Vertical scaling can get very expensive, and creates an increasingly unwieldy single point of failure for your application or service, eventually reaching a limit that requires the adoption of sharding. Scaling up (or vertically scaling) requires adding more CPU to handle more concurrent connections adding RAM so more queries can run in memory instead of paging out to disk and adding more disk to get more capacity for the data and indexes to speed up queries. When you’ve optimized your application code, caching, and query patterns and yet can’t squeeze any more performance out of your database layer, it’s time to scale up or scale out. The fundamental flaw with an RDBMS, unfortunately, is scalability. The overwhelming majority of transactional applications are written on top of traditional relational databases like PostgreSQL. But that is challenging to do on a manually sharded database, which makes sharding bad for business. To stay ahead you need to be able to flexibly and responsively adapt your app to changes in your customers and the market.
For the near future, at least, the only thing certain is uncertainty. Your business faces costs and risks when technology can’t evolve fast enough to meet changing business needs.Įconomic conditions have been turbulent lately. After having worked with Spanner in the past, it’s like going back to the stone age, like we’re only a step above babysitting individual machines in a closet. Even basic things like deploying schema changes to every shard are an unbelievable pain in the ass, and changing the number of shards on a major database is a high-touch, multi-day operation. My current employer uses sharded and replicated Postgres via RDS. How seven companies are cutting costs and moving faster with Distributed SQL.
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