Data is growing at an unprecedented pace, but storage capacity hasn’t evolved to keep up. The increased emphasis on creating a unique, tailored customer experience demands businesses store and process vast amounts of data. Driven by IoT devices, security, privacy regulations, and general data growth, businesses cannot sustain this exponential explosion of data. As the data continues growing, data infrastructures are breaking down, unable to maintain the scale.
Solving a Problem with a Problem
To address this data growth challenge, it has become common to split the dataset. This process of breaking it into smaller, more logical pieces and running multiple datasets is known as sharding. However, sharding requires another layer of code on top of the storage engine (aka key-value storage engine), which is the software component used by databases to sort and index data. The problem is that existing storage engines such as RocksDB are based on architectures that were not designed to support the scales of modern datasets. Thus, they are stretched out to their limits trying to keep up with the ever-growing volumes of data.
To keep things going, developers find themselves spending more and more time dealing with sharding, resulting in increased complexity and management overhead on top of regular maintenance. Increased complexity is an ongoing problem in every layer of the IT stack but it is particularly egregious in this scenario.
Unfortunately, despite the short-term benefits of sharding, introducing a new layer of code makes it increasingly more complicated to manage the multiplying data-sets. As the business continues to shard it creates a challenging situation where the new datasets demand more support and development efforts to sustain. The multiplicative nature of sharding means that developers must now contend with an even wider range of datasets amid their ongoing storage crisis.
Devoting more resources to manage these new datasets comes at the expense of other activities. Developers must devote a set amount of time to partition the data and distribute it among shards on an ongoing basis. By making storage engine maintenance a daily task, developers are being distracted from focusing on higher-value work. This creates a widespread problem of reduced efficiency that impacts profits, productivity, and the business’s ability to remain competitive in the marketplace.
This increased complexity has one outcome—the developers work for the storage engine. Instead of utilizing the embedded Key-value library to support the business, the developers are tied up sorting, splitting, and organizing the data. But this begs the question: if it takes so much effort to maintain the storage engines, why use it in the first place?
Speedb was founded to eliminate the inefficiencies associated with having to constantly deal with sharding and storage engine maintenance. By redesigning the basic components of the existing RocksDB engine we created a next-generation embedded key-value store that can be used as a drop-in replacement solution for RocksDB.
With Speedb, sharding becomes an option, not a requirement. Speedb can scale a single dataset into the PBs without adding complexity or maintenance. This makes dataset maintenance a simple task instead of an all-consuming challenge that must be tackled by the entire dev team. Even if the scale of the dataset grows beyond what was once considered too large, Speedb will simply continue to scale without hiccups or breakdowns.
Even better, sharding can be postponed or undertaken strategically. Sharding does have several benefits, such as: replacing expensive servers with cheaper, smaller ones, creating specific isolated datasets, replication, and more. While data will continue to grow exponentially, sharding will still serve a purpose. However, sharding need not be the only option for dealing with data growth. With Speedb, businesses can once again focus on how they can leverage the data they store instead of seeing it as a burden.
Ultimately, Speedb frees businesses from the operational burden involved with hyper-scale data operations. They are no longer beholden to massive datasets preventing them from offering services and tending to their customers. Data has become an essential part of the business, but its explosive growth has created as many problems as it has solved. With Speedb, businesses are free from that challenge and can return to the days of data serving the business.