假设我有这样一张表格:
我希望能够做到这个:
产生的结果如下所示:
CREATE TABLE time_series (
snapshot_date DATE,
sales INTEGER,
PRIMARY KEY (snapshot_date));
使用这样的值:
INSERT INTO time_series SELECT '2017-01-01'::DATE AS snapshot_date,10 AS sales;
INSERT INTO time_series SELECT '2017-01-02'::DATE AS snapshot_date,4 AS sales;
INSERT INTO time_series SELECT '2017-01-03'::DATE AS snapshot_date,13 AS sales;
INSERT INTO time_series SELECT '2017-01-04'::DATE AS snapshot_date,7 AS sales;
INSERT INTO time_series SELECT '2017-01-05'::DATE AS snapshot_date,15 AS sales;
INSERT INTO time_series SELECT '2017-01-06'::DATE AS snapshot_date,8 AS sales;
我希望能够做到这个:
SELECT a.snapshot_date,
AVG(b.sales) AS sales_avg,
COUNT(*) AS COUNT
FROM time_series AS a
JOIN time_series AS b
ON a.snapshot_date > b.snapshot_date
GROUP BY a.snapshot_date
产生的结果如下所示:
*---------------*-----------*-------*
| snapshot_date | sales_avg | count |
*---------------*-----------*-------*
| 2017-01-02 | 10.0 | 1 |
| 2017-01-03 | 7.0 | 2 |
| 2017-01-04 | 9.0 | 3 |
| 2017-01-05 | 8.5 | 4 |
| 2017-01-06 | 9.8 | 5 |
-------------------------------------
在这个例子中,由于行数很少,所以查询非常快。问题是我必须处理数百万行数据,在Redshift上(与Postgres类似的语法),我的查询需要几天才能运行。速度极慢,而且这是我最常用的查询模式之一。我怀疑问题是由于数据中O(n^2)的增长而产生的,而不是更可取的O(n)。
在Python中,我采用了一种O(n)的实现方法,如下所示:
rows = [('2017-01-01',10),
('2017-01-02',4),
('2017-01-03',13),
('2017-01-04',7),
('2017-01-05',15),
('2017-01-06',8)]
sales_total_previous = 0
count = 0
for index, row in enumerate(rows):
snapshot_date = row[0]
sales = row[1]
if index == 0:
sales_total_previous += sales
continue
count += 1
sales_avg = sales_total_previous / count
print((snapshot_date,sales_avg, count))
sales_total_previous += sales
以下是查询结果(与SQL查询相同):
('2017-01-02', 10.0, 1)
('2017-01-03', 7.0, 2)
('2017-01-04', 9.0, 3)
('2017-01-05', 8.5, 4)
('2017-01-06', 9.8, 5)
我正在考虑转换到Apache Spark,这样我就可以使用python查询,但是几百万行的数据并不算太大(最多只有3-4 GB),使用100 GB RAM的Spark集群似乎有些浪费。是否有一种高效且易于理解的方法可以在SQL中实现O(n)的效率,最好在Postgres/Redshift中实现?