PHP high concurrency and large traffic solution (PHP interview theory question)

PHP High concurrency and high traffic solutions

One 、 The concept of high concurrency

In the age of the Internet , Concurrent , High concurrency usually refers to concurrent access . At some point in time , How many visits come at the same time .

Two 、 Concepts related to high concurrency Architecture

1、QPS ( Query rate per second ) : The number of requests or queries per second , In the field of Internet , It refers to the number of response requests per second ( finger HTTP request )

2、PV（Page View）： Comprehensive views , That is, page views or clicks , A visitor is in 24 Number of pages visited in hours

– notes ： The same person browsing the same page of your website , Just remember to do it once pv

3、 throughput （fetches/sec） ： Number of requests processed per unit time （ Usually by QPS And concurrency ）

4、 response time ： The time from the request to the response

5、 Independent visitor （UV）： Within a certain time frame , The same visitors visit the website many times , Only calculated as 1 An independent visitor

6、 bandwidth ： There are two indicators to pay attention to when calculating bandwidth , Peak traffic and average page size

7、 Daily website bandwidth ： PV/ Count the time （ Convert to seconds ） * Average page size （kb）* 8

3、 ... and 、 You need to pay attention to ：

1、QPS Not equal to the number of concurrent connections （QPS Every second HTTP Number of requests , The number of concurrent connections is the number of requests processed by the system at the same time ）

2、 Peak requests per second （QPS）= （ total PV Count 80%）/ （ Six hours and seconds 20%）【 representative 80% All of our visits are focused on 20% In time 】

3、 Pressure test ： Test the maximum number of concurrent that can be tolerated And test the most bearable QPS value

4、 Common performance testing tools 【ab,wrk,httpload,Web Bench,Siege,Apache JMeter】

Four 、 Optimize

1、 When QPS Less than 50 when

Optimization plan : For general small websites , Don't think about optimization

2、 When QPS achieve 100 when , Encountered data query bottleneck

Optimization plan : Database cache layer , Database load balancing

3、 When QPS achieve 800 when , Bandwidth bottleneck

Optimization plan :CDN Speed up , Load balancing

4、 When QPS achieve 1000 when

Optimization plan : do html Static cache

5、 When QPS achieve 2000 when

Optimization plan : Do business separation , Distributed storage

5、 ... and 、 High concurrency solution cases :

1. Traffic optimization

Anti theft chain handling ( Remove malicious requests )

2. Front end optimization

• Reduce HTTP request [ take css,js Etc ]
• Add asynchronous request ( Don't show all the data to the user first , The user triggers an event , To request data asynchronously )
• Enable browser caching and file compression
• CDN Speed up
• Set up an independent image server ( Reduce I/O)

3. Server optimization

• Page static
• Concurrent processing
• Queue processing
• Control large file downloads , It is recommended to put large files on another server

4. Database optimization

• Database cache
• Sub database and sub table , Partition
• Read / write separation
• Load balancing
• Try not to query select *
• Avoid related subqueries
• Add indexes to frequently queried

5. web Server optimization

• nginx Reverse agent to achieve load balancing
• lvs Load balancing