Decision comparison
SingleStore vs StarRocks
SingleStore and StarRocks are both MPP analytical databases with MySQL-compatible interfaces and cost-based optimisers that handle joins, and db-engines lists them side by side. The differences are licensing and scope: SingleStore is commercial and also carries transactional writes; StarRocks is open source and reads Iceberg, Hudi and Delta Lake tables in place.
Direct comparison. These are reviewed substitutes bought for the same job, so the differences below are the ones that decide between them.
All 2 are OLAP databases.
Quick Comparison
| Decision factor | SingleStore | StarRocks |
|---|---|---|
| What it is | A commercial distributed database combining rowstore and columnstore for analytics and transactions | An open-source MPP analytical database with fast joins and direct lakehouse querying |
| Licensing | Commercial, with a free tier and a managed cloud service | Open source, with managed offerings from several providers |
| Transactions | Supports transactional writes alongside analytics in one engine | Analytical by design, with record-level updates but not a transactional store |
| Lakehouse access | Queries data in object storage, with its own storage as the primary home | Queries Iceberg, Hudi, Delta Lake and Hive tables in place alongside its own storage |
| Interface | MySQL wire protocol | MySQL wire protocol |
| Joins | Cost-based optimiser over normalised schemas | Cost-based optimiser with join performance as a headline property |
| Best fit | One system for operational writes and analytics, with commercial support | Analytics over normalised data and open lake formats, at no licence cost |
SingleStore
- What it is:
- A commercial distributed database combining rowstore and columnstore for analytics and transactions
- Licensing:
- Commercial, with a free tier and a managed cloud service
- Transactions:
- Supports transactional writes alongside analytics in one engine
- Lakehouse access:
- Queries data in object storage, with its own storage as the primary home
- Interface:
- MySQL wire protocol
- Joins:
- Cost-based optimiser over normalised schemas
- Best fit:
- One system for operational writes and analytics, with commercial support
StarRocks
- What it is:
- An open-source MPP analytical database with fast joins and direct lakehouse querying
- Licensing:
- Open source, with managed offerings from several providers
- Transactions:
- Analytical by design, with record-level updates but not a transactional store
- Lakehouse access:
- Queries Iceberg, Hudi, Delta Lake and Hive tables in place alongside its own storage
- Interface:
- MySQL wire protocol
- Joins:
- Cost-based optimiser with join performance as a headline property
- Best fit:
- Analytics over normalised data and open lake formats, at no licence cost
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | SingleStore | StarRocks |
|---|---|---|
| Docker Hub pulls(Product adoption) | 960.1k | 906.6k |
| GitHub commits, 90d(Developer adoption) | 7 | Not available |
| GitHub stars(Developer adoption) | 37 | Not available |
| Search interest(Market interest) | 0 | 1 |
| Hacker News mentions, 90d(Community interest) | 0 | 0 |
| npm weekly downloads(Developer adoption) | 235 | Not available |
| PyPI weekly downloads(Developer adoption) | 62.2k | 131.3k |
| Stack Overflow questions(Community interest) | 403 | 11 |
| GitHub commits, 90d(Product adoption) | Not available | 1.3k |
| GitHub stars(Product adoption) | Not available | 12,000+ |
| Product Hunt comments(Community interest) | Not available | 0 |
| Product Hunt reviews(Community interest) | Not available | 0 |
| Product Hunt votes(Community interest) | Not available | 2 |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
SingleStore
September 21, 2026Package vulnerabilities
npm · singlestore-nodejs@1.0.1 · PyPI · singlestoredb@1.17.3
0 vulnerabilities
across 2 packages
Repository security score
Not available
StarRocks
September 21, 2026Package vulnerabilities
PyPI · starrocks@1.3.4
0 vulnerabilities
across 1 package
Repository security score
Not available
Interface Preview
SingleStore
StarRocks
Feature Comparison
| Feature | SingleStore | StarRocks |
|---|---|---|
| Query | ||
| Sub-second aggregation over large datasets | Full support | Full support |
| Fast multi-table joins | Full support | Full support |
| Materialised views | Full support | Full support |
| High concurrency | Full support | Full support |
| Model | ||
| Columnar storage | Full support | Full support |
| Row storage for operational writes | Full support | Not verified |
| Transactional writes | Full support | Not verified |
