Flink1.12流批一体API
Flink1.12流批一体APIFlink 的核心 API 最初是针对特定的场景设计的,尽管 Table API / SQL 针对流处理和批处理已经实现了统一的 API,但当用户使用较底层的 API 时,仍然需要在批处理(DataSet API)和流处理(DataStream API)这两种不同的 API 之间进行选择。鉴于批处理是流处理的一种特例,将这两种 API 合并成统一的 API。Flin
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Flink1.12流批一体API
Flink 的核心 API 最初是针对特定的场景设计的,尽管 Table API / SQL 针对流处理和批处理已经实现了统一的 API,但当用户使用较底层的 API 时,仍然需要在批处理(DataSet API)和流处理(DataStream API)这两种不同的 API 之间进行选择。鉴于批处理是流处理的一种特例,将这两种 API 合并成统一的 API。
Flink 应用程序结构主要包含三部分,Source、Transformation、Sink。
1、Flink的数据源Source
1.1、基于集合的Source
package cn.exam.source;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import java.util.Arrays;
/**
* Author SKY
* Desc 演示DataStream-Source-基于集合
*/
public class SourceDemo01_Collection {
public static void main(String[] args) throws Exception {
//TODO 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
DataStream<String> ds1 = env.fromElements("hadoop spark flink", "hadoop spark flink");
DataStream<String> ds2 = env.fromCollection(Arrays.asList("hadoop spark flink", "hadoop spark flink"));
DataStream<Long> ds3 = env.generateSequence(1, 100);
DataStream<Long> ds4 = env.fromSequence(1, 100);
//TODO 2.transformation
//TODO 3.sink
ds1.print();
ds2.print();
ds3.print();
ds4.print();
//TODO 4.execute
env.execute();
}
}
1.2 基于文件的Source
package cn.exam.source;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
/**
* Author SKY
* Desc 演示DataStream-Source-基于本地/HDFS的文件/文件夹/压缩文件
*/
public class SourceDemo02_File {
public static void main(String[] args) throws Exception {
//TODO 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
DataStream<String> ds1 = env.readTextFile("data/input/words.txt");
DataStream<String> ds2 = env.readTextFile("data/input/dir");
DataStream<String> ds3 = env.readTextFile("data/input/wordcount.txt.gz");
//TODO 2.transformation
//TODO 3.sink
ds1.print();
ds2.print();
ds3.print();
//TODO 4.execute
env.execute();
}
}
1.3 基于Socket的Source
package cn.exam.source;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;
/**
* Author SKY
* Desc 演示DataStream-Source-基于Socket
*/
public class SourceDemo03_Socket {
public static void main(String[] args) throws Exception {
//TODO 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
DataStream<String> lines = env.socketTextStream("node1", 9999);
//TODO 2.transformation
//注意:下面的操作将上面的2步合成了1步,直接切割单词并记为1返回
SingleOutputStreamOperator<Tuple2<String, Integer>> wordAndOne = lines.flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {
@Override
public void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception {
String[] arr = value.split(" ");
for (String word : arr) {
out.collect(Tuple2.of(word, 1));
}
}
});
SingleOutputStreamOperator<Tuple2<String, Integer>> result = wordAndOne.keyBy(t -> t.f0).sum(1);
//TODO 3.sink
result.print();
//TODO 4.execute
env.execute();
}
}
1.4 自定义数据源Source代码随机生成数据
package cn.exam.source;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;
import java.util.Random;
import java.util.UUID;
/**
* Author SKY
* Desc 演示DataStream-Source-自定义数据源
* 需求:
*/
public class SourceDemo04_Customer {
public static void main(String[] args) throws Exception {
//TODO 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
DataStream<Order> orderDS = env.addSource(new MyOrderSource()).setParallelism(2);
//TODO 2.transformation
//TODO 3.sink
orderDS.print();
//TODO 4.execute
env.execute();
}
@Data
@AllArgsConstructor
@NoArgsConstructor
public static class Order{
private String id;
private Integer userId;
private Integer money;
private Long createTime;
}
public static class MyOrderSource extends RichParallelSourceFunction<Order>{
private Boolean flag = true;
//执行并生成数据
@Override
public void run(SourceContext<Order> ctx) throws Exception {
Random random = new Random();
while (flag) {
String oid = UUID.randomUUID().toString();
int userId = random.nextInt(3);
int money = random.nextInt(101);
long createTime = System.currentTimeMillis();
ctx.collect(new Order(oid,userId,money,createTime));
Thread.sleep(1000);
}
}
//执行cancel命令的时候执行
@Override
public void cancel() {
flag = false;
}
}
}
1.5 自定义数据源-MySQL
package cn.exam.source;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
/**
* Author SKY
* Desc 演示DataStream-Source-自定义数据源-MySQL
* 需求:
*/
public class SourceDemo05_Customer_MySQL {
public static void main(String[] args) throws Exception {
//TODO 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
DataStream<Student> studentDS = env.addSource(new MySQLSource()).setParallelism(1);
