最有效的方式加入兩個時間序列

Question

想象一下，我有一個這樣的表：

 CREATE TABLE time_series (
        snapshot_date DATE,
        sales INTEGER,
PRIMARY KEY (snapshot_date));

使用這樣的值：

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;

我希望能夠做到這一點：

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

這會產生如下結果：

*---------------*-----------*-------*
| 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  |
-------------------------------------

如此示例中的行數非常少，查詢運行速度非常快。 問題是我必須為數百萬行執行此操作，而在Redshift上（類似於Postgres的語法），我的查詢需要數天才能運行。 它非常慢，但這是我最常見的查詢模式之一。 我懷疑問題是由於數據中O（n ^ 2）的增長與更優選的O（n）的增長。

我在python中的O（n）實現將是這樣的：

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

使用這樣的結果（與SQL查詢相同）：

('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)

我正在考慮切換到Apache Spark，以便我可以完成那個python查詢，但是幾百萬行並不是那么大（最多3-4 GB）並且使用具有100 GB RAM的Spark集群似乎矯枉過正。 有一種高效且易於閱讀的方式我可以在SQL中獲得O（n）效率，最好是在Postgres / Redshift中嗎？

Answer 1

你似乎想要：

SELECT ts.snapshot_date, 
       AVG(ts.sales) OVER (ORDER BY ts.snapshot_date) AS sales_avg,
       ROW_NUMBER() OVER (ORDER BY ts.snapshot_date) AS COUNT
FROM time_series ts;

你會發現使用窗口函數效率更高。