table.local-time-zone
- table.local-time-zone
- DataStream-to-Table Conversion(拓展知识)
- 代码测试
- flinksql代码
- 执行结果截图
- 1. Asia/Shanghai 结果如下
- 2. UTC结果如下
table.local-time-zone
table.local-time-zone可用于设置flinksql的时区。
flink的内置数据类型TIMESTAMP(n)或者是TIMESTAMP_LTZ(n), 我们设置水位线都是基于这两种类型,不同的是前者本质是字符串形势,后者本质是long,也因此前者不受时区影响,后者受时区影响类型。(n指的毫秒级的精度取值范围是 0~9)
原始数据库如果不是时间类型,可能要用TO_TIMESTAMP(字符串格式的时间)或者TO_TIMESTAMP_LTZ(long数字,n)
如果原始数据库是string则需要用TO_TIMESTAMP(字符串格式的时间字段)转成TIMESTAMP(n)
如果原始数据库中是long则需要用TO_TIMESTAMP_LTZ(long数字,n) 转成TIMESTAMP_LTZ(n)
DataStream-to-Table Conversion(拓展知识)
datastream API到Table Api转换的时候,是以后string的形式传递event_time, 并且这个string在DataStream Api是以UTC时区转换的,如果你的原始数据中是long, 如果不做处理展示出来的string就是UTC字符串,为了在东八区展示,则需要将long再加上8小时
// 水位线 允许乱序
WatermarkStrategy<String> waterStrategy = WatermarkStrategy.<String>forMonotonousTimestamps() //ofSeconds(20)
.withTimestampAssigner(new SerializableTimestampAssigner<String>() {
@Override
public long extractTimestamp(String element, long recordTimestamp) {
try {
Mybook book= JSON.parseObject(element,Mybook.class);
return boo.time+8*60*60*1000 //转成东八区
}catch (Exception e){
return recordTimestamp;
}
}
}).withIdleness(Duration.ofSeconds(timeWindowIdleness));
SingleOutputStreamOperator<UserSlotGame> processStream = env
.fromSource(source, waterStrategy, "readKafka")
.process(new ProcessFunction<String, UserSlotGame>() {
@Override
public void processElement(String value, Context ctx, Collector<UserSlotGame> out) throws Exception {
// 省略
}
}) ;
代码测试
mysql时区是Asia/Shanghai
CREATE TABLE `versioned_rates` (
`operation_code` int DEFAULT NULL,
`update_time` varchar(255) DEFAULT NULL, -- 注意这是字符串
`product_id` varchar(255) DEFAULT NULL,
`product_name` varchar(255) DEFAULT NULL,
`price` float DEFAULT NULL,
`time_long` bigint NOT NULL DEFAULT '0' -- 注意这是long
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci
INSERT INTO flink.versioned_rates
(operation_code, update_time, product_id, product_name, price, time_long)
VALUES(1, '2024-01-01 00:01:00', 'p_001', 'scooter', 11.11, 1730346179000);
INSERT INTO flink.versioned_rates
(operation_code, update_time, product_id, product_name, price, time_long)
VALUES(1, '2024-01-01 00:02:00', 'p_002', 'basketball', 23.11, 1730346179000);
INSERT INTO flink.versioned_rates
(operation_code, update_time, product_id, product_name, price, time_long)
VALUES(2, '2024-01-01 12:00:00', 'p_001', 'scooter', 11.11, 1730346179000);
INSERT INTO flink.versioned_rates
(operation_code, update_time, product_id, product_name, price, time_long)
VALUES(3, '2024-01-01 12:00:00', 'p_001', 'scooter', 12.99, 1730346179000);
INSERT INTO flink.versioned_rates
(operation_code, update_time, product_id, product_name, price, time_long)
VALUES(2, '2024-01-01 12:00:00', 'p_002', 'basketball', 23.11, 1730346179000);
INSERT INTO flink.versioned_rates
(operation_code, update_time, product_id, product_name, price, time_long)
VALUES(3, '2024-01-01 12:00:00', 'p_002', 'basketball', 19.99, 1730346179000);
INSERT INTO flink.versioned_rates
(operation_code, update_time, product_id, product_name, price, time_long)
VALUES(4, '2024-01-01 18:00:00', 'p_001', 'scooter', 12.99, 1730346179000);
flinksql代码
package com.pg.TableAndDataStreamApi;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.api.config.TableConfigOptions;
/*
*
* */
public class version_table {
private static final String SOURCE="CREATE TABLE source_table(\n" +
"\toperation_code int,\n" +
"\tupdate_time string,\n" +
"\tup_t AS TO_TIMESTAMP(update_time),\n" +
"\ttime_long bigint,\n" +
"\tbbb AS TO_TIMESTAMP_LTZ(time_long,3) \n" +
" ) WITH (\n" +
" 'connector' = 'jdbc',\n" +
" 'url' = 'jdbc:mysql://ip:3306/flink',\n" +
" 'driver'='com.mysql.cj.jdbc.Driver',\n "+
" 'username'='用户名',\n"+
" 'password'='密码',\n"+
" 'table-name' = 'versioned_rates'\n" +
")";
public static void main(String[] args) throws Exception {
// 创建执行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
tableEnv.executeSql(SOURCE);
Configuration configuration = new Configuration();
// configuration.set(TableConfigOptions.LOCAL_TIME_ZONE, "UTC");
configuration.set(TableConfigOptions.LOCAL_TIME_ZONE, "Asia/Shanghai");
tableEnv.getConfig().addConfiguration(configuration);
// 从 MySQL 表中选择所有行
Table t = tableEnv.sqlQuery("select * from source_table");
t.execute().print();
}
}
执行结果截图
TO_TIMESTAMP_LTZ 受时区影响
而TO_TIMESTAMP()意味着原始数据中本就是string, 是不会受到时区影响的
- 下方第一个红色列不管是UTC还是 Asia/Shanghai 我们看大的string都是一样的
- 下方第一个红色列UTC比 Asia/Shanghai 少了8个小时