SingleStore, Inc. is an American data management software company based in San Francisco. Its platform aims to bring transactional operations and analytical queries together in a single engine, two use cases often handled by separate infrastructures. It is aimed particularly at teams developing applications that require fast responses across large volumes of data: operational dashboards, service personalization, or event tracking. Its official website is singlestore.com.
From MemSQL to SingleStore
The company was founded in 2011 by Eric Frenkiel and Nikita Shamgunov under the name MemSQL. Its initial positioning was based on a distributed SQL database using RAM to accelerate processing. Over successive releases, the product expanded its storage and analytics capabilities to support datasets that extend beyond what memory alone can hold.
In 2020, MemSQL became SingleStore. This name change reflects an ambition already present in its architecture: to limit the separation between the data used by applications and the data used for analysis. The company thus positions itself in the market for databases known as HTAP, short for “Hybrid Transactional and Analytical Processing,” while also developing a managed cloud service offering.
A distributed database for mixed workloads
SingleStore’s core product is a distributed relational database that can be queried using SQL. Its architecture distributes data and computation across multiple machines. It combines storage and indexing mechanisms suited both to targeted access and to analyses spanning many rows. Compatibility with the MySQL protocol facilitates integration with part of the existing ecosystem, without implying that all features of the two databases are interchangeable.
SingleStore offers managed cloud deployments as well as self-managed options. Ingestion features allow the database to be fed from streams and files, particularly to keep analytics updated as business events occur. The goal is to reduce the time between data arriving and being put to use. The benefits nevertheless depend on the chosen architecture, the workload, and the quality of upstream pipelines.
The platform also includes vector search capabilities. These make it possible to match content based on similarity and combine this search with SQL filters. They can be used in particular by artificial intelligence applications that need to retrieve relevant information before generating a response.
What’s next?
SingleStore operates in a competitive landscape where relational databases, cloud data warehouses, and analytics platforms are expanding their capabilities. Its challenge is to demonstrate the operational value of a shared engine for multiple use cases without increasing costs or administrative overhead. The growth of AI applications could support demand for accessible, up-to-date data. The relevance of its offering will nevertheless remain tied to each organization’s specific needs: latency, query concurrency, governance, and ease of operation.