应用程序除了像使用 FT.SEARCH 命令那样检索文档列表信息外，还有一个常见需求是进行一些“聚合”操作。
例如，如果我们查看电影文档，您可能希望按上映年份对电影数量进行分组，从最新的电影开始排列。
为此，Redis Stack 提供了 FT.AGGREGATE 命令，聚合操作被描述为一个数据处理管道。
让我们看一些示例。
在前面的示例中，您使用了 query string 参数来选择所有文档 ("*") 或文档的子集 ("@gender:{female}")
还可以使用与每个结果中的值相关的谓词表达式来过滤结果。这在查询后应用，并且与管道的当前状态有关。这是通过使用 FILTER 参数完成的。
> FT.AGGREGATE "idx:movie" "*" GROUPBY 1 @release_year REDUCE COUNT 0 AS nb_of_movies
1) (integer) 60
2) 1) "release_year"
2) "1964"
3) "nb_of_movies"
4) "9"
...
61) 1) "release_year"
2) "2010"
3) "nb_of_movies"
4) "15"> FT.AGGREGATE "idx:movie" "*" GROUPBY 1 @release_year REDUCE COUNT 0 AS nb_of_movies SORTBY 2 @release_year DESC
1) (integer) 60
2) 1) "release_year"
2) "2019"
3) "nb_of_movies"
4) "14"
...
11) 1) "release_year"
2) "2010"
3) "nb_of_movies"
4) "15"> FT.AGGREGATE idx:movie "*" GROUPBY 1 @genre REDUCE COUNT 0 AS nb_of_movies REDUCE SUM 1 votes AS nb_of_votes REDUCE AVG 1 rating AS avg_rating SORTBY 4 @avg_rating DESC @nb_of_votes DESC
1) (integer) 26
2) 1) "genre"
2) "fantasy"
3) "nb_of_movies"
4) "1"
5) "nb_of_votes"
6) "1500090"
7) "avg_rating"
8) "8.8"
...
11) 1) "genre"
2) "romance"
3) "nb_of_movies"
4) "2"
5) "nb_of_votes"
6) "746"
7) "avg_rating"
8) "6.65"> FT.AGGREGATE idx:user "@gender:{female}" GROUPBY 1 @country REDUCE COUNT 0 AS nb_of_users SORTBY 2 @nb_of_users DESC
1) (integer) 193
2) 1) "country"
2) "china"
3) "nb_of_users"
4) "537"
...
11) 1) "country"
2) "ukraine"
3) "nb_of_users"
4) "72"> FT.AGGREGATE idx:user * APPLY year(@last_login) AS year APPLY "monthofyear(@last_login) + 1" AS month GROUPBY 2 @year @month REDUCE count 0 AS num_login SORTBY 4 @year ASC @month ASC
1) (integer) 13
2) 1) "year"
2) "2019"
3) "month"
4) "9"
5) "num_login"
6) "230"
...
14) 1) "year"
2) "2020"
3) "month"
4) "9"
5) "num_login"
6) "271"
使用日期/时间 Apply 函数可以从时间戳中提取工作日，因此让我们看看登录次数在一周内的分布情况。
> FT.AGGREGATE idx:user * APPLY "dayofweek(@last_login) +1" AS dayofweek GROUPBY 1 @dayofweek REDUCE count 0 AS num_login SORTBY 2 @dayofweek ASC
1) (integer) 7
2) 1) "dayofweek"
2) "1"
3) "num_login"
4) "815"
...
8) 1) "dayofweek"
2) "7"
3) "num_login"
4) "906"
> FT.AGGREGATE idx:user "@gender:{female}" GROUPBY 1 @country REDUCE COUNT 0 AS nb_of_users FILTER "@country!='china' && @nb_of_users > 100" SORTBY 2 @nb_of_users DESC
1) (integer) 163
2) 1) "country"
2) "indonesia"
3) "nb_of_users"
4) "309"
...
6) 1) "country"
2) "brazil"
3) "nb_of_users"
4) "108"这与之前的查询类似，但增加了按年份过滤的条件。
> FT.AGGREGATE idx:user * APPLY year(@last_login) AS year APPLY "monthofyear(@last_login) + 1" AS month GROUPBY 2 @year @month REDUCE count 0 AS num_login FILTER "@year==2020" SORTBY 2 @month ASC
1) (integer) 13
2) 1) "year"
2) "2020"
3) "month"
4) "1"
5) "num_login"
6) "520"
...
10) 1) "year"
2) "2020"
3) "month"
4) "9"
5) "num_login"
6) "271"