How to optimize database query plan through thinkorm to reduce resource consumption

How to optimize database query plan to reduce resource consumption through thinkorm

Introduction:
In most applications, the database is a core component. In order to improve the performance of the application, we need to optimize the query plan of the database to reduce resource consumption. ThinkORM is a popular PHP ORM framework that can help us achieve this goal. This article will introduce how to use ThinkORM to optimize database query plans and give some code examples.

1. Use of index
   Index is an important means to optimize database queries. ThinkORM provides flexible ways to create and use indexes. Here are some common index usage tips.

1.1 Consider creating appropriate indexes
When designing database tables, we should consider creating indexes suitable for queries. For example, creating indexes on frequently used query condition fields can greatly improve query efficiency. In ThinkORM, we can use the index method to create an index. The following is an example:

class User extends Model
{
    protected $table = 'users';

    protected $index = [
        'name',
        'email',
    ];
}

In the above example, the name and email fields are indexed so that matching can be found faster when querying Record.

1.2 Multi-field index
Sometimes, we need to query based on multiple fields. In this case, we can use multi-field indexes to improve query efficiency. In ThinkORM, we can use the compoundIndex method to create a multi-field index. Here is an example:

class Order extends Model
{
    protected $table = 'orders';

    protected $compoundIndex = [
        ['user_id', 'status'],
    ];
}

In the above example, a multi-field index is created based on the user_id and status fields.

2. Related query optimization
   Related query is one of the common database query operations. In ThinkORM, we can use hasOne, hasMany and other methods to perform related queries. In order to optimize related queries, we can consider using the eagerlyLoad method to preload related data and reduce the number of queries.

The following is an example:

class User extends Model
{
    protected $table = 'users';

    public function orders()
    {
        return $this->hasMany(Order::class);
    }
}

$users = User::with('orders')->get();

In the above example, through the with method, we can load the User model in one go All Order models are related, instead of executing a related query for each query. This can greatly improve query efficiency.

3. Paging query optimization
   In the case of large amounts of data, the efficiency of paging query may be low. In order to optimize paging queries, we can try to use cursor paging instead of the traditional limit and offset methods. In ThinkORM, we can use the cursor method to implement cursor paging.

Here is an example:

$lastId = 0;

$users = User::cursor(function ($query) use (&$lastId) {
    $query->where('id', '>', $lastId)
        ->orderBy('id')
        ->limit(100);
})->get();

In the above example, we execute the query through the cursor method and use where The condition specifies the starting position of cursor paging, and uses the limit method to limit the amount of data returned per page. In this way, we can avoid the performance problems caused by using offset in traditional paging queries.

4. Avoid N 1 query problem
   In related queries, if we do not use preloading technology correctly, it is easy to encounter N 1 query problems. When there are multiple associations, each query results in additional queries. In order to avoid this problem, we can use the withCount method to load the number of related queries at one time.

The following is an example:

class User extends Model
{
    protected $table = 'users';

    public function orders()
    {
        return $this->hasMany(Order::class);
    }
}

$users = User::withCount('orders')->get();

In the above example, we loaded the User model association in one go using the withCount method The quantity information of the Order model avoids the N 1 query problem.

Conclusion:
By rationally using indexes, optimizing related queries, paging queries and avoiding N 1 query problems, we can optimize database query plans through ThinkORM, thereby reducing resource consumption and improving application performance. I hope the content of this article can be helpful to readers.

Reference:

1. ThinkORM documentation: https://think-orm.gitee.io/model/
2. Introduction to Indexes in Database Systems, https:/ /www.studytonight.com/dbms/indexing-in-dbms
3. Eloquent ORM - Laravel, https://laravel.com/docs/8.x/eloquent

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