream = inputStream.map((str) -> {
            String[] split = str.split(" ");
            return new SensorReading(split[0], Long.parseLong(split[1]), Double.parseDouble(split[2])).toString();
        });

        mapStream.addSink(new FlinkKafkaProducer011<>("192.168.1.77:9092", "sinktest", new SimpleStringSchema()));
        env.execute();

kafka消费者：

./bin/kafka-console-consumer.sh --bootstrap-server 192.168.1.77:9092 --topic sinktest

效果：

Redis

添加依赖：

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

代码：

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        DataStream<String> inputStream = env.readTextFile("D:\project\flink-demo\src\main\resources\sensor.txt");

        DataStream<SensorReading> mapStream = inputStream.map((str) -> {
            String[] split = str.split(" ");
            return new SensorReading(split[0], Long.parseLong(split[1]), Double.parseDouble(split[2]));
        });

        FlinkJedisPoolConfig config = new FlinkJedisPoolConfig.Builder()
                .setHost("192.168.1.77")
                .setPort(6379).build();
        mapStream.addSink(new RedisSink<>(config, new MyRedisMapper()));
        env.execute();
    }

    public static class MyRedisMapper implements RedisMapper<SensorReading> {

        //返回redis操作信息
        @Override
        public RedisCommandDescription getCommandDescription() {
            return new RedisCommandDescription(RedisCommand.HSET, "sensor");
        }

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

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

运行结果：

ElasticSearch

导入依赖:

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

代码：

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        DataStream<String> inputStream = env.readTextFile("D:\project\flink-demo\src\main\resources\sensor.txt");

        DataStream<SensorReading> mapStream = inputStream.map((str) -> {
            String[] split = str.split(" ");
            return new SensorReading(split[0], Long.parseLong(split[1]), Double.parseDouble(split[2]));
        });

        List<HttpHost> httpHosts = Collections.singletonList(new HttpHost("192.168.1.77", 9200));
        ElasticsearchSink<SensorReading> elasticsearchSink = new ElasticsearchSink.Builder<SensorReading>(httpHosts, new MyEsSinkFunction()).build();

        mapStream.addSink(elasticsearchSink);

        env.execute();
    }

    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("timestamp", element.getTimestamp().toString());

            //创建请求,作为向es发起的写入命令
            IndexRequest indexRequest = Requests.indexRequest().index("sensors")
                    .type("sensors").source(dataSource);
            //发送请求
            indexer.add(indexRequest);
        }
    }

访问：http://192.168.1.77:9200/sensors/_search?pretty

可见数据以及输出到ElasticSearch中去了

Mysql

添加依赖：

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

表sql：

DROP TABLE IF EXISTS `sensor_temp`;
CREATE TABLE `sensor_temp`  (
  `id` varchar(20) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL,
  `temp` double NOT NULL,
  PRIMARY KEY (`id`) USING BTREE
) ENGINE = InnoDB CHARACTER SET = utf8 COLLATE = utf8_general_ci ROW_FORMAT = Dynamic;
SET FOREIGN_KEY_CHECKS = 1;

java代码：

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        DataStream<String> inputStream = env.readTextFile("D:\project\flink-demo\src\main\resources\sensor.txt");

        DataStream<SensorReading> mapStream = inputStream.map((str) -> {
            String[] split = str.split(" ");
            return new SensorReading(split[0], Long.parseLong(split[1]), Double.parseDouble(split[2]));
        });
        mapStream.addSink(new MysqlRichSinkFunction());
        env.execute();
    }

    public static class MysqlRichSinkFunction extends RichSinkFunction<SensorReading> {

        Connection conn = null;
        PreparedStatement insertStmt = null;
        PreparedStatement updateStmt = null;

        // open 主要是创建连接
        @Override
        public void open(Configuration parameters) throws Exception {
            conn = DriverManager.getConnection("jdbc:mysql://192.168.1.77:3306/sensor", "root", "1234");
            // 创建预编译器，有占位符，可传入参数
            insertStmt = conn.prepareStatement("INSERT INTO sensor_temp (id, temp) VALUES (?, ?)");
            updateStmt = conn.prepareStatement("UPDATE sensor_temp SET temp = ? WHERE id  = ?");
        }

        @Override
        public void invoke(SensorReading value, Context context) throws Exception {
            // 执行更新语句，注意不要留 super
            updateStmt.setDouble(1, value.getTemperature());
            updateStmt.setString(2, value.getId());
            updateStmt.execute();
            // 如果刚才 update 语句没有更新，那么插入
            if (updateStmt.getUpdateCount() == 0) {
                insertStmt.setString(1, value.getId());
                insertStmt.setDouble(2, value.getTemperature());
                insertStmt.execute();
            }
        }

        @Override
        public void close() throws Exception {
            insertStmt.close();
            updateStmt.close();
            conn.close();
        }

    }

运行结果：

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原文地址：https://www.cnblogs.com/wwjj4811/p/15219415.html