Database Clustering is the process of combining more than one servers or instances connecting a single database. Sometimes one server may not be adequate to manage the amount of data or the number of requests, that is when a Data Cluster is needed. Database clustering, SQL server clustering, and SQL clustering are closely associated with SQL is the language used to manage the database information.
The main reasons for database clustering are its advantages a server receives; Data redundancy, Load balancing, High availability, and lastly, Monitoring and automation.
Multiple computers work together to store data amongst each other with database clustering. This gives the advantage of data redundancy. All the computers are synchronised that means each node is going to have the exact same data as all the other nodes. In a database, we need to avoid the kinds of repetitions (redundancies) that lead to data ambiguity. The kind of redundancy that clustering offers is certain because of the synchronization. In case if a computer malfunctions, we will have all the data available to others.
Load balancing or scalability doesn’t come by default with the database. It has to be brought by clustering regularly. It also depends on the setup. Basically, what load balancing does is allocating the workload among the different computers that are part of the cluster. This indicates that more users can be supported and if for some reasons if a huge spike in the traffic appears, there is a higher assurance that it will be able to support the new traffic. One machine is not going to get all of the hits. This can provide scaling seamlessly as required. This links directly to high availability. Without load balancing, a particular machine could get overworked and traffic would slow down, leading to decrement of the traffic to zero.
When you can access a database, it implies that it is available. High availability refers the amount of time a database is considered available. The amount of availability you need greatly depends on the number of transactions you are running on your database and how often you are running any kind of analytics on your data. With database clustering, we can reach extremely high levels of availability due to load balancing and have extra machines. In case a server got shut down the database will, however, be available.
A normal database can be used for this task because monitoring and automation can be done easily with software. The advantage grows more evident when a cluster is present. Typically, the advantage is that clustering allows to automate a lot of the processes of the database at the same time it permits to set up rules to warn potential issues. This prevents going back to check everything manually. With a clustered database, automation is helpful because it will allow getting notifications if a system is being demanded too much. However, a cluster will have a designated machine that will be used as database management system/control panel for the whole cluster. This selected machine can have scripts that run automatically for the entire database cluster and work with all of the database nodes.
In cluster architecture, all requests are split with many computers so that an individual user request is executed and produced by a number of computer systems. The clustering is serviceable definitely by the ability of load balancing and high-availability. If one node collapses, the request is handled by another node. Consequently, there are few or no possibilities of absolute system failures.
Types of Database Clusters
Database Cluster is highly comprehensive. It covers multiple tiers and arrangements depending on the requirement of the system. Here, we will brief three types of cluster computing architectures. Failover clusters, high-performance clusters, and load balancing clusters.
1. Failover/High Availability clusters: A machine can go wrong or stop working anytime. System administrators manage such failovers and fix issues efficiently. Here, the clusters come to the rescue. Cluster prepares the service availability by replicating servers and by redundant software and hardware reconfiguration. So, every system is controlling the other and works on requests if any one node fails. These types of clusters are profitable for those users who depend on their computer systems completely. For example, e-commerce, websites, etc.
The system should be capable enough to know which all systems are running, from which IP is running, which request and what would be the progression of action in case of a crash. The important thing is that the servers should not stop working anyway.
2. High-Performance Clusters: The purpose of developing high-performance database clusters is to produce high performing computer systems. They operate co-extending programs that are needed for time-exhaustive computations. Such variety of clusters are commonly preferred by the scientific industries. The basic aim is intelligently sharing the workload.
3. Load Balancing Clusters: These database clusters serve for distributing loads between different servers. They strive to provide an increased network capacity, finally increasing the performance. Systems in this network integrate their nodes, with the help of which user requests are equally divided across the participating nodes. The system is not working together, rather it redirects requests individually as they occur.
Despite all these technology distributions at the back-end, it appears to be a single system to the user. The use of clusters varies from enterprise to enterprise, depending on the kind of processes and level of performance required.