流式概念 - 时区 - 《Apache Flink v1.13 官方中文文档》

概述
TIMESTAMP vs TIMESTAMP_LTZ
- TIMESTAMP 类型
- TIMESTAMP_LTZ 类型
时区的作用
- 确定时间函数的返回值
- TIMESTAMP_LTZ 字符串表示
时间属性和时区
- 处理时间和时区
- 事件时间和时区
  - TIMESTAMP 上的事件时间属性
  - TIMESTAMP_LTZ 上的事件时间属性
夏令时支持
Batch 模式和 Streaming 模式的区别

概述

Flink 为日期和时间提供了丰富的数据类型，包括 DATE， TIME， TIMESTAMP， TIMESTAMP_LTZ， INTERVAL YEAR TO MONTH， INTERVAL DAY TO SECOND (更多详情请参考 Date and Time)。 Flink 支持在 session （会话）级别设置时区（更多详情请参考 table.local-time-zone）。 Flink 对多种时间类型和时区的支持使得跨时区的数据处理变得非常容易。

TIMESTAMP vs TIMESTAMP_LTZ

TIMESTAMP 类型

TIMESTAMP(p) 是 TIMESTAMP(p) WITHOUT TIME ZONE 的简写，精度 p 支持的范围是0-9，默认是6。
TIMESTAMP 用于描述年，月，日，小时，分钟，秒和小数秒对应的时间戳。
TIMESTAMP 可以通过一个字符串来指定，例如：

Flink SQL> SELECT TIMESTAMP '1970-01-01 00:00:04.001';
+-------------------------+
| 1970-01-01 00:00:04.001 |
+-------------------------+

TIMESTAMP_LTZ 类型

TIMESTAMP_LTZ(p) 是 TIMESTAMP(p) WITH LOCAL TIME ZONE 的简写，精度 p 支持的范围是0-9，默认是6。
TIMESTAMP_LTZ 用于描述时间线上的绝对时间点，使用 long 保存从 epoch 至今的毫秒数，使用int保存毫秒中的纳秒数。 epoch 时间是从 java 的标准 epoch 时间 1970-01-01T00:00:00Z 开始计算。在计算和可视化时，每个 TIMESTAMP_LTZ 类型的数据都是使用的 session （会话）中配置的时区。
TIMESTAMP_LTZ 没有字符串表达形式因此无法通过字符串来指定，可以通过一个 long 类型的 epoch 时间来转化(例如: 通过 Java 来产生一个 long 类型的 epoch 时间 System.currentTimeMillis())

Flink SQL> CREATE VIEW T1 AS SELECT TO_TIMESTAMP_LTZ(4001, 3);
Flink SQL> SET table.local-time-zone=UTC;
Flink SQL> SELECT * FROM T1;
+---------------------------+
| TO_TIMESTAMP_LTZ(4001, 3) |
+---------------------------+
|   1970-01-01 00:00:04.001 |
+---------------------------+
Flink SQL> SET table.local-time-zone=Asia/Shanghai;
Flink SQL> SELECT * FROM T1;
+---------------------------+
| TO_TIMESTAMP_LTZ(4001, 3) |
+---------------------------+
|   1970-01-01 08:00:04.001 |
+---------------------------+

TIMESTAMP_LTZ 可以用于跨时区的计算，因为它是一个基于 epoch 的绝对时间点（比如上例中的 4001 毫秒）代表的就是不同时区的同一个绝对时间点。补充一个背景知识：在同一个时间点，全世界所有的机器上执行 System.currentTimeMillis() 都会返回同样的值。 (比如上例中的 4001 milliseconds), 这就是绝对时间的定义。

时区的作用

本地时区定义了当前 session（会话）所在的时区，你可以在 Sql client 或者应用程序中配置。

SQL Client

-- 设置为 UTC 时区
Flink SQL> SET table.local-time-zone=UTC;
-- 设置为上海时区
Flink SQL> SET table.local-time-zone=Asia/Shanghai;
-- 设置为Los_Angeles时区
Flink SQL> SET table.local-time-zone=America/Los_Angeles;

