Decision comparison
ClickHouse vs SingleStore
ClickHouse and SingleStore both answer analytical queries in milliseconds and are compared directly on db-engines and in 2026 OLAP guides. ClickHouse is open source and columnar, built around scan and aggregation throughput with explicit physical design. SingleStore is commercial and combines rowstore and columnstore in one engine, so transactional writes and analytics can share a database.
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 | ClickHouse | SingleStore |
|---|---|---|
| What it is | An open-source columnar database built for high-throughput analytical queries | A commercial distributed database combining row and column storage for analytics and transactions together |
| Storage model | Columnar, with table engines and ordering keys chosen explicitly | Rowstore and columnstore in one engine, so operational and analytical work share a system |
| Query shape | Scans and aggregations over wide, largely denormalised tables | Aggregations and joins, with the optimiser handling normalised schemas directly |
| Interface | Its own SQL dialect, with HTTP and native protocols | MySQL wire protocol, so existing MySQL clients and drivers connect |
| Licensing | Open source, with ClickHouse Cloud from the project's own vendor | Commercial, with a free tier and managed cloud |
| Transactions | Analytical by design; not built for transactional workloads | Supports transactional writes alongside analytics in the same database |
| Best fit | Analytical throughput over event data, self-hosted or managed, at no licence cost | Workloads that need fast analytics and operational writes without two systems |
ClickHouse
- What it is:
- An open-source columnar database built for high-throughput analytical queries
- Storage model:
- Columnar, with table engines and ordering keys chosen explicitly
- Query shape:
- Scans and aggregations over wide, largely denormalised tables
- Interface:
- Its own SQL dialect, with HTTP and native protocols
- Licensing:
- Open source, with ClickHouse Cloud from the project's own vendor
- Transactions:
- Analytical by design; not built for transactional workloads
- Best fit:
- Analytical throughput over event data, self-hosted or managed, at no licence cost
SingleStore
- What it is:
- A commercial distributed database combining row and column storage for analytics and transactions together
- Storage model:
- Rowstore and columnstore in one engine, so operational and analytical work share a system
- Query shape:
- Aggregations and joins, with the optimiser handling normalised schemas directly
- Interface:
- MySQL wire protocol, so existing MySQL clients and drivers connect
- Licensing:
- Commercial, with a free tier and managed cloud
- Transactions:
- Supports transactional writes alongside analytics in the same database
- Best fit:
- Workloads that need fast analytics and operational writes without two systems
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 | ClickHouse | SingleStore |
|---|---|---|
| Docker Hub pulls(Product adoption) | 301.2M | 960.1k |
| GitHub commits, 90d(Product adoption) | 38.7k | Not available |
| GitHub stars(Product adoption) | 50,000+ | Not available |
| Search interest(Market interest) | 8 | 0 |
| Hacker News mentions, 90d(Community interest) | 156 | 0 |
| npm weekly downloads(Developer adoption) | 2.5M | 235 |
| Product Hunt comments(Community interest) | 0 | Not available |
| Product Hunt rating(Community interest) | 5.0/5 | Not available |
| Product Hunt reviews(Community interest) | 28 | Not available |
| Product Hunt votes(Community interest) | 12 | Not available |
| PyPI weekly downloads(Developer adoption) | 6.5M | 62.2k |
| Stack Overflow questions(Community interest) | 2.2k | 403 |
| GitHub commits, 90d(Developer adoption) | Not available | 7 |
| GitHub stars(Developer adoption) | Not available | 37 |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
ClickHouse
September 21, 2026Package vulnerabilities
npm · @clickhouse/client@1.23.1 · PyPI · clickhouse-connect@1.8.0
0 vulnerabilities
across 2 packages
Repository security score
github.com/ClickHouse/ClickHouse
4.4/10
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
Interface Preview
SingleStore
Feature Comparison
| Feature | ClickHouse | SingleStore |
|---|---|---|
| Query | ||
| Sub-second aggregation over large datasets | Full support | Full support |
| Fast multi-table joins | Partial 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 | Not verified | Full support |
| Transactional writes | Not verified | Full support |
| Record-level updates | Partial support | Full support |
| Access | ||
| MySQL protocol compatibility | Not verified | Full support |
| Real-time ingestion from Kafka | Full support | Full support |
| Standard BI connectivity | Full support | Full support |
| Query data in object storage | Partial support | Full support |
| Platform | ||
| Open source | Full support | Not verified |
| Self-hosted deployment | Full support | Full support |
| Managed cloud service | Full support | Full support |
| Tiered storage to object storage | Full support | Full 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
Real-time ingestion from Kafka
Standard BI connectivity
Query data in object storage
Platform
Open source
Self-hosted deployment
Managed cloud service
Tiered storage to object storage
Which to choose
ClickHouse and SingleStore both answer analytical queries in milliseconds and are compared directly on db-engines and in 2026 OLAP guides. ClickHouse is open source and columnar, built around scan and aggregation throughput with explicit physical design. SingleStore is commercial and combines rowstore and columnstore in one engine, so transactional writes and analytics can share a database.
Best-fit scenarios
Choose ClickHouse if:
Choose ClickHouse when the workload is analytical and you want no licence cost. Explicit table engines, ordering keys and materialised views give direct influence over performance, the community is large enough that most operational problems are documented publicly, and ClickHouse Cloud is available when you would rather not run the cluster yourself.
Choose SingleStore if:
Choose SingleStore when the same data has to serve operational writes and analytical reads without two systems and a pipeline between them. Rowstore and columnstore in one engine remove that split, MySQL protocol compatibility means existing clients connect, and the optimiser handles normalised schemas so tables do not have to be flattened first.
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.
Is combining transactions and analytics worth a licence?
It is when the alternative is two databases and a pipeline keeping them in sync, which is a system you build, monitor and debug forever. It is not when your analytical data arrives from elsewhere anyway and nothing writes to it transactionally. Count the pipelines you would remove; if the answer is none, the combined engine is capability you would be paying for and not using.
How much does MySQL compatibility help?
It removes an integration step. Existing MySQL clients, drivers and many BI tools connect without a new connector, and engineers who know MySQL syntax are productive immediately. ClickHouse uses its own dialect and protocols, which are well documented and widely supported, but are another thing to learn and another integration to maintain.
What does explicit physical design buy on ClickHouse?
Control. Choosing the table engine, the ordering key and which materialised views exist decides how a query performs, and a team that knows its access patterns can tune precisely. The cost is that somebody has to know them, and to revisit the choices as the product changes. A cost-based optimiser makes more of those decisions for you and gives you less to get wrong or right.
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.