在pyspark中，可以通过传递时间戳和时区从UTC时间获取本地时间to the function from_utc_timestamp
>>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
>>> df.select(from_utc_timestamp(df.t, "PST").alias('t')).collect()
[Row(t=datetime.datetime(1997, 2, 28, 2, 30))]
此处的时区以字符串文字(“PST”)的形式提供。如果一个人具有以下数据结构:
+--------------------------+---------+
| utc_time |timezone |
+--------------------------+---------+
| 2018-08-03T23:27:30.000Z| PST |
| 2018-08-03T23:27:30.000Z| GMT |
| 2018-08-03T23:27:30.000Z| SGT |
+--------------------------+---------+
如何实现以下新列(最好没有 UDF)？
+--------------------------+-----------------------------------+
| utc_time |timezone | local_time |
+--------------------------+-----------------------------------+
| 2018-08-03T23:27:30.000Z| PST | 2018-08-03T15:27:30.000 |
| 2018-08-03T23:27:30.000Z| GMT | 2018-08-04T00:27:30.000 |
| 2018-08-03T23:27:30.000Z| SGT | 2018-08-04T07:27:30.000 |
+--------------------------+-----------------------------------+
请您参考如下方法:
使用 pyspark.sql.functions.expr() rather the the dataframe API ，这可以通过以下方式实现:
import pyspark.sql.functions as F
df = df.select(
'*',
F.expr('from_utc_timestamp(utc_time, timezone)').alias("timestamp_local")
)
但是，不推荐使用 3 个字母的时区。根据 Java docs :
For compatibility with JDK 1.1.x, some other three-letter time zone IDs (such as "PST", "CTT", "AST") are also supported. However, their use is deprecated because the same abbreviation is often used for multiple time zones (for example, "CST" could be U.S. "Central Standard Time" and "China Standard Time"), and the Java platform can then only recognize one of them.