Java

 EnvironmentSettings envSetting = EnvironmentSettings.newInstance().build();
 TableEnvironment tEnv = TableEnvironment.create(envSetting);
 // 设置为 UTC 时区
 tEnv.getConfig().setLocalTimeZone(ZoneId.of("UTC"));
// 设置为上海时区
 tEnv.getConfig().setLocalTimeZone(ZoneId.of("Asia/Shanghai"));
// 设置为 Los_Angeles 时区
 tEnv.getConfig().setLocalTimeZone(ZoneId.of("America/Los_Angeles"));

Scala

val envSetting = EnvironmentSettings.newInstance.build
val tEnv = TableEnvironment.create(envSetting)
// 设置为 UTC 时区
tEnv.getConfig.setLocalTimeZone(ZoneId.of("UTC"))
// 设置为上海时区
tEnv.getConfig.setLocalTimeZone(ZoneId.of("Asia/Shanghai"))
// 设置为 Los_Angeles 时区
tEnv.getConfig.setLocalTimeZone(ZoneId.of("America/Los_Angeles"))

session（会话）的时区设置在 Flink SQL 中非常有用，它的主要用法如下:

确定时间函数的返回值

session （会话）中配置的时区会对以下函数生效。

LOCALTIME
LOCALTIMESTAMP
CURRENT_DATE
CURRENT_TIME
CURRENT_TIMESTAMP
CURRENT_ROW_TIMESTAMP()
NOW()
PROCTIME()

Flink SQL> SET sql-client.execution.result-mode=tableau;
Flink SQL> CREATE VIEW MyView1 AS SELECT LOCALTIME, LOCALTIMESTAMP, CURRENT_DATE, CURRENT_TIME, CURRENT_TIMESTAMP, CURRENT_ROW_TIMESTAMP(), NOW(), PROCTIME();
Flink SQL> DESC MyView1;

+------------------------+-----------------------------+-------+-----+--------+-----------+
|                   name |                        type |  null | key | extras | watermark |
+------------------------+-----------------------------+-------+-----+--------+-----------+
|              LOCALTIME |                     TIME(0) | false |     |        |           |
|         LOCALTIMESTAMP |                TIMESTAMP(3) | false |     |        |           |
|           CURRENT_DATE |                        DATE | false |     |        |           |
|           CURRENT_TIME |                     TIME(0) | false |     |        |           |
|      CURRENT_TIMESTAMP |            TIMESTAMP_LTZ(3) | false |     |        |           |
|CURRENT_ROW_TIMESTAMP() |            TIMESTAMP_LTZ(3) | false |     |        |           |
|                  NOW() |            TIMESTAMP_LTZ(3) | false |     |        |           |
|             PROCTIME() | TIMESTAMP_LTZ(3) *PROCTIME* | false |     |        |           |
+------------------------+-----------------------------+-------+-----+--------+-----------+

Flink SQL> SET table.local-time-zone=UTC;
Flink SQL> SELECT * FROM MyView1;

+-----------+-------------------------+--------------+--------------+-------------------------+-------------------------+-------------------------+-------------------------+
| LOCALTIME |          LOCALTIMESTAMP | CURRENT_DATE | CURRENT_TIME |       CURRENT_TIMESTAMP | CURRENT_ROW_TIMESTAMP() |                   NOW() |              PROCTIME() |
+-----------+-------------------------+--------------+--------------+-------------------------+-------------------------+-------------------------+-------------------------+
|  15:18:36 | 2021-04-15 15:18:36.384 |   2021-04-15 |     15:18:36 | 2021-04-15 15:18:36.384 | 2021-04-15 15:18:36.384 | 2021-04-15 15:18:36.384 | 2021-04-15 15:18:36.384 |
+-----------+-------------------------+--------------+--------------+-------------------------+-------------------------+-------------------------+-------------------------+

Flink SQL> SET table.local-time-zone=Asia/Shanghai;
Flink SQL> SELECT * FROM MyView1;

