Just create a collection users and insert as many random documents as you need.
FOR i IN 1..1100000
INSERT {
name: CONCAT("test", i),
year: 1970 + FLOOR(RAND() * 55),
gender: i % 2 == 0 ? 'male' : 'female'
} IN users
Then do the count:
FOR user IN users
FILTER user.gender == 'male'
COLLECT WITH COUNT INTO number
RETURN {
number: number
}
And if you use this query in production, make sure to add an index too. On my machine it reduces the execution time by factor > 100x (0.043 sec / 1.1mio documents).
Check your query with EXPLAIN to further estimate how "expensive" the execution will be.
Query string:
FOR user IN users
FILTER user.gender == 'male'
COLLECT WITH COUNT INTO number
RETURN {
number: number
}
Execution plan:
Id NodeType Est. Comment
1 SingletonNode 1 * ROOT
8 IndexRangeNode 550001 - FOR user IN users /* hash index scan */
5 AggregateNode 1 - COLLECT WITH COUNT INTO number /* sorted*/
6 CalculationNode 1 - LET #4 = { "number" : number } /* simple expression */
7 ReturnNode 1 - RETURN #4
Indexes used:
Id Type Collection Unique Sparse Selectivity Est. Fields Ranges
8 hash users false false 0.00 % `gender` [ `gender` == "male" ]
Optimization rules applied:
Id RuleName
1 use-index-range
2 remove-filter-covered-by-index