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This paper focuses on the actual practice of full-link stress testing, aiming to elaborate on the methods to ensure the stability of high-concurrency systems. First, the core value and key links of full-link stress testing are summarized, and then the practical points of the whole process from goal setting, scenario design, environment construction to traffic injection, monitoring analysis, and problem optimization are explained in detail, covering stress testing strategies, tool selection and common problem solving, and finally summarizing the implementation significance and continuous optimization direction of full-link stress testing, providing practical guidance for the stable operation of high-concurrency systems.

1. The core value of full-link stress testing

With the rapid development of Internet business, high-concurrency scenarios are becoming more and more frequent, and the stability of the system is directly related to user experience and corporate revenue. As a key means to ensure the stability of high-concurrency systems, the core value of full-link stress testing is to break through the limitations of traditional single-system stress testing, starting from the user request entrance, simulating full-link traffic in real business scenarios, covering all related components from front-end pages, API gateways, application services to databases, caches, message queues, etc., and accurately discovering the bottleneck points of the system under high concurrency, such as insufficient resources, interface performance deterioration, link call abnormalities, etc., so as to warn and optimize in advance to avoid online faults.

Traditional stress testing is mostly aimed at a single service or module, which is difficult to reflect the collaboration problem between various components of the system, while full-link stress testing can truly restore the call relationship of the service link, expose hidden problems across systems and modules, provide data support for system expansion and architecture optimization, and is a "preventive needle" for the stable operation of high-concurrency systems.

2. Key links and practical points of full-link stress testing

(1) Clarify the objectives and scope of pressure testing

Before conducting full-link stress testing, it is necessary to clearly set goals, such as determining the maximum number of concurrent users that the system can withstand, the response time threshold of key interfaces, and the upper limit of transactions per second (TPS) of the database. At the same time, define the scope of stress testing, sort out the core business links, such as the "product browsing-add to cart-order payment" link of e-commerce, and clarify the application services, middleware, databases and other components involved in the link to ensure that key nodes are covered.

(2) Scene design and traffic simulation

The scenario design needs to fit the real business, analyze historical traffic characteristics, such as user access peak hours, request distribution laws, traffic proportion of different business scenarios, etc., to build diversified stress testing scenarios. For example, for e-commerce platforms, daily browsing scenarios, promotional rush purchase scenarios, return and exchange scenarios, etc. can be designed.

To achieve accurate and controllable traffic simulation, it is necessary to consider the magnitude of traffic (such as simulating 100,000 concurrent users), traffic distribution (such as uniform traffic, burst traffic), and request parameters (such as the random distribution of different product IDs and user IDs). The tool can generate virtual user requests that comply with business rules to ensure the authenticity of simulated traffic and avoid distortion of stress test results due to single parameters.

(3) Construction of pressure testing environment

The full-link stress test environment should be as consistent as possible with the production environment, including hardware configuration (server CPU, memory, disk), software version (operating system, application server, database), network topology (bandwidth, routing), etc., to ensure the reference value of the stress test results.

In order to avoid affecting the production system, shadow library and shadow table technology are usually used to isolate the stress test traffic from the real business traffic. For example, by creating a shadow table in the database, the data generated by the stress test is stored, and it is directly cleaned after the test is completed without interfering with the production data. Differentiate stress test requests by identity in Application Service and route them to shadow resources to ensure the security of the production environment.

(4) Traffic injection and monitoring system construction

The flow injection needs to be gradual, using a stepped pressurization method, gradually increasing from low concurrency to the target pressure, and observing the performance of the system under different pressures. At the same time, combined with pulsed pressurization, the sudden flow scenario is simulated to test the elasticity of the system. Commonly used traffic injection tools include JMeter, LoadRunner, Gatling, etc., which can be selected according to the complexity of the scene, such as JMeter is suitable for small and medium-sized scenarios, and Gatling has better performance under high concurrency.

Building a comprehensive monitoring system is the key to full-link stress testing, which needs to cover the indicators of all links of the whole link, such as:

Application layer: interface response time, error rate, thread pool status, JVM (Java Virtual Machine) parameters (heap memory usage, GC frequency), etc.

