这个查询很快：

select date, name_id,
case @i when name_id then @i:=name_id else (@i:=name_id)
and (@n:=0)
and (@a0:=0) and (@a1:=0) and (@a2:=0) and (@a3:=0) and (@a4:=0) and (@a5:=0) and (@a6:=0) and (@a7:=0) and (@a8:=0)
end as a,
case @n when 9 then @n:=9 else @n:=@n+1 end as n,
@a0:=@a1,@a1:=@a2,@a2:=@a3,@a3:=@a4,@a4:=@a5,@a5:=@a6,@a6:=@a7,@a7:=@a8,@a8:=close,
(@a0+@a1+@a2+@a3+@a4+@a5+@a6+@a7+@a8)/@n as av
from tbl,
(select @i:=0, @n:=0,
        @a0:=0, @a1:=0, @a2:=0, @a3:=0, @a4:=0, @a5:=0, @a6:=0, @a7:=0, @a8:=0) a
where name_id=2
order by name_id, date

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如果你需要一个超过50或100个值的平均值，写起来很乏味，但值得努力。速度接近有序选择。

移动中位数（如果有人需要）

SELECT date,
   (SELECT AVG(close) AS median
    FROM
        (SELECT close, 
            ROW_NUMBER() OVER(ORDER BY close) AS r,
            COUNT(close) OVER() AS c
        FROM tbl
        WHERE datediff(t.date, date) 
                BETWEEN 1 AND 9
        ) AS t2

    WHERE t2.r BETWEEN FLOOR(t2.c/2)+MOD(t2.c,2) 
                    AND FLOOR(t2.c/2)+1
   ) AS mvgMedian
FROM tbl t
GROUP BY date
ORDER BY date

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如果你想要每个日期的移动平均值，那么试试这个：

SELECT date, SUM(close),
       (select avg(close) from tbl t2 where t2.name_id = t.name_id and datediff(t2.date, t.date) <= 9
       ) as mvgAvg
FROM tbl t
WHERE date <= '2002-07-05' and
      name_id = 2
GROUP BY date
ORDER BY date DESC

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它使用相关子查询来计算9个值的平均值。

从MySQL 8开始，你应该使用窗口函数来实现这一点。使用window RANGE子句，你可以在一个时间间隔内创建一个logical window，这是非常强大的。像这样：

SELECT
  date,
  close,
  AVG (close) OVER (ORDER BY date DESC RANGE INTERVAL 9 DAY PRECEDING)
FROM tbl
WHERE date <= DATE '2002-07-05'
AND name_id = 2
ORDER BY date DESC

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举例来说：

WITH t (date, `close`) AS (
  SELECT DATE '2020-01-01', 50 UNION ALL
  SELECT DATE '2020-01-03', 54 UNION ALL
  SELECT DATE '2020-01-05', 51 UNION ALL
  SELECT DATE '2020-01-12', 49 UNION ALL
  SELECT DATE '2020-01-13', 59 UNION ALL
  SELECT DATE '2020-01-15', 30 UNION ALL
  SELECT DATE '2020-01-17', 35 UNION ALL
  SELECT DATE '2020-01-18', 39 UNION ALL
  SELECT DATE '2020-01-19', 47 UNION ALL
  SELECT DATE '2020-01-26', 50
)
SELECT
  date,
  `close`,
  COUNT(*) OVER w AS c,
  SUM(`close`) OVER w AS s,
  AVG(`close`) OVER w AS a
FROM t
WINDOW w AS (ORDER BY date DESC RANGE INTERVAL 9 DAY PRECEDING)
ORDER BY date DESC

型
导致：

date      |close|c|s  |a      |
----------|-----|-|---|-------|
2020-01-26|   50|1| 50|50.0000|
2020-01-19|   47|2| 97|48.5000|
2020-01-18|   39|3|136|45.3333|
2020-01-17|   35|4|171|42.7500|
2020-01-15|   30|4|151|37.7500|
2020-01-13|   59|5|210|42.0000|
2020-01-12|   49|6|259|43.1667|
2020-01-05|   51|3|159|53.0000|
2020-01-03|   54|3|154|51.3333|
2020-01-01|   50|3|155|51.6667|

型

使用类似于

SELECT 
  sum(close) as sum,
  avg(close) as average
FROM (
    SELECT 
      (close)
    FROM 
      tbl
    WHERE 
      date <= '2002-07-05'
      AND name_id = 2
    ORDER BY 
      date DESC
    LIMIT 9 ) temp

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内部查询以desc顺序返回所有筛选的行，然后对返回的行进行avg、sum。
您给出的query不起作用的原因是，首先计算sum，然后在计算sum之后应用LIMIT子句，从而为您提供所有行的sum

另一个技巧是做一个表格：

CREATE TABLE `tinyint_asc` (
 `value` tinyint(3) unsigned NOT NULL default '0',
 PRIMARY KEY (value)
) ;
​
INSERT INTO `tinyint_asc` VALUES (0),(1),(2),(3),(4),(5),(6),(7),(8),(9),(10),(11),(12),(13),(14),(15),(16),(17),(18),(19),(20),(21),(22),(23),(24),(25),(26),(27),(28),(29),(30),(31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41),(42),(43),(44),(45),(46),(47),(48),(49),(50),(51),(52),(53),(54),(55),(56),(57),(58),(59),(60),(61),(62),(63),(64),(65),(66),(67),(68),(69),(70),(71),(72),(73),(74),(75),(76),(77),(78),(79),(80),(81),(82),(83),(84),(85),(86),(87),(88),(89),(90),(91),(92),(93),(94),(95),(96),(97),(98),(99),(100),(101),(102),(103),(104),(105),(106),(107),(108),(109),(110),(111),(112),(113),(114),(115),(116),(117),(118),(119),(120),(121),(122),(123),(124),(125),(126),(127),(128),(129),(130),(131),(132),(133),(134),(135),(136),(137),(138),(139),(140),(141),(142),(143),(144),(145),(146),(147),(148),(149),(150),(151),(152),(153),(154),(155),(156),(157),(158),(159),(160),(161),(162),(163),(164),(165),(166),(167),(168),(169),(170),(171),(172),(173),(174),(175),(176),(177),(178),(179),(180),(181),(182),(183),(184),(185),(186),(187),(188),(189),(190),(191),(192),(193),(194),(195),(196),(197),(198),(199),(200),(201),(202),(203),(204),(205),(206),(207),(208),(209),(210),(211),(212),(213),(214),(215),(216),(217),(218),(219),(220),(221),(222),(223),(224),(225),(226),(227),(228),(229),(230),(231),(232),(233),(234),(235),(236),(237),(238),(239),(240),(241),(242),(243),(244),(245),(246),(247),(248),(249),(250),(251),(252),(253),(254),(255);

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你可以这样使用它：

select 
    date_add(tbl.date, interval tinyint_asc.value day) as mydate, 
    count(*),
    sum(myvalue)
from tbl inner 
    join tinyint_asc.value <= 30 -- for a 30 day moving average
where date( date_add(o.created_at, interval tinyint_asc.value day ) ) between '2016-01-01' and current_date()
    group by mydate

型