TrustRadius Insights for SingleStore are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
Real-Time Data Processing Capabilities: Users have consistently praised SingleStore for its efficient real-time data processing capabilities, noting its effectiveness in online transaction processing and big-data batch handling. The seamless integration with external services like Kafka and S3 has also been highlighted as a significant advantage.
Super Fast Data Queries: Reviewers have emphasized the exceptional speed of data queries on SingleStore, enabling them to quickly and efficiently retrieve information for their needs. This feature is seen as a key benefit that enhances overall productivity and decision-making processes.
Scalability and Performance Improvements: Users appreciate SingleStore's scalability for both writes and reads, along with notable performance enhancements. These include faster request processing rates, improved algorithm processing times, and the ability to handle growing workloads without compromising efficiency or reliability.
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SingleStore Reviews
32 Reviews
Engineering
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We use SingleStore for a super fast client experience, running real time analytics on billions of events arriving every day from various publishers channels.
Pros
Performance - Milliseconds response of 80 tables Joined queries
Scalability - Ability to grow with no downtimes
Client success - Attentive to business needs, deep level support, patches and fixes
Efficiency - Built-in Kafka / S3 / MySQL integrations well adjusted to leverage SingleStore architecture and hardware
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Cons
Add Iceberg tables / files Pipeline
CDC out in form of logfile / binlog / producer to Kafka
Efficiency with multi shard-key use case: Joined three tables when one of them holds both shard keys of the other two.
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Likelihood to Recommend
SingleStore shines as a unified solution of high OLTP & OLAP workloads. The technology suits big data systems with mutual identity (shard key/s), fast JSON processing, vector search for AI features and streaming.
The client success attentiveness and the consistent support of experts in any matter shows the company maturity and their vision for success. No doubt this is a long term partnership.
Performing real-time risk calculations on complex financial instruments. Advanced analytics at scale helps with risk management and compliance with regulatory reporting requirements. Other usages include an anomaly detection system, order management platform, and tracking and optimization movement across multiple regions in real-time. The distributed architecture and sub second query responses helps manage huge systems with ease.
Pros
Distributed architecture.
Sub-second query responses.
Handling time series data with high write and query performance.
Cons
The UI can be made more user-friendly.
Kubernetes integration.
Compression and storage efficiency.
Likelihood to Recommend
Well-Suited Scenarios: Real-Time Analytics: Financial trading platforms requiring instant insights. Operational Dashboards: Retail businesses monitoring live sales. IoT Data Processing: Smart device monitoring with high data ingestion. Fraud Detection: Banks detect suspicious transactions instantly. Less Appropriate Scenarios: Archival Storage: Cold data storage with infrequent access. Low-Volume Workloads: Small-scale apps with minimal data processing needs. Complex ETL Pipelines: Heavy data transformations without real-time demands.
SingleStore has allowed us to simplify our analytical queries. It has allowed us to eliminate intensive jobs that compiled stats for the previous day and instead given us a way to run those same queries on demand eliminating discrepancies and giving our organization the ability to tackle any analysis without having worry about it impacting other aspects of our stack.
Pros
Statistical analysis
Data ingestion
S3 integration
Cons
Schema altering
Lucene engine column compatibility
Use as a primary transactional db
Likelihood to Recommend
If analytical workloads are necessary this solves many problems and is a viable solution. It has lots of ways to ingest and export. Its not suitable as a heavy traffic transactional db, but as a piggy back it is ideal. It has been very reliable to boot, we love it!
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.
Likelihood to Recommend
In my opinion, it is well suited for analytics use cases. However, it is less appropriate for transactional data as row tables are saved in memory, and single nodes are more costly than traditional databases.
SingleStore is our main database. We use it to serve our application including user and account information as well as millions and millions of rows of data that we gather every day. SingleStore has enabled us to only need one storage solution which simplifies our processes a lot. It has also enabled us to do very complex and computational intense queries on the fly to serve up results to our customers.
Pros
Ingesting large amounts of data relatively fast using pipelines.
Runs large/intense data analytical queries very fast and efficient.
