1.group by 可以包含任意数目的列2.group by 中每个列都必须是检索列或有效的表达式(但不能使聚集函数)3.除聚集函数外, select 语句中的每个列都必须在 group by 子句中出现 4. 如果分组列有 Null , Null 将作为一个分组返回 5. group by 子句必须出现在
<code>1.group by 可以包含任意数目的列 2.group by 中每个列都必须是检索列或有效的表达式(但不能使聚集函数) 3.除聚集函数外,<span class="hljs-operator"><span class="hljs-keyword">select</span>语句中的每个列都必须在<span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span>子句中出现 <span class="hljs-number">4.</span>如果分组列有<span class="hljs-literal">Null</span>值,<span class="hljs-literal">Null</span>将作为一个分组返回 <span class="hljs-number">5.</span><span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span> 子句必须出现在<span class="hljs-keyword">where</span>子句之后, <span class="hljs-keyword">order</span> <span class="hljs-keyword">by</span> 之前 where 和 have 的区别: where在分组前过滤,having在分组后过滤 SELECT vend_id ,COUNT(*) AS num_prods FROM products GROUP BY vend_id; <img src="/static/imghwm/default1.png" data-src="https://segmentfault.com/img/bVlQKE" class="lazy" alt="" style="max-width:90%"> <strong> GROUP BY子句指示MySQL分组数据,然后对每个组而不是整个结果集进行聚集。 在使用GROUP BY子句之前,需要知道一些重要的规定: 1. GROUP BY 子句可以包含任意数目的列。这使得能对分组进行嵌套,为数据分组提供更细致的控制。 2. 如果GROUP BY子句中嵌套了分组,数据将最后规定的分组上进行汇总。换句话说,在建立分组时,指定的所有列都一起计算(所以不能从个别的列取回数据) 3. GROUP BY 子句中列出的每个列都必须是检索列或有效的表达式(但不能是聚集函数)。如果在SELECT 中使用表达式,必须在GROUP BY子句中指定相同的表达式。不能使用别名。 4.除聚集计算语句外,SELECT 语句中的每个列都必须在GROUP BY 子句中给出。 5.如果分组列中具有NULL 值,则将NULL作为一个分组返回 6.GROUP BY 子句必须出现在where子句之后。ORDER BY 子句之前。 注意:使用ROLLUP 使用WITH ROLLUP 关键字,可以得到每个分组以及每个分组汇总级别(针对每个分组)的值。 SELECT vend_id ,COUNT(*) AS num_prods FROM products GROUPS BY vend_id WITH ROLLUP; 二 :过滤分组 我们已经看到了WHERE 子句的作用,但是这个例子中WHERE 不能完成任务。因为WHERE过滤指定的行而不是分组。事实上WHERE 没有分组的概念。MYSQL中为此目的 提供了类似的语句,那就是HAVING子句。HAVING非常类似于WHERE。事实上,目前为止所有学过的所有类型的WHERE子句都可以用HAVING来替代。唯一的差别是WHERE过滤行 ,而HAVING过滤分组。 SELECT cust_id,COUNT(*) AS orders FROM orders GROUP BY cust_id HAVING COUNT(*)>=2; 注意:HAVING 与WHERE 的区别 WHERE 在数据分组前进行过滤,HAVING在数据分组后进行过滤。这是一个重要区别,WHERE排除的行不会出现在分组中。可能改变分组值,从而影响HAVING子句中基于这些值过滤掉分组。 为了更好的理解,请看下面一个例子,它列出具有2个以上,价格为10以上的产品的供应商。 SELECT vend_id,COUNT(*) AS num_prods FROM products where prod_price>=10 GROUP BY vend_id HAVING COUNT(*)>=2; 需要注意的是:在使用GROUP BY 子句时,应该也给出ORDER BY子句。这保证数据正确排序的唯一方法。 SELECT order_num,SUM(quantity*item_price) AS ordertotal FROM orderitems GROUP by order_num HAVING SUM(quantity* item_price)>=50 ORDER BY ordertotal; </strong></span></code>

MySQL index cardinality has a significant impact on query performance: 1. High cardinality index can more effectively narrow the data range and improve query efficiency; 2. Low cardinality index may lead to full table scanning and reduce query performance; 3. In joint index, high cardinality sequences should be placed in front to optimize query.

The MySQL learning path includes basic knowledge, core concepts, usage examples, and optimization techniques. 1) Understand basic concepts such as tables, rows, columns, and SQL queries. 2) Learn the definition, working principles and advantages of MySQL. 3) Master basic CRUD operations and advanced usage, such as indexes and stored procedures. 4) Familiar with common error debugging and performance optimization suggestions, such as rational use of indexes and optimization queries. Through these steps, you will have a full grasp of the use and optimization of MySQL.

MySQL's real-world applications include basic database design and complex query optimization. 1) Basic usage: used to store and manage user data, such as inserting, querying, updating and deleting user information. 2) Advanced usage: Handle complex business logic, such as order and inventory management of e-commerce platforms. 3) Performance optimization: Improve performance by rationally using indexes, partition tables and query caches.

SQL commands in MySQL can be divided into categories such as DDL, DML, DQL, DCL, etc., and are used to create, modify, delete databases and tables, insert, update, delete data, and perform complex query operations. 1. Basic usage includes CREATETABLE creation table, INSERTINTO insert data, and SELECT query data. 2. Advanced usage involves JOIN for table joins, subqueries and GROUPBY for data aggregation. 3. Common errors such as syntax errors, data type mismatch and permission problems can be debugged through syntax checking, data type conversion and permission management. 4. Performance optimization suggestions include using indexes, avoiding full table scanning, optimizing JOIN operations and using transactions to ensure data consistency.

InnoDB achieves atomicity through undolog, consistency and isolation through locking mechanism and MVCC, and persistence through redolog. 1) Atomicity: Use undolog to record the original data to ensure that the transaction can be rolled back. 2) Consistency: Ensure the data consistency through row-level locking and MVCC. 3) Isolation: Supports multiple isolation levels, and REPEATABLEREAD is used by default. 4) Persistence: Use redolog to record modifications to ensure that data is saved for a long time.

MySQL's position in databases and programming is very important. It is an open source relational database management system that is widely used in various application scenarios. 1) MySQL provides efficient data storage, organization and retrieval functions, supporting Web, mobile and enterprise-level systems. 2) It uses a client-server architecture, supports multiple storage engines and index optimization. 3) Basic usages include creating tables and inserting data, and advanced usages involve multi-table JOINs and complex queries. 4) Frequently asked questions such as SQL syntax errors and performance issues can be debugged through the EXPLAIN command and slow query log. 5) Performance optimization methods include rational use of indexes, optimized query and use of caches. Best practices include using transactions and PreparedStatemen

MySQL is suitable for small and large enterprises. 1) Small businesses can use MySQL for basic data management, such as storing customer information. 2) Large enterprises can use MySQL to process massive data and complex business logic to optimize query performance and transaction processing.

InnoDB effectively prevents phantom reading through Next-KeyLocking mechanism. 1) Next-KeyLocking combines row lock and gap lock to lock records and their gaps to prevent new records from being inserted. 2) In practical applications, by optimizing query and adjusting isolation levels, lock competition can be reduced and concurrency performance can be improved.


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