Leecode_SQL50_1661. Average Time of Process per Machine

Leecode_SQL50_1661. Average Time of Process per Machine

Problem description

Table: Activity

±---------------±--------+

| Column Name | Type |

±---------------±--------+

| machine_id | int |

| process_id | int |

| activity_type | enum |

| timestamp | float |

±---------------±--------+

The table shows the user activities for a factory website.

(machine_id, process_id, activity_type) is the primary key (combination of columns with unique values) of this table.

machine_id is the ID of a machine.

process_id is the ID of a process running on the machine with ID machine_id.

activity_type is an ENUM (category) of type ('start', 'end').

timestamp is a float representing the current time in seconds.

'start' means the machine starts the process at the given timestamp and 'end' means the machine ends the process at the given timestamp.

The 'start' timestamp will always be before the 'end' timestamp for every (machine_id, process_id) pair.

It is guaranteed that each (machine_id, process_id) pair has a 'start' and 'end' timestamp.

There is a factory website that has several machines each running the same number of processes. Write a solution to find the average time each machine takes to complete a process.

The time to complete a process is the 'end' timestamp minus the 'start' timestamp. The average time is calculated by the total time to complete every process on the machine divided by the number of processes that were run.

The resulting table should have the machine_id along with the average time as processing_time, which should be rounded to 3 decimal places.

Return the result table in any order.

The result format is in the following example.

Example 1:

Input:

Activity table:

±-----------±-----------±--------------±----------+

| machine_id | process_id | activity_type | timestamp |

±-----------±-----------±--------------±----------+

| 0 | 0 | start | 0.712 |

| 0 | 0 | end | 1.520 |

| 0 | 1 | start | 3.140 |

| 0 | 1 | end | 4.120 |

| 1 | 0 | start | 0.550 |

| 1 | 0 | end | 1.550 |

| 1 | 1 | start | 0.430 |

| 1 | 1 | end | 1.420 |

| 2 | 0 | start | 4.100 |

| 2 | 0 | end | 4.512 |

| 2 | 1 | start | 2.500 |

| 2 | 1 | end | 5.000 |

±-----------±-----------±--------------±----------+

Output:

±-----------±----------------+

| machine_id | processing_time |

±-----------±----------------+

| 0 | 0.894 |

| 1 | 0.995 |

| 2 | 1.456 |

±-----------±----------------+

Explanation:

There are 3 machines running 2 processes each.

Machine 0's average time is ((1.520 - 0.712) + (4.120 - 3.140)) / 2 = 0.894

Machine 1's average time is ((1.550 - 0.550) + (1.420 - 0.430)) / 2 = 0.995

Machine 2's average time is ((4.512 - 4.100) + (5.000 - 2.500)) / 2 = 1.456

My solution

sql 复制代码
WITH st AS (
    SELECT machine_id, process_id, timestamp
    FROM Activity
    WHERE activity_type = 'start'
), 
en AS (
    SELECT machine_id, process_id, timestamp
    FROM Activity
    WHERE activity_type = 'end'
), 
b AS (
    SELECT st.machine_id, st.process_id, (en.timestamp - st.timestamp) AS time_taken
    FROM st 
        JOIN en 
            ON st.machine_id = en.machine_id 
            AND st.process_id = en.process_id
)

SELECT b.machine_id, ROUND(AVG(time_taken), 3) AS processing_time
FROM b
GROUP BY b.machine_id

Other solutions

sql 复制代码
select a1.machine_id, round(avg(a2.timestamp-a1.timestamp), 3) as processing_time 
from Activity a1
	join Activity a2 
		on a1.machine_id=a2.machine_id 
			and a1.process_id=a2.process_id
			and a1.activity_type='start' 
			and a2.activity_type='end'
group by a1.machine_id
sql 复制代码
Select a.machine_id,ROUND(AVG(b.timestamp-a.timestamp), 3)as processing_time 
from Activity a, Activity b
where a.process_id=b.process_id 
	AND a.machine_id=b.machine_id
	AND a.activity_type="start" 
	AND b.activity_type="end"
group by machine_id;
相关推荐
muddjsv1 小时前
大中小型企业数据层配置规模分析与选型指南
数据库
Runawayliquor1 小时前
opbase:CANN 所有算子的公共地基
大数据·数据库·人工智能·算法
yangshicong2 小时前
第11章:结构化输出与数据提取 —— 让 AI 直接返回你想要的数据格式
数据库·人工智能·redis·python·langchain·ai编程
chimchim662 小时前
pg dblink使用查询
数据库
Java面试题总结3 小时前
java高频面试题(2026最新)
java·开发语言·jvm·数据库·spring·缓存
绝知此事3 小时前
【算法突围 02】树形结构与数据库索引:树形结构与数据库索引:从 BST 到 B+ 树的演化与 MySQL 优化
数据库·mysql·算法·面试·b+树
吴可可1234 小时前
用Teigha修改并保存CAD文件
数据库·算法·c#
yuzhiboyouye5 小时前
内连接,左连接,右连接怎么区别开来?
数据库
铭毅天下5 小时前
Easysearch 版本进化全图——从 ES 国产替代到 AI Native 搜索数据库
大数据·数据库·人工智能·elasticsearch·搜索引擎
muddjsv6 小时前
SQL 最常用技能详解与实战示例
数据库·sql·mysql