Flink学习(四) Flink Table & SQL 实现wordcount Java版本
Flink Table & SQL WordCount
Flink SQL 是 Flink 实时计算为简化计算模型,降低用户使用实时计算门槛而设计的一套符合标准 SQL 语义的开发语言。
一个完整的 Flink SQL 编写的程序包括如下三部分。
Source Operator:是对外部数据源的抽象, 目前 Apache Flink 内置了很多常用的数据源实现,比如 MySQL、Kafka 等。
Transformation Operators:算子操作主要完成比如查询、聚合操作等,目前 Flink SQL 支持了 Union、Join、Projection、Difference、Intersection 及 window 等大多数传统数据库支持的操作。
Sink Operator:是对外结果表的抽象,目前 Apache Flink 也内置了很多常用的结果表的抽象,比如 Kafka Sink 等。
我们也是通过用一个最经典的 WordCount 程序作为入门,上面已经通过 DataSet/DataStream API 开发,那么实现同样的 WordCount 功能, Flink Table & SQL 核心只需要一行代码:
//省略掉初始化环境等公共代码 SELECT word, COUNT(word) FROM table GROUP BY word;
首先,整个工程中我们 pom 中的依赖如下图所示:
<dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-java</artifactId> <version>1.10.0</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-streaming-java_2.11 <version>1.10.0</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-table-api-java-bridge_2.11</artifactId> <version>1.10.0</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-table-planner-blink_2.11</artifactId> <version>1.10.0</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-table-planner_2.11</artifactId> <version>1.10.0</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-table-api-scala-bridge_2.11</artifactId> <version>1.10.0</version> </dependency>
第一步,创建上下文环境:
ExecutionEnvironment fbEnv = ExecutionEnvironment.getExecutionEnvironment();
BatchTableEnvironment fbTableEnv = BatchTableEnvironment.create(fbEnv);
第二步,读取一行模拟数据作为输入:
String words = "hello flink hello lagou"; String[] split = words.split("\\W+"); ArrayList<WC> list = new ArrayList<>(); for(String word : split){ WC wc = new WC(word,1); list.add(wc); } DataSet<WC> input = fbEnv.fromCollection(list);
第三步,注册成表,执行 SQL,然后输出:
//DataSet 转sql, 指定字段名 Table table = fbTableEnv.fromDataSet(input, "word,frequency"); table.printSchema(); //注册为一个表 fbTableEnv.createTemporaryView("WordCount", table); Table table02 = fbTableEnv.sqlQuery("select word as word, sum(frequency) as frequency from WordCount GROUP BY word"); //将表转换DataSet DataSet<WC> ds3 = fbTableEnv.toDataSet(table02, WC.class); ds3.printToErr();
整体代码结构如下:
package wyh.tableApi;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.java.BatchTableEnvironment;
import java.util.ArrayList;
public class WCTableApi {
public static void main(String[] args) {
ExecutionEnvironment fbEnv = ExecutionEnvironment.getExecutionEnvironment();
BatchTableEnvironment fbTableEnv = BatchTableEnvironment.create(fbEnv);
String words="hello flink hello shujia";
String[] split = words.split("\\W+");
ArrayList<WC> list = new ArrayList<>();
for (String word : split) {
WC wc = new WC(word, 1L);
list.add(wc);
}
// DataSet<WC> input = fbEnv.fromCollection(list);
DataSource<WC> input = fbEnv.fromCollection(list);
Table table = fbTableEnv.fromDataSet(input, "word,frequency");
table.printSchema();
fbTableEnv.createTemporaryView("wordcount",table);
Table table1 = fbTableEnv.sqlQuery("select word,sum(frequency) as frequency from wordcount group by word");
DataSet<WC> ds3 = fbTableEnv.toDataSet(table1, WC.class);
try {
ds3.printToErr();
} catch (Exception e) {
e.printStackTrace();
}
}
public static class WC{
public String word;
public Long frequency;
public WC() {
}
public WC(String word, Long frequency) {
this.word = word;
this.frequency = frequency;
}
@Override
public String toString() {
return "WC{" +
"word='" + word + '\'' +
", frequency=" + frequency +
'}';
}
}
}
我们直接运行该程序,在控制台可以看到输出结果: