1.flink 窗口的分类
1.1 分类
2. 先看基于处理时间的窗口
2.1 处理时间的滚动窗口
2.1.1 先可以看看官网的描述
·https://ci.apache.org/projects/flink/flink-docs-master/docs/dev/table/tableapi/
proctime() 需要注意的是这个指定的处理时间
package com.wudl.flink.sql;
import com.wudl.flink.bean.WaterSensor;
import org.apache.flink.api.common.functions.MapFunction;
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.Table;
import org.apache.flink.table.api.Tumble;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.lit;
/**
* @ClassName : Flink_Group_Window -- 基于处理时间的混动窗口
* @Description : Flink sql 窗口
* @Author :wudl
* @Date: 2021-08-04 23:13
*/
public class Flink_Group_Window {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
StreamTableEnvironment tableEnvironment = StreamTableEnvironment.create(env);
DataStreamSource<String> streamSource = env.socketTextStream("192.168.1.180", 9999);
SingleOutputStreamOperator<WaterSensor> waterDS = streamSource.map(new MapFunction<String, WaterSensor>() {
@Override
public WaterSensor map(String s) throws Exception {
String[] split = s.split(",");
return new WaterSensor(split[0], Long.parseLong(split[1]), Integer.parseInt(split[2]));
}
});
// 将流转化为表
Table table = tableEnvironment.fromDataStream(waterDS,
$("id"),
$("ts"),
$("vc"),
$("pt").proctime());
// 开窗滚动窗口计算wordCound
Table result = table.window(Tumble.over(lit(5).seconds()).on($("pt")).as("tw"))
.groupBy($("id"), $("tw"))
.select($("id"), $("id").count());
// 将结果表转化为流进行输出
tableEnvironment.toAppendStream(result, Row.class).print();
env.execute();
}
}
3.基于处理时间的滑动窗口
package com.wudl.flink.sql;
import com.wudl.flink.bean.WaterSensor;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Slide;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.Tumble;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.lit;
/**
* @ClassName : 基于处理时间的滑动窗口
* @Description : Flink sql 窗口
* @Author :wudl
* @Date: 2021-08-04 23:13
*/
public class Flink_Group_Sliding_Window {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
StreamTableEnvironment tableEnvironment = StreamTableEnvironment.create(env);
DataStreamSource<String> streamSource = env.socketTextStream("192.168.1.180", 9999);
SingleOutputStreamOperator<WaterSensor> waterDS = streamSource.map(new MapFunction<String, WaterSensor>() {
@Override
public WaterSensor map(String s) throws Exception {
String[] split = s.split(",");
return new WaterSensor(split[0], Long.parseLong(split[1]), Integer.parseInt(split[2]));
}
});
// 将流转化为表
Table table = tableEnvironment.fromDataStream(waterDS,
$("id"),
$("ts"),
$("vc"),
$("pt").proctime());
// 开窗滑动窗口计算wordCound
Table result = table.window(Slide.over(lit(5).seconds())
.every(lit(2).seconds())
.on($("pt"))
.as("sw"))
.groupBy($("id"),$("sw"))
.select($("id"), $("id").count());
// 将结果表转化为流进行输出
tableEnvironment.toAppendStream(result, Row.class).print();
env.execute();
}
}
4. 基于处理时间的会话窗口
package com.wudl.flink.sql;
import com.wudl.flink.bean.WaterSensor;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Session;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.Tumble;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.lit;
/**
* @ClassName : Flink_Group_Window -- 基于处理时间的会话窗口
* @Description : Flink sql 窗口
* @Author :wudl
* @Date: 2021-08-04 23:13
*/
public class Flink_Group_session_Window {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
StreamTableEnvironment tableEnvironment = StreamTableEnvironment.create(env);
DataStreamSource<String> streamSource = env.socketTextStream("192.168.1.180", 9999);
SingleOutputStreamOperator<WaterSensor> waterDS = streamSource.map(new MapFunction<String, WaterSensor>() {
@Override
public WaterSensor map(String s) throws Exception {
String[] split = s.split(",");
return new WaterSensor(split[0], Long.parseLong(split[1]), Integer.parseInt(split[2]));
}
});
// 将流转化为表
Table table = tableEnvironment.fromDataStream(waterDS,
$("id"),
$("ts"),
$("vc"),
$("pt").proctime());
// 开窗滚动窗口计算wordCound
Table result = table.window(Session.withGap(lit(5).seconds()).on($("pt")).as("sw"))
.groupBy($("id"), $("sw"))
.select($("id"), $("id").count());
// 将结果表转化为流进行输出
tableEnvironment.toAppendStream(result, Row.class).print();
env.execute();
}
}