| Record-level updates | Full support | Full support |
| Access | ||
| MySQL protocol compatibility | Full support | Full support |
| Query Iceberg and Delta Lake in place | Partial support | Full support |
| Real-time ingestion from Kafka | Full support | Full support |
| Standard BI connectivity | Full support | Full support |
| Platform | ||
| Open source | Not verified | Full support |
| Self-hosted deployment | Full support | Full support |
| Managed cloud service | Full support | Full support |
| Commercial support from one vendor | Full support | Partial support |
Query
Sub-second aggregation over large datasets
Fast multi-table joins
Materialised views
High concurrency
Model
Columnar storage
Row storage for operational writes
Transactional writes
Record-level updates
Access
MySQL protocol compatibility
Query Iceberg and Delta Lake in place
Real-time ingestion from Kafka
Standard BI connectivity
Platform
Open source
Self-hosted deployment
Managed cloud service
Commercial support from one vendor
Which to choose
SingleStore and StarRocks are both MPP analytical databases with MySQL-compatible interfaces and cost-based optimisers that handle joins, and db-engines lists them side by side. The differences are licensing and scope: SingleStore is commercial and also carries transactional writes; StarRocks is open source and reads Iceberg, Hudi and Delta Lake tables in place.
Best-fit scenarios
Choose SingleStore if:
Choose SingleStore when operational writes and analytical reads touch the same data and you would rather not run two systems with a pipeline between them. Rowstore alongside columnstore removes that split, and a commercial vendor with support is accountable for the result rather than a community.
Choose StarRocks if:
Choose StarRocks when the workload is analytical and you want no licence cost. Joins at query time mean normalised schemas work directly, MySQL protocol compatibility keeps existing clients working, and querying Iceberg, Hudi and Delta Lake in place means lake data does not need a second copy inside the database.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
What kind of workload are these built for?
Aggregation over large volumes of data, answered in milliseconds for many concurrent users — the shape of user-facing analytics, operational dashboards and embedded reporting. Neither is a general-purpose warehouse built for overnight batch transformation, and neither is a transactional database. If your queries run once a night over years of history, compare warehouses instead.
Do we need transactional writes in the analytical database?
Only if the same rows are written by an application and read by analysts. Then one engine removes a change-data-capture or batch pipeline, its latency, its failure modes and the window in which two systems disagree. If analytical data arrives from event streams or other teams and nothing writes to it transactionally, the capability removes nothing and you would be paying for it unused.
What does querying the lake in place give us?
One less copy. If data already sits in Iceberg or Delta Lake on object storage, reading it directly means the database serves recent hot data while history stays where it is. That removes both the duplication and the pipeline keeping the second copy current, which is a common source of quiet disagreement between two systems that should match.
How much does commercial support matter here?
It matters in proportion to how much of your revenue depends on the queries. A commercial vendor is accountable for a fix and can be escalated to; an open-source project has a community whose response time you do not control, though managed providers sell support for it. Decide whether you need somebody contractually on the hook before weighing the licence.
What does running one of these involve?
Several node roles with different scaling behaviour, a metadata layer, storage management and capacity planning as volume grows, plus ingestion supervision if data arrives from Kafka. Both are distributed databases and operating one is a specialism. Budget a meaningful fraction of an engineer's time indefinitely, or buy a managed service and pay for that time instead.
How do we control cost?
Keep recent data on fast storage and older data on object storage, and reduce what queries scan through materialised views or pre-aggregation. Ingestion method matters as much: real-time ingestion from Kafka costs more in resources than batch loading the same volume, so anything that does not need second-level freshness should arrive in batches. Unlike a warehouse there is no idle period to save money in, because the point of the system is that it is always ready.