大数据之Flink（一）

1、简介

flink是一个分布式计算/处理引擎，用于对无界和有界数据流进行状态计算。

flink处理流程

应用场景：

电商销售：实时报表、广告投放、实时推荐
物联网：实时数据采集、实时报警
物流配送、服务：订单状态跟踪、信息推送
银行、金融：实时结算、风险检测

有状态的流式处理

用内存的本地状态替代传统的数据库

flink核心特点：高吞吐低延迟、结果准确性、精确一次的状态一致性保证、兼容性好、高可用和动态扩展。

分层API：

SQL-最高层语言
Table API-声明式领域专用语言
DataStream/ DataSet API-核心API
有状态流处理-底层API

Flink与Spark对比

数据模型
spark采用RDD模型，spark streaming的DStream实际上也是一组小批量数据集（RDD的集合）
flink基本数据模型是数据流以及时间序列
运行时架构
spark是批计算，将DAG划分为不同的stage，一个完成后才计算下一个
flink是标准的流执行模式，一个事件在一个节点处理完后直接发往下一个节点进行处理
2、测试wordcount

maven的pom文件

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><groupId>org.lizhe</groupId><artifactId>flinkDemo</artifactId><version>1.0-SNAPSHOT</version><build><plugins><!----><!--net.alchim31.maven--><!--scala-maven-plugin--><!--3.4.6--><!----><!----><!----><!--compile--><!----><!----><!----><!----><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-assembly-plugin</artifactId><version>3.0.0</version><configuration><descriptorRefs><descriptorRef>jar-with-dependencies</descriptorRef></descriptorRefs></configuration><executions><execution><id>make-assembly</id><phase>package</phase><goals><goal>single</goal></goals></execution></executions></plugin><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-compiler-plugin</artifactId><configuration><source>8</source><target>8</target></configuration></plugin></plugins></build><dependencies><dependency><groupId>org.apache.flink</groupId><artifactId>flink-java</artifactId><version>1.13.0</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java_2.12</artifactId><version>1.13.0</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients_2.12</artifactId><version>1.13.0</version></dependency><dependency><groupId>org.apache.logging.log4j</groupId><artifactId>log4j-core</artifactId><version>2.8.2</version></dependency></dependencies></project>

java代码（批处理）

package FlinkDemo;import org.apache.flink.api.common.functions.FlatMapFunction;import org.apache.flink.api.common.typeinfo.Types;import org.apache.flink.api.java.ExecutionEnvironment;import org.apache.flink.api.java.operators.AggregateOperator;import org.apache.flink.api.java.operators.DataSource;import org.apache.flink.api.java.operators.FlatMapOperator;import org.apache.flink.api.java.operators.UnsortedGrouping;import org.apache.flink.api.java.tuple.Tuple2;import org.apache.flink.util.Collector;/** * @Title: FlinkWordCount * @Author lizhe * @Package FlinkDemo * @Date 2024/2/24 22:03 * @description: FLinkDemo */public class FlinkWordCount {public static void main(String[] args) throws Exception {//初始化环境ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();DataSource<String> dataSource = env.readTextFile("input/words.txt");//对每行进行分词，并转换成元组类型FlatMapOperator<String, Tuple2<String, Long>> wordAndOneTuple = dataSource.flatMap((String line, Collector<Tuple2<String, Long>> col) -> {String[] words = line.split(" ");for (String word : words) {col.collect(Tuple2.of(word, 1L));}}) .returns(Types.TUPLE(Types.STRING,Types.LONG));//returns 方法指定的返回数据类型Tuple2，就是 Flink 自带的二元组数据类型。//按照第一个字段进行聚合UnsortedGrouping<Tuple2<String, Long>> group = wordAndOneTuple.groupBy(0);//按照第二个字段进行求和AggregateOperator<Tuple2<String, Long>> sum = group.sum(1);sum.print();}}

