标签：specific 例子 mvn snapshot cat 获得 string exe rem
1. API基本概念
Flink程序可以对分布式集合进行转换（例如： filtering, mapping, updating state, joining, grouping, defining windows, aggregating）
集合最初是从源创建的（例如，从文件、kafka主题或本地内存集合中读取）
结果通过sink返回，例如，可以将数据写入(分布式)文件，或者写入标准输出(例如，命令行终端)
根据数据源的类型（有界或无界数据源），可以编写批处理程序或流处理程序，其中使用DataSet API进行批处理，并使用DataStream API进行流处理。
Flink有特殊的类DataSet和DataStream来表示程序中的数据。在DataSet的情况下，数据是有限的，而对于DataStream，元素的数量可以是无限的。
Flink程序看起来像转换数据集合的常规程序。每个程序都包含相同的基本部分：
为了方便演示，先创建一个项目，可以从maven模板创建，例如：
mvn archetype:generate \ -DarchetypeGroupId=org.apache.flink \ -DarchetypeArtifactId=flink-quickstart-java \ -DarchetypeVersion=1.10.0 \ -DgroupId=com.cjs.example \ -DartifactId=flink-quickstart \ -Dversion=1.0.0-SNAPSHOT \ -Dpackage=com.cjs.example.flink \ -DinteractiveMode=false
也可以直接创建SpringBoot项目，自行引入依赖：
<dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-java</artifactId> <version>1.10.0</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-streaming-java_2.11</artifactId> <version>1.10.0</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-connector-kafka-0.10_2.11</artifactId> <version>1.10.0</version> </dependency>
StreamExecutionEnvironment是所有Flink程序的基础。你可以在StreamExecutionEnvironment上使用以下静态方法获得一个：
getExecutionEnvironment() createLocalEnvironment() createRemoteEnvironment(String host, int port, String... jarFiles)
通常，只需要使用getExecutionEnvironment()即可，因为该方法会根据上下文自动推断出当前的执行环境
从文件中读取数据，例如：
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); DataStream<String> text = env.readTextFile("file:///path/to/file");
对DataStream应用转换，例如：
DataStream<String> input = ...; DataStream<Integer> parsed = input.map(new MapFunction<String, Integer>() { @Override public Integer map(String value) { return Integer.parseInt(value); } });
通过创建一个sink将结果输出，例如：
writeAsText(String path)
print()
最后，调用StreamExecutionEnvironment上的execute()执行：
// Triggers the program execution env.execute(); // Triggers the program execution asynchronously final JobClient jobClient = env.executeAsync(); final JobExecutionResult jobExecutionResult = jobClient.getJobExecutionResult(userClassloader).get();
下面通过单词统计的例子来加深对这一流程的理解，WordCount程序之于大数据就相当于是HelloWorld之于Java，哈哈哈
package com.cjs.example.flink; import org.apache.flink.api.common.functions.FlatMapFunction; import org.apache.flink.api.java.DataSet; import org.apache.flink.api.java.ExecutionEnvironment; import org.apache.flink.api.java.tuple.Tuple2; import org.apache.flink.util.Collector; /** * Map-Reduce思想 * 先分组，再求和 * @author ChengJianSheng * @date 2020-05-26 */ public class WordCount { public static void main(String[] args) throws Exception { ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); DataSet<String> text = env.readTextFile("/Users/asdf/Desktop/input.txt"); DataSet<Tuple2<String, Integer>> counts = // split up the lines in pairs (2-tuples) containing: (word,1) text.flatMap(new Tokenizer()) // group by the tuple field "0" and sum up tuple field "1" .groupBy(0) .sum(1); counts.writeAsCsv("/Users/asdf/Desktop/aaa", "\n", " "); env.execute(); } static class Tokenizer implements FlatMapFunction<String, Tuple2<String, Integer>> { @Override public void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception { // normalize and split the line String[] tokens = value.toLowerCase().split("\\W+"); // emit the pairs for (String token : tokens) { if (token.length() > 0) { out.collect(new Tuple2<>(token, 1)); } } } } }
为Tuple定义keys
Python中也有Tuple（元组）
DataStream<Tuple3<Integer,String,Long>> input = // [...] KeyedStream<Tuple3<Integer,String,Long>,Tuple> keyed = input.keyBy(0)
元组按第一个字段(整数类型的字段)分组
还可以使用POJO的属性来定义keys，例如：
// some ordinary POJO (Plain old Java Object) public class WC { public String word; public int count; } DataStream<WC> words = // [...] DataStream<WC> wordCounts = words.keyBy("word").window(/*window specification*/);
先来了解一下KeyedStream
因此可以通过KeySelector方法来自定义
// some ordinary POJO public class WC {public String word; public int count;} DataStream<WC> words = // [...] KeyedStream<WC> keyed = words .keyBy(new KeySelector<WC, String>() { public String getKey(WC wc) { return wc.word; } });
如何指定转换方法呢？
方式一：匿名内部类
data.map(new MapFunction<String, Integer> () { public Integer map(String value) { return Integer.parseInt(value); } });
方式二：Lamda
data.filter(s -> s.startsWith("http://"));
data.reduce((i1,i2) -> i1 + i2);
2. DataStream API
下面这个例子，每10秒钟统计一次来自Web Socket的单词次数
package com.cjs.example.flink; 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.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.api.windowing.time.Time; import org.apache.flink.util.Collector; public class WindowWordCount { public static void main(String[] args) throws Exception { StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); DataStream<Tuple2<String, Integer>> dataStream = env.socketTextStream("localhost", 9999) .flatMap(new Splitter()) .keyBy(0) .timeWindow(Time.seconds(10)) .sum(1); dataStream.print(); env.execute("Window WordCount"); } static class Splitter implements FlatMapFunction<String, Tuple2<String, Integer>> { @Override public void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception { String[] words = value.split("\\W+"); for (String word : words) { out.collect(new Tuple2<String, Integer>(word, 1)); } } } }
为了运行此程序，首先要在终端启动一个监听
nc -lk 9999
https://ci.apache.org/projects/flink/flink-docs-release-1.10/dev/datastream_api.html
标签：specific 例子 mvn snapshot cat 获得 string exe rem
原文地址：https://www.cnblogs.com/cjsblog/p/12967555.html
