问题描述
我发现了一个类似的问题(复制记录以填补 Google BigQuery 中日期之间的空白)，但是使用不同的场景并且那里的答案不适用.
I've found a similar question (Duplicating records to fill gap between dates in Google BigQuery), however with a different scenario and the answer there does not apply.
我有这样结构的数据(基本上是多个产品和合作伙伴的价格变化历史):
I have data structured like so (which is basically price-change history for multiple products and partners):
+------------+---------+---------+-------+
| date | product | partner | value |
+------------+---------+---------+-------+
| 2017-01-01 | a | x | 10 |
| 2017-01-01 | b | x | 15 |
| 2017-01-01 | a | y | 11 |
| 2017-01-01 | b | y | 16 |
| 2017-01-05 | b | x | 13 |
| 2017-01-07 | a | y | 15 |
| 2017-01-07 | a | x | 15 |
+------------+---------+---------+-------+
我需要的是一个查询(特别是用 BigQuery 标准 SQL 编写的)，给定一个日期范围(在这种情况下，2017-01-01 到 2017-01-10)，输出如下结果:
What I need is a query (specifically written in BigQuery Standard SQL) that, given a date range (in this case, 2017-01-01 to 2017-01-10), outputs the following result:
+--------------+---------+---------+-------+
| date | product | partner | value |
+--------------+---------+---------+-------+
| 2017-01-01 | a | x | 10 |
| 2017-01-02 | a | x | 10 |
| 2017-01-03 | a | x | 10 |
| 2017-01-04 | a | x | 10 |
| 2017-01-05 | a | x | 10 |
| 2017-01-06 | a | x | 10 |
| 2017-01-07 | a | x | 15 |
| 2017-01-08 | a | x | 15 |
| 2017-01-09 | a | x | 15 |
| 2017-01-10 | a | x | 15 |
| 2017-01-01 | a | y | 11 |
| 2017-01-02 | a | y | 11 |
| 2017-01-03 | a | y | 11 |
| 2017-01-04 | a | y | 11 |
| 2017-01-05 | a | y | 11 |
| 2017-01-06 | a | y | 11 |
| 2017-01-07 | a | y | 15 |
| 2017-01-08 | a | y | 15 |
| 2017-01-09 | a | y | 15 |
| 2017-01-10 | a | y | 15 |
| 2017-01-01 | b | x | 15 |
| 2017-01-02 | b | x | 15 |
| 2017-01-03 | b | x | 15 |
| 2017-01-04 | b | x | 15 |
| 2017-01-05 | b | x | 13 |
| 2017-01-06 | b | x | 13 |
| 2017-01-07 | b | x | 13 |
| 2017-01-08 | b | x | 13 |
| 2017-01-09 | b | x | 13 |
| 2017-01-10 | b | x | 13 |
| 2017-01-01 | b | y | 16 |
| 2017-01-02 | b | y | 16 |
| 2017-01-03 | b | y | 16 |
| 2017-01-04 | b | y | 16 |
| 2017-01-05 | b | y | 16 |
| 2017-01-06 | b | y | 16 |
| 2017-01-07 | b | y | 16 |
| 2017-01-08 | b | y | 16 |
| 2017-01-09 | b | y | 16 |
| 2017-01-10 | b | y | 16 |
+--------------+---------+---------+-------+
基本上是针对产品和合作伙伴的每种组合填充所有日期间隔的价格历史记录.
Basically a price history with all date gaps filled, for every combination of product and partner.
我很难弄清楚如何完成这项工作，尤其是如何在没有发生价格变化的同一日期生成多行.有什么想法吗?
I'm having a hard time figuring out how to get this done, especially how to generate multiple rows for the same date where no price change has happened. Any ideas?
推荐答案
试试下面的
#standardSQL
WITH history AS (
SELECT '2017-01-01' AS d, 'a' AS product, 'x' AS partner, 10 AS value UNION ALL
SELECT '2017-01-01' AS d, 'b' AS product, 'x' AS partner, 15 AS value UNION ALL
SELECT '2017-01-01' AS d, 'a' AS product, 'y' AS partner, 11 AS value UNION ALL
SELECT '2017-01-01' AS d, 'b' AS product, 'y' AS partner, 16 AS value UNION ALL
SELECT '2017-01-05' AS d, 'b' AS product, 'x' AS partner, 13 AS value UNION ALL
SELECT '2017-01-07' AS d, 'a' AS product, 'y' AS partner, 15 AS value UNION ALL
SELECT '2017-01-07' AS d, 'a' AS product, 'x' AS partner, 15 AS value
),
daterange AS (
SELECT date_in_range
FROM UNNEST(GENERATE_DATE_ARRAY('2017-01-01', '2017-01-10')) AS date_in_range
),
temp AS (
SELECT d, product, partner, value, LEAD(d) OVER(PARTITION BY product, partner ORDER BY d) AS next_d
FROM history
ORDER BY product, partner, d
)
SELECT date_in_range, product, partner, value
FROM daterange
JOIN temp
ON daterange.date_in_range >= PARSE_DATE('%Y-%m-%d', temp.d)
AND (daterange.date_in_range < PARSE_DATE('%Y-%m-%d', temp.next_d) OR temp.next_d IS NULL)
ORDER BY product, partner, date_in_range
这篇关于重复记录组以填补 Google BigQuery 中的多个日期间隔的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！