Arangodb连接查询优化

Question

问题：在数据库中，每个集合中有3个集合，每个集合中包含10万个文档，每个文档中的属性数为150，每个集合中的<属性是唯一的哈希索引。

现在我要合并所有集合并返回所有集合中

“ ID”

相匹配的文档例如：collection1包含文档{"id":1,"firstname":"alex","gender":"male"}collection2包含文档{"id":1,"middlename":"wilson","age":23}collection3包含文档{"id":1,"lastname","alive":true}

所以我想像{"id":1,"firstname":"alex","gender":"male","middlename":"wilson","age":23,"lastname","alive":true}

查询工作正常，并给了我预期的结果，但是在大型集合中花费了太多时间，所以我想知道任何其他方法来编写查询或我的aql需要进行任何优化

AQL查询：

查询字符串（219个字符，可缓存：true）：

   FOR c1 IN coll1 
     sort c1.id ASC
         FOR c2 IN coll2
           FOR c3 IN coll3
              FILTER ((c1.id == c2.id ) && (c2.id == c3.id))
              limit 10000
     RETURN MERGE(c1, c2,c3)

AQL解释：

Execution plan:
 Id   NodeType             Est.   Comment
  1   SingletonNode           1   * ROOT
 15   IndexNode         1000000     - FOR c1 IN coll1   /* hash index scan */
 13   IndexNode         1000000       - FOR c2 IN coll2   /* hash index scan */
 12   IndexNode         1000000         - FOR c3 IN coll3   /* hash index scan */
  9   LimitNode           10000           - LIMIT 0, 10000
 10   CalculationNode     10000           - LET #7 = MERGE(c1, c2, c3)   /* simple expression */   /* collections used: c1 : coll1, c2 : coll2, c3 : coll3 */
 11   ReturnNode          10000           - RETURN #7

Indexes used:
 By   Name                      Type   Collection   Unique   Sparse   Selectivity   Fields              Ranges
 15   idx_1650897367446061056   hash   coll1        true     false       100.00 %   [ `id` ]   *
 13   idx_1650897883340210176   hash   coll2        true     false       100.00 %   [ `id` ]   (c1.`id` == c2.`id`)
 12   idx_1650895606437117952   hash   coll3        true     false       100.00 %   [ `id` ]   (c2.`id` == c3.`id`)

Functions used:
 Name    Deterministic   Cacheable   Uses V8
 MERGE   true            true        false  

Optimization rules applied:
 Id   RuleName
  1   use-indexes
  2   remove-filter-covered-by-index
  3   use-index-for-sort
  4   remove-unnecessary-calculations-2

Answer 1

使用该查询执行查询所花费的时间更少