#1 most frequent
United States of America
61.8%268 installations of 434
What is SingleStore?
SingleStore aims to enable organizations to scale from one to one million customers, handling SQL, JSON, full text and vector workloads in one unified platform.
Categories & Use Cases
Videos
Top Performing Features
Flexible deployment
The database can be deployed in a variety of environments and operating systems.
Category average: 8.3
Database monitoring
Teams can monitor database usage and performance metrics.
Category average: 8.8
Encryption
Data is encrypted in-motion and at rest, preventing unauthorized access and security breaches.
Category average: 8.7
Areas for Improvement
Database locking
Locking mechanisms prevent users from accessing data while it is being updated.
Category average: 8.7
ACID compliance
The database complies with ACID (atomicity, consistency, isolation, durability), ensuring transactions are processed reliably.
Category average: 8.7
Disaster recovery
Data can be recovered in the event of a disaster.
Category average: 8.5
Who Buys & Uses SingleStore
#2 most frequent
India
5.8%25 installations of 434
#3 most frequent
United Kingdom of Great Britain and Northern Ireland
5.5%24 installations of 434
Pros
- Delivers fast query performance, often sub-second, for complex analytical queries and joined tables.
- Provides horizontal scalability and a distributed architecture for managing large data volumes.
- Efficiently ingests large volumes of data from diverse sources like Kafka and S3.
Cons
- Limitations in data integration pipeline parameterization and monitoring capabilities.
- Maturity gaps in core database features such as column mutations, foreign keys, and unique constraints for columnstore tables.
- Difficulties in diagnosing root causes of issues like slow queries or memory failures.
Great database solution for apps that require analytics and those who don't want to juggle db providers
Use Cases and Deployment Scope
We are using SingleStore to power our whole app database layer.
We use it both for our basic tables and also for the analytical ones.
The magic is that we don't need to have multiple types of databases, it's just a single database which is a drop-in replacement for MySQL and it works almost exactly the same at the API level with some differences, which we can live with but hope they can improve upon (like column adjustments on the fly, foreign keys, etc.)
We use it both for our basic tables and also for the analytical ones.
The magic is that we don't need to have multiple types of databases, it's just a single database which is a drop-in replacement for MySQL and it works almost exactly the same at the API level with some differences, which we can live with but hope they can improve upon (like column adjustments on the fly, foreign keys, etc.)
Pros
- Analytical queries
- Basic SQL usage similar to how you would use MySQL
Cons
- Column mutations (from null to not null, or type changes)
- Foreign keys
- Unique columns (for columnstore)
Return on Investment
- No engineering overhead for maintaining the db so no makes up for the price in a way
- We are able to provide better analytics to our customers - providing more value
Alternatives Considered
ClickHouse and Elasticsearch
Other Software Used
Hetzner IaaS, Symfony, Vue.js
Usability
Best in market for developers to work with enterprise level data without any hassle
Use Cases and Deployment Scope
SingleStore resolved my latency issues with vector database usage by taking heavy lifting on their.
I have created an application to chat with POS orders done for e-commerce website.
Lot of insights & Analysis of order data is correct and precise.
Retail data is messy but vector database of SingleStore given me wings to play with retail & e-commerce data
I have created an application to chat with POS orders done for e-commerce website.
Lot of insights & Analysis of order data is correct and precise.
Retail data is messy but vector database of SingleStore given me wings to play with retail & e-commerce data
Pros
- SQL query fastness
- Vector database
- Contextual & Real time application
Cons
- It should give more tutorials
- Documentation is little fussy & confusing
- Not defined use cases
Return on Investment
- Efficient
- Good developer experience
- Impact measurable
Alternatives Considered
YES-POS, Weaviate and Quadrant Platform
Other Software Used
Azure AI Search, AWS Chatbot, GitHub
Good Analytics Database.
Use Cases and Deployment Scope
We use a single store for an analytics use case in our organization. The previous database was not distributed, and scaling issues were occurring, so a single store with a distributed nature helped us solve this issue. We mainly use the columnar tables in a single store for analytics.
Pros
- Sharding/Distributed Database.
- Analytics Queries.
- Good Observability.
Cons
- Row tables.
- Query Profiler.
Return on Investment
- Reduced Costs.
- Improved User experience due to faster queries.
Alternatives Considered
Google BigQuery
Usability
100% recommend Singlestore for data saas companies.
Use Cases and Deployment Scope
SingleStore is our only database solution we currently use. It is used at the application level to serve and store user information. We are a data company and serve up millions and millions of datapoints to customers and all that data is stored in SingleStore. SingleStore enables us to do quick analytical queries on millions of rows of data in seconds allowing us to be a market leader in the data space.
Pros
- Efficiency
- Reliability
- Speed
Cons
- Customer Service
Return on Investment
- Faster application
- Less database management
Alternatives Considered
Amazon DynamoDB and Amazon Elasticsearch Service
Other Software Used
AWS Batch, AWS Lambda
Master performance
Use Cases and Deployment Scope
We use for real time analytical and data store and serving platform for our ML workloads. It enables us to run low-latency analytics and model driven use cases at scale which is quite difficult for OLAP and OLTP databases alone.
Pros
- Serving ML features with low latency
- Real time analytics on continuous incoming data
- Fast ingestion without heavy ETL infrastructure
Cons
- Diagnosing slow queries, skewed partitions can be difficult
- Evolving schemas for real time data requires careful coordination
- It lacks advanced features like fuzzy logic matches to dedup string formatted data. It should be quite intelligent enough to do that.
Return on Investment
- to move faster for prod use cases
- reduced infra and data management costs
Alternatives Considered
Databricks Data Intelligence Platform
Other Software Used
Databricks Data Intelligence Platform