+-----------+-------------------------+--------------+--------------+-------------------------+-------------------------+-------------------------+-------------------------+
| LOCALTIME |          LOCALTIMESTAMP | CURRENT_DATE | CURRENT_TIME |       CURRENT_TIMESTAMP | CURRENT_ROW_TIMESTAMP() |                   NOW() |              PROCTIME() |
+-----------+-------------------------+--------------+--------------+-------------------------+-------------------------+-------------------------+-------------------------+
|  23:18:36 | 2021-04-15 23:18:36.384 |   2021-04-15 |     23:18:36 | 2021-04-15 23:18:36.384 | 2021-04-15 23:18:36.384 | 2021-04-15 23:18:36.384 | 2021-04-15 23:18:36.384 |
+-----------+-------------------------+--------------+--------------+-------------------------+-------------------------+-------------------------+-------------------------+

`TIMESTAMP_LTZ` 字符串表示

当一个 TIMESTAMP_LTZ 值转为 string 格式时， session 中配置的时区会生效。例如打印这个值，将类型强制转化为 STRING 类型，将类型强制转换为 TIMESTAMP ，将 TIMESTAMP 的值转化为 TIMESTAMP_LTZ 类型：

Flink SQL> CREATE VIEW MyView2 AS SELECT TO_TIMESTAMP_LTZ(4001, 3) AS ltz, TIMESTAMP '1970-01-01 00:00:01.001'  AS ntz;
Flink SQL> DESC MyView2;

+------+------------------+-------+-----+--------+-----------+
| name |             type |  null | key | extras | watermark |
+------+------------------+-------+-----+--------+-----------+
|  ltz | TIMESTAMP_LTZ(3) |  true |     |        |           |
|  ntz |     TIMESTAMP(3) | false |     |        |           |
+------+------------------+-------+-----+--------+-----------+

Flink SQL> SET table.local-time-zone=UTC;
Flink SQL> SELECT * FROM MyView2;

+-------------------------+-------------------------+
|                     ltz |                     ntz |
+-------------------------+-------------------------+
| 1970-01-01 00:00:04.001 | 1970-01-01 00:00:01.001 |
+-------------------------+-------------------------+

Flink SQL> SET table.local-time-zone=Asia/Shanghai;
Flink SQL> SELECT * FROM MyView2;

+-------------------------+-------------------------+
|                     ltz |                     ntz |
+-------------------------+-------------------------+
| 1970-01-01 08:00:04.001 | 1970-01-01 00:00:01.001 |
+-------------------------+-------------------------+

Flink SQL> CREATE VIEW MyView3 AS SELECT ltz, CAST(ltz AS TIMESTAMP(3)), CAST(ltz AS STRING), ntz, CAST(ntz AS TIMESTAMP_LTZ(3)) FROM MyView2;

Flink SQL> DESC MyView3;
+-------------------------------+------------------+-------+-----+--------+-----------+
|                          name |             type |  null | key | extras | watermark |
+-------------------------------+------------------+-------+-----+--------+-----------+
|                           ltz | TIMESTAMP_LTZ(3) |  true |     |        |           |
|     CAST(ltz AS TIMESTAMP(3)) |     TIMESTAMP(3) |  true |     |        |           |
|           CAST(ltz AS STRING) |           STRING |  true |     |        |           |
|                           ntz |     TIMESTAMP(3) | false |     |        |           |
| CAST(ntz AS TIMESTAMP_LTZ(3)) | TIMESTAMP_LTZ(3) | false |     |        |           |
+-------------------------------+------------------+-------+-----+--------+-----------+

Flink SQL> SELECT * FROM MyView3;

+-------------------------+---------------------------+-------------------------+-------------------------+-------------------------------+
|                     ltz | CAST(ltz AS TIMESTAMP(3)) |     CAST(ltz AS STRING) |                     ntz | CAST(ntz AS TIMESTAMP_LTZ(3)) |
+-------------------------+---------------------------+-------------------------+-------------------------+-------------------------------+
| 1970-01-01 08:00:04.001 |   1970-01-01 08:00:04.001 | 1970-01-01 08:00:04.001 | 1970-01-01 00:00:01.001 |       1970-01-01 00:00:01.001 |
+-------------------------+---------------------------+-------------------------+-------------------------+-------------------------------+

