Shulou(Shulou.com)05/31 Report--

This article will explain in detail how to analyze the Etcd in K8s. The content of the article is of high quality, so the editor shares it for you as a reference. I hope you will have some understanding of the relevant knowledge after reading this article.

I. introduction

Etcd is described as the brain of a Kubernetes cluster and a key component of Kubernetes because it stores the entire state of the cluster: its configuration, specifications, and the status of running workloads.

In the Kubernetes world, etcd acts as the back end of service discovery and stores the state of the cluster and its configuration.

Etcd is deployed as a cluster, and the communication between several nodes is handled by the Raft algorithm. In a production environment, the cluster contains an odd number of nodes and requires at least three.

About etcd

Etcd . The name "etcd" comes from two ideas, the unix "/ etc" folder and the "d" distributed system. The "/ etc" folder is the location where configuration data for a single system is stored, while etcd is used to store massively distributed configuration information. Therefore, the "/ etc" assigned to "d" is "etcd".

Etcd is designed as a universal substrate for large-scale distributed systems. These large systems need to avoid brain-splitting operations and are willing to sacrifice availability to achieve this. Etcd stores metadata in a consistent and fault-tolerant manner. Etcd clusters are designed to provide key-value storage with stability, reliability, scalability, and performance.

Distributed systems use etcd as a consistent key storage component for configuration management, service discovery, and coordination of distributed work. Many organizations use etcd on production systems, such as container schedulers, service discovery services, and distributed data storage. Common distributed patterns using etcd include leader election, distributed locking, and monitoring machine activity.

And ZooKeeper

ZooKeeper solves the same problems as etcd: distributed system coordination and metadata storage. However, etcd stepped on the shoulders of its predecessors, referring to the design and implementation experience of ZooKeeper. The lessons learned from Zookeeper undoubtedly provide support for the design of etcd, which helps it support large systems such as Kubernetes. Etcd improvements to Zookeeper include:

Dynamically reconfigure cluster members

Stable reading and writing under high load

Multi-version concurrency control data model

Reliable key value monitoring

Lease primitive decouples connections in session

API for distributed shared locks

In addition, etcd supports multiple languages and frameworks right out of the box. Zookeeper has its own custom Jute RPC protocol, which is completely unique to Zookeeper and limits its supported language bindings, while the client protocol of etcd is based on gRPC. GRP is a popular RPC framework with go,C + +, Java and other language support. Similarly, gRPC can be serialized to JSON through HTTP, so even general command-line utilities such as curl can communicate with it. Because systems can choose from a variety of options, they are built on etcd with native tools, rather than on a fixed set of technologies built around etcd.

When considering functionality, support, and stability, etcd is more suitable as a component for consistent key-value storage than Zookeeper.

2. Etcd in Kubernetes

In the context of Kubernetes clusters, etcd instances can be deployed as Pod on master nodes (this is the example we will use in this article).

On how to analyze the Etcd in K8s to share here, I hope the above content can be of some help to you, can learn more knowledge. If you think the article is good, you can share it for more people to see.

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.