案例三
需求
使用Flink SQL来统计5秒内 每个用户的 订单总数、订单的最大金额、订单的最小金额
也就是每隔5秒统计最近5秒的每个用户的订单总数、订单的最大金额、订单的最小金额
上面的需求使用流处理的Window的基于时间的滚动窗口就可以搞定!
那么接下来使用FlinkTable&SQL-API来实现
编码步骤
1.创建环境
2.使用自定义函数模拟实时流数据
3.设置事件时间和Watermaker
4.注册表
5.执行sql-可以使用sql风格或table风格(了解)
6.输出结果
7.触发执行
代码实现-方式1
代码语言：javascript复制package cn.it.sql;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.RichSourceFunction;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
import java.time.Duration;
import java.util.Random;
import java.util.UUID;
import java.util.concurrent.TimeUnit;
import static org.apache.flink.table.api.Expressions.$;
/**
* Author lanson
* Desc
*/
public class FlinkSQL_Table_Demo04 {
public static void main(String[] args) throws Exception {
//1.准备环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);
//2.Source
DataStreamSource<Order> orderDS = env.addSource(new RichSourceFunction<Order>() {
private Boolean isRunning = true;
@Override
public void run(SourceContext<Order> ctx) throws Exception {
Random random = new Random();
while (isRunning) {
Order order = new Order(UUID.randomUUID().toString(), random.nextInt(3), random.nextInt(101), System.currentTimeMillis());
TimeUnit.SECONDS.sleep(1);
ctx.collect(order);
}
}
@Override
public void cancel() {
isRunning = false;
}
});
//3.Transformation
DataStream<Order> watermakerDS = orderDS
.assignTimestampsAndWatermarks(
WatermarkStrategy.<Order>forBoundedOutOfOrderness(Duration.ofSeconds(2))
.withTimestampAssigner((event, timestamp) -> event.getCreateTime())
);
//4.注册表
tEnv.createTemporaryView("t_order", watermakerDS,
$("orderId"), $("userId"), $("money"), $("createTime").rowtime());
//5.执行SQL
String sql = "select "
"userId,"
"count(*) as totalCount,"
"max(money) as maxMoney,"
"min(money) as minMoney "
"from t_order "
"group by userId,"
"tumble(createTime, interval '5' second)";
Table ResultTable = tEnv.sqlQuery(sql);
//6.Sink
//将SQL的执行结果转换成DataStream再打印出来
//toAppendStream → 将计算后的数据append到结果DataStream中去
//toRetractStream → 将计算后的新的数据在DataStream原数据的基础上更新true或是删除false
DataStream<Tuple2<Boolean, Row>> resultDS = tEnv.toRetractStream(ResultTable, Row.class);
resultDS.print();
env.execute();
}
@Data
@AllArgsConstructor
@NoArgsConstructor
public static class Order {
private String orderId;
private Integer userId;
private Integer money;
private Long createTime;
}
}toAppendStream → 将计算后的数据append到结果DataStream中去
toRetractStream → 将计算后的新的数据在DataStream原数据的基础上更新true或是删除false
代码实现-方式2
代码语言：javascript复制package cn.it.sql;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.RichSourceFunction;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.Tumble;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
import java.time.Duration;
import java.util.Random;
import java.util.UUID;
import java.util.concurrent.TimeUnit;
import static org.apache.flink.table.api.Expressions.$;
import static org.apache.flink.table.api.Expressions.lit;
/**
* Author lanson
* Desc
*/
public class FlinkSQL_Table_Demo05 {
public static void main(String[] args) throws Exception {
//1.准备环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);
//2.Source
DataStreamSource<Order> orderDS = env.addSource(new RichSourceFunction<Order>() {
private Boolean isRunning = true;
@Override
public void run(SourceContext<Order> ctx) throws Exception {
Random random = new Random();
while (isRunning) {
Order order = new Order(UUID.randomUUID().toString(), random.nextInt(3), random.nextInt(101), System.currentTimeMillis());
TimeUnit.SECONDS.sleep(1);
ctx.collect(order);
}
}
@Override
public void cancel() {
isRunning = false;
}
});
//3.Transformation
DataStream<Order> watermakerDS = orderDS
.assignTimestampsAndWatermarks(
WatermarkStrategy.<Order>forBoundedOutOfOrderness(Duration.ofSeconds(2))
.withTimestampAssigner((event, timestamp) -> event.getCreateTime())
);
//4.注册表
tEnv.createTemporaryView("t_order", watermakerDS,
$("orderId"), $("userId"), $("money"), $("createTime").rowtime());
//查看表约束
tEnv.from("t_order").printSchema();
//5.TableAPI查询
Table ResultTable = tEnv.from("t_order")
//.window(Tumble.over("5.second").on("createTime").as("tumbleWindow"))
.window(Tumble.over(lit(5).second())
.on($("createTime"))
.as("tumbleWindow"))
.groupBy($("tumbleWindow"), $("userId"))
.select(
$("userId"),
$("userId").count().as("totalCount"),
$("money").max().as("maxMoney"),
$("money").min().as("minMoney"));
//6.将SQL的执行结果转换成DataStream再打印出来
DataStream<Tuple2<Boolean, Row>> resultDS = tEnv.toRetractStream(ResultTable, Row.class);
resultDS.print();
//7.excute
env.execute();
}
@Data
@AllArgsConstructor
@NoArgsConstructor
public static class Order {
private String orderId;
private Integer userId;
private Integer money;
private Long createTime;
}
}
