“Seckilling activities”, “grabbing red envelopes”, “hot search on Weibo”, “grabbing tickets on 12306”, “driving new shared bikes” are all typical business scenarios with high concurrency. Then how to solve the difficult problems behind these business scenarios?

  • How can I locate and solve service bottlenecks when QPS reaches 100,000 /s in the second kill system?
  • Star dating topic constantly bow | weibo hot search, how to ensure the system downtime?
  • How to ensure that shared bikes are not oversold?
  • .

The high frequency access of a large number of users at the same time is a challenge for any platform, but the good news is that although the business scenario is different, the design and optimization ideas are always the same. If you have mastered the core technical points of high concurrency system design (caching, pooling, asynchronization, load balancing, queuing, demoted circuit breakers, etc.) and deepened your knowledge, you will be able to solve these business problems with ease.

In xiaobian’s opinion, many engineers with strong technical ability will still be trapped by “high concurrency”, which has a lot to do with the lack of knowledge reserve and the inability to systematically master the core technology. Technical personnel should constantly absorb new nutrition, but also to be able to apply technical knowledge to the actual business, so as to enhance competitiveness and break through the workplace bottleneck.

High concurrency system design knowledge framework diagram

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basis

Understand the design concept of high concurrency architecture together, and establish a preliminary understanding of high concurrency systems. For example, how to make your system better support high performance, availability, and scalability, for example, by taking you to the core technical points of architecture layering.

  • High Concurrency systems: What is its general design approach
  • Architectural layering: Why do we have to do it?
  • System design Objective (1) : How to improve system performance?
  • System design Objective (2) : How does the system achieve high availability?
  • System design Objective (3) : How to make the system easy to expand?

The database

  • Pooling techniques: How can you reduce the performance cost of frequently creating database connections?
  • Database optimization scheme (a) : when the query request increases, how to separate master from slave?
  • Database optimization scheme (2) : how to realize the sub-database sub-table when the amount of written data increases?
  • Sender: how to ensure the global uniqueness of ID after sub-database sub-table?
  • NOSQL: How do databases and NOSQL complement each other in high concurrency scenarios?

The cache

Caching is one of the techniques we often use to do architecture work, which greatly alleviates the impact of large users on the system. With the evolution of system architecture, the use of cache has never stopped changing, and the types of cache frameworks are also increasing.

The use of caching may seem very simple, but there are a number of techniques that can help us maximize the effectiveness of caching and reduce the number of online production accidents caused by caching errors. How to do the selection of cache framework, which framework is more suitable for their current situation, these are the difficult problems we need to face and solve.

  • Caching: How can queries for dynamic data be accelerated when the database becomes a bottleneck?
  • Cache usage posture (1) : How to choose the cache read and write strategy?
  • Cache usage posture (2) : How to make cache highly available?
  • Cache usage posture (3) : What if the cache is penetrated?
  • CDN: How can static resources be accelerated?

The message queue

If you understand the underlying technology of message queuing, you can analyze the problem at the level of principle, take a brief look at the API and related configuration items, and quickly learn how to configure message queuing and write high performance and reliable applications.

  • Message queuing: How to handle the tens of thousands of order requests per second in seckill?
  • Message delivery: How to ensure that messages are consumed only once?
  • Message queuing: How can message latency be reduced in message queuing systems?

Distributed service

  • System architecture: Should a system with 10,000 requests per second be servified unbundled?
  • Microservice Architecture: How to transform the system architecture after microservice?
  • RPC Framework: How to implement millisecond service invocation under 100,000 QPS?
  • Registry: How are distributed systems addressed?
  • Distributed Trace: How do I troubleshoot slow requests that span dozens of distributed components?
  • Load balancing: How can systems scale horizontally?
  • API Gateway: What about the face of the system?
  • Multi-room Deployment: What about distributed systems across geographies?
  • Service Mesh: How to mask the Service governance details of a servified system?

maintenance

  • Adding eyes to the system: How to do server-side monitoring?
  • Application Performance Management: How should the user experience be monitored?
  • Pressure test: how to design a full-link pressure test platform?
  • Configuration management: How to manage thousands of configuration items?
  • Degraded fuses: How to shield non-core System failures?
  • Flow Control: How do we manipulate traffic in high-concurrency systems?

In actual combat

Take the design of no-reading system and the design of information flow as an example. Not reading system actual combat, mainly explains how to design a scheme to resist hundreds of thousands of times per second to obtain user not reading requests; Information flow design practice, explain how to do the general information flow system push mode and pull mode. The actual combat content is operational, can test your mastery of technical points, and the degree of flexible use, improve the important link of your knowledge system.

Learning speeches

Take you first set up the intuitive understanding of high concurrent systems design, and then to the most simple framework to evolve to support the millions and millions of concurrent distributed architecture as a case, take you to solve the pain points of the problems in the process, improve business processing capabilities, complete a real system evolution, finally combining with the actual combat to optimize the design of the overall train of thought.

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