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1.10.Flink DataStreamAPI(API的抽象级别、Data Sources、connectors、Source容错性保证、Sink容错性保证、自定义sink、partition等)

發(fā)布時(shí)間：2024/9/27 编程问答 27 豆豆

生活随笔收集整理的這篇文章主要介紹了 1.10.Flink DataStreamAPI(API的抽象级别、Data Sources、connectors、Source容错性保证、Sink容错性保证、自定义sink、partition等) 小編覺得挺不錯(cuò)的,現(xiàn)在分享給大家,幫大家做個(gè)參考.

1.10.Flink DataStreamAPI
1.10.1.Flink API的抽象級(jí)別
1.10.2.DatSource部分詳解
1.10.2.1.DataStream API之Data Sources
1.10.2.2.DataSources API
1.10.2.3.DataStream內(nèi)置connectors
1.10.2.4.Source容錯(cuò)性保證
1.10.2.5.Sink容錯(cuò)性保證
1.10.2.6.自定義sink
1.10.2.7.Table & SQL Connectors
1.10.2.8.自定義source
1.10.2.9.DataStream API之Transformations部分詳解
1.10.2.10.DataStream API之partition
1.10.2.11.DataStream API之Data Sink

1.10.Flink DataStreamAPI

1.10.1.Flink API的抽象級(jí)別

?Flink API 最底層的抽象為有狀態(tài)實(shí)時(shí)流處理。其抽象實(shí)現(xiàn)是 Process Function，并且 Process Function 被 Flink 框架集成到了 DataStream API 中來為我們使用。它允許用戶在應(yīng)用程序中自由地處理來自單流或多流的事件（數(shù)據(jù)），并提供具有全局一致性和容錯(cuò)保障的狀態(tài)。此外，用戶可以在此層抽象中注冊(cè)事件時(shí)間（event time）和處理時(shí)間（processing time）回調(diào)方法，從而允許程序可以實(shí)現(xiàn)復(fù)雜計(jì)算。

?Flink API 第二層抽象是 Core APIs。實(shí)際上，許多應(yīng)用程序不需要使用到上述最底層抽象的 API，而是可以使用 Core APIs 進(jìn)行編程：其中包含 DataStream API（應(yīng)用于有界/無界數(shù)據(jù)流場(chǎng)景）和 DataSet API（應(yīng)用于有界數(shù)據(jù)集場(chǎng)景）兩部分。Core APIs 提供的流式 API（Fluent API）為數(shù)據(jù)處理提供了通用的模塊組件，例如各種形式的用戶自定義轉(zhuǎn)換（transformations）、聯(lián)接（joins）、聚合（aggregations）、窗口（windows）和狀態(tài)（state）操作等。此層 API 中處理的數(shù)據(jù)類型在每種編程語言中都有其對(duì)應(yīng)的類。

Process Function 這類底層抽象和 DataStream API 的相互集成使得用戶可以選擇使用更底層的抽象 API 來實(shí)現(xiàn)自己的需求。DataSet API 還額外提供了一些原語，比如循環(huán)/迭代（loop/iteration）操作。

?Flink API 第三層抽象是 Table API。Table API 是以表（Table）為中心的聲明式編程（DSL）API，例如在流式數(shù)據(jù)場(chǎng)景下，它可以表示一張正在動(dòng)態(tài)改變的表。Table API 遵循（擴(kuò)展）關(guān)系模型：即表擁有 schema（類似于關(guān)系型數(shù)據(jù)庫中的 schema），并且 Table API 也提供了類似于關(guān)系模型中的操作，比如 select、project、join、group-by 和 aggregate 等。Table API 程序是以聲明的方式定義應(yīng)執(zhí)行的邏輯操作，而不是確切地指定程序應(yīng)該執(zhí)行的代碼。盡管 Table API 使用起來很簡潔并且可以由各種類型的用戶自定義函數(shù)擴(kuò)展功能，但還是比 Core API 的表達(dá)能力差。此外，Table API 程序在執(zhí)行之前還會(huì)使用優(yōu)化器中的優(yōu)化規(guī)則對(duì)用戶編寫的表達(dá)式進(jìn)行優(yōu)化。

表和 DataStream/DataSet 可以進(jìn)行無縫切換，Flink 允許用戶在編寫應(yīng)用程序時(shí)將 Table API 與 DataStream/DataSet API 混合使用。

?Flink API 最頂層抽象是 SQL。這層抽象在語義和程序表達(dá)式上都類似于 Table API，但是其程序?qū)崿F(xiàn)都是 SQL 查詢表達(dá)式。SQL 抽象與 Table API 抽象之間的關(guān)聯(lián)是非常緊密的，并且 SQL 查詢語句可以在 Table API 中定義的表上執(zhí)行。

