SingleStore vs. ClickHouse: Why SingleStore Stands Out

In today’s data-driven world, the ability to quickly analyze large volumes of information is paramount.

Data teams, whether working on real-time streaming analytics, interactive dashboards or machine learning workloads, need databases that can balance speed, scalability and flexibility. Two popular choices in the modern data stack are SingleStore and ClickHouse. Both databases are designed to handle analytic, data warehouse workloads efficiently, but they differ in architecture, ecosystem integration, scalability and use-case fit. In this article, we’ll dive deep into the capabilities and strengths of SingleStore and ClickHouse to help you decide which one best aligns with your organizational needs.

Understanding SingleStore vs. Clickhouse

What is SingleStore?

SingleStore is a real-time, distributed SQL database. With familiar SQL tooling and MySQL wire protocol compatibility, SingleStore eliminates the need for specialized databases and simplifies database architectures.

SingleStore is also built to handle multiple data types — including JSON, time-series, geospatial and full-text search— delivering high-speed data ingestion on a unified transactional (OLTP) and analytical (OLAP) foundation.

SingleStore architecture

What is ClickHouse?

ClickHouse, developed by Yandex and maintained by ClickHouse, Inc., is a real-time data warehouse and open-source database that uses a highly performant columnar database built primarily for analytics. It is known for its exceptionally fast query performance on large datasets. ClickHouse leverages vertical partitioning, vectorized query execution and advanced compression techniques, making it a go-to solution for log analytics, observability and time-series workloads.

Key differences between SingleStore and ClickHouse include:

Aspect	SingleStore	ClickHouse
Primary focus	Converged (OLTP + OLAP) workloads	High-performance analytical (OLAP) workloads
Storage model	Universal columnstore for both OLTP and OLAP	Columnar storage optimized for analytical queries
Query execution engine	Distributed SQL engine with compute pushdown	MPP engine with vectorized execution and compression
Data ingestion	Real-time ingestion with inbuilt pipeline support for multiple sources like Kafka, S3, Iceberg format, JSON	High ingestion rates, typically with batch insert
Data updates	Supports standard SQL DML (INSERT, UPDATE, DELETE) for transactions	Limited support; best for append-only or batch updates
Workload flexibility	Handles mixed workloads (operational + analytical) in one system	Primarily optimized for analytical, read-heavy workloads
Scalability	Both horizontal and vertical online scaling	Horizontal scaling by adding more nodes; excels at massive scale analytics
Compression	High compression ratio (60-70%)	Support compression
Sharding	Automatic	Manual
Data transformation	Inbuilt support of TRANSFORM using pipelines	No built-in syntax or feature
Use case fit	Real-time analytics, mixed workload consolidation, simplifying data infrastructure	Analytical queries
Vector data types	Mature	New
JSON support	Fully integrated JSON data type and functions	“Object” data type and related functions to handle JSON data.
CDC out	Inbuilt CDC out feature	Not inbuilt
WASM	Allows you to create UDFs/TVFs in a language of your choice and run them in a sandboxed environment for enhanced security	No support

How to setup SingleStore and ClickHouse

SingleStore

On-premises. Please follow the instructions here to download and install SingleStore.
Cloud. Spin up your cluster on the cloud of your choice (AWS/GC/Azure) using the link here.

ClickHouse

On-premises. Follow the instructions to download and install ClickHouse.

SingleStore and ClickHouse Configuration

ClickHouse

Installation type: Cloud (Region: Ohio)

ClickHouse server version 24.8

Scale up to 48 vCPU, 192 GiB (From 6 vCPU, 24 GiB)

Engine: MergeTree

SingleStore

Installation type: Cloud (Region: Ohio)

SingleStore version: 8.7.1

Size: S-1, 8vCPU , 64 GiBPartition: 32

Table type: Columnstore

Performance

Here's a query performance comparison on 20 million rows

	Query	Clickhouse	SingleStore
1	select payment_type_,count(trip_id) from trips_raw group by payment_type_;	0.620s	0.242s
2	select pickup_datetime,sum(fare_amount) from trips_raw group by pickup_datetime;	0.736s	0.237s
3	select pickup_datetime,sum(trip_distance), sum(fare_amount),sum(tip_amount) from trips_raw group by pickup_datetime;	1.816s	0.245s
4	select pickup_date,count(trip_id) from trips_raw group by pickup_date order by pickup_date;	0.480s	0.237s