Spark高级玩法 2020-06-09
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