Middleware layer: cache hit rate, message queue stack, Redis (Remote Dictionary Server) response time, etc.

Data layer: database CPU usage, lock latency, SQL execution efficiency, etc.

Infrastructure layer: server CPU, memory, disk I/O, network bandwidth usage, etc.

Through APM (Application Performance Management) tools such as Pinpoint and SkyWalking, the request link can be tracked in real time and the specific location of performance bottlenecks can be located.

(5) Data analysis and problem optimization

After the stress test, the monitoring data is comprehensively analyzed to identify the bottlenecks of the system. For example, if the response time of an interface increases rapidly with the increase in concurrency, it may be due to complex interface logic or insufficient performance of dependent services. If the database CPU usage is too high, there may be issues with slow SQL or unreasonable index design.

Optimize the problem found:

· For insufficient resources, such as insufficient server memory, you can expand the capacity or adjust the resource allocation.

· For interface performance issues, code logic can be optimized and caches (such as Redis) can be introduced to reduce database access.

· For database bottlenecks, SQL statements can be optimized, indexes can be added, and database and table shunt strategies can be adopted.

· For link call problems, you can adjust the service timeout time and retry mechanism to avoid cascading failures.

After optimization, a secondary stress test is required to verify the optimization effect until the system performance reaches the expected target.

3. Strategy and tool selection of full-link stress testing

(1) Stress testing strategy

Incremental stress test: First conduct stress testing on a single system to verify that its performance meets the standard, and then gradually add the associated system to expand to the full link to reduce the complexity of one-time stress test.

Grayscale stress test: Select part of the flow rate in the production environment for small-scale pressure measurement, monitor the system performance in real time, and then expand the pressure test scale after there are no abnormalities to balance the authenticity and safety of the pressure test.

Continuous stress testing: Regularly carry out full-link stress testing in conjunction with the service iteration cycle, especially before major versions go live, to ensure the stability of new features under high concurrency.

(2) Tool selection

Traffic generation tools: JMeter supports graphical interface operation, which is easy to get started and is suitable for stress testing of multiple protocols. Gatling is based on the Scala language, which has high performance and is suitable for scripted stress testing in high-concurrency scenarios. Locust is written in Python and supports distributed stress testing for flexibility.

Monitoring and analysis tools: Prometheus combined with Grafana can realize the collection and visualization of indicators; ELK (Elasticsearch, Logstash, Kibana) stack for log collection and analysis; SkyWalking enables distributed tracking and performance analysis to help locate problems quickly.

4. Common problems and solutions of full-link stress testing

(1) Data pollution

If the stress test data is mixed with production data, it may affect the normal operation of the business. Solution: Use shadow tables and shadow databases to store stress measurement data, and distinguish between stress measurement traffic and real traffic through request identification to ensure data isolation.

(2) The pressure testing environment is inconsistent with the production environment

Environmental differences can lead to distortion of stress test results. Solution: Try to reuse the configuration parameters of the production environment, such as server specifications and database parameters. Replicate production environment data (desensitization) to the stress testing environment through mirroring technology to ensure data consistency.

(3) Incomplete link coverage

Missing critical links can lead to undetected issues. Solution: Sort out all service dependencies through a service registry (e.g., Nacos, Eureka) before stress testing, and combine them with business flow diagrams to ensure that both core and non-core but high-impact links are covered.

5. Summary and outlook

By simulating the full-link traffic of real business scenarios, system bottlenecks can be accurately discovered and optimized in advance, effectively reducing the risk of online failures. Its implementation requires key links such as goal setting, scenario design, environment construction, traffic injection, monitoring and analysis, and problem optimization, combined with scientific stress testing strategies and appropriate tools to solve common problems such as data pollution and environmental inconsistency.

In the future, with the popularization of microservices and cloud-native architectures, full-link stress testing will develop in the direction of automation and intelligence, such as combining AI technology to realize automatic generation of stress testing scenarios and intelligent diagnosis of bottlenecks, further improving the efficiency and accuracy of stress testing. Enterprises need to integrate full-link stress testing into the whole life cycle of system development, operation and maintenance, and continue to iterate and optimize to escort the stable operation of high-concurrency systems.