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Server farms achieve high scalability and high availability through server load balancing, a technique that makes the server farm appear to clients as a single server. In this two-part article, Gregor Roth explores server load balancing architectures, with a focus on open source solutions. Part 1 covers server load balancing basics and discusses the pros and cons of transport-level server load balancing. Part 2 covers application-level server load balancing architectures, which address some of the limitations of the architectures discussed in Part 1.
The barrier to entry for many Internet companies is low. Anyone with a good idea can develop a small application, purchase a domain name, and set up a few PC-based servers to handle incoming traffic. The initial investment is small, so the start-up risk is minimal. But a successful low-cost infrastructure can become a serious problem quickly. A single server that handles all the incoming requests may not have the capacity to handle high traffic volumes once the business becomes popular. In such a situations companies often start to scale up: they upgrade the existing infrastructure by buying a larger box with more processors or add more memory to run the applications.
Scaling up, though, is only a short-term solution. And it's a limited approach because the cost of upgrading is disproportionately high relative to the gains in server capability. For these reasons most successful Internet companies follow a scale out approach. Application components are processed as multiple instances on server farms, which are based on low-cost hardware and operating systems. As traffic increases, servers are added.
The server-farm approach has its own unique demands. On the software side, you must design applications so that they can run as multiple instances on different servers. You do this by splitting the application into smaller components that can be deployed independently. This is trivial if the application components are stateless. Because the components don't retain any transactional state, any of them can handle the same requests equally. If more processing power is required, you just add more servers and install the application components.
A more challenging problem arises when the application components are stateful. For instance, if the application component holds shopping-cart data, an incoming request must be routed to an application component instance that holds that requester's shopping-cart data. Later in this article, I'll discuss how to handle such application-session data in a distributed environment. However, to reduce complexity, most successful Internet-based application systems try to avoid stateful application components whenever possible.
On the infrastructure side, the processing load must be distributed among the group of servers. This is known as server load balancing. Load balancing technologies also pertain to other domains, for instance spreading work among components such as network links, CPUs, or hard drives. This article focuses on server load balancing.
spymemcached, an improved
memcachedclient for Java.