本技術(shù)文檔上案例所需的pom.xml如下：

<?xml version="1.0" encoding="UTF-8"?> <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>xxxxx.demo</groupId><artifactId>flink-demo</artifactId><version>1.0-SNAPSHOT</version><properties><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><maven.test.skip>false</maven.test.skip><maven.javadoc.skip>false</maven.javadoc.skip><maven.compiler.source>1.8</maven.compiler.source><maven.compiler.target>1.8</maven.compiler.target><rocketmq.version>4.7.1</rocketmq.version><flink.version>1.11.1</flink.version><commons-lang.version>2.5</commons-lang.version><scala.binary.version>2.12</scala.binary.version></properties><distributionManagement><repository><id>releases</id><layout>default</layout><url>http://ip/nexus/content/repositories/releases/</url></repository><snapshotRepository><id>snapshots</id><name>snapshots</name><url>http://ip/nexus/content/repositories/snapshots/</url></snapshotRepository></distributionManagement><repositories><repository><id>releases</id><layout>default</layout><url>http://ip/nexus/content/repositories/releases/</url></repository><repository><id>snapshots</id><name>snapshots</name><url>http://ip/nexus/content/repositories/snapshots/</url><snapshots><enabled>true</enabled><updatePolicy>always</updatePolicy><checksumPolicy>warn</checksumPolicy></snapshots></repository><repository><id>tianque</id><name>tianque</name><url>http://ip/nexus/content/repositories/tianque/</url></repository><repository><id>public</id><name>public</name><url>http://ip/nexus/content/groups/public/</url></repository><repository><id>cloudera</id><url>https://repository.cloudera.com/artifactory/cloudera-repos/</url></repository></repositories><dependencies><dependency><groupId>org.apache.flink</groupId><artifactId>flink-java</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java_${scala.binary.version}</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients_${scala.binary.version}</artifactId><version>${flink.version}</version></dependency><dependency><groupId>com.tianque.doraemon</groupId><artifactId>issue-business-api</artifactId><version>1.0.6.RELEASE</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-scala_${scala.binary.version}</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-scala_${scala.binary.version}</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-connector-kafka_${scala.binary.version}</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.kafka</groupId><artifactId>kafka-clients</artifactId><version>2.0.0</version></dependency><dependency><groupId>org.slf4j</groupId><artifactId>slf4j-api</artifactId><version>1.7.25</version></dependency><dependency><groupId>org.slf4j</groupId><artifactId>slf4j-log4j12</artifactId><version>1.7.25</version><scope>test</scope></dependency><dependency><groupId>org.apache.bahir</groupId><artifactId>flink-connector-redis_2.11</artifactId><version>1.0</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-statebackend-rocksdb_${scala.binary.version}</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-queryable-state-runtime_${scala.binary.version}</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.rocketmq</groupId><artifactId>rocketmq-client</artifactId><version>${rocketmq.version}</version></dependency><dependency><groupId>org.apache.rocketmq</groupId><artifactId>rocketmq-acl</artifactId><version>${rocketmq.version}</version></dependency><dependency><groupId>org.apache.rocketmq</groupId><artifactId>rocketmq-common</artifactId><version>${rocketmq.version}</version><exclusions><exclusion><groupId>io.netty</groupId><artifactId>netty-tcnative</artifactId></exclusion></exclusions></dependency><dependency><groupId>commons-lang</groupId><artifactId>commons-lang</artifactId><version>${commons-lang.version}</version></dependency><dependency><groupId>junit</groupId><artifactId>junit</artifactId><scope>test</scope><version>4.12</version></dependency><dependency><groupId>org.powermock</groupId><artifactId>powermock-module-junit4</artifactId><version>1.5.5</version><scope>test</scope></dependency><dependency><groupId>org.powermock</groupId><artifactId>powermock-api-mockito</artifactId><version>1.5.5</version><scope>test</scope></dependency><dependency><groupId>org.apache.rocketmq</groupId><artifactId>rocketmq-namesrv</artifactId><version>${rocketmq.version}</version><scope>test</scope></dependency><dependency><groupId>org.apache.rocketmq</groupId><artifactId>rocketmq-broker</artifactId><version>${rocketmq.version}</version><scope>test</scope></dependency><dependency><groupId>com.tianque</groupId><artifactId>caterpillar-sdk</artifactId><version>0.1.4</version></dependency><dependency><groupId>com.alibaba</groupId><artifactId>fastjson</artifactId><version>1.2.73</version></dependency><dependency><groupId>redis.clients</groupId><artifactId>jedis</artifactId><version>3.3.0</version></dependency></dependencies><build><plugins><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-compiler-plugin</artifactId><version>3.6.0</version><configuration><source>${maven.compiler.source}</source><target>${maven.compiler.target}</target><encoding>UTF-8</encoding><compilerVersion>${maven.compiler.source}</compilerVersion><showDeprecation>true</showDeprecation><showWarnings>true</showWarnings></configuration></plugin><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-surefire-plugin</artifactId><version>2.12.4</version><configuration><skipTests>${maven.test.skip}</skipTests></configuration></plugin><plugin><groupId>org.apache.rat</groupId><artifactId>apache-rat-plugin</artifactId><version>0.12</version><configuration><excludes><exclude>README.md</exclude></excludes></configuration></plugin><plugin><artifactId>maven-checkstyle-plugin</artifactId><version>2.17</version><executions><execution><id>verify</id><phase>verify</phase><configuration><configLocation>style/rmq_checkstyle.xml</configLocation><encoding>UTF-8</encoding><consoleOutput>true</consoleOutput><failsOnError>true</failsOnError><includeTestSourceDirectory>false</includeTestSourceDirectory><includeTestResources>false</includeTestResources></configuration><goals><goal>check</goal></goals></execution></executions></plugin><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-javadoc-plugin</artifactId><version>2.10.4</version><configuration><aggregate>true</aggregate><reportOutputDirectory>javadocs</reportOutputDirectory><locale>en</locale></configuration></plugin><plugin><groupId>net.alchim31.maven</groupId><artifactId>scala-maven-plugin</artifactId><version>3.1.6</version><configuration><scalaCompatVersion>2.11</scalaCompatVersion><scalaVersion>2.11.12</scalaVersion><encoding>UTF-8</encoding></configuration><executions><execution><id>compile-scala</id><phase>compile</phase><goals><goal>add-source</goal><goal>compile</goal></goals></execution><execution><id>test-compile-scala</id><phase>test-compile</phase><goals><goal>add-source</goal><goal>testCompile</goal></goals></execution></executions></plugin><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-assembly-plugin</artifactId><version>2.6</version><configuration><descriptorRefs><descriptorRef>jar-with-dependencies</descriptorRef></descriptorRefs><archive><manifest><mainClass>xxxxx.SocketWindowWordCountJava</mainClass></manifest></archive></configuration><executions><execution><id>make-assembly</id><phase>package</phase><goals><goal>single</goal></goals></execution></executions></plugin></plugins></build></project>

1.10.2.DatSource部分詳解

1.10.2.1.DataStream API之Data Sources

?source是程序的數(shù)據(jù)源輸入，你可以通過StreamExecutionEnvironment.addSource(sourceFunction)來為你的程序添加一個(gè)source。

