![]() With monoliths, there was generally one database instance and one application server. Developers began to realize that microservices came with some serious drawbacks. While microservices solved the scalability and availability issues that had been fundamentally blocking software growth, not all was well. Microservices also brought flexibility to teams and companies as they provided clear lines of responsibility and separation for organizational architectures. ![]() And because each microservice maintained its own state it meant your application was no longer limited to what fit on a single machine! Developers could finally build applications that met the scale demands of an increasingly connected world. Microservices seemed great initially because they enabled applications to be broken down into relatively self-contained units that could be scaled independently. The answer they found was microservices (well, service-oriented architectures). They began looking for alternative architectures that would alleviate the scalability issues they were experiencing. Developers and businesses needed to find ways to keep up with rapid global growth and demanding user expectations. Unfortunately, monoliths start to fall apart when scale and availability become requirements. Reliability and availability are no longer features, they are requirements. Every company is now expected to have software products. Software isn’t just powering things behind the scenes anymore, it’s become the end-user experience itself. Companies like Twitter and Facebook have made 24/7 always-online table stakes. Today, applications are expected to serve a global market from day one. Over the last 20 years, demand for software wouldn’t stop growing. There were a handful of companies like Amazon and Google that were running at “scale,” but they were the rare exception, not the rule. Even the biggest of software giants were operating at a scale that seems minuscule today. There weren’t a ton of connected users, which meant the scale requirements for software was minimal. In turn, this meant developers didn’t have to constantly write code to guess at the state of things.įor a long time, monoliths just made good sense. Therefore, monoliths provided a great experience for developers as they meant there was no chance of failed transactions resulting in an inconsistent state. A monolith can mutate any of its state within a single transaction, which means it yields a binary outcome-it worked or it didn’t. Monoliths by nature rely on a single database, which means all state is centralized. The model is simple, consistent, and similar to the experience you get programming in a local environment. Twenty years ago, developers almost always built monolithic applications. In full disclosure, I’m the Head of Product for Temporal, so I might be biased, but I think this approach is the future. After that, we’ll talk about Temporal’s stateful execution model and how it attempts to solve the problems introduced by service-oriented architectures (SOA). In this article, I’ll discuss how we reached this point with a brief stroll through the history of networked applications. Distributing things also introduces a whole new world of problems, many of which were previously solved by monoliths. Distributed architectures have enabled developers to scale and meet the needs of an increasingly connected world. In just 20 years, software engineering has shifted from architecting monoliths with a single database and centralized state to microservices where everything is distributed across multiple containers, servers, data centers, and even continents.
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