[Dry] System throughput (TPS), user concurrency, performance test concepts and formulas

In the Taobao environment, assuming that we stress test the TPS is 100, then the system's daily throughput =100*11*3600=396 million

This is in the case of simple (single URL), some pages, a page has multiple request, the actual throughput of the system is also small

Regardless of time-to-think (T_think), there is a relationship between the measured TPS value and the number of concurrent virtual users (u_concurrent) and the transaction response time (t_response) of LoadRunner read (Stable operation):

Tps=u_concurrent/(T_response+t_think)

Relationship between concurrency number, QPS and average response time

First, the focus of software performance

Performance testing of a software needs to focus on those performance.

We think about what roles are involved in software design, deployment, use, and maintenance, and then consider what the performance points are for each of these roles, and what we should focus on as a software performance test engineer.

First of all, the purpose of software development is to allow users to use, we first stand in the user's perspective analysis, users need to pay attention to what performance.

For the user, when clicking on a button, link or issuing an instruction to start, to the system to show the results of the user-perceived form, the process of the time spent is the user of the software performance of the intuitive impression. That is what we call response time, when the corresponding hours, the user experience is very good, of course, the user experience response time includes individual subjective factors and objective response time, in the design of software, we need to consider how to better combine these two parts to achieve the best user experience. Such as: The user in the large data volume query, we can first extract the data presented to the user, in the process of the user to continue the data retrieval, then the user does not know what we are doing in the background.

The user is concerned about the corresponding time of the user action.

Second, we stand in the administrator's perspective to consider the performance points that need attention

1, the corresponding time

2, the use of server resources is reasonable

3, Application Server and database resources use is reasonable

4, the system can be extended

5, the maximum number of user access to support the system, the maximum amount of business processing capacity

6. Where can the bottleneck of system performance exist

7, replace those devices can improve performance

8, the system can support 7x24 hours of business access

Once again, stand on the development (design) staff angle to consider

1, the structure design is reasonable

2, the database design is reasonable

3. Is there any performance problem with the code?

4. Is there any unreasonable memory usage in the system?

5, whether there is unreasonable thread synchronization in the system

6, whether there is unreasonable resource competition in the system

So what do we have to look at in terms of performance test engineers?

In a word, we should focus on all of the above performance points

Ii. Several key terms of software performance

1. Response Time: The time required to respond to the request

Network transfer time: N1+N2+N3+N4

Application Server processing time: A1+A3

Database server processing time: A2

Response Time =n1+n2+n3+n4+a1+a3+a2

2, the calculation formula of concurrent user number

Number of system users: the number of system-rated users, such as an OA system, the total number of users who may use the system is 5,000, then this number is the number of system users

Simultaneous online Users: The largest number of simultaneous online users in a certain time frame

Simultaneous number of users on-line = number of requests per second RPS (throughput) + Concurrent connections + average user think time

Calculation of the average concurrent user count: c=nl/t

Where c is the average number of concurrent users, n is the average number of access users per day (login session), L is the average time of the day the user logs on to the exit (the average time of the login session), T is the length of the study (how long the user uses the system within a day)

Peak Concurrent User count: c^ approximately equals C + p

Where c^ is the peak of concurrent users and C is the average number of concurrent users, the formula follows the Poisson distribution theory