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Kubernetes solves this problem by providing a scalable, declarative platform that can automate container management to achieve high availability, resilience, and scale.

Kubernetes is not just a container management tool. Kubernetes has multiple built-in controllers that can be mapped to various layers of the cloud-native architecture.

Serverless architecture (Faas/Serverless) is a hot topic in the field of software architecture. But what is serverless and why (or not) is it worth considering?

Most DevOps-friendly security tools that can be well integrated into continuous workflows are often free. This article lists a few of the most promising DevOps-friendly free tools.

DevOps is a software development and delivery process. It can help emphasize communication between product management, software development and operations professionals, and cross-functional collaboration.

Currently, there are many DevOps tools in the industry that can help users simplify and automate software delivery pipelines to ensure effective implementation.

Nowadays, we are in an era of continuous integration and development of container, orchestration, microservices and DevOps functions.

The continuous integration and continuous deployment mentioned by DevOps are inseparable from the auxiliary role of continuous monitoring.

With the vigorous development of Devops, cloud computing, microservices, containers, etc., what kind of architecture and technical solutions are more suitable for such huge and complex monitoring needs?

The technical stack of DevOps operation and maintenance monitoring is roughly introduced above, but in fact, some open source monitoring software has comprehensive functions.

DevOps monitoring is the process of collecting, analyzing, and using information to track applications and infrastructure to guide business decisions.

Generator can be understood as a data type, which automatically implements the iterator protocol, so the generator is an iterable object.

Although the process of creating a Python iterator is powerful, it is often inconvenient to use. In Python, the mechanism of calculating while looping is called a generator.

In Generator, a very common usage is the use of yield. Every time the next() function of the Generator is called, the execution will start from the last yield statement executed.

We can use lists to store data, but when the data is large, creating a list of stored data will take up memory. The generator is a method that does not take up much computer resources.

When creating a list is only an intermediate process, in order to avoid creating a huge list, we can use generator expressions to complete.

Although the process of creating a Python iterator is powerful, it is often inconvenient to use. The generator is a simple way to complete the iteration.

Python generator is a simple way to create iterators. The generator is a function that returns an object (iterator) that we can iterate, and the iterator returns one value at a time.

We can use lists to store data, but when the data is large, creating a list of stored data will take up memory. The generator is a method that does not take up much computer resources.

We can use lists to store data, but when the data is large, creating a list of stored data will take up memory. The generator is a method that does not take up much computer resources.