¶ MemSQL (now SingleStore)
¶ Database Technologies
/jpg/image.png)
¶ Introduction to MemSQL (now SingleStore): The Journey Toward a Real-Time, Unified Data Engine
When people talk about modern data systems today, the conversation almost always circles back to a common desire: speed. Not the kind of speed that feels fast on paper, but the kind that lets a business see what’s happening right now, react instantly, and make sense of streams of information without waiting for nightly batches. The world has shifted from dealing with data as a static resource to treating it as something alive, constantly moving and constantly reshaping the decisions we make. This shift is precisely what gave rise to MemSQL—rebranded in recent years as SingleStore—a database born out of the need to process data at the pace of modern life.
Before SingleStore came along, companies were already wrestling with data problems that traditional databases struggled to handle. Web applications needed low-latency reads and writes. Analytics teams needed enormous fact tables scanned across many dimensions. Business dashboards needed fresh data. Machine learning systems needed to ingest events at scale. But people ended up stitching tools together: OLTP databases for transactions, OLAP systems for analytics, message queues for events, caching layers for speed, and warehouses for queries. Each solved a piece of the puzzle, but stitching them together introduced complexity, latency, brittle pipelines, and operational overhead.
What made MemSQL different from the start was its refusal to accept that you needed separate databases for transactions and analytics. It set out to prove that a single database could handle both—what the industry calls HTAP, a hybrid transactional–analytical processing model. Many companies talked about this idea. MemSQL actually built it. And with time, this ambitious goal transformed into SingleStore, a platform used by some of the most demanding applications on the planet.
Part of what makes SingleStore so interesting is how it blends familiar ideas with unconventional ones. It looks like a relational database. It speaks SQL, the language developers and analysts already know. It behaves like a distributed system under the hood. And it stores data in formats optimized both for blazing-fast transactions and for massive-scale analytics. This balance gives SingleStore a kind of versatility that is rare in the world of data technology.
But to appreciate why SingleStore matters for the modern era, it helps to rewind the clock and look at the environment in which MemSQL originally emerged. Silicon Valley in the early 2010s was in the middle of a data explosion. Real-time analytics was quickly becoming the lifeblood of decision-making. Social networks, ecommerce platforms, logistics systems, and financial engines all demanded faster systems. Companies like Facebook and Google had built internal, proprietary solutions to deal with the velocity of their data, but most organizations didn’t have the same engineering might. They needed tools that worked out of the box.
MemSQL’s founders were inspired by this challenge. They saw a world where developers needed the expressive power of SQL, but with the speed of in-memory systems and the elasticity of distributed architectures. The early versions of MemSQL did something unusual: they compiled SQL queries down to native machine code. This gave the database the ability to execute operations faster than many competitors, making it attractive for real-time applications. Over time, as the platform matured, MemSQL expanded well beyond its initial in-memory engine and embraced a more balanced architecture combining memory, disk, columnstores, and rowstores, all designed with speed in mind.
What never changed, though, was the focus on unifying the transactional and analytical workloads. Traditional databases separate these worlds for good reason—they have different performance requirements, different storage patterns, and different expectations about concurrency and throughput. But SingleStore challenged the idea that separation was a necessity. Instead, it built a system that lets the same dataset serve lightning-fast transactional queries and deep analytical scans without costly data movement.
A big piece of the puzzle is SingleStore’s distributed architecture. Instead of one monolithic server, it uses a cluster of nodes that store data across multiple partitions. This allows workloads to scale horizontally. When you need more performance, add more nodes; the cluster rebalances data and workload automatically. This distributed nature aligns perfectly with the types of applications that dominate the digital era: high-velocity data streams, globally available platforms, and dashboards that update in near real time.
Another distinctive feature is the dual storage format. SingleStore doesn’t force developers to choose between row-based storage (great for transactions) and columnar storage (great for analytics). Instead, it supports both within the same database. You can store fast-changing entity data in rowstores while keeping vast fact tables in columnstores. Because both play inside the same environment, queries can flow across them seamlessly. This design creates a single engine capable of delivering results for apps that once needed entire ecosystems of tools.
What has always made SingleStore appealing is how intuitively it fits into existing development workflows. Companies don’t need to abandon SQL. They don’t need to rewrite their applications. They don’t have to retrain their analytics teams. Instead, they get a system that behaves like a familiar relational database but performs like a real-time distributed engine. This is especially valuable in organizations that need powerful analytics but don’t want the complexity of traditional data warehouses, Hadoop-style clusters, or bolt-on caching layers.
The rebranding to SingleStore signaled an evolution more than anything else. MemSQL started as a high-performance in-memory SQL database. But as its capabilities expanded—columnstores, disk-based persistence, cloud-native elasticity, pipelines for real-time ingestion, and distributed query execution—the name “MemSQL” stopped capturing the full picture. “SingleStore” reflected the broader mission: providing a single, unified store for all workloads.
