Build cloud-native applications from scratch using Docker and SpringBoot Summary: Cloud-native applications have become a trend in modern software development. By using container technology and microservice architecture, rapid deployment and scaling can be achieved, and the reliability and maintainability of applications can be improved. . This article will introduce how to use Docker and SpringBoot to build cloud native applications and provide specific code examples. 1. Background introduction Cloud native application (CloudNativeApplication) refers to

When using Kubernetes, you will inevitably encounter problems in the cluster, which need to be debugged and repaired to ensure that Pods and services can run normally. Whether you are a beginner or an expert in dealing with complex environments, debugging processes within a cluster is not always easy and can become time-consuming and tedious. In Kubernetes, the key to diagnosing problems is understanding the relationship between the various components and how they interact with each other. Logging and monitoring tools are key to problem solving and can help you quickly locate and resolve faults. In addition, an in-depth understanding of Kubernetes resource configuration and scheduling mechanisms is also an important part of solving problems. When faced with a problem, first make sure your cluster and application are configured correctly. Then, by looking at the logs,

Technological waves such as cloud computing, big data, artificial intelligence, and blockchain have given continuous vitality to financial technology innovation. However, at the same time, new economic forms represented by the digital economy have also brought changes to traditional financial formats and existing underlying technologies. Profound changes and huge challenges. In the context of a complex international situation, the country has put forward higher requirements for safe, reliable, independent and controllable technologies. The financial industry information system has independent research and development capabilities, and reducing dependence on commercial products has become an urgent task. Since the financial industry involves people's livelihood, once problems occur in the business, it will have a serious impact on the entire public opinion. Therefore, ensuring the system stability of the financial industry is particularly important. However, financial companies that are going digital have unpredictable, uncontrollable, and highly complex businesses.

Designing cloud-native applications involves managing a complex system of microservices and serverless components that need to communicate with each other efficiently. Synchronous communication uses HTTP or gRPC calls, waiting for a response within a specified time range, providing real-time feedback, and is suitable for scenarios that require immediate response. Asynchronous communication utilizes message brokers (such as RabbitMQ or Kafka) to exchange messages without requiring immediate responses, enhancing the scalability of the system. By understanding the advantages and disadvantages of each communication mode, architects can design systems that effectively coordinate these independent elements to deliver high-performance, scalable, and reliable cloud-native applications.

Building a cloud-native web application using C++ involves the following steps: Create a new project and add the necessary libraries. Write business logic and create HTTP routes. Use Dockerfile to create container images. Build and push the image to the registry. Deploy applications on Kubernetes.

Among cloud-native architectures, Go is favored for its concurrency, cross-platform features, and ease of use. It enables easy building of highly concurrent applications, deployment on multiple platforms, and has rich network support. A cloud-native microservice built in Go can create routes, define endpoints, handle requests and return responses. Therefore, Go is well suited for cloud-native development and can optimize the performance and scalability of microservices and applications.

ChatGPT is popular on the Internet, and the AI ??model training behind it has also attracted widespread attention. IBM Research recently announced that the cloud-native supercomputer Vela it developed can be quickly deployed and used to train basic AI models. Since May 2022, dozens of the company’s researchers have been using this supercomputer to train AI models with tens of billions of parameters. Base models are AI models trained on large amounts of unlabeled data, and their versatility means they can be used for a range of different tasks with just fine-tuning. Their scale is enormous and requires massive and costly computing power. Therefore, as experts say, computing power will become the biggest bottleneck in developing the next generation of large-scale basic models, and training them requires a lot of computing power and time. Training available

Comparing the similarities and differences between Spring Cloud and Spring Boot from the architectural level. Spring Cloud and Spring Boot are currently the most popular microservice development frameworks in the Java field. They are both derived from Spring Framework. Although they are both used to build enterprise-level applications, there are some differences at the architectural level. This article will compare SpringCloud and SpringBoot from the architectural level, and through specific