How to build a highly available distributed system using Go language

With the continuous development of Internet applications, the construction of distributed systems is becoming more and more common. High availability is a very important aspect of these distributed systems, because when any system fails or goes down, it will lead to user disappointment and loss of profits. As an emerging programming language, Go language’s efficiency and simplicity have attracted more and more developers. So how to use Go language to build a highly available distributed system? This article will explain it to you one by one.

1. Choose a suitable architecture

Before starting to build a distributed system, we need to choose a suitable architecture. Different architectures are suitable for different application scenarios. Common architectures include: single node, master-slave, peer-to-peer, sharding, etc. The single-node architecture is simple and easy to understand, but it cannot meet high availability requirements; the master-slave architecture is suitable for scenarios where there is more reading and less writing; the peer-to-peer architecture is suitable for scenarios of peer-to-peer interaction between nodes; and the sharding architecture is suitable for large-scale data scene. Therefore, when choosing an architecture, you need to choose the most suitable architecture based on the actual situation of the system.

2. Achieve high availability of data

Data storage is a very important part of the distributed system. When using Go language to build a distributed system, we can use some high-availability data storage technologies for different scenarios, such as Zookeeper, Etcd, Redis Cluster, etc.

Zookeeper is a distributed coordination service that can coordinate and manage distributed applications. It provides a hierarchical namespace that can automatically detect node failures and perform data replication and master selection to ensure high data availability.

Etcd is a distributed key-value storage system that uses the Raft algorithm for cluster management. It has good data reading and writing performance and supports high-speed reading, so it is suitable for storing small data.

Redis Cluster is a solution officially launched by Redis. It supports multi-node clusters, has functions such as automatic failover and replication, and is suitable for storing large amounts of data.

3. Ensure high availability of the system

Once a node failure or network delay occurs, we need to take a series of measures to ensure the high availability of the system. It mainly includes the following three aspects:

3.1 Load Balancing

When the request volume is too large, we need to distribute the requests to multiple nodes through load balancing to avoid single points of failure; at the same time, Load balancing can also make the load of each node in the cluster even.

Common load balancing software includes:

Nginx load balancing module, which can perform load balancing of protocols such as HTTP and TCP.

HAProxy is an open source load balancing software that supports a variety of load balancing algorithms, including polling, weighted polling, IP hash, random, etc.

3.2 Automatic failover

Automatic failover means that when a node fails, requests can be quickly transferred to other normal nodes to ensure high availability of the system. Commonly used automatic failover tools include:

Pacemaker is a cluster management software that can detect whether the nodes are working properly and maintain the nodes, thus ensuring the stability of the cluster.

Keepalived is a high-availability software based on the VRRP protocol. It can manage multiple servers and ensure that when the main node fails, the backup node can quickly take over.

3.3 Monitoring and real-time alarm

Monitoring is the key to timely detection of faults. Therefore, some professional monitoring tools need to be used for monitoring. These tools can monitor the status of the system in real time and trigger alarms in a timely manner when problems occur. Commonly used monitoring tools include:

Prometheus is an open source monitoring system that can monitor various components in the cluster and record their running status to accurately understand the health status of the system.

Elasticsearch is a powerful search and analysis engine that can quickly query and analyze large amounts of distributed data, and can also perform real-time alerts and alarm notifications.

Summary

When building a highly available distributed system, multiple aspects need to be considered, including data storage, system high availability, load balancing, automatic failover, monitoring and real-time alarms, etc. As an efficient and concise programming language, Go language can implement the above functions in a variety of ways while ensuring the maintainability and scalability of the code.

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