//TODO 2.transformation
//TODO 3.sink
studentDS.print();
//TODO 4.execute
env.execute();
}
/**
CREATE TABLE `t_student` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`name` varchar(255) DEFAULT NULL,
`age` int(11) DEFAULT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=7 DEFAULT CHARSET=utf8;
INSERT INTO `t_student` VALUES ('1', 'jack', '18');
INSERT INTO `t_student` VALUES ('2', 'tom', '19');
INSERT INTO `t_student` VALUES ('3', 'rose', '20');
INSERT INTO `t_student` VALUES ('4', 'tom', '19');
INSERT INTO `t_student` VALUES ('5', 'jack', '18');
INSERT INTO `t_student` VALUES ('6', 'rose', '20');
*/
@Data
@NoArgsConstructor
@AllArgsConstructor
public static class Student {
private Integer id;
private String name;
private Integer age;
}
public static class MySQLSource extends RichParallelSourceFunction<Student> {
private boolean flag = true;
private Connection conn = null;
private PreparedStatement ps =null;
private ResultSet rs = null;
//open只执行一次,适合开启资源
@Override
public void open(Configuration parameters) throws Exception {
conn = DriverManager.getConnection("jdbc:mysql://localhost:3306/bigdata", "root", "123456");
String sql = "select id,name,age from t_student";
ps = conn.prepareStatement(sql);
}
@Override
public void run(SourceContext<Student> ctx) throws Exception {
while (flag) {
rs = ps.executeQuery();//执行查询
while (rs.next()) {
int id = rs.getInt("id");
String name = rs.getString("name");
int age = rs.getInt("age");
ctx.collect(new Student(id,name,age));
}
Thread.sleep(5000);
}
}
//接收到cancel命令时取消数据生成
@Override
public void cancel() {
flag = false;
}
//close里面关闭资源
@Override
public void close() throws Exception {
if(conn != null) conn.close();
if(ps != null) ps.close();
if(rs != null) rs.close();
}
}
}
2.Flink的Sink
2.1 基于控制台和文件
package cn.exam.sink;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
/**
* Author SKY
* Desc 演示DataStream-Sink-基于控制台和文件
*/
public class SinkDemo01 {
public static void main(String[] args) throws Exception {
//TODO 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
DataStream<String> ds = env.readTextFile("data/input/words.txt");
//TODO 2.transformation
//TODO 3.sink
ds.print();
ds.print("输出标识");
ds.printToErr();//会在控制台上以红色输出
ds.printToErr("输出标识");//会在控制台上以红色输出
ds.writeAsText("data/output/result1").setParallelism(1);
ds.writeAsText("data/output/result2").setParallelism(2);
//TODO 4.execute
env.execute();
}
}
2.2 自定义Sink-MySQL
package cn.exam.sink;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
/**
* Author SKY
* Desc 演示DataStream-Sink-自定义Sink
*/
public class SinkDemo02 {
public static void main(String[] args) throws Exception {
//TODO 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
DataStream<Student> studentDS = env.fromElements(new Student(null, "tony", 18));
//TODO 2.transformation
//TODO 3.sink
studentDS.addSink(new MySQLSink());
//TODO 4.execute
env.execute();
}
@Data
@NoArgsConstructor
@AllArgsConstructor
public static class Student {
private Integer id;
private String name;
private Integer age;
}
public static class MySQLSink extends RichSinkFunction<Student> {
private Connection conn = null;
private PreparedStatement ps =null;
@Override
public void open(Configuration parameters) throws Exception {
conn = DriverManager.getConnection("jdbc:mysql://localhost:3306/bigdata", "root", "123456");
String sql = "INSERT INTO `t_student` (`id`, `name`, `age`) VALUES (null, ?, ?);";
ps = conn.prepareStatement(sql);
}
@Override
public void invoke(Student value, Context context) throws Exception {
//设置?占位符参数值
ps.setString(1,value.getName());
ps.setInt(2,value.getAge());
//执行sql
ps.executeUpdate();
}
@Override
public void close() throws Exception {
if(conn != null) conn.close();
if(ps != null) ps.close();
}
}
}
2.3 Flink官方提供的JdbcSink
package cn.exam.connectors;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.connector.jdbc.JdbcConnectionOptions;
import org.apache.flink.connector.jdbc.JdbcSink;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
/**
* Author SKY
* Desc 演示Flink官方提供的JdbcSink
*/
public class JDBCDemo {
public static void main(String[] args) throws Exception {
//TODO 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
DataStream<Student> studentDS = env.fromElements(new Student(null, "tony2", 18));
//TODO 2.transformation
//TODO 3.sink
studentDS.addSink(JdbcSink.sink(
"INSERT INTO `t_student` (`id`, `name`, `age`) VALUES (null, ?, ?)",
(ps, value) -> {
ps.setString(1, value.getName());
ps.setInt(2, value.getAge());
