本篇内容介绍了“flinksql env的定义”的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！
1、编写 pom
<?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>org.example</groupId>
<artifactId>flinksqldemo</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<!-- Encoding -->
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<scala.binary.version>2.11</scala.binary.version>
<scala.version>2.11.8</scala.version>
<kafka.version>0.10.2.1</kafka.version>
<flink.version>1.12.0</flink.version>
<hadoop.version>2.7.3</hadoop.version>
<!-- scope 本地调试时注销 设定为默认的 compile 打包时设定为 provided -->
<setting.scope>compile</setting.scope>
</properties>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<configuration>
<source>8</source>
<target>8</target>
</configuration>
</plugin>
</plugins>
</build>
<dependencies>
<!--flink start-->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-planner-blink_2.11</artifactId>
<version>1.12.0</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>${flink.version}</version>
<scope>${setting.scope}</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_2.11</artifactId>
<version>${flink.version}</version>
<scope>${setting.scope}</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients_2.11</artifactId>
<version>${flink.version}</version>
<scope>${setting.scope}</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-kafka-0.10_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-scala_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
<scope>${setting.scope}</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-filesystem_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
</dependency>
<!--<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-statebackend-rocksdb_${scala.binary.version}</artifactId>
<version>${flink.version}</version>
</dependency>-->
<!-- flink end-->
<!-- kafka start -->
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_${scala.binary.version}</artifactId>
<version>${kafka.version}</version>
<scope>${setting.scope}</scope>
</dependency>
<!-- kafka end-->
<!-- hadoop start -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>${hadoop.version}</version>
<scope>${setting.scope}</scope>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>${hadoop.version}</version>
<scope>${setting.scope}</scope>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
<scope>${setting.scope}</scope>
</dependency>
<!-- hadoop end -->
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
<version>1.7.25</version>
</dependency>
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>fastjson</artifactId>
<version>1.2.72</version>
</dependency>
<dependency>
<groupId>redis.clients</groupId>
<artifactId>jedis</artifactId>
<version>2.7.3</version>
</dependency>
<dependency>
<groupId>com.google.guava</groupId>
<artifactId>guava</artifactId>
<version>29.0-jre</version>
</dependency>
</dependencies>
</project>2、编写代码
package com.jd.data;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.ExecutionEnvironment;
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.table.api.EnvironmentSettings;
import org.apache.flink.table.api.TableEnvironment;
import org.apache.flink.table.api.bridge.java.BatchTableEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
public class FlinkTableApiDemo {
public static void main(String[] args) {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
DataStreamSource<String> stream = env.readTextFile("/Users/liuhaijing/Desktop/flinktestword/aaa.txt");
// 1、创建表执行环节
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
// ==============================================
// 1.1 老版本planner的流式查询
EnvironmentSettings set = EnvironmentSettings.newInstance()
.useOldPlanner() //用老版本
.inStreamingMode() //流式处理
.build();
// 老版本的流式处理执行环境
StreamTableEnvironment oldStreamingEnv = StreamTableEnvironment.create(env, set);
// 1.2 老版本批处理环境
ExecutionEnvironment executionEnvironment = ExecutionEnvironment.getExecutionEnvironment();
BatchTableEnvironment batchTableEnvironment = BatchTableEnvironment.create(executionEnvironment);
// =========================================================
// 1.3 blink 版本的流式查询
EnvironmentSettings settings = EnvironmentSettings.newInstance()
.useBlinkPlanner()
.inStreamingMode()
.build();
StreamTableEnvironment blinkTableEnv = StreamTableEnvironment.create(env, settings);
// 1.4 blink 版本的批处理查询
EnvironmentSettings bsettings = EnvironmentSettings.newInstance()
.useBlinkPlanner()
.inBatchMode()
.build();
TableEnvironment blinkBatchTableEnvironment = TableEnvironment.create(settings);
}
}“flinksql env的定义”的内容就介绍到这里了，感谢大家的阅读。如果想了解更多行业相关的知识可以关注亿速云网站，小编将为大家输出更多高质量的实用文章！
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