时间属性和时区

更多时间属性相关的详细介绍，请参考 Time Attribute 。

处理时间和时区

Flink SQL 使用函数 PROCTIME() 来定义处理时间属性，该函数返回的类型是 TIMESTAMP_LTZ 。

在 Flink1.13 之前， PROCTIME() 函数返回的类型是 TIMESTAMP ，返回值是UTC时区下的 TIMESTAMP 。例如：当上海的时间为 2021-03-01 12:00:00 时， PROCTIME() 显示的时间却是错误的 2021-03-01 04:00:00 。这个问题在 Flink 1.13 中修复了，因此用户不用再去处理时区的问题了。

PROCTIME() 返回的是本地时区的时间，使用 TIMESTAMP_LTZ 类型也可以支持夏令时时间。

Flink SQL> SET table.local-time-zone=UTC;
Flink SQL> SELECT PROCTIME();

+-------------------------+
|              PROCTIME() |
+-------------------------+
| 2021-04-15 14:48:31.387 |
+-------------------------+

Flink SQL> SET table.local-time-zone=Asia/Shanghai;
Flink SQL> SELECT PROCTIME();

+-------------------------+
|              PROCTIME() |
+-------------------------+
| 2021-04-15 22:48:31.387 |
+-------------------------+

Flink SQL> CREATE TABLE MyTable1 (
                  item STRING,
                  price DOUBLE,
                  proctime as PROCTIME()
            ) WITH (
                'connector' = 'socket',
                'hostname' = '127.0.0.1',
                'port' = '9999',
                'format' = 'csv'
           );
Flink SQL> CREATE VIEW MyView3 AS
            SELECT
                TUMBLE_START(proctime, INTERVAL '10' MINUTES) AS window_start,
                TUMBLE_END(proctime, INTERVAL '10' MINUTES) AS window_end,
                TUMBLE_PROCTIME(proctime, INTERVAL '10' MINUTES) as window_proctime,
                item,
                MAX(price) as max_price
            FROM MyTable1
                GROUP BY TUMBLE(proctime, INTERVAL '10' MINUTES), item;
Flink SQL> DESC MyView3;

+-----------------+-----------------------------+-------+-----+--------+-----------+
|           name  |                        type |  null | key | extras | watermark |
+-----------------+-----------------------------+-------+-----+--------+-----------+
|    window_start |                TIMESTAMP(3) | false |     |        |           |
|      window_end |                TIMESTAMP(3) | false |     |        |           |
| window_proctime | TIMESTAMP_LTZ(3) *PROCTIME* | false |     |        |           |
|            item |                      STRING | true |     |        |           |
|       max_price |                      DOUBLE |  true |     |        |           |
+-----------------+-----------------------------+-------+-----+--------+-----------+

在终端执行以下命令写入数据到 MyTable1 ：

> nc -lk 9999
A,1.1
B,1.2
A,1.8
B,2.5
C,3.8

Flink SQL> SET table.local-time-zone=UTC;
Flink SQL> SELECT * FROM MyView3;

+-------------------------+-------------------------+-------------------------+------+-----------+
|            window_start |              window_end |          window_procime | item | max_price |
+-------------------------+-------------------------+-------------------------+------+-----------+
| 2021-04-15 14:00:00.000 | 2021-04-15 14:10:00.000 | 2021-04-15 14:10:00.005 |    A |       1.8 |
| 2021-04-15 14:00:00.000 | 2021-04-15 14:10:00.000 | 2021-04-15 14:10:00.007 |    B |       2.5 |
| 2021-04-15 14:00:00.000 | 2021-04-15 14:10:00.000 | 2021-04-15 14:10:00.007 |    C |       3.8 |
+-------------------------+-------------------------+-------------------------+------+-----------+