}, new JdbcConnectionOptions.JdbcConnectionOptionsBuilder()
.withUrl("jdbc:mysql://localhost:3306/bigdata")
.withUsername("root")
.withPassword("123456")
.withDriverName("com.mysql.jdbc.Driver")
.build()));
//TODO 4.execute
env.execute();
}
@Data
@NoArgsConstructor
@AllArgsConstructor
public static class Student {
private Integer id;
private String name;
private Integer age;
}
}
Flink对接Kafka
准备主题
/export/server/kafka/bin/kafka-topics.sh --create --zookeeper node1:2181 --replication-factor 2 --partitions 3 --topic flink_kafka
启动控制台生产者发送数据
/export/server/kafka/bin/kafka-console-producer.sh --broker-list node1:9092 --topic flink_kafka
启动程序FlinkKafkaConsumer
观察控制台输出结果
3.1 Flink-Connectors-KafkaComsumer/Source
package cn.exam.connectors;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import java.util.Properties;
/**
* Author SKY
* Desc 演示Flink-Connectors-KafkaComsumer/Source
*/
public class KafkaComsumerDemo {
public static void main(String[] args) throws Exception {
//TODO 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
//准备kafka连接参数
Properties props = new Properties();
props.setProperty("bootstrap.servers", "node1:9092");//集群地址
props.setProperty("group.id", "flink");//消费者组id
props.setProperty("auto.offset.reset","latest");//latest有offset记录从记录位置开始消费,没有记录从最新的/最后的消息开始消费 /earliest有offset记录从记录位置开始消费,没有记录从最早的/最开始的消息开始消费
props.setProperty("flink.partition-discovery.interval-millis","5000");//会开启一个后台线程每隔5s检测一下Kafka的分区情况,实现动态分区检测
props.setProperty("enable.auto.commit", "true");//自动提交(提交到默认主题,后续学习了Checkpoint后随着Checkpoint存储在Checkpoint和默认主题中)
props.setProperty("auto.commit.interval.ms", "2000");//自动提交的时间间隔
//使用连接参数创建FlinkKafkaConsumer/kafkaSource
FlinkKafkaConsumer<String> kafkaSource = new FlinkKafkaConsumer<String>("flink_kafka", new SimpleStringSchema(), props);
//使用kafkaSource
DataStream<String> kafkaDS = env.addSource(kafkaSource);
//TODO 2.transformation
//TODO 3.sink
kafkaDS.print();
//TODO 4.execute
env.execute();
}
}
准备主题 flink_kafka
/export/server/kafka/bin/kafka-topics.sh --create --zookeeper node1:2181 --replication-factor 2 --partitions 3 --topic flink_kafka
准备主题 flink_kafka2
/export/server/kafka/bin/kafka-topics.sh --create --zookeeper node1:2181 --replication-factor 2 --partitions 3 --topic flink_kafka2
启动控制台生产者发送数据
/export/server/kafka/bin/kafka-console-producer.sh --broker-list node1:9092 --topic flink_kafka
准备的数据
//log:2021-07-10 success xxx
//log:2021-07-10 success xxx
//log:2021-07-10 success xxx
//log:2021-07-10 fail xxx
启动控制台消费者消费数据
/export/server/kafka/bin/kafka-console-consumer.sh --bootstrap-server node1:9092 --topic flink_kafka2 --from-beginning
启动程序FlinkKafkaConsumer
观察控制台输出结果
3.2 Flink-Connectors-KafkaProducer/Sin
package cn.exam.connectors;
import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import java.util.Properties;
/**
* Author SKY
* Desc 演示Flink-Connectors-KafkaComsumer/Source + KafkaProducer/Sink
*/
public class KafkaSinkDemo {
public static void main(String[] args) throws Exception {
//TODO 0.env
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);
//TODO 1.source
//准备kafka连接参数
Properties props = new Properties();
props.setProperty("bootstrap.servers", "node1:9092");//集群地址
props.setProperty("group.id", "flink");//消费者组id
props.setProperty("auto.offset.reset","latest");//latest有offset记录从记录位置开始消费,没有记录从最新的/最后的消息开始消费 /earliest有offset记录从记录位置开始消费,没有记录从最早的/最开始的消息开始消费
props.setProperty("flink.partition-discovery.interval-millis","5000");//会开启一个后台线程每隔5s检测一下Kafka的分区情况,实现动态分区检测
props.setProperty("enable.auto.commit", "true");//自动提交(提交到默认主题,后续学习了Checkpoint后随着Checkpoint存储在Checkpoint和默认主题中)
props.setProperty("auto.commit.interval.ms", "2000");//自动提交的时间间隔
//使用连接参数创建FlinkKafkaConsumer/kafkaSource
FlinkKafkaConsumer<String> kafkaSource = new FlinkKafkaConsumer<String>("flink_kafka", new SimpleStringSchema(), props);
//使用kafkaSource
DataStream<String> kafkaDS = env.addSource(kafkaSource);
//TODO 2.transformation
SingleOutputStreamOperator<String> etlDS = kafkaDS.filter(new FilterFunction<String>() {
@Override
public boolean filter(String value) throws Exception {
return value.contains("success");
}
});
//TODO 3.sink
etlDS.print();
Properties props2 = new Properties();
props2.setProperty("bootstrap.servers", "node1:9092");
FlinkKafkaProducer<String> kafkaSink = new FlinkKafkaProducer<>("flink_kafka2", new SimpleStringSchema(), props2);
etlDS.addSink(kafkaSink);
//TODO 4.execute
env.execute();
}
}
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