Ideal for building modern applications, a distributed microservices architecture breaks complex systems into small, autonomous services, each of which can be independently developed, tested, and deployed. In the actual development process, how to implement an efficient distributed microservice architecture? Based on his many years of practical experience, the author will briefly discuss the key technologies in the actual combat process and the specific implementation plan under high concurrency conditions.
1. Service registration and discovery
In the distributed microservice architecture, service registration and discovery is a crucial technology. It solves the dynamic changes and communication problems of service instances, allowing different services to discover and call each other. Netflix Eureka and Consul are the two main implementation components. Why do we need service registration and discovery?
In a microservices architecture, services are dynamically started, shut down, or migrated, so a mechanism is needed to track and manage the location information of these service instances. Service registration and discovery solve the following key issues:
- Service registration: A service instance registers its network address and metadata with the registration center when it starts, so that other services can find it.
- Service discovery: When a client or other service needs to call a service, it can obtain available service instances by querying the registration center.
- Load balancing: The registration center can provide load balancing function and distribute requests to different service instances, thereby improving system performance and availability.
Therefore, it can be seen that the stability of the service registration center is crucial in the entire microservice architecture. Let’s take Netflix Eureka as an example to introduce in detail some key implementation strategies in high concurrency environments:
- Registration center cluster: Eureka supports building multiple registration center instances that register with each other to form a cluster. This avoids single points of failure and improves availability.
- Self-protection mode: Eureka introduces a self-protection mechanism. When the registration center node cannot receive heartbeat information for a period of time, it will enter the self-protection mode and will not immediately eliminate all disconnected instances. This can prevent a large number of instances from being evicted due to network jitter and other reasons, thereby affecting the stability of the system.
- Caching mechanism: When the Eureka client queries available service instances, it will cache the registry information. This can reduce the pressure on the registration center and improve the response speed of queries.
- Dynamic refresh: The Eureka client will regularly update the registry information from the registration center to keep the instance information real-time. This mechanism ensures the synchronization of the registry and service instances.
- High availability: By building multiple registration center instances and registering with each other, a highly available registration center cluster can be achieved. At the same time, the Eureka client can configure the addresses of multiple registration centers to automatically switch to other available instances when a registration center is unavailable.
2. Load balancing
In a distributed microservice architecture, load balancing is one of the key technologies to ensure system stability and high performance. By properly allocating requests to different service instances, load balancing can avoid overloading certain instances, thereby improving the throughput and availability of the entire system. In the application architecture, there are many load balancing middleware, such as Nginx and HAProxy, but the author of this article will use Netflix Ribbon as an example to introduce the load balancing component in the microservice component in detail.
Netflix Ribbon is a load balancing component widely used in Spring Cloud. It is a component that acts directly on the client, or more precisely, on the application side. It provides a wealth of load balancing strategies and dynamic adjustment mechanisms, and can well support high-concurrency scenarios. It has the following characteristics:
- Configuring load balancing strategies When using Netflix Ribbon, you can configure appropriate load balancing strategies according to actual needs, such as polling, random, weighted polling, etc. These policies can be set in configuration files.
- Dynamically adjust the weight Netflix Ribbon supports dynamic adjustment of the weight of the instance. By observing the load condition of the instance, it automatically adjusts the weight to ensure optimal performance.
- Failover and circuit breaker Netflix Ribbon has a built-in failover and circuit breaker mechanism. When a service instance is unavailable, it will automatically transfer requests to other available instances to ensure the availability of the entire system.
3. Fuse
In a distributed microservice architecture, the failure of one service may affect other services that depend on it, resulting in cascading failures. In order to cope with this situation, the circuit breaker (Circuit Breaker) was introduced. It is a fault-tolerant mechanism that can quickly interrupt the call to the fault service when a fault occurs, prevent the fault from spreading, and thus ensure the stability of the entire system. Netflix Hystrix is a mainstream implementation library. Below we will introduce in detail the implementation principle of circuit breaker and its implementation method in a high concurrency environment.
- The principle of fuse
The principle of a fuse is similar to that of a fuse in a circuit. When the current is too large, the fuse will break to prevent damage to the circuit. In the microservice architecture, circuit breakers determine whether a service fails by monitoring the failure rate or response time of service calls.
It usually has the following states:
(1) Closed : In this state, all requests will be allowed to pass, and the fuse will monitor the frequency of faults.
(2) Open state : When the frequency of faults reaches a certain threshold, the fuse will enter the open state, all requests will be quickly failed, and no more attempts will be made to call the faulty service.
(3) Half-Open : After a period of time, the fuse will automatically enter the half-open state, allowing a request to pass to detect whether the fault has been restored.
In order to support circuit breakers in high concurrency situations, the following implementation strategies need to be considered:
(4) Threshold setting
Reasonably set the fault threshold that triggers the circuit breaker to prevent misjudgments due to instantaneous high concurrency. Thresholds can be based on metrics such as failure rate, number of errors, or response time.
(5) Timeout setting
Set an appropriate timeout. When the service response time exceeds the predetermined threshold, a circuit breaker is triggered to avoid long waiting times for requests.
(6) Adaptive fuse
Dynamically adjust the fuse threshold based on actual request conditions to avoid being too conservative or aggressive.
(7) Fuse recovery
When the fuse is restored, gradually increase the traffic and observe the stability of the service to avoid premature recovery that will cause the system to become unstable again.