In recent years, SingleStore has also embraced the cloud-native world. It now supports fully managed services, integrates smoothly with modern ecosystems, and offers the kind of elasticity developers expect in an age where workloads spike without warning. Real-time pipelines can ingest event streams from platforms like Kafka with remarkable efficiency, making it a natural fit for high-speed streaming use cases.
One of the subtle, but important, philosophical beliefs behind SingleStore is that data systems should get out of your way. Developers shouldn’t have to babysit ETL jobs all day. Analysts shouldn’t have to wait for dashboards to refresh. Data scientists shouldn’t have to pull extracts that grow stale within hours. SingleStore’s architecture is built to keep data as close to real time as possible, allowing teams to focus on insights rather than pipelines.
This mindset has led to several innovations. For example, the database can ingest millions of rows per second while still serving queries with sub-second latency. It can compress columnstore data aggressively while still performing rapid scans. It supports vectorized execution to process large chunks of data efficiently. It can execute distributed joins, which many systems avoid because of their complexity. These capabilities make it well-suited for demanding tasks such as fraud detection, personalization engines, logistics optimization, and financial analytics.
The beauty of SingleStore is that it caters to both ends of the spectrum: developers building applications and analysts performing heavy explorations. From the developer’s perspective, it behaves like a fast relational database that responds quickly, even under stress. From the analyst’s perspective, it behaves like an analytical engine capable of scanning billions of records. And importantly, both can query the same data without interfering with one another, thanks to an architecture carefully designed around workload isolation.
SingleStore also reflects a broader trend in the database world: simplification. For decades, businesses adopted more and more tools, each offering specialized abilities but adding their own complexity. Data lakes, warehouses, message queues, ingestion engines, caching layers, query accelerators, and real-time analytics platforms piled on top of each other. SingleStore is part of a new wave of technologies trying to consolidate these functions so data organizations can be leaner, faster, and more adaptable.
This course will gradually uncover the layers and thinking behind SingleStore. We’ll explore how its query engine works, why its storage format is designed the way it is, and what makes its ingestion capabilities unique. You’ll see how it scales, how it handles distributed queries, and how it maintains performance even under heavy workloads. You’ll explore the practical side—how to model data, write queries, optimize performance, and build applications on top of it. And we’ll also zoom out to consider how SingleStore fits within the broader landscape of database technologies, where it shines, and where its design decisions come with tradeoffs.
By the end of this journey, SingleStore will no longer feel like just another database on a list of systems to memorize. It will make sense in the context of the tradeoffs every data system must face. It will illustrate how modern businesses are redefining the meaning of “real time.” And more importantly, it will show you what happens when a database is engineered not just for storage or queries, but for the way people actually use data in the age of streaming information and on-demand insights.
The evolution from MemSQL to SingleStore mirrors the evolution of the data world itself—from separate systems stitched together to unified platforms that can handle everything from high-speed ingestion to deep analytics. This transformation isn’t just a technical shift; it’s a philosophical one. It’s about embracing simplicity without sacrificing power, about letting developers and analysts work directly with fresh, live data, and about shaping a future where the database becomes a partner rather than a bottleneck.
As you dive into the upcoming articles, you’ll see how SingleStore accomplishes this through clever architecture, thoughtful engineering, and a persistent focus on performance. You’ll get comfortable with its tooling, understand its strengths, and gain an intuition for designing systems that leverage its capabilities effectively.
The path ahead will take you from the fundamental building blocks of its distributed engine to the nuances of tuning queries, designing schemas, and powering real-time applications. You’ll learn how SingleStore fits into modern data stacks, how it compares to other systems, and how it empowers teams to build things that once felt too complex or too slow.
For now, what matters is understanding what SingleStore represents: a unified database built for today’s pace, designed to bring the worlds of transactions and analytics into one harmonious engine. It’s a system crafted for teams who want speed without complexity, scalability without fragmentation, and real-time insights without the maze of pipelines that used to define enterprise data operations.
This introduction is just the beginning. The rest of the course will take you deep into the core of SingleStore, unraveling the ideas and engineering choices that make it one of the most distinctive and forward-thinking databases in the modern ecosystem.
¶ MemSQL (now SingleStore)
¶ Beginner (Chapters 1-25)
