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目錄
When should you use GROUP BY?
How does GROUP BY actually work?
Common mistakes and how to avoid them
Can GROUP BY be used with multiple columns?
首頁(yè) 數(shù)據(jù)庫(kù) mysql教程 該小組的子句如何工作?

該小組的子句如何工作?

Jun 17, 2025 am 09:39 AM
sql group by

GROUP BY在SQL中用于將具有相同列值的行分組為聚合數(shù)據(jù)。它通常與COUNT、SUM、AVG、MAX或MIN等聚合函數(shù)一起使用,以對(duì)每組數(shù)據(jù)進(jìn)行計(jì)算而非整個(gè)表。1. 當(dāng)需要基于一個(gè)或多個(gè)類(lèi)別匯總數(shù)據(jù)時(shí)應(yīng)使用GROUP BY,例如計(jì)算每個(gè)地區(qū)的總銷(xiāo)售額;2. GROUP BY的工作原理是掃描指定列、將相同值的行分組并應(yīng)用聚合函數(shù);3. 常見(jiàn)錯(cuò)誤包括SELECT中包含未聚合或未分組的列、GROUP BY列過(guò)多導(dǎo)致分組過(guò)細(xì)以及誤解NULL值的處理;4. GROUP BY可以與多列一起使用以實(shí)現(xiàn)更細(xì)致的分組,如按部門(mén)和職位統(tǒng)計(jì)員工數(shù)量。正確匹配SELECT和GROUP BY可避免多數(shù)錯(cuò)誤。

How does the GROUP BY clause work?

The GROUP BY clause in SQL is used to group rows that have the same values in specified columns into aggregated data. It’s commonly used with aggregate functions like COUNT, SUM, AVG, MAX, or MIN to perform calculations on each group of rows rather than the whole table.


When should you use GROUP BY?

You'll want to use GROUP BY when your goal is to summarize or aggregate data based on one or more categories. For example, if you're working with a sales table and want to know total sales per region, you'd group by the "region" column.

Here are some typical situations:

  • Calculating totals per category (e.g., total orders per customer)
  • Finding averages within groups (e.g., average salary per department)
  • Counting occurrences in each group (e.g., number of employees per team)

If you try to select a non-aggregated column without including it in the GROUP BY, most SQL databases will throw an error. That’s because SQL doesn’t know how to display individual values when grouped.


How does GROUP BY actually work?

When you run a query with GROUP BY, here's what happens behind the scenes:

  1. The database scans the table and looks at the columns you've listed in GROUP BY.
  2. It sorts the rows so that rows with the same value in those columns are grouped together.
  3. Then it applies the aggregate function(s) you’ve specified (like SUM() or COUNT()) to each group.

For example:

SELECT department, AVG(salary)
FROM employees
GROUP BY department;

This query tells SQL to:

  • Group all rows where the department is the same
  • Calculate the average salary for each group

So instead of getting every single employee row, you get one row per department with the average salary for that department.


Common mistakes and how to avoid them

A few pitfalls come up often when using GROUP BY. Here are some things to watch out for:

  • Including ungrouped columns in the SELECT list
    If you include a column in SELECT that isn't in an aggregate function or in the GROUP BY clause, SQL won't know which value to show and will return an error.

  • Grouping by too many columns
    If you add too many columns to GROUP BY, you might end up with too many small groups — sometimes even one row per group. That defeats the purpose of grouping.

  • Misunderstanding NULLs in grouping
    Rows with NULL in the GROUP BY column are grouped together as their own category. So if you see a result with NULL as a group label, that means the original data had missing values in that column.


Can GROUP BY be used with multiple columns?

Yes, absolutely. You can group by more than one column when you need more specific groupings. Think of it like creating a hierarchy: the first column is the main group, and the second column breaks that group down further.

Example:

SELECT department, job_title, COUNT(*)
FROM employees
GROUP BY department, job_title;

This would give you the number of employees in each combination of department and job title — like how many software engineers are in engineering vs. QA.

Just remember: the more columns you add to GROUP BY, the more granular (and possibly less summarized) your results become.


That’s basically how GROUP BY works. It helps turn raw data into summaries by organizing rows into logical groups and applying aggregations. Just make sure to match your SELECT and GROUP BY carefully, and you’ll avoid most common errors.

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