获取本地化日期的毫秒数,考虑到夏令时

Get number of milliseconds for a localised date, taking into account daylight savings

我在 Google BigQuery 中有这样的数据:


sample_date_time_UTC     time_zone       milliseconds_between_samples
--------                 ---------       ----------------------------
2019-03-31 01:06:03 UTC  Europe/Paris    60000
2019-03-31 01:16:03 UTC  Europe/Paris    60000
...

预计数据样本会定期出现,由 milliseconds_between_samples 字段的值指示:

time_zone 是一个代表 Google 云的字符串 Supported Timezone Value


然后我将检查任何特定日期的实际样本数与预期样本数的比率,对于任何一天的范围(表示为本地日期,对于给定的 time_zone):

with data as 
  ( 
    select 
      -- convert sample_date_time_UTC to equivalent local datetime for the timezone
      DATETIME(sample_date_time_UTC,time_zone) as localised_sample_date_time, 
      milliseconds_between_samples 
    from  `mytable` 
    where sample_date_time between '2019-03-31 00:00:00.000000+01:00' and '2019-04-01 00:00:00.000000+02:00'
  ) 

select date(localised_sample_date_time) as localised_date, count(*)/(86400000/avg(milliseconds_between_samples)) as ratio_of_daily_sample_count_to_expected 
from data 
group by localised_date 
order by localised_date 

问题是这有一个错误,因为我已经将一天中的预期毫秒数硬编码为 86400000。这是不正确的,因为当夏令时在指定的 time_zone (Europe/Paris) 开始时,一天会缩短 1 小时。夏令时结束时,一天会延长 1 小时。

所以,上面的查询是不正确的。它查询 Europe/Paris 时区今年 3 月 31 日的数据(这是该时区开始夏令时的时间)。那天的毫秒应该是82800000.

在查询中,如何获得指定 localised_date 的正确毫秒数?

更新:

我试过这样做是为了看看效果如何 returns:

select DATETIME_DIFF(DATETIME('2019-04-01 00:00:00.000000+02:00', 'Europe/Paris'), DATETIME('2019-03-31 00:00:00.000000+01:00', 'Europe/Paris'), MILLISECOND)

那没用 - 我得到 86400000

您可以通过删除 +01:00+02:00 来获得两个时间戳的毫秒差值。请注意,这给出了 UTC 时间戳之间的差异:90000000,这与实际经过的毫秒数不同。

您可以像这样获取一天的毫秒数:

select 86400000 + (86400000 - DATETIME_DIFF(DATETIME('2019-04-01 00:00:00.000000', 'Europe/Paris'), DATETIME('2019-03-31 00:00:00.000000', 'Europe/Paris'), MILLISECOND))

感谢@Juta,关于使用 UTC 时间进行计算的提示。当我按本地化日期对每天的数据进行分组时,我发现我可以通过获取 'localised' 日期的开始和结束日期时间(UTC 格式)来计算每天的毫秒数,使用以下逻辑:

-- get UTC start datetime for localised date
-- get UTC end datetime for localised date

-- this then gives the milliseconds for that localised date:
datetime_diff(utc_end_datetime, utc_start_datetime, MILLISECOND);

因此,我的完整查询变为:

with daily_sample_count as (
  with data as 
    ( 
      select 
        -- get the date in the local timezone, for sample_date_time_UTC
        DATE(sample_date_time_UTC,time_zone) as localised_date, 
        milliseconds_between_samples 
      from  `mytable` 
      where sample_date_time between '2019-03-31 00:00:00.000000+01:00' and '2019-04-01 00:00:00.000000+02:00'
    ) 

  select
    localised_date,
    count(*) as daily_record_count,
    avg(milliseconds_between_samples) as daily_avg_millis_between_samples,
    datetime(timestamp(localised_date, time_zone)) as utc_start_datetime,
    datetime(timestamp(date_add(localised_date, interval 1 day), time_zone)) as utc_end_datetime
  from data 
)

select
  localised_date,
  -- apply calculation for ratio_of_daily_sample_count_to_expected
  -- based on the actual vs expected number of samples for the day
  -- no. of milliseconds in the day changes, when transitioning in/out of daylight saving - so we calculate milliseconds in the day
  daily_record_count/(datetime_diff(utc_end_datetime, utc_start_datetime, MILLISECOND)/daily_avg_millis_between_samples) as ratio_of_daily_sample_count_to_expected
from
  daily_sample_count