1. Introduction to MemSQL: What is SingleStore?
2. Installing and Setting Up SingleStore
3. Understanding the MemSQL Architecture: Distributed and In-Memory
4. Overview of MemSQL’s Hybrid Storage Model
5. Introduction to SQL and Distributed Querying in MemSQL
6. Connecting to SingleStore Using SQL Clients
7. Your First Query in SingleStore
8. Managing Databases and Tables in SingleStore
9. Basic Data Types and Operations in SingleStore
10. Working with Schemas and Databases in SingleStore
11. Introduction to Data Modeling in MemSQL
12. Loading Data into MemSQL: Bulk Insert and Import
13. Basic SQL Queries in MemSQL
14. Working with Indexes in SingleStore
15. Filtering and Sorting Data: SELECT Queries
16. Understanding Transactions in SingleStore
17. Implementing Data Integrity Constraints (Primary, Foreign Keys)
18. Working with Joins in MemSQL
19. Using Group By and Aggregation in SingleStore
20. Introduction to Full-Text Search in MemSQL
21. Securing SingleStore: Authentication and Authorization
22. Backup and Restore Procedures in MemSQL
23. Working with Views in MemSQL
24. Introduction to Stored Procedures and Functions
25. Error Handling and Debugging Queries in MemSQL
¶ Intermediate (Chapters 26-50)
26. Using Partitioning for Better Performance in MemSQL
27. Advanced Query Techniques in SingleStore
28. Optimizing SQL Queries in MemSQL
29. Using MemSQL’s In-Memory Capabilities for Speed
30. Querying Real-Time Data in SingleStore
31. Advanced Indexing: Columnstore and Rowstore
32. Multi-Table Joins and Subqueries in MemSQL
33. Creating and Managing Tablespaces in SingleStore
34. Working with Data Consistency and ACID Properties
35. Real-Time Data Streaming and Pipelines in MemSQL
36. Monitoring and Managing Performance in SingleStore
37. Using Materialized Views for Faster Queries
38. Time Series Data Handling in MemSQL
39. Using SQL Window Functions for Complex Analysis
40. Optimizing Full-Text Search in SingleStore
41. Integrating MemSQL with External Data Sources
42. Data Replication and Sharding in SingleStore
43. Implementing Data Governance and Auditing
44. Using MemSQL for Analytical Queries in Real-Time
45. Managing User Roles and Permissions in SingleStore
46. Working with JSON Data in MemSQL
47. Advanced Data Modeling Techniques for MemSQL
48. Implementing Change Data Capture (CDC) in SingleStore
49. Using Temporary Tables in MemSQL
50. Querying Large Datasets Efficiently in MemSQL
¶ Advanced (Chapters 51-75)
51. Advanced Sharding Techniques in MemSQL
52. Working with Distributed Transactions in SingleStore
53. Query Optimization Techniques in MemSQL
54. Managing High Availability with MemSQL Clusters
55. SingleStore’s Distributed Query Execution Engine
56. Building Scalable Applications with SingleStore
57. Integration with Apache Kafka for Streaming Data
58. Handling Large Volumes of Streaming Data in MemSQL
59. Managing Data Durability in MemSQL
60. Real-Time Analytics with MemSQL and BI Tools
61. Advanced Indexing Techniques for Performance Tuning
62. Horizontal Scaling and Load Balancing in SingleStore
63. Best Practices for Configuring SingleStore for Production
64. Deep Dive into SingleStore’s Execution Plans
65. Optimizing Resource Allocation in SingleStore
66. Working with Foreign Data Wrappers in SingleStore
67. Building Complex ETL Pipelines with MemSQL
68. Integrating MemSQL with Machine Learning Frameworks
69. Implementing Multi-Tenant Applications in MemSQL
70. Understanding the Distributed Nature of MemSQL Queries
71. Using Advanced SQL Functions and Expressions in SingleStore
72. Advanced User and Role Management in MemSQL
73. Ensuring Data Security: Encryption and Compliance
74. Replicating Data Across Multiple Data Centers in SingleStore
75. Debugging and Profiling Distributed Queries in MemSQL
¶ Expert (Chapters 76-100)
76. Architecting Large-Scale Applications with SingleStore
77. Using MemSQL as a Hybrid Transactional/Analytical Processing (HTAP) Engine
78. Optimizing MemSQL for Big Data Analytics
79. Best Practices for Real-Time Data Processing in SingleStore
80. Automating Deployments with SingleStore and CI/CD
81. Implementing Global Data Distribution in MemSQL
82. Architecting for Low Latency in MemSQL
83. Managing Complex Data Models with MemSQL
84. Advanced Integration with External Data Warehouses
85. Building a Data Lake with MemSQL and Real-Time Queries
86. SingleStore as a Microservices Database: Patterns and Best Practices
87. Implementing a Graph Database Solution with MemSQL
88. Using MemSQL with Cloud-Native Architectures
89. Automating Data Sharding in SingleStore
90. Using MemSQL for Financial and Real-Time Trading Systems
91. Performance Tuning for Complex Query Workloads in SingleStore
92. Advanced Techniques for Data Consistency Across Nodes
93. Handling Multi-Region, Multi-Cloud Deployments with SingleStore
94. SingleStore’s Integration with Kubernetes and Docker
95. Implementing Data Warehousing with SingleStore
96. Building and Scaling Real-Time Dashboards with SingleStore
97. Leveraging MemSQL for IoT and Sensor Data Applications
98. Customizing SingleStore’s Query Optimizer for Specialized Workloads
99. Advanced Backup and Disaster Recovery Strategies in MemSQL
100. The Future of MemSQL: Trends in Distributed Databases