?Flink提供了大量的已經(jīng)實(shí)現(xiàn)好的source方法，你也可以自定義source

?通過實(shí)現(xiàn)sourceFunction接口來自定義無并行度的source
?或者你也可以通過實(shí)現(xiàn)ParallelSourceFunction接口or繼承RichParallelSourceFunction來自定義有并行度的source。

以下是自定義Source相關(guān)的內(nèi)容

import org.apache.flink.streaming.api.functions.source.SourceFunction;/*** 自定義實(shí)現(xiàn)并行度為1的source** 模擬產(chǎn)生從1開始的遞增數(shù)字*** 注意：* SourceFunction 和 SourceContext 都需要指定數(shù)據(jù)類型，如果不指定，代碼運(yùn)行的時(shí)候會(huì)報(bào)錯(cuò)* Caused by: org.apache.flink.api.common.functions.InvalidTypesException:* The types of the interface org.apache.flink.streaming.api.functions.source.SourceFunction could not be inferred.* Support for synthetic interfaces, lambdas, and generic or raw types is limited at this point** Created by xxxx on 2020/10/09 on 2018/10/23.*/ public class MyNoParalleSource implements SourceFunction<Long>{private long count = 1L;private boolean isRunning = true;/*** 主要的方法* 啟動(dòng)一個(gè)source* 大部分情況下，都需要在這個(gè)run方法中實(shí)現(xiàn)一個(gè)循環(huán)，這樣就可以循環(huán)產(chǎn)生數(shù)據(jù)了** @param ctx* @throws Exception*/@Overridepublic void run(SourceContext<Long> ctx) throws Exception {while(isRunning){ctx.collect(count);count++;//每秒產(chǎn)生一條數(shù)據(jù)Thread.sleep(1000);}}/*** 取消一個(gè)cancel的時(shí)候會(huì)調(diào)用的方法**/@Overridepublic void cancel() {isRunning = false;} }

scala代碼：

import org.apache.flink.streaming.api.functions.source.SourceFunction import org.apache.flink.streaming.api.functions.source.SourceFunction.SourceContext/*** 創(chuàng)建自定義并行度為1的source** 實(shí)現(xiàn)從1開始產(chǎn)生遞增數(shù)字** Created by xxxx on 2020/10/09 on 2018/10/23.*/ class MyNoParallelSourceScala extends SourceFunction[Long]{var count = 1Lvar isRunning = trueoverride def run(ctx: SourceContext[Long]) = {while(isRunning){ctx.collect(count)count+=1Thread.sleep(1000)}}override def cancel() = {isRunning = false}} import org.apache.flink.streaming.api.functions.source.ParallelSourceFunction;/*** 自定義實(shí)現(xiàn)一個(gè)支持并行度的source* Created by xxxx on 2020/10/09 on 2018/10/23.*/ public class MyParalleSource implements ParallelSourceFunction<Long> {private long count = 1L;private boolean isRunning = true;/*** 主要的方法* 啟動(dòng)一個(gè)source* 大部分情況下，都需要在這個(gè)run方法中實(shí)現(xiàn)一個(gè)循環(huán)，這樣就可以循環(huán)產(chǎn)生數(shù)據(jù)了** @param ctx* @throws Exception*/@Overridepublic void run(SourceContext<Long> ctx) throws Exception {while(isRunning){ctx.collect(count);count++;//每秒產(chǎn)生一條數(shù)據(jù)Thread.sleep(1000);}}/*** 取消一個(gè)cancel的時(shí)候會(huì)調(diào)用的方法**/@Overridepublic void cancel() {isRunning = false;} } ```java import org.apache.flink.configuration.Configuration; import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;/*** 自定義實(shí)現(xiàn)一個(gè)支持并行度的source** RichParallelSourceFunction 會(huì)額外提供open和close方法* 針對(duì)source中如果需要獲取其他鏈接資源，那么可以在open方法中獲取資源鏈接，在close中關(guān)閉資源鏈接** Created by xxxx on 2020/10/09 on 2018/10/23.*/ public class MyRichParalleSource extends RichParallelSourceFunction<Long> {private long count = 1L;private boolean isRunning = true;/*** 主要的方法* 啟動(dòng)一個(gè)source* 大部分情況下，都需要在這個(gè)run方法中實(shí)現(xiàn)一個(gè)循環(huán)，這樣就可以循環(huán)產(chǎn)生數(shù)據(jù)了** @param ctx* @throws Exception*/@Overridepublic void run(SourceContext<Long> ctx) throws Exception {while(isRunning){ctx.collect(count);count++;//每秒產(chǎn)生一條數(shù)據(jù)Thread.sleep(1000);}}/*** 取消一個(gè)cancel的時(shí)候會(huì)調(diào)用的方法**/@Overridepublic void cancel() {isRunning = false;}/*** 這個(gè)方法只會(huì)在最開始的時(shí)候被調(diào)用一次* 實(shí)現(xiàn)獲取鏈接的代碼* @param parameters* @throws Exception*/@Overridepublic void open(Configuration parameters) throws Exception {System.out.println("open.............");super.open(parameters);}/*** 實(shí)現(xiàn)關(guān)閉鏈接的代碼* @throws Exception*/@Overridepublic void close() throws Exception {super.close(); }}