Flink SQL> SET table.local-time-zone=Asia/Shanghai;
Flink SQL> SELECT * FROM MyView3;

相比在 UTC 时区下的计算结果，在 Asia/Shanghai 时区下计算的窗口开始时间，窗口结束时间和窗口处理时间是不同的。

+-------------------------+-------------------------+-------------------------+------+-----------+
|            window_start |              window_end |          window_procime | item | max_price |
+-------------------------+-------------------------+-------------------------+------+-----------+
| 2021-04-15 22:00:00.000 | 2021-04-15 22:10:00.000 | 2021-04-15 22:10:00.005 |    A |       1.8 |
| 2021-04-15 22:00:00.000 | 2021-04-15 22:10:00.000 | 2021-04-15 22:10:00.007 |    B |       2.5 |
| 2021-04-15 22:00:00.000 | 2021-04-15 22:10:00.000 | 2021-04-15 22:10:00.007 |    C |       3.8 |
+-------------------------+-------------------------+-------------------------+------+-----------+

处理时间窗口是不确定的，每次运行都会返回不同的窗口和聚合结果。以上的示例只用于说明时区如何影响处理时间窗口。

事件时间和时区

Flink 支持在 TIMESTAMP 列和 TIMESTAMP_LTZ 列上定义时间属性。

TIMESTAMP 上的事件时间属性

如果 source 中的时间用于表示年-月-日-小时-分钟-秒，通常是一个不带时区的字符串，例如: 2020-04-15 20:13:40.564。推荐在 TIMESTAMP 列上定义事件时间属性。

Flink SQL> CREATE TABLE MyTable2 (
                  item STRING,
                  price DOUBLE,
                  ts TIMESTAMP(3), -- TIMESTAMP data type
                  WATERMARK FOR ts AS ts - INTERVAL '10' SECOND
            ) WITH (
                'connector' = 'socket',
                'hostname' = '127.0.0.1',
                'port' = '9999',
                'format' = 'csv'
           );
Flink SQL> CREATE VIEW MyView4 AS
            SELECT
                TUMBLE_START(ts, INTERVAL '10' MINUTES) AS window_start,
                TUMBLE_END(ts, INTERVAL '10' MINUTES) AS window_end,
                TUMBLE_ROWTIME(ts, INTERVAL '10' MINUTES) as window_rowtime,
                item,
                MAX(price) as max_price
            FROM MyTable2
                GROUP BY TUMBLE(ts, INTERVAL '10' MINUTES), item;
Flink SQL> DESC MyView4;

+----------------+------------------------+------+-----+--------+-----------+
|           name |                   type | null | key | extras | watermark |
+----------------+------------------------+------+-----+--------+-----------+
|   window_start |           TIMESTAMP(3) | true |     |        |           |
|     window_end |           TIMESTAMP(3) | true |     |        |           |
| window_rowtime | TIMESTAMP(3) *ROWTIME* | true |     |        |           |
|           item |                 STRING | true |     |        |           |
|      max_price |                 DOUBLE | true |     |        |           |
+----------------+------------------------+------+-----+--------+-----------+

在终端执行以下命令用于写入数据到 MyTable2 ：

> nc -lk 9999
A,1.1,2021-04-15 14:01:00
B,1.2,2021-04-15 14:02:00
A,1.8,2021-04-15 14:03:00 
B,2.5,2021-04-15 14:04:00
C,3.8,2021-04-15 14:05:00       
C,3.8,2021-04-15 14:11:00

Flink SQL> SET table.local-time-zone=UTC; 
Flink SQL> SELECT * FROM MyView4;

+-------------------------+-------------------------+-------------------------+------+-----------+
|            window_start |              window_end |          window_rowtime | item | max_price |
+-------------------------+-------------------------+-------------------------+------+-----------+
| 2021-04-15 14:00:00.000 | 2021-04-15 14:10:00.000 | 2021-04-15 14:09:59.999 |    A |       1.8 |
| 2021-04-15 14:00:00.000 | 2021-04-15 14:10:00.000 | 2021-04-15 14:09:59.999 |    B |       2.5 |
| 2021-04-15 14:00:00.000 | 2021-04-15 14:10:00.000 | 2021-04-15 14:09:59.999 |    C |       3.8 |
+-------------------------+-------------------------+-------------------------+------+-----------+