java代码（流处理）

package FlinkDemo;import org.apache.flink.api.common.typeinfo.Types;import org.apache.flink.api.java.functions.KeySelector;import org.apache.flink.api.java.tuple.Tuple;import org.apache.flink.api.java.tuple.Tuple2;import org.apache.flink.streaming.api.datastream.DataStreamSource;import org.apache.flink.streaming.api.datastream.KeyedStream;import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;import org.apache.flink.util.Collector;/** * @Title: BoundedStreamWordCount * @Author lizhe * @Package FlinkDemo * @Date 2024/2/25 18:57 * @description: BoundedStreamWordCount */public class BoundedStreamWordCount {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env=StreamExecutionEnvironment.getExecutionEnvironment();DataStreamSource<String> streamSource = env.readTextFile("input/words.txt");SingleOutputStreamOperator<Tuple2<String, Long>> singleOutputStreamOperator = streamSource.flatMap((String line, Collector<Tuple2<String, Long>> col) -> {String[] words = line.split(" ");for (String word : words) {col.collect(Tuple2.of(word, 1L));}}).returns(Types.TUPLE(Types.STRING, Types.LONG));KeyedStream<Tuple2<String, Long>, Object> keyedStream = singleOutputStreamOperator.keyBy(new KeySelector<Tuple2<String, Long>, Object>() {@Overridepublic Object getKey(Tuple2<String, Long> stringLongTuple2) throws Exception {return stringLongTuple2.f0;}});SingleOutputStreamOperator<Tuple2<String, Long>> sum = keyedStream.sum(1);sum.print();env.execute();}}

输出结果：线程号>(单词，个数)

7> (flink,1)8> (hadoop,1)3> (hello,1)5> (world,1)3> (hello,2)3> (hello,3)

监听socket流

启动netcat
```
nc -lk 7777
```

编写代码

package FlinkDemo;import org.apache.flink.api.common.typeinfo.Types;import org.apache.flink.api.java.ExecutionEnvironment;import org.apache.flink.api.java.functions.KeySelector;import org.apache.flink.api.java.tuple.Tuple2;import org.apache.flink.streaming.api.datastream.DataStreamSource;import org.apache.flink.streaming.api.datastream.KeyedStream;import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;import org.apache.flink.util.Collector;/** * @Title: StreamWordCount * @Author lizhe * @Package FlinkDemo * @Date 2024/2/25 20:34 * @description: StreamWordCount */public class StreamWordCount {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStreamSource<String> dataStreamSource = env.socketTextStream("192.168.132.101", 7777);SingleOutputStreamOperator<Tuple2<String, Long>> singleOutputStreamOperator = dataStreamSource.flatMap((String line, Collector<Tuple2<String, Long>> col) -> {String[] words = line.split(" ");for (String word : words) {col.collect(Tuple2.of(word, 1L));}}).returns(Types.TUPLE(Types.STRING, Types.LONG));KeyedStream<Tuple2<String, Long>, Object> keyedStream = singleOutputStreamOperator.keyBy(new KeySelector<Tuple2<String, Long>, Object>() {@Overridepublic Object getKey(Tuple2<String, Long> stringLongTuple2) throws Exception {return stringLongTuple2.f0;}});SingleOutputStreamOperator<Tuple2<String, Long>> sum = keyedStream.sum(1);sum.print();env.execute();}}

netcat发送

查看结果

3> (hello,1)7> (flink,1)5> (world,1)3> (hello,2)3> (hello,3)8> (hadoop,1)

3、flink快速部署

fink提交作业和执行任务需要：客户端（Client）、作业管理器（JobManager）、任务管理器（TaskManager）

JobManager对作业进行调度管理，把任务分发给TaskManager，TaskManager对数据进行处理。

本地启动

下载flink，地址：https://flink.apache.org/downloads/#apache-flink-downloads

解压后直接启动

[root@hadoop101 soft] cd /export/soft/flink-1.13.0[root@hadoop101 flink-1.13.0] bin/start-cluster.shStarting cluster.Starting standalonesession daemon on host hadoop101.Starting taskexecutor daemon on host hadoop101.

访问webUI：http://hadoop101:8081/

关闭命令

bin/stop-cluster.sh

集群启动
修改flink下的conf/flink-conf.yaml，修改jobmanager.rpc.address:hadoop100，并修改works文件，其中添加hadoop101，hadoop102

在master节点输入启动命令，得到

[root@hadoop100 flink-1.13.0]# bin/start-cluster.shStarting cluster.Starting standalonesession daemon on host hadoop100.Starting taskexecutor daemon on host hadoop101.Starting taskexecutor daemon on host hadoop102.

集群停止同本地启动。

向集群提交作业
程序使用maven打包，并提交到web端

启动netcat，并在netcat中发送数据

大数据之Flink（一）

1、简介

2、测试wordcount

3、flink快速部署

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大数据之Flink（一）

1、简介

2、测试wordcount

3、flink快速部署

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