Free Tier is nice to get started.
Cons
Customer Service tickets take way to long to get responses when something critical is breaking.
Pricing can at times be a bit unclear.
Pipelines are great but can be a bit of a black box.
Likelihood to Recommend
If you need 1 database to handle all your use cases SingleStore works great. Enabling you to store user/account information on the same database as analytical time-series data is game changing. If you don't have large amounts of data that you need to run very fast analytical queries then SingleStore is probably a bit overkill for your use case.
Singlestore powers everything for us. It is our entire dataware house and powers all of our main business functions. All of our business analytics related to customers, churn, retention. We use it for all of our data ingest which is hundreds of gb per day and it handles all of that nicely. We power our frontend app which displays all kinds of data. Timeseries, AI, etc.
Pros
Data Ingestion
Scaling Compute
OLTP and OLAP queries
Cons
Pipeline Monitoring
Data Migration Tooling
Multiple SQL editors with AI completion in web UI
Likelihood to Recommend
If you are doing data larger than normal app data (I.e. 1 - 2 mb / day) Singlestore is great and can scale with you as you grow.
Maybe if you're data isn't growing constantly you could get away with another solution.
The SingleStore is high-performance, scalable, and real-time analytics on large datasets, particularly for hybrid transactional and analytical processing (HTAP); we had a requirement to handle fast OLTP workloads (e.g., processing transactions, user interactions, etc.). It is a Fast column store database with a Pipeline concept. In our current use case, we do data ingestion directly into the database from external sources like Kafka, S3, and various other data streams with their "data pipeline model." It allows for high-throughput data ingestion without needing separate ETL (Extract, Transform, Load) processes, enabling SingleStore to handle transactional and analytical workloads with minimal latency.
Pros
Very fast columnar store database.
Data pipeline model.
Designed for horizontal scalability across distributed clusters.
Cons
It does not release a patch to have back porting; it just releases a new version and stops support; it's difficult to keep up to that pace.
Support engineers lack expertise, but they seem to be improving organically.
Lacks enterprise CDC capability: Change data capture (CDC) is a process that tracks and records changes made to data in a database and then delivers those changes to other systems in real time.
For enterprise-level backup & restore capability, we had to implement our model via Velero snapshot backup.
Likelihood to Recommend
Lack of enterprise capabilities in a patch release, large-sized backup and restore, CDC, and tech support.
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
Likelihood to Recommend
If multiple terabytes of data are involved and analytical queries are required on all of the data then SingleStore is a great option. We have tried other database providers and they were unable to handle the amount of data in a fast enough manner to serve up an application. However we have had issues with customer service resolving issues that were causing us problems on our end.
We recommend SingleStore to our customers who face scalability issues with their OLTP or OLAP systems. Many companies use database engines that are inappropriate for specific data computing tasks. For example, MySQL is not meant for analytics or real-time analytics. SingleStore is. SingleStore compresses data, which lowers your disk footprint, thus saving storage costs. Most of the time, we'll do a discovery about our customer pain points and determine if SingleStore is the right fit. We'll then work with SingleStore to do a POC to ensure we meet all customer requirements.
Pros
Data Ingestion
Scaling Writes and Reads
Compressing Data
Support
Short learning curve
Cons
Mysql to SingleStore replication
Likelihood to Recommend
SingleStore HTAP engine is well suited for real-time analytics, fast ingestion, scaling OLTP system like MySQL. When you need to run reports or perform aggregates on billions of rows and you get result in seconds, you cannot get this experience with other OLTP engines. I wish DBtLab was a little more developer and supported for SingleStore. This would allow to perform better data transformation. You can use stored procedures, but DBTLabs has become a standard for dimensional modeling in data warehousing projects. This is probably why SingleStore has trouble piercing in the data warehouse world. It is definately capable to compete with Snowflake when it comes to scalability, query performance, data compression, but Snowflake has ravaged the data warehouse market in few years and large corporations have already invested lots of money in migrating into Snowflake. The SingleStore community needs to grow. Everyone who uses SingleStore loves it.
