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
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);
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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);
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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(); } } }
