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())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。- 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4001milliseconds), 这就是绝对时间的定义。
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 |+---------------------------+时区设置
scala设置：
val envSetting = EnvironmentSettings.inStreamingMode()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"))SQL Client 设置：
-- 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';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。
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 9999A,1.1B,1.2A,1.8B,2.5C,3.8Flink 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。
事件时间和时区
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 9999A,1.1,2021-04-15 14:01:00B,1.2,2021-04-15 14:02:00A,1.8,2021-04-15 14:03:00 B,2.5,2021-04-15 14:04:00C,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 14:01:00B,1.2,1618495320000 # The corresponding utc timestamp is 2021-04-15 14:02:00A,1.8,1618495380000 # The corresponding utc timestamp is 2021-04-15 14:03:00B,2.5,1618495440000 # The corresponding utc timestamp is 2021-04-15 14:04:00C,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 |+-------------------------+-------------------------+-------------------------+------+-----------+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：
- Streaming 模式下这些函数是每条记录都会计算一次
- 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()
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