Question

我正在使用Django和Postgres并拥有以下表格：

class Person(models.Model):
    person_id=models.IntegerField(primary_key=True)
    name = models.CharField(max_length=500, blank=True)
    description = models.ManyToManyField('descriptions.Description', through='DescriptionPersonUser')

class DescriptionPersonUser(models.Model):
    person = models.ForeignKey(Person)
    description = models.ForeignKey('descriptions.Description')
    user = models.ForeignKey(User)

    class Meta:
        managed = True
        unique_together = ('person', 'description', 'user')

class Description(models.Model):
    description_id=models.AutoField (primary_key=True)
    description_word=models.CharField(max_length=50, blank=True, unique=True)

class AuthUser(models.Model):
    id = models.IntegerField(primary_key=True)  # AutoField?
   ...
    username = models.CharField(unique=True, max_length=30)

Person表的行数超过1.5 mio，其他表的行数不超过100行。据我所知，执行“正常”执行的查询仍然是合理的。我想通过DescriptionPersonUser表中的计数注释计数来订购Person表。

person_list = Person.objects.annotate(count=Count('descriptionpersonuser')).order_by('-count')[:10]

此查询需要加载cca 50000毫秒。比我尝试在原始sql中执行它并且改进了很多cca 1900 ms。

person_list= Person.objects.raw('SELECT "person"."person_id", COUNT("persons_descriptionpersonuser"."id") AS "count" FROM "person" LEFT OUTER JOIN "persons_descriptionpersonuser" ON ( "person"."person_id" = "persons_descriptionspersonuser"."person_id" ) GROUP BY "person"."person_id" ORDER BY "count" DESC, "person"."person_id" ASC LIMIT 10'),

我还在persons_descriptionpersonuser上创建了索引：

CREATE INDEX index_descriptionpersonuser ON persons_descriptionpersonuser (person_id, description_id, id);

所以我的问题是：

是否仍然可以加快查询速度？或者对于1+ mio行查询是否为1900 ms？
由于我没有看到查询速度与创建的索引有任何差异，我该如何检查索引是否正常或是否影响查询？

已编辑（根据Tomasz Jakub Rup建议添加EXPLAIN ANALYZE结果）：

没有index_descriptionpersonuser：

Limit  (cost=138185.30..138185.33 rows=10 width=8) (actual time=2470.974..2470.976 rows=10 loops=1)
   ->  Sort  (cost=138185.30..142177.82 rows=1597006 width=8) (actual time=2470.973..2470.975 rows=10 loops=1)
         Sort Key: (count(persons_descriptionpersonuser.id)), person.person_id
         Sort Method: top-N heapsort  Memory: 25kB
         ->  GroupAggregate  (cost=0.56..103674.58 rows=1597006 width=8) (actual time=0.402..1945.107 rows=1597006 loops=1)
               Group Key: person.person_id
               ->  Merge Left Join  (cost=0.56..79719.49 rows=1597006 width=8) (actual time=0.378..1014.179 rows=1597016 loops=1)
                     Merge Cond: (person.person_id = persons_descriptionpersonuse.person_id)
                     ->  Index Only Scan using person_pkey on person  (cost=0.43..75718.86 rows=1597006 width=4) (actual time=0.359..610.272 rows=1597006 loops=1)
                           Heap Fetches: 235898
                     ->  Index Scan using persons_descriptionpersonuse_person_id on persons_descriptionpersonuser  (cost=0.14..12.42 rows=19 width=8) (actual time=0.014..0.025 rows=20 loops=1)
 Planning time: 17.879 ms
 Execution time: 2472.821 ms
(13 rows)

使用index_descriptionpersonuser：

Limit  (cost=138185.55..138185.58 rows=10 width=8) (actual time=2341.349..2341.352 rows=10 loops=1)
   ->  Sort  (cost=138185.55..142178.07 rows=1597006 width=8) (actual time=2341.325..2341.325 rows=10 loops=1)
         Sort Key: (count(persons_descriptionpersonuser.id)), person.person_id
         Sort Method: top-N heapsort  Memory: 25kB
         ->  GroupAggregate  (cost=0.56..103674.83 rows=1597006 width=8) (actual time=0.106..1819.330 rows=1597006 loops=1)
               Group Key: person.person_id
               ->  Merge Left Join  (cost=0.56..79719.74 rows=1597006 width=8) (actual time=0.092..877.874 rows=1597016 loops=1)
                     Merge Cond: (person.person_id = persons_descriptionpersonuser.person_id)
                     ->  Index Only Scan using person_pkey on person  (cost=0.43..75718.86 rows=1597006 width=4) (actual time=0.023..473.046 rows=1597006 loops=1)
                           Heap Fetches: 235898
                     ->  Index Only Scan using index_descriptionpersonuser on persons_descriptionpersonuser  (cost=0.14..12.44 rows=20 width=8) (actual time=0.059..0.085 rows=20 loops=1)
                           Heap Fetches: 20
 Planning time: 0.715 ms
 Execution time: 2343.815 ms
(14 rows)

如Tomasz Jakub Rup所示，优化的sql查询现在需要cca 40 ms。结果如下：

Limit  (cost=1.50..1.52 rows=8 width=8) (actual time=0.061..0.064 rows=10 loops=1)
   ->  Sort  (cost=1.50..1.52 rows=8 width=8) (actual time=0.060..0.061 rows=10 loops=1)
         Sort Key: (count(id)), person_id
         Sort Method: quicksort  Memory: 25kB
         ->  HashAggregate  (cost=1.30..1.38 rows=8 width=8) (actual time=0.039..0.044 rows=10 loops=1)
               Group Key: person_id
               ->  Seq Scan on persons_descriptionpersonuser  (cost=0.00..1.20 rows=20 width=8) (actual time=0.011..0.018 rows=20 loops=1)
 Planning time: 0.175 ms
 Execution time: 0.129 ms
(9 rows)

由于

Answer 1

回答你的第二个问题：

看看

的结果

EXPLAIN ANALYZE SELECT "person"."person_...

查询。如果您在结果中找到index_descriptionpersonuser，则查询使用索引。如果不是，请尝试创建其他索引。也许只在person_id？

第一个问题：是的，这个查询可以更快。显示EXPLAIN ANALYZE...的结果，然后我们尝试加快查询速度。

注意

原始查询可能更快，因为它们从PostgreSQL缓存中获取数据。

Django查询性能提高

1 个答案: