In an era where milliseconds translate to millions in revenue, achieving the lowest latency in database operations has become one of the most critical aspects of the modern technology infrastructure. From e-commerce giants handling festival sales to gaming platforms managing real-time player interactions, the ability to process data with minimal delay can make or break the experience.
Aerospike, a distributed NoSQL database platform, has a different approach to achieving industry-leading low latency starting with its innovative hybrid memory architecture. It combines SSDs with DRAM, making storage costs significantly lower since SSDs are 10 times cheaper per bit than DRAM.
“We have invented our platform with over a dozen patents for algorithms that move all the way from storage to CPU, network, and distributed systems concepts,” Srini V Srinivasan, founder and CTO of Aerospike, told AIM.
Aerospike faces competition from several prominent database platforms including ScyllaDB, MarkLogic, DataStax, Couchbase, Redis, and MongoDB. The company claims that its architecture enables sub-millisecond latency for write-intensive workloads while requiring fewer nodes than its competitors to handle similar workloads.
The Foundation of Speed
The effectiveness of this approach has been demonstrated in real-world scenarios. During Flipkart’s Big Billion Days, the platform handled an impressive 95 million transactions per second. Similarly, Dream11 relies on this architecture to manage massive spikes during IPL seasons.
When it comes to the architectural choices, Aerospike went with the tried-and-true classic: C. While many modern platforms opt for Java or Rust, Aerospike’s decision to build its entire stack in C has been both a blessing and a challenge. “C is a language that is not for the faint-hearted,” acknowledged Srinivasan. “If we were to rebuild Aerospike now, we probably would seriously consider using Rust instead of C.”
This is one of the reasons companies cannot leave C/C++ for legacy systems, as C once was the preferred choice for efficiency and low-resource language. Writing an entire stack is not a good choice, especially when the company’s framework is built on top of it.
Apart from using C and hybrid architecture, the other reason is how the platform achieves its performance through extensive parallelism across CPU, network, and storage. “Everything has to be parallel. And then some parallel processing has to be followed by sequential resolution of any conflicts,” explained Srinivasan.
The system implements specialised query nodes with caching capabilities, achieving 90% hit rates while only keeping 10-20% of data in the cache. This sophisticated approach to caching and data management has made the platform particularly attractive for high-throughput applications.
The Scaling Part
For every software company, scaling is the most important task after gaining an initial set of customers. When AIM asked how a company like Aerospike manages the scaling part, especially in the AI era, Srinivasan said, “The biggest advantage, or biggest issue, is virtually any developer can build an application on any platform fairly fast. The challenge starts when the applications start to scale.”
One of the most significant scaling challenges involves observability and management. When organisations deploy hundreds of nodes across multiple clusters, traditional management approaches fall short. Aerospike addressed this through Kubernetes integration, providing an efficient way to install and manage large-scale deployments.
For vector operations, the platform employs a unique parallel composition approach. “We’ve invented an algorithm where we can compose the vector index in pieces in parallel and then resolve conflicts,” Srinivasan said. This allows the system to handle thousands of concurrent vector queries while simultaneously ingesting new vector data.
This sophisticated approach to scaling has enabled customers like InMobi to grow their deployments by factors of 10x to 1000x over their initial deployment over a 13-year period.
The idea of becoming the industry’s lowest latency platform isn’t just about raw speed, it’s about creating a sophisticated symphony of hardware utilisation and optimised algorithms. As the demands of AI applications and real-time analytics continue to grow, the importance of minimal latency will only increase, making these innovations more crucial than ever.
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