Users won’t have to wait for a single struggling server to finish its previous tasks. Users experience faster, uninterrupted service.By balancing application requests across multiple servers, a load balancer reduces individual server load and prevents any one application server from becoming a single point of failure, thus improving overall application availability and responsiveness. Typically a load balancer sits between the client and the server accepting incoming network and application traffic and distributing the traffic across multiple backend servers using various algorithms. If a server is not available to take new requests or is not responding or has elevated error rate, LB will stop sending traffic to such a server. LB also keeps track of the status of all the resources while distributing requests. It helps to spread the traffic across a cluster of servers to improve responsiveness and availability of applications, websites or databases. Load Balancer (LB) is another critical component of any distributed system. In the next section, we will go through Consistent Hashing, CAP Theorem, Load Balancing, Caching, Data Partitioning, Indexes, Proxies, Queues, Replication, and choosing between SQL vs. Familiarizing these concepts would greatly benefit in understanding distributed system concepts. In the following chapters, we will try to define some of the core building blocks of scalable systems. Investing in scaling before it is needed is generally not a smart business proposition however, some forethought into the design can save valuable time and resources in the future. How can we best utilize these pieces, what are the right tradeoffs?.How do these pieces work with each other?.What are different architectural pieces that can be used?.Whenever we are designing a large system, we need to consider few things:
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