使用自己定義的source

import org.apache.flink.api.common.functions.MapFunction; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.datastream.DataStreamSource; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.api.windowing.time.Time;/*** 使用并行度為1的source** Created by xxxx on 2020/10/09 on 2018/10/23.*/ public class StreamingDemoWithMyNoPralalleSource {public static void main(String[] args) throws Exception {//獲取Flink的運(yùn)行環(huán)境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//獲取數(shù)據(jù)源DataStreamSource<Long> text = env.addSource(new MyNoParalleSource()).setParallelism(1);//注意：針對(duì)此source，并行度只能設(shè)置為1DataStream<Long> num = text.map(new MapFunction<Long, Long>() {@Overridepublic Long map(Long value) throws Exception {System.out.println("接收到數(shù)據(jù)：" + value);return value;}});//每2秒鐘處理一次數(shù)據(jù)DataStream<Long> sum = num.timeWindowAll(Time.seconds(2)).sum(0);//打印結(jié)果sum.print().setParallelism(1);String jobName = StreamingDemoWithMyNoPralalleSource.class.getSimpleName();env.execute(jobName);} } import org.apache.flink.api.common.functions.MapFunction; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.datastream.DataStreamSource; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.api.windowing.time.Time;/*** 使用多并行度的source** Created by xxxx on 2020/10/09 on 2018/10/23.*/ public class StreamingDemoWithMyPralalleSource {public static void main(String[] args) throws Exception {//獲取Flink的運(yùn)行環(huán)境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//獲取數(shù)據(jù)源DataStreamSource<Long> text = env.addSource(new MyParalleSource()).setParallelism(2);DataStream<Long> num = text.map(new MapFunction<Long, Long>() {@Overridepublic Long map(Long value) throws Exception {System.out.println("接收到數(shù)據(jù)：" + value);return value;}});//每2秒鐘處理一次數(shù)據(jù)DataStream<Long> sum = num.timeWindowAll(Time.seconds(2)).sum(0);//打印結(jié)果sum.print().setParallelism(1);String jobName = StreamingDemoWithMyPralalleSource.class.getSimpleName();env.execute(jobName);} } import org.apache.flink.api.common.functions.MapFunction; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.datastream.DataStreamSource; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.api.windowing.time.Time;/*** 使用多并行度的source** Created by xxxx on 2020/10/09 on 2018/10/23.*/ public class StreamingDemoWithMyRichPralalleSource {public static void main(String[] args) throws Exception {//獲取Flink的運(yùn)行環(huán)境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//獲取數(shù)據(jù)源DataStreamSource<Long> text = env.addSource(new MyRichParalleSource()).setParallelism(2);DataStream<Long> num = text.map(new MapFunction<Long, Long>() {@Overridepublic Long map(Long value) throws Exception {System.out.println("接收到數(shù)據(jù)：" + value);return value;}});//每2秒鐘處理一次數(shù)據(jù)DataStream<Long> sum = num.timeWindowAll(Time.seconds(2)).sum(0);//打印結(jié)果sum.print().setParallelism(1);String jobName = StreamingDemoWithMyRichPralalleSource.class.getSimpleName();env.execute(jobName);} }

以下是scala的實(shí)現(xiàn)

import org.apache.flink.streaming.api.functions.source.SourceFunction import org.apache.flink.streaming.api.functions.source.SourceFunction.SourceContext/*** 創(chuàng)建自定義并行度為1的source** 實(shí)現(xiàn)從1開始產(chǎn)生遞增數(shù)字** Created by xxxx on 2020/10/09 on 2018/10/23.*/ class MyNoParallelSourceScala extends SourceFunction[Long]{var count = 1Lvar isRunning = trueoverride def run(ctx: SourceContext[Long]) = {while(isRunning){ctx.collect(count)count+=1Thread.sleep(1000)}}override def cancel() = {isRunning = false}} import org.apache.flink.streaming.api.functions.source.ParallelSourceFunction import org.apache.flink.streaming.api.functions.source.SourceFunction.SourceContext/*** 創(chuàng)建自定義并行度為1的source** 實(shí)現(xiàn)從1開始產(chǎn)生遞增數(shù)字** Created by xxxx on 2020/10/09 on 2018/10/23.*/ class MyParallelSourceScala extends ParallelSourceFunction[Long]{var count = 1Lvar isRunning = trueoverride def run(ctx: SourceContext[Long]) = {while(isRunning){ctx.collect(count)count+=1Thread.sleep(1000)}}override def cancel() = {isRunning = false}} import org.apache.flink.configuration.Configuration import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction import org.apache.flink.streaming.api.functions.source.SourceFunction.SourceContext/*** 創(chuàng)建自定義并行度為1的source** 實(shí)現(xiàn)從1開始產(chǎn)生遞增數(shù)字** Created by xxxx on 2020/10/09 on 2018/10/23.*/ class MyRichParallelSourceScala extends RichParallelSourceFunction[Long]{var count = 1Lvar isRunning = trueoverride def run(ctx: SourceContext[Long]) = {while(isRunning){ctx.collect(count)count+=1Thread.sleep(1000)}}override def cancel() = {isRunning = false}override def open(parameters: Configuration): Unit = super.open(parameters)override def close(): Unit = super.close() } import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment import org.apache.flink.streaming.api.windowing.time.Time/*** Created by xxxx on 2020/10/09 on 2018/10/23.*/ object StreamingDemoWithMyNoParallelSourceScala {def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironment//隱式轉(zhuǎn)換import org.apache.flink.api.scala._val text = env.addSource(new MyNoParallelSourceScala)val mapData = text.map(line=>{println("接收到的數(shù)據(jù)："+line)line})val sum = mapData.timeWindowAll(Time.seconds(2)).sum(0)sum.print().setParallelism(1)env.execute("StreamingDemoWithMyNoParallelSourceScala")}} import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment import org.apache.flink.streaming.api.windowing.time.Time/*** Created by xxxx on 2020/10/09 on 2018/10/23.*/ object StreamingDemoWithMyParallelSourceScala {def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironment//隱式轉(zhuǎn)換import org.apache.flink.api.scala._val text = env.addSource(new MyParallelSourceScala).setParallelism(2)val mapData = text.map(line=>{println("接收到的數(shù)據(jù)："+line)line})val sum = mapData.timeWindowAll(Time.seconds(2)).sum(0)sum.print().setParallelism(1)env.execute("StreamingDemoWithMyNoParallelSourceScala")}} import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment import org.apache.flink.streaming.api.windowing.time.Time/*** Created by xxxx on 2020/10/09 on 2018/10/23.*/ object StreamingDemoWithMyRichParallelSourceScala {def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironment//隱式轉(zhuǎn)換import org.apache.flink.api.scala._val text = env.addSource(new MyRichParallelSourceScala).setParallelism(2)val mapData = text.map(line=>{println("接收到的數(shù)據(jù)："+line)line})val sum = mapData.timeWindowAll(Time.seconds(2)).sum(0)sum.print().setParallelism(1)env.execute("StreamingDemoWithMyNoParallelSourceScala")}}

