我有一个存储JSONB字段的表(
data
)包含类似Facebook的数据。数据结构为:
-
id | 9403
kind | 'likes'
data | [{ id: "1", name: "Pluto", category: "Planet"}, { id: "2", name: "Saturn", category: "Planet" }]
-
id | 9403
kind | 'likes'
data | [{ id: "2", name: "Neptune", category: "Planet"}, { id: "3", name: "Mars", category: "Planet" }]
目标是编写按类别聚合每个类别前N(5)个赞的查询。我有以下子查询,我不知道如何优化(使用索引或通过重写)。目标是获得名称和类别的计数分组,以便对它们进行排名。我从更简单的问题开始,高效地选择N个最受欢迎的:
SELECT
likes.entry->>'name' AS name,
likes.entry->>'category' AS category,
COUNT(*) AS count
FROM (SELECT json_array_elements(metadata.data::JSON) AS entry FROM metadata WHERE metadata.kind = 'likes') AS likes
GROUP BY name, category
ORDER BY count DESC
LIMIT 5
该查询的运行时间已超过5秒(粘贴解释/分析):
Limit (cost=39971.07..39971.07 rows=5 width=32) (actual time=5468.952..5468.954 rows=5 loops=1)
-> Sort (cost=39971.07..39971.17 rows=200 width=32) (actual time=5468.952..5468.954 rows=5 loops=1)
Sort Key: (count(*))
Sort Method: top-N heapsort Memory: 25kB
-> HashAggregate (cost=39969.61..39970.41 rows=200 width=32) (actual time=5241.143..5376.502 rows=392515 loops=1)
Group Key: (likes.entry ->> 'name'::text), (likes.entry ->> 'category'::text)
-> Subquery Scan on likes (cost=0.00..34491.46 rows=3652100 width=32) (actual time=0.104..4552.531 rows=880073 loops=1)
-> Seq Scan on metadata (cost=0.00..19883.06 rows=3652100 width=703) (actual time=0.097..2146.678 rows=880073 loops=1)
Filter: ((kind)::text = 'likes'::text)
Rows Removed by Filter: 90145
我是否可以在不使用物化视图的情况下对其进行重构以提高速度/添加一些索引?我尝试添加以下(无用)索引:
CREATE INDEX index_metadata_on_likes_raw ON metadata USING gin(data) WHERE (kind = 'likes');
CREATE INDEX index_metadata_on_likes_targeted ON metadata ((data ->> 'name'), (data ->> 'category')) WHERE (kind = 'likes');