Flink SQL> SET table.local-time-zone=Asia/Shanghai; 
Flink SQL> SELECT * FROM MyView4;

相比在 UTC 时区下的计算结果，在 Asia/Shanghai 时区下计算的窗口开始时间，窗口结束时间和窗口的 rowtime 是相同的。

+-------------------------+-------------------------+-------------------------+------+-----------+
|            window_start |              window_end |          window_rowtime | item | max_price |
+-------------------------+-------------------------+-------------------------+------+-----------+
| 2021-04-15 14:00:00.000 | 2021-04-15 14:10:00.000 | 2021-04-15 14:09:59.999 |    A |       1.8 |
| 2021-04-15 14:00:00.000 | 2021-04-15 14:10:00.000 | 2021-04-15 14:09:59.999 |    B |       2.5 |
| 2021-04-15 14:00:00.000 | 2021-04-15 14:10:00.000 | 2021-04-15 14:09:59.999 |    C |       3.8 |
+-------------------------+-------------------------+-------------------------+------+-----------+

TIMESTAMP_LTZ 上的事件时间属性

如果源数据中的时间为一个 epoch 时间，通常是一个 long 值，例如: 1618989564564 ，推荐将事件时间属性定义在 TIMESTAMP_LTZ 列上。

Flink SQL> CREATE TABLE MyTable3 (
                  item STRING,
                  price DOUBLE,
                  ts BIGINT, -- long time value in epoch milliseconds
                  ts_ltz AS TO_TIMESTAMP_LTZ(ts, 3),
                  WATERMARK FOR ts_ltz AS ts_ltz - INTERVAL '10' SECOND
            ) WITH (
                'connector' = 'socket',
                'hostname' = '127.0.0.1',
                'port' = '9999',
                'format' = 'csv'
           );
Flink SQL> CREATE VIEW MyView5 AS 
            SELECT 
                TUMBLE_START(ts_ltz, INTERVAL '10' MINUTES) AS window_start,        
                TUMBLE_END(ts_ltz, INTERVAL '10' MINUTES) AS window_end,
                TUMBLE_ROWTIME(ts_ltz, INTERVAL '10' MINUTES) as window_rowtime,
                item,
                MAX(price) as max_price
            FROM MyTable3
                GROUP BY TUMBLE(ts_ltz, INTERVAL '10' MINUTES), item;
Flink SQL> DESC MyView5;

+----------------+----------------------------+-------+-----+--------+-----------+
|           name |                       type |  null | key | extras | watermark |
+----------------+----------------------------+-------+-----+--------+-----------+
|   window_start |               TIMESTAMP(3) | false |     |        |           |
|     window_end |               TIMESTAMP(3) | false |     |        |           |
| window_rowtime | TIMESTAMP_LTZ(3) *ROWTIME* |  true |     |        |           |
|           item |                     STRING |  true |     |        |           |
|      max_price |                     DOUBLE |  true |     |        |           |
+----------------+----------------------------+-------+-----+--------+-----------+

MyTable3 的输入数据为：

A,1.1,1618495260000  # The corresponding utc timestamp is 2021-04-15 14:01:00
B,1.2,1618495320000  # The corresponding utc timestamp is 2021-04-15 14:02:00
A,1.8,1618495380000  # The corresponding utc timestamp is 2021-04-15 14:03:00
B,2.5,1618495440000  # The corresponding utc timestamp is 2021-04-15 14:04:00
C,3.8,1618495500000  # The corresponding utc timestamp is 2021-04-15 14:05:00       
C,3.8,1618495860000  # The corresponding utc timestamp is 2021-04-15 14:11:00

Flink SQL> SET table.local-time-zone=UTC; 
Flink SQL> SELECT * FROM MyView5;