1.10.2.2.DataSources API

?基于文件

?readTextFile(path)
?讀取文本文件，文件遵循TextInputFormat讀取規(guī)則，逐行讀取并返回。
?基于socket
?從socket中讀取數(shù)據(jù)，元素可以通過一個(gè)分隔符切開。
?基于集合
?fromCollection(Collection)
?通過java 的collection集合創(chuàng)建一個(gè)數(shù)據(jù)流，集合中的所有元素必須是相同類型的。
?自定義輸入
?addSource 可以實(shí)現(xiàn)讀取第三方數(shù)據(jù)源的數(shù)據(jù)
?系統(tǒng)內(nèi)置提供了一批connectors，連接器會(huì)提供對(duì)應(yīng)的source支持【kafka】

基于集合的案例fromCollection(Collection)：

1.10.2.3.DataStream內(nèi)置connectors

一些比較基本的 Source 和 Sink 已經(jīng)內(nèi)置在 Flink 里。預(yù)定義 data sources 支持從文件、目錄、socket，以及 collections 和 iterators 中讀取數(shù)據(jù)。預(yù)定義 data sinks 支持把數(shù)據(jù)寫入文件、標(biāo)準(zhǔn)輸出（stdout）、標(biāo)準(zhǔn)錯(cuò)誤輸出（stderr）和socket。
?Apache Kafka (source/sink)
?Apache Cassandra (sink)
?Amazon Kinesis Streams (source/sink)
?Elasticsearch (sink)
?Hadoop FileSystem (sink)
?RabbitMQ (source/sink)
?Apache NiFi (source/sink)
?Twitter Streaming API (source)
?Google PubSub (source/sink)
?JDBC (sink)

在使用一種連接器時(shí)，通常需要額外的第三方組件，比如：數(shù)據(jù)存儲(chǔ)服務(wù)器或者消費(fèi)隊(duì)列。要注意這些列舉的連接器是Flink工程的一部分，包含在發(fā)布的源碼中，但是不包含在二進(jìn)制發(fā)行版本中。

1.10.2.4.Source容錯(cuò)性保證

SourceGuaranteesNotes

Apache Kafka	精確一次	根據(jù)你的版本恰當(dāng)?shù)腒afka連接器
AWS Kinesis Streams	精確一次
RabbitMQ	至多一次 (v 0.10) / 精確一次 (v 1.0)
Twitter Streaming API	至多一次
Google PubSub	至少一次
Collections	精確一次
Files	精確一次
Sockets	至多一次

為了保證端到端精確一次的數(shù)據(jù)交付（在精確一次的狀態(tài)語義上更進(jìn)一步），sink需要參與checkpointing。下表列舉了Flink與其自帶Sink的交付保證（假設(shè)精確一次狀態(tài)更新）

1.10.2.5.Sink容錯(cuò)性保證

SinkGuaranteesNotes

HDFS BucketingSink	精確一次	實(shí)現(xiàn)方法取決于 Hadoop 的版本
Elasticsearch	至少一次
Kafka producer	至少一次/精確一次	當(dāng)使用事務(wù)生產(chǎn)者時(shí)，保證精確一次 (v 0.11+)
Cassandra sink	至少一次 / 精確一次
AWS Kinesis Streams	至少一次
File sinks	精確一次
Socket sinks	至少一次
Standard output	至少一次
Redis sink	至少一次

1.10.2.6.自定義sink

?實(shí)現(xiàn)自定義的sink

?實(shí)現(xiàn)SinkFunction接口
?或者繼承RichSinkFunction

1.10.2.7.Table & SQL Connectors

?Formats
?Kafka
?JDBC
?Elasticsearch
?FileSystem
?HBASE
?DataGen
?Print
?BlackHole

1.10.2.8.自定義source

?實(shí)現(xiàn)并行度為1的自定義source

?實(shí)現(xiàn)SourceFunction
?一般不需要實(shí)現(xiàn)容錯(cuò)性保證
?處理好cancel方法(cancel應(yīng)用的時(shí)候，這個(gè)方法會(huì)被調(diào)用)
?實(shí)現(xiàn)并行化的自定義source
?實(shí)現(xiàn)ParallelSourceFunction
?或者繼承RichParallelSourceFunction

繼承RichParallelSourceFunction的那些SourceFunction意味著它們都是并行執(zhí)行的并且可能有一些資源需要open/close

1.10.2.9.DataStream API之Transformations部分詳解

?map：輸入一個(gè)元素，然后返回一個(gè)元素，中間可以做一些清洗轉(zhuǎn)換等操作
?flatmap：輸入一個(gè)元素，可以返回零個(gè)，一個(gè)或者多個(gè)元素
?filter：過濾函數(shù)，對(duì)傳入的數(shù)據(jù)進(jìn)行判斷，符合條件的數(shù)據(jù)會(huì)被留下

