Flink流处理API大合集：掌握所有flink流处理技术，看这一篇就够了

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创作时间：2022 年 5 月 18 日
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前言

在之前的文章中有提到过，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
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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;}@Overridepublic 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 1env.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;@Overridepublic 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);}}@Overridepublic 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
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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>() {@Overridepublic 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>() {@Overridepublic 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>() {@Overridepublic 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。

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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>() {@Overridepublic 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>() {@Overridepublic 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>() {@Overridepublic 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。

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Select

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

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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。

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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。

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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。

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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>() {@Overridepublic 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>() {@Overridepublic 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>>() {@Overridepublic 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>() {@Overridepublic Object map1(Tuple2<String, Double> value) throws Exception {return new Tuple3<>(value.f0, value.f1, "high temp warning");}@Overridepublic 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>() {@Overridepublic 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>() {@Overridepublic 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RedisMapperpublic static class MyRedisMapper implements RedisMapper<SensorReading>{//自定义保存数据到Redis的命令，存成hash表Hset@Overridepublic RedisCommandDescription getCommandDescription() {return new RedisCommandDescription(RedisCommand.HSET,"sensor_temp");}@Overridepublic String getKeyFromData(SensorReading data) {return data.getId();}@Overridepublic 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> {@Overridepublic void process(SensorReading element, RuntimeContext ctx, RequestIndexer indexer) {// 定义写入的数据sourceHashMap<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>() {@Overridepublic 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SinkFunctionpublic static class MyJDBCSink extends RichSinkFunction<SensorReading> {//声明连接和预编译Connection connection=null;PreparedStatement insert=null;PreparedStatement update=null;@Overridepublic 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@Overridepublic 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@Overridepublic 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>() {@Overridepublic 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>>{@Overridepublic 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>>{@Overridepublic Tuple2<String, Integer> map(SensorReading value) throws Exception {// getRuntimeContext().getState()return new Tuple2<String,Integer>(value.getId(),getRuntimeContext().getIndexOfThisSubtask());}@Overridepublic 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);}@Overridepublic void close() throws Exception {// 关闭链接，收尾状态System.out.println("close");// super.close();}}
}