4. Distributed data management
In a distributed microservice architecture, different microservices may have their own databases, and a business operation may involve multiple microservices and multiple databases. If one of the steps fails, how to ensure data consistency and avoid the situation where some operations succeed and some operations fail?
One solution is distributed transactions. The currently popular solution is to use Seata (formerly Fescar), which provides a distributed transaction management mechanism based on the Two-Phase Commit protocol to ensure the database operations of each microservice. Either all succeed or all fail. So how to ensure the consistency and performance of distributed data management in high concurrency situations? To ensure the consistency and performance of distributed data management under high concurrency conditions, there are several strategies:
- Read and write separation
The read operations and write operations of the database are processed separately. The read operations can support high concurrent reads by replicating multiple read-only copies.
- Database sharding
The database is stored in shards according to certain rules, and each shard is responsible for a portion of the data. This can improve the ability to read and write concurrently.
- Cache optimization
For highly concurrent read operations, caching technology such as Redis can be used to reduce the load on the database and improve response speed.
- Asynchronous processing
Asynchronousize some non-real-time operations that do not require high data consistency to reduce the pressure of synchronous operations on the database.
The following is a brief introduction to how to use Seata to implement distributed transactions in the project:
(1) First, you must introduce Seata dependencies: introduce Seata dependencies in each microservice project, and configure Seata's registration center, transaction group and other information.
(2) In business operations, use the @GlobalTransactional annotation to mark a global transaction. Seata automatically coordinates the transaction operations of each microservice.
(3) When all microservice operations are successful, Seata will initiate a global commit to ensure that each branch transaction can be submitted correctly.
(4) If any branch transaction fails, Seata will initiate a global rollback to ensure that all branch transactions can be rolled back correctly.
5.API gateway
In the distributed microservice architecture, the API gateway acts as the entrance to the system and plays multiple roles such as routing, authentication, authentication, and current limiting. In a high-concurrency environment, the design and implementation of API gateways become particularly critical. Many large enterprises often make API gateways a separate gateway support system. Here the author will briefly introduce the functions and implementation methods of the API gateway, as well as its specific implementation in a high-concurrency environment.
- API Gateway Features
API Gateway is not just a forwarder of requests, it also undertakes the following functions:
(1) Routing and load balancing: forward external requests to the corresponding microservice instances, while supporting load balancing to ensure balanced pressure on each instance.
(2) Authentication and authentication: Authentication of requests and authentication based on permissions and policies to protect the system from unauthorized access.
(3) Current limiting and circuit breaker: Limit the rate of requests to prevent too many requests from a single user or IP from affecting the entire system. At the same time, a circuit breaker mechanism is implemented to prevent service avalanche.
(4) Logging and monitoring: Logs that record requests and responses for troubleshooting and performance monitoring.
Implementation strategy to support API gateway in high concurrency environment:
(1) Distributed cache
Use distributed cache such as Redis to store API routing information to speed up route lookup.
(2) Response cache
Cache responses to frequent requests to avoid repeated calculations and improve response speed.
(3) Current limiting and fusing
Set a current limiting strategy based on the system's capacity to limit the number of requests and prevent system overload. At the same time, a circuit breaker mechanism is implemented to promptly interrupt requests for the service when there is a problem with the service to protect the stability of the system.
(4) Asynchronous processing
For some requests that do not need to return results in real time, they can be processed asynchronously to reduce the time required to wait in queue.
(5) Hotspot data cache
For hot data, caching is used to reduce database access pressure and improve data reading speed.
Currently, the mainstream component of microservice API gateway is Spring Cloud Gateway.
6. Logging and Monitoring
In a distributed microservice architecture, due to the large number of microservices, how to quickly locate system problems and effectively solve them? How to track the running status of the system in real time to help identify potential problems and take timely measures? Accurate logging and effective monitoring are key. As high concurrency situations increase, the management of logging and monitoring becomes more important. Here the author will briefly introduce the role of logging and monitoring, implementation methods, and strategies in high concurrency situations.
- The implementation strategy to support logs in high concurrency environments is as follows:
(1) Asynchronous log
Use an asynchronous logging mechanism to write the log to the buffer and return immediately to avoid blocking the request processing flow.
(2) Distributed log collection
To collect the logs of each microservice in one place, you can use tools such as ELK (Elasticsearch, Logstash, Kibana), etc.
(3) Log compression
Compress logs to reduce disk usage and network transmission overhead.
(4) Log level setting
Set different log levels according to business needs to avoid unnecessary detailed logging.
- The implementation strategies to support monitoring in high-concurrency environments are as follows:
(1) Real-time monitoring
Use monitoring tools to monitor system performance in real time to detect and solve problems in a timely manner.
(2) Abnormal alarm
Set up an exception alarm mechanism to promptly notify operation and maintenance personnel when an abnormality occurs in the system.
(3) Data analysis
By analyzing monitoring data, we can identify system bottlenecks and optimization space, thereby improving system performance.
(4) Visual display
Use dashboards, charts and other forms to visually display monitoring data to facilitate analysis by operation and maintenance personnel.
Key technologies in distributed microservice architecture provide important support for building high-concurrency systems. By rationally selecting technologies such as service registration and discovery, load balancing, circuit breakers, distributed data management, API gateways, and logging and monitoring, combined with appropriate high-concurrency support solutions, we can build a stable and scalable distributed system to achieve Excellent performance in high-concurrency environments. However, when applying these technologies, it is important to adjust and optimize them according to the actual situation to achieve the best results.