import org.apache.flink.api.common.functions.FilterFunction; import org.apache.flink.api.common.functions.MapFunction; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.datastream.DataStreamSource; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.api.windowing.time.Time; import xxx.xxx.streaming.custormSource.MyNoParalleSource;/*** Filter演示** Created by xxxx on 2020/10/09 on 2018/10/23.*/ public class StreamingDemoFilter {public static void main(String[] args) throws Exception {//獲取Flink的運(yùn)行環(huán)境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//獲取數(shù)據(jù)源//注意：針對(duì)此source，并行度只能設(shè)置為1DataStreamSource<Long> text = env.addSource(new MyNoParalleSource()).setParallelism(1);DataStream<Long> num = text.map(new MapFunction<Long, Long>() {@Overridepublic Long map(Long value) throws Exception {System.out.println("原始接收到數(shù)據(jù)：" + value);return value;}});//執(zhí)行filter過濾，滿足條件的數(shù)據(jù)會(huì)被留下DataStream<Long> filterData = num.filter(new FilterFunction<Long>() {//把所有的奇數(shù)過濾掉@Overridepublic boolean filter(Long value) throws Exception {return value % 2 == 0;}});DataStream<Long> resultData = filterData.map(new MapFunction<Long, Long>() {@Overridepublic Long map(Long value) throws Exception {System.out.println("過濾之后的數(shù)據(jù)：" + value);return value;}});//每2秒鐘處理一次數(shù)據(jù)DataStream<Long> sum = resultData.timeWindowAll(Time.seconds(2)).sum(0);//打印結(jié)果sum.print().setParallelism(1);String jobName = StreamingDemoFilter.class.getSimpleName();env.execute(jobName);} } import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment import org.apache.flink.streaming.api.windowing.time.Time import xxx.xxx.streaming.custormSource.MyNoParallelSourceScala/*** Created by xxxx on 2020/10/09 on 2018/10/23.*/ object StreamingDemoFilterScala {def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironment//隱式轉(zhuǎn)換import org.apache.flink.api.scala._val text = env.addSource(new MyNoParallelSourceScala)val mapData = text.map(line=>{println("原始接收到的數(shù)據(jù)："+line)line}).filter(_ % 2 == 0)val sum = mapData.map(line=>{println("過濾之后的數(shù)據(jù)："+line)line}).timeWindowAll(Time.seconds(2)).sum(0)sum.print().setParallelism(1)env.execute("StreamingDemoWithMyNoParallelSourceScala")}}

?keyBy：根據(jù)指定的key進(jìn)行分組，相同key的數(shù)據(jù)會(huì)進(jìn)入同一個(gè)分區(qū)【典型用法見備注】
?reduce：對(duì)數(shù)據(jù)進(jìn)行聚合操作，結(jié)合當(dāng)前元素和上一次reduce返回的值進(jìn)行聚合操作，然后返回一個(gè)新的值
?aggregations：sum(),min(),max()等
?window：在后面單獨(dú)詳解
?Union：合并多個(gè)流，新的流會(huì)包含所有流中的數(shù)據(jù)，但是union是一個(gè)限制，就是所有合并的流類型必須是一致的。

?Connect：和union類似，但是只能連接兩個(gè)流，兩個(gè)流的數(shù)據(jù)類型可以不同，會(huì)對(duì)兩個(gè)流中的數(shù)據(jù)應(yīng)用不同的處理方法。

import org.apache.flink.api.common.functions.MapFunction; import org.apache.flink.streaming.api.datastream.ConnectedStreams; import org.apache.flink.streaming.api.datastream.DataStreamSource; import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.api.functions.co.CoMapFunction; import xxx.xxx.streaming.custormSource.MyNoParalleSource;/*** connect* 和union類似，但是只能連接兩個(gè)流，兩個(gè)流的數(shù)據(jù)類型可以不同，會(huì)對(duì)兩個(gè)流中的數(shù)據(jù)應(yīng)用不同的處理方法** Created by xxxx on 2020/10/09 on 2018/10/23.*/ public class StreamingDemoConnect {public static void main(String[] args) throws Exception {//獲取Flink的運(yùn)行環(huán)境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//獲取數(shù)據(jù)源//注意：針對(duì)此source，并行度只能設(shè)置為1DataStreamSource<Long> text1 = env.addSource(new MyNoParalleSource()).setParallelism(1);DataStreamSource<Long> text2 = env.addSource(new MyNoParalleSource()).setParallelism(1);SingleOutputStreamOperator<String> text2_str = text2.map(new MapFunction<Long, String>() {@Overridepublic String map(Long value) throws Exception {return "str_" + value;}});ConnectedStreams<Long, String> connectStream = text1.connect(text2_str);SingleOutputStreamOperator<Object> result = connectStream.map(new CoMapFunction<Long, String, Object>() {@Overridepublic Object map1(Long value) throws Exception {return value;}@Overridepublic Object map2(String value) throws Exception {return value;}});//打印結(jié)果result.print().setParallelism(1);String jobName = StreamingDemoConnect.class.getSimpleName();env.execute(jobName);} } import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment import xxx.xxx.streaming.custormSource.MyNoParallelSourceScala/*** Created by xxxx on 2020/10/09 on 2018/10/23.*/ object StreamingDemoConnectScala {def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironment//隱式轉(zhuǎn)換import org.apache.flink.api.scala._val text1 = env.addSource(new MyNoParallelSourceScala)val text2 = env.addSource(new MyNoParallelSourceScala)val text2_str = text2.map("str" + _)val connectedStreams = text1.connect(text2_str)val result = connectedStreams.map(line1=>{line1},line2=>{line2})result.print().setParallelism(1)env.execute("StreamingDemoWithMyNoParallelSourceScala")}}

