我有每5分钟记录一次的时间戳天气数据,我想以15分钟为间隔进行分组。我发现下面的floor函数看起来很有前途,但BQ不支持UNIX_TIMESTAMP函数
SELECT
FLOOR(UNIX_TIMESTAMP(utc_timestamp)/(15 * 60)) AS timekey
GROUP BY
timekey做这件事最好的方法是什么?
发布于 2019-03-16 07:12:24
下面是针对BigQuery标准SQL的说明
#standardSQL
SELECT
TIMESTAMP_SECONDS(15*60 * DIV(UNIX_SECONDS(utc_timestamp), 15*60)) timekey,
AVG(metric) metric
FROM `project.dataset.table`
GROUP BY timekey您可以使用以下示例中的虚拟数据来测试和处理上面的内容
#standardSQL
WITH `project.dataset.table` AS (
SELECT TIMESTAMP '2019-03-15 00:00:00' utc_timestamp, 1 metric UNION ALL
SELECT '2019-03-15 00:05:00', 2 UNION ALL
SELECT '2019-03-15 00:10:00', 3 UNION ALL
SELECT '2019-03-15 00:15:00', 4 UNION ALL
SELECT '2019-03-15 00:20:00', 5 UNION ALL
SELECT '2019-03-15 00:25:00', 6 UNION ALL
SELECT '2019-03-15 00:30:00', 7 UNION ALL
SELECT '2019-03-15 00:35:00', 8 UNION ALL
SELECT '2019-03-15 00:40:00', 9
)
SELECT
TIMESTAMP_SECONDS(15*60 * DIV(UNIX_SECONDS(utc_timestamp), 15*60)) timekey,
AVG(metric) metric
FROM `project.dataset.table`
GROUP BY timekey
-- ORDER BY timekey 有结果
Row timekey metric
1 2019-03-15 00:00:00 UTC 2.0
2 2019-03-15 00:15:00 UTC 5.0
3 2019-03-15 00:30:00 UTC 8.0 显然,您可以使用逻辑所需的任何聚合-我使用AVG()只是为了举例
https://stackoverflow.com/questions/55191664
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