+-------------------------+-------------------------+-------------------------+------+-----------+
|            window_start |              window_end |          window_rowtime | item | max_price |
+-------------------------+-------------------------+-------------------------+------+-----------+
| 2021-04-15 14:00:00.000 | 2021-04-15 14:10:00.000 | 2021-04-15 14:09:59.999 |    A |       1.8 |
| 2021-04-15 14:00:00.000 | 2021-04-15 14:10:00.000 | 2021-04-15 14:09:59.999 |    B |       2.5 |
| 2021-04-15 14:00:00.000 | 2021-04-15 14:10:00.000 | 2021-04-15 14:09:59.999 |    C |       3.8 |
+-------------------------+-------------------------+-------------------------+------+-----------+

Flink SQL> SET table.local-time-zone=Asia/Shanghai; 
Flink SQL> SELECT * FROM MyView5;

相比在 UTC 时区下的计算结果，在 Asia/Shanghai 时区下计算的窗口开始时间，窗口结束时间和窗口的 rowtime 是不同的。

+-------------------------+-------------------------+-------------------------+------+-----------+
|            window_start |              window_end |          window_rowtime | item | max_price |
+-------------------------+-------------------------+-------------------------+------+-----------+
| 2021-04-15 22:00:00.000 | 2021-04-15 22:10:00.000 | 2021-04-15 22:09:59.999 |    A |       1.8 |
| 2021-04-15 22:00:00.000 | 2021-04-15 22:10:00.000 | 2021-04-15 22:09:59.999 |    B |       2.5 |
| 2021-04-15 22:00:00.000 | 2021-04-15 22:10:00.000 | 2021-04-15 22:09:59.999 |    C |       3.8 |
+-------------------------+-------------------------+-------------------------+------+-----------+

夏令时支持

Flink SQL支持在 TIMESTAMP_LTZ列上定义时间属性，基于这一特征，Flink SQL 在窗口中使用 TIMESTAMP 和 TIMESTAMP_LTZ 类型优雅地支持了夏令时。

Flink 使用时间戳的字符格式来分割窗口并通过每条记录对应的 epoch 时间来分配窗口。这意味着 Flink 窗口开始时间和窗口结束时间使用的是 TIMESTAMP 类型（例如: TUMBLE_START 和 TUMBLE_END），窗口的时间属性使用的是 TIMESTAMP_LTZ 类型（例如: TUMBLE_PROCTIME， TUMBLE_ROWTIME）。给定一个 tumble window示例，在 Los_Angele 时区下夏令时从 2021-03-14 02:00:00 开始：

long epoch1 = 1615708800000L; // 2021-03-14 00:00:00
long epoch2 = 1615712400000L; // 2021-03-14 01:00:00
long epoch3 = 1615716000000L; // 2021-03-14 03:00:00, 手表往前拨一小时，跳过 (2021-03-14 02:00:00)
long epoch4 = 1615719600000L; // 2021-03-14 04:00:00

在 Los_angele 时区下， tumble window [2021-03-14 00:00:00, 2021-03-14 00:04:00] 将会收集3个小时的数据，在其他非夏令时的时区下将会收集4个小时的数据，用户只需要在 TIMESTAMP_LTZ 列上声明时间属性即可。

Flink 的所有窗口（如 Hop window， Session window， Cumulative window）都会遵循这种方式， Flink SQL 中的所有操作都很好地支持了 TIMESTAMP_LTZ 类型，因此Flink可以非常优雅的支持夏令时。

Batch 模式和 Streaming 模式的区别

以下函数：

LOCALTIME
LOCALTIMESTAMP
CURRENT_DATE
CURRENT_TIME
CURRENT_TIMESTAMP
NOW()

Flink 会根据执行模式来进行不同计算，在 Streaming 模式下这些函数是每条记录都会计算一次，但在 Batch 模式下，只会在 query 开始时计算一次，所有记录都使用相同的结果。

以下时间函数无论是在 Streaming 模式还是 Batch 模式下，都会为每条记录计算一次结果：

CURRENT_ROW_TIMESTAMP()
PROCTIME()

时区

概述