?CoMap, CoFlatMap：在ConnectedStreams中需要使用這種函數(shù)，類似于map和flatmap
?Split：根據(jù)規(guī)則把一個(gè)數(shù)據(jù)流切分為多個(gè)流

import org.apache.flink.streaming.api.collector.selector.OutputSelector; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.datastream.DataStreamSource; import org.apache.flink.streaming.api.datastream.SplitStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import xxx.xxx.streaming.custormSource.MyNoParalleSource;import java.util.ArrayList;/*** split** 根據(jù)規(guī)則把一個(gè)數(shù)據(jù)流切分為多個(gè)流** 應(yīng)用場(chǎng)景：* 可能在實(shí)際工作中，源數(shù)據(jù)流中混合了多種類似的數(shù)據(jù)，多種類型的數(shù)據(jù)處理規(guī)則不一樣，所以就可以在根據(jù)一定的規(guī)則，* 把一個(gè)數(shù)據(jù)流切分成多個(gè)數(shù)據(jù)流，這樣每個(gè)數(shù)據(jù)流就可以使用不用的處理邏輯了** Created by xxxx on 2020/10/09 on 2018/10/23.*/ public class StreamingDemoSplit {public static void main(String[] args) throws Exception {//獲取Flink的運(yùn)行環(huán)境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//獲取數(shù)據(jù)源//注意：針對(duì)此source，并行度只能設(shè)置為1DataStreamSource<Long> text = env.addSource(new MyNoParalleSource()).setParallelism(1);//對(duì)流進(jìn)行切分，按照數(shù)據(jù)的奇偶性進(jìn)行區(qū)分SplitStream<Long> splitStream = text.split(new OutputSelector<Long>() {@Overridepublic Iterable<String> select(Long value) {ArrayList<String> outPut = new ArrayList<>();if (value % 2 == 0) {outPut.add("even");//偶數(shù)} else {outPut.add("odd");//奇數(shù)}return outPut;}});//選擇一個(gè)或者多個(gè)切分后的流DataStream<Long> evenStream = splitStream.select("even");DataStream<Long> oddStream = splitStream.select("odd");DataStream<Long> moreStream = splitStream.select("odd","even");//打印結(jié)果moreStream.print().setParallelism(1);String jobName = StreamingDemoSplit.class.getSimpleName();env.execute(jobName);} } import java.utilimport org.apache.flink.streaming.api.collector.selector.OutputSelector import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment import xxx.xxx.streaming.custormSource.MyNoParallelSourceScala/*** Created by xxxx on 2020/10/09 on 2018/10/23.*/ object StreamingDemoSplitScala {def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironment//隱式轉(zhuǎn)換import org.apache.flink.api.scala._val text = env.addSource(new MyNoParallelSourceScala)val splitStream = text.split(new OutputSelector[Long] {override def select(value: Long) = {val list = new util.ArrayList[String]()if(value%2 == 0){list.add("even")// 偶數(shù)}else{list.add("odd")// 奇數(shù)}list}})val evenStream = splitStream.select("even")evenStream.print().setParallelism(1)env.execute("StreamingDemoWithMyNoParallelSourceScala")}}

?Select：和split配合使用，選擇切分后的流

兩種典型用法：
dataStream.keyBy(“someKey”) // 指定對(duì)象中的 "someKey"字段作為分組key
dataStream.keyBy(0) //指定Tuple中的第一個(gè)元素作為分組key

注意：以下類型是無法作為key的

1：一個(gè)實(shí)體類對(duì)象，沒有重寫hashCode方法，并且依賴object的hashCode方法
2：一個(gè)任意形式的數(shù)組類型
3：基本數(shù)據(jù)類型，int，long

1.10.2.10.DataStream API之partition

?Random partitioning：隨機(jī)分區(qū)

?dataStream.shuffle()
?Rebalancing：對(duì)數(shù)據(jù)集進(jìn)行再平衡，重分區(qū)，消除數(shù)據(jù)傾斜
?dataStream.rebalance()
?Rescaling：解釋見備注
?dataStream.rescale()
?Custom partitioning：自定義分區(qū)
?自定義分區(qū)需要實(shí)現(xiàn)Partitioner接口
?dataStream.partitionCustom(partitioner, “someKey”)
?或者dataStream.partitionCustom(partitioner, 0);
?Broadcasting：在后面單獨(dú)詳解

Rescaling解釋：
舉個(gè)例子：
如果上游操作有2個(gè)并發(fā)，而下游操作有4個(gè)并發(fā)，那么上游的一個(gè)并發(fā)結(jié)果分配給下游的兩個(gè)并發(fā)操作，另外的一個(gè)并發(fā)結(jié)果分配給了下游的另外兩個(gè)并發(fā)操作.另一方面，下游有兩個(gè)并發(fā)操作而上游又4個(gè)并發(fā)操作，那么上游的其中兩個(gè)操作的結(jié)果分配給下游的一個(gè)并發(fā)操作而另外兩個(gè)并發(fā)操作的結(jié)果則分配給另外一個(gè)并發(fā)操作。

Rescaling與Rebalancing的區(qū)別：
Rebalancing會(huì)產(chǎn)生全量重分區(qū)，而Rescaling不會(huì)。

自定義分區(qū)案例：

import org.apache.flink.api.common.functions.Partitioner;/*** Created by xxxx on 2020/10/09*/ public class MyPartition implements Partitioner<Long> {@Overridepublic int partition(Long key, int numPartitions) {System.out.println("分區(qū)總數(shù)："+numPartitions);if(key % 2 == 0){return 0;}else{return 1;}} } import org.apache.flink.api.common.functions.MapFunction; import org.apache.flink.api.java.tuple.Tuple1; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.datastream.DataStreamSource; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import xxx.xxx.streaming.custormSource.MyNoParalleSource;/**** 使用自定義分析* 根據(jù)數(shù)字的奇偶性來分區(qū)** Created by xxxx on 2020/10/09 on 2018/10/23.*/ public class SteamingDemoWithMyParitition {public static void main(String[] args) throws Exception{StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(2);DataStreamSource<Long> text = env.addSource(new MyNoParalleSource());//對(duì)數(shù)據(jù)進(jìn)行轉(zhuǎn)換，把long類型轉(zhuǎn)成tuple1類型DataStream<Tuple1<Long>> tupleData = text.map(new MapFunction<Long, Tuple1<Long>>() {@Overridepublic Tuple1<Long> map(Long value) throws Exception {return new Tuple1<>(value);}});//分區(qū)之后的數(shù)據(jù)DataStream<Tuple1<Long>> partitionData = tupleData.partitionCustom(new MyPartition(), 0);DataStream<Long> result = partitionData.map(new MapFunction<Tuple1<Long>, Long>() {@Overridepublic Long map(Tuple1<Long> value) throws Exception {System.out.println("當(dāng)前線程id：" + Thread.currentThread().getId() + ",value: " + value);return value.getField(0);}});result.print().setParallelism(1);env.execute("SteamingDemoWithMyParitition");} }

