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
夏炳辉.42 分钟前
PostgreSQL 高可用集群核心配置参数全解:从原生流复制到 Patroni 企业级方案
数据库·postgresql
努力的小雨2 小时前
KES 开启 SSL 前,证书、端口和客户端要一起验
数据库
DevOps老兵2 小时前
AI全栈知识07:向量数据库 - Milvus/Chroma实战
数据库·ai·milvus
这个DBA有点耶2 小时前
当数据库从“存储”走向“决策”:金仓数据库的融合架构之路
数据库·架构·aigc
意疏3 小时前
2026年远控软件安全横评:六款主流工具逐项核查——官方文档、一手实测与安全事件,全摊开
大数据·前端·数据库
名不经传的养虾人3 小时前
从0到1:企业级AI项目迭代日记 Vol.90|Agent变快了,Judge定下来了
大数据·数据库·人工智能·ai编程·企业ai
冰之杍3 小时前
MySQL utf8mb3 → utf8mb4 完整修改方案
数据库·mysql
老纪的技术唠嗑局4 小时前
端侧智能爆火之后,为何模型反而不是主角了?
数据库·人工智能
这个DBA有点耶4 小时前
一文讲透数据库分类:关系型、非关系型、OLTP、OLAP、分布式、多模……
数据库·mysql·架构
Macbethad4 小时前
使用Rigol DHO924示波器连接上位机进行24小时波形数据记录的技术报告
数据库