SingleStore (formerly MemSQL) is a proprietary, cloud-native database designed for data-intensive applications. A distributed, relational, SQL database management system (RDBMS) that features ANSI SQL support, it is known for speed in data ingest, transaction processing, and query processing.
SingleStore primarily stores relational data, though it can also store JSON data, graph data, and time series data. It supports blended workloads, commonly referred to as HTAP workloads, as well as more traditional OLTP and OLAP use cases. For queries, it compiles Structured Query Language (SQL) into machine code. The SingleStore database engine can be run in various Linux environments, including on-premises installations, public and private cloud providers, in containers via a Kubernetes operator, or as a hosted service in the cloud known as SingleStore Managed Service.
History
1999–2010 (First era) Cloud data consisted of retrofitting on-premise and general-purpose SQL databases.
2010–2017 (Second era) The marketplace saw a retreat from more commonly used SQL to achieve speed and scale, instead favoring the adoption of special-purpose, NoSQL databases, and cheap object storage.
2017 — Present (Third era) Several trends drove database market disruption: faster internet, growth in the adoption of modern applications for streaming, gaming, IoT, and more, and modern applications that drive the need for converged streaming data, transactional, and analytical processing for multi-model data. The third-era began in 2017 with real-time, hybrid multi-cloud, multi-model, and relational databases. This evolution in cloud data drove SingleStore to develop a frictionless, unified distributed SQL database for real-time, data-intensive applications.
On April 23, 2013, SingleStore launched its first generally available version of the database to the public as MemSQL. Early versions only supported row-oriented tables, and were highly optimized for cases where all data can fit within main memory. This design was based on the idea that the cost of RAM would continue to decrease exponentially over time, in a trend similar to Moore's law. This would eventually allow most use cases for database systems to store their data exclusively in memory.
Shortly after launch, MemSQL added general support for an on-disk column-based storage format to work alongside the in-memory rowstore. The decreases in cost of memory slowed over time, and the market for purely in-memory database systems largely failed to materialize, with increasing demand for disk-based OLAP workloads. Thus, over time, MemSQL's columnstore became a major focus and a crucial feature for customers.
On October 27, 2020, MemSQL rebranded to SingleStore to reflect a shift in focus away from exclusively in-memory workloads. The new name highlights the goal of achieving a universal storage format capable of supporting both transactional and analytical use cases.
In its current product release, v.7.5, SingleStore became the first and only database to combine separation of storage and compute plus system of record into a single platform. Headquartered in San Francisco, California, in June 2021 SingleStore opened an office in Raleigh, North Carolina. As part of the office opening, SingleStore launched Launch Pad, a center for innovation to incubate and prototype solutions. Its other offices include Sunnyvale, California, Seattle, Washington, and Lisbon, Portugal.
Funding
In January 2013, SingleStore announced it raised $5 million. Since then, the company has raised $318.1M from various investors including Khosla Ventures, Accel, Google Ventures, Dell Capital and HPE, among others.
Architecture
Row and column table formats
SingleStore can store data in either row-oriented tables ("rowstores") or column-oriented tables ("columnstores"). The format used is determined by the user when creating the table.
Rowstore tables, as the name implies, store information in row format, which is the traditional data format used by RDBMS systems. Rowstores are optimized for singleton or small insert, update or delete queries and are most closely associated with OLTP (transactional) use cases. Data for rowstore tables is stored completely in-memory, making random reads fast, with snapshots and transaction logs persisted to disk.