SingleStore
APISingleStore is a distributed SQL database for real-time analytics and transactions, combining OLTP and OLAP workloads in
www.singlestore.comLast updated: April 2026
SingleStore is a distributed SQL database for real-time analytics and transactions, combining OLTP and OLAP workloads in a single unified engine.
About
SingleStore is a high-performance distributed SQL database designed to handle both transactional (OLTP) and analytical (OLAP) workloads simultaneously without the need for separate database systems or ETL pipelines between them. This HTAP (Hybrid Transactional and Analytical Processing) capability makes SingleStore particularly valuable for applications that need to both write data quickly and query it in real time for analytics.
The architectural foundation of SingleStore is a combination of row-oriented storage for transactional workloads and column-oriented storage for analytical queries, all within a single engine. The row store provides fast single-row lookups, point queries, and high-frequency writes, similar to traditional RDBMS systems. The column store provides efficient bulk scans and aggregations over large datasets, similar to dedicated analytical databases. Data is automatically moved from the row store to the column store in the background as it ages, ensuring that fresh data is available for real-time querying while historical data benefits from analytical optimizations.
SingleStore's distributed architecture enables horizontal scaling by sharding data across multiple nodes in a cluster. The partitioning key determines how data is distributed, and queries that include the partition key can be executed on the relevant node directly, while queries that require data from multiple nodes are distributed and their results merged. This parallel execution model enables linear scaling of both write throughput and query performance as nodes are added.
The MySQL-compatible SQL interface makes SingleStore accessible to any application or tool that supports MySQL. Most MySQL drivers, ORMs, and BI tools connect to SingleStore without modification, lowering the barrier to adoption and enabling straightforward migration of MySQL-based applications that have outgrown MySQL's performance capabilities.
Real-time data ingestion in SingleStore is a critical capability for streaming analytics use cases. The Pipelines feature enables direct ingestion from Apache Kafka, Amazon S3, Google Cloud Storage, Azure Blob Storage, and other sources without intermediate ETL layers. Data is ingested, parsed, transformed, and made queryable in sub-second timeframes, enabling dashboards and alerts that reflect the true current state of the data.
Full-text search capabilities are built directly into SingleStore through a distributed inverted index that integrates naturally with SQL queries. This eliminates the need for separate Elasticsearch deployments for search-heavy applications.
SingleStore is available as a managed cloud service (SingleStore Helios) on AWS, Azure, and Google Cloud, and as a self-managed deployment on Kubernetes or bare metal. The combination of speed, versatility, and MySQL compatibility makes it a compelling choice for real-time analytics applications, operational dashboards, and fraud detection systems.
Positioning
SingleStore is a distributed SQL database engine designed to handle both transactional and analytical workloads in a single unified system. Formerly known as MemSQL, the platform combines the speed of in-memory processing with the durability of disk-based storage, eliminating the need for separate OLTP and OLAP databases in data architectures.
By supporting real-time analytics on operational data without ETL pipelines, SingleStore enables enterprises to make decisions on live data rather than stale copies. The database scales horizontally across commodity hardware and runs identically in cloud and on-premises environments, making it a practical choice for organizations consolidating fragmented data infrastructure.
What You Get
- Unified HTAP Engine
Single database that handles transactions and analytics simultaneously using a universal storage format with rowstore and columnstore tables - Real-Time Ingest Pipeline
Native pipeline connectors for Kafka, S3, HDFS, and other sources that load data continuously without external ETL tools - Distributed Query Optimizer
Cost-based optimizer that parallelizes queries across cluster nodes with support for ANSI SQL and JSON operations - Vector Search
Built-in vector similarity search using dot product, Euclidean distance, and cosine similarity for AI/ML application workloads - Managed Cloud Service
Fully managed deployment on AWS and Azure with automatic scaling, built-in high availability, and usage-based pricing
Core Areas
Real-Time Analytics
Sub-second query performance on billions of rows for dashboards, reporting, and operational intelligence applications
Machine Learning & AI
Vector storage and similarity search integrated with SQL for building recommendation engines, RAG applications, and feature stores
Financial Services
Low-latency transaction processing for fraud detection, risk analysis, and real-time trading analytics
Operational Data Platforms
Consolidating multiple purpose-built databases into a single system that serves both application backends and analytical queries
Why It Matters
Most enterprises maintain separate databases for transactions and analytics, connected by fragile ETL pipelines that introduce latency and data inconsistency. SingleStore eliminates this architectural complexity by handling both workload types natively, which means analytics always run on current data rather than batch-loaded copies from hours ago.
As AI workloads increasingly require combining structured SQL queries with vector similarity search, SingleStore's unified approach avoids the operational burden of synchronizing data between a traditional database and a separate vector store.
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