![]() Let’s look at the topics we’re going to explore: Lastly, we’re going to dive a bit deeper into Dgraph’s approach to sharding data and how it addresses problems seen in existing solutions. We will also look at some methods of doing so that aim to mitigate limitations on the performance of the database. Since Dgraph is a graph database, we’re going to look at how a graph database can distribute its graphs across several machines to achieve horizontal scalability. The idea of database sharding has become a cornerstone of modern database architecture, especially at scale. This article discusses one of the most common and modern options for scaling: database sharding. As a fundamental part of most modern applications, there are many ways that you could begin to scale your database functionalities. Part of the equation likely involves scaling your database to increase resources to serve the increasing demand. Complexity mainly depends on the architecture of your system and the underlying technologies. ![]() Scaling a system can be very complex and involve many different facets. To do that, since the market is more or less always unpredictable, you have to ensure that your products can scale as you’re growing. This is the benchmark for all modern applications: faster and unrestricted access to data. Application performance is a key factor in the success of giants of the tech arena like Amazon, Facebook, and Google, that proved performance at mega-scale is possible. The effect of such issues is even more apparent as your application begins to scale, potentially leading to poor user experience and a loss of users. Database performance bottlenecks can make or break a product. ![]() However, that journey can include a range of hurdles that many developers and technologists neglect or forget about, particularly when it comes to scaling a database.Īs the core component of most modern architectures, databases have a tremendous responsibility. Whether you’re building a prototype or expanding your existing product, the goal is to reach a massive user base. Database sharding: How to scale a graph databaseĮveryone wants to build the next unicorn of a modern application. ![]()
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