scala案例：

import org.apache.flink.api.common.functions.Partitioner/*** Created by xxxx on 2020/10/09 on 2018/10/23.*/ class MyPartitionerScala extends Partitioner[Long]{override def partition(key: Long, numPartitions: Int) = {println("分區(qū)總數(shù)："+numPartitions)if(key % 2 ==0){0}else{1}}} import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment import xxx.xxx.streaming.custormSource.MyNoParallelSourceScala/*** Created by xxxx on 2020/10/09 on 2018/10/23.*/ object StreamingDemoMyPartitionerScala {def main(args: Array[String]): Unit = {val env = StreamExecutionEnvironment.getExecutionEnvironmentenv.setParallelism(2)//隱式轉(zhuǎn)換import org.apache.flink.api.scala._val text = env.addSource(new MyNoParallelSourceScala)//把long類型的數(shù)據(jù)轉(zhuǎn)成tuple類型val tupleData = text.map(line=>{Tuple1(line)// 注意tuple1的實(shí)現(xiàn)方式})val partitionData = tupleData.partitionCustom(new MyPartitionerScala,0)val result = partitionData.map(line=>{println("當(dāng)前線程id："+Thread.currentThread().getId+",value: "+line)line._1})result.print().setParallelism(1)env.execute("StreamingDemoWithMyNoParallelSourceScala")}}

1.10.2.11.DataStream API之Data Sink

?writeAsText()：將元素以字符串形式逐行寫入，這些字符串通過調(diào)用每個(gè)元素的toString()方法來獲取
?print() / printToErr()：打印每個(gè)元素的toString()方法的值到標(biāo)準(zhǔn)輸出或者標(biāo)準(zhǔn)錯(cuò)誤輸出流中
?自定義輸出addSink【kafka、redis】

關(guān)于redis sink的案例：

import org.apache.flink.api.common.functions.MapFunction; import org.apache.flink.api.java.tuple.Tuple2; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.datastream.DataStreamSource; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.connectors.redis.RedisSink; import org.apache.flink.streaming.connectors.redis.common.config.FlinkJedisPoolConfig; import org.apache.flink.streaming.connectors.redis.common.mapper.RedisCommand; import org.apache.flink.streaming.connectors.redis.common.mapper.RedisCommandDescription; import org.apache.flink.streaming.connectors.redis.common.mapper.RedisMapper;/*** 接收socket數(shù)據(jù)，把數(shù)據(jù)保存到redis中** list** lpush list_key value** Created by xxxx on 2020/10/09 on 2018/10/23.*/ public class StreamingDemoToRedis {public static void main(String[] args) throws Exception{StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStreamSource<String> text = env.socketTextStream("hadoop100", 9000, "\n");//lpsuh l_words word//對(duì)數(shù)據(jù)進(jìn)行組裝,把string轉(zhuǎn)化為tuple2<String,String>DataStream<Tuple2<String, String>> l_wordsData = text.map(new MapFunction<String, Tuple2<String, String>>() {@Overridepublic Tuple2<String, String> map(String value) throws Exception {return new Tuple2<>("l_words", value);}});//創(chuàng)建redis的配置FlinkJedisPoolConfig conf = new FlinkJedisPoolConfig.Builder().setHost("hadoop110").setPort(6379).build();//創(chuàng)建redissinkRedisSink<Tuple2<String, String>> redisSink = new RedisSink<>(conf, new MyRedisMapper());l_wordsData.addSink(redisSink);env.execute("StreamingDemoToRedis");}public static class MyRedisMapper implements RedisMapper<Tuple2<String, String>>{//表示從接收的數(shù)據(jù)中獲取需要操作的redis key@Overridepublic String getKeyFromData(Tuple2<String, String> data) {return data.f0;}//表示從接收的數(shù)據(jù)中獲取需要操作的redis value@Overridepublic String getValueFromData(Tuple2<String, String> data) {return data.f1;}@Overridepublic RedisCommandDescription getCommandDescription() {return new RedisCommandDescription(RedisCommand.LPUSH);}} } import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment import org.apache.flink.streaming.connectors.redis.RedisSink import org.apache.flink.streaming.connectors.redis.common.config.FlinkJedisPoolConfig import org.apache.flink.streaming.connectors.redis.common.mapper.{RedisCommand, RedisCommandDescription, RedisMapper}/***** Created by xxxx on 2020/10/09 .*/ object StreamingDataToRedisScala {def main(args: Array[String]): Unit = {//獲取socket端口號(hào)val port = 9000//獲取運(yùn)行環(huán)境val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment//鏈接socket獲取輸入數(shù)據(jù)val text = env.socketTextStream("hadoop100",port,'\n')//注意：必須要添加這一行隱式轉(zhuǎn)行，否則下面的flatmap方法執(zhí)行會(huì)報(bào)錯(cuò)import org.apache.flink.api.scala._val l_wordsData = text.map(line=>("l_words_scala",line))val conf = new FlinkJedisPoolConfig.Builder().setHost("hadoop110").setPort(6379).build()val redisSink = new RedisSink[Tuple2[String,String]](conf,new MyRedisMapper)l_wordsData.addSink(redisSink)//執(zhí)行任務(wù)env.execute("Socket window count");}class MyRedisMapper extends RedisMapper[Tuple2[String,String]]{override def getKeyFromData(data: (String, String)) = {data._1}override def getValueFromData(data: (String, String)) = {data._2}override def getCommandDescription = {new RedisCommandDescription(RedisCommand.LPUSH)}} }

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