The ‘nail in the coffin’ for Azure Federations was simply that this custom implementation of Azure SQL Databases could not continue to be supported in the rapidly evolving Azure world. ![]() Azure Federations are being retired with Web and Business Editions of Azure SQL Databases. For me, the key issues were that sharded databases (Federation Members) could only be accessed via a logical container database (FederationRoot), restrictions were imposed on the design of database tables, and there was no built-in support for fan-out queries. I was an early (and enthusiastic) adopter but I soon realised that it had a number of serious limitations. So why did Microsoft choose to retire Azure SQL Database Federations and how does Elastic Scale address the lessons they learnt?Īzure Federations was a bold attempt to implement an elastic scale-out model in the database tier with built-in support for sharding. Azure Elastic Scale provides this flexibility. The more flexibility you have to place and move your shards as data changes, the more freedom you have to design a database that meets your application’s requirements but can still be scaled. This brings a host of challenges which can result in the data elasticity capabilities of the platform influencing the design of the data model. increasing hardware resources), so most of the focus is on the design and maintenance of distributed databases. For on-premise solutions, there are limited opportunities for scaling-up (i.e. The challenges to scaling out relational database management systems are well known, and the patterns for sharding are well developed. In this respect, Azure SQL databases are the perfect candidates for sharding because they can be created or deleted on demand, provide near-zero administration, and have built-in fault tolerance. It also adds more administrative overhead, and increases the number of points of failure. ![]() ![]() Scaling out (or sharding) by adding more databases usually requires careful planning and provisioning to ensure even distribution of data.
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