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ClickHouse vs SingleStore

Compare and contrast ClickHouse and SingleStore by architecture, ingestion, queries, performance, and scalability.

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ClickHouse Architecture vs SingleStore

Architecture
ClickHouse
SingleStore
Separation of compute and storage
No - although ClickHouse Cloud decouples compute and cloud storage
Yes - Singlestore Cloud uses cloud object storage for separation of compute and storage
Isolation of ingest and query
No
No - databases share ingest and queries
Isolation for multiple applications
No
No
Use of storage hierarchy
• Designed to use hard disk drives for storage • Can also use SSD if available
• Memory - for data requiring the highest performance • High-performance block storage for persistent cache - the working dataset should fit within the persistent cache • Cloud object storage for long-term retention
Deployment model
• Self-managed on-premises or on cloud infrastructure • Several managed cloud services available
Self managed and SaaS deployment options

ClickHouse is open source and can be deployed anywhere. Several vendors such as ClickHouse, the company, and Tinybird also offer cloud versions. Compute and storage are tightly coupled, although ClickHouse Cloud was rearchitected to decouple compute and storage. ClickHouse Cloud pricing is based on compute and storage usage.

SingleStore is a proprietary distributed relational database that handles both transactional and analytical workloads. It relies on memory and a persistent cache to deliver low latency queries. For longer term data retention, SingleStore Cloud separates compute from cloud object storage. SingleStore Cloud pricing is based on compute and storage usage.


ClickHouse Ingestion vs SingleStore

Ingestion
ClickHouse
SingleStore
Data sources
• Core integrations for ingestion from Kafka, S3, Google Cloud Storage • Other partner and community integrations available
Integrations to: Amazon S3, Apache Beam, GCS, HDFS, Kafka, Spark, Qlik Replicate, HVR
Semi structured data
• JSON Object type for handling nested JSON • Automatically infers schema from a subset of rows
Ingests JSON as a JSON column type
Transformations and rollups
Yes - several storage engines can pre-aggregate data
SingleStore pipelines do common data shaping including normalizing and denormalizing data, adding computed columns, filtering data, mapping data, splitting records into multiple destination tables

ClickHouse has core integrations from common sources such as Kafka and S3. It recently introduced greater ability to handle semi-structured data using the JSON Object type and automatic schema inference.

SingleStore has integrations to common data lakes and streams. With SingleStore pipelines, users can perform common data transformations during the ingestion process. SingleStore provides limited support for semi-structured data with its JSON column type. Many users structure data prior to ingestion for optimal query performance.

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ClickHouse Queries vs SingleStore

Queries
ClickHouse
SingleStore
Query language
SQL
SQL
Joins
Yes
Yes
Developer tooling
• API for querying SQL via POST command • Python, Java, Node.js and Go language clients
• API for querying data via POST command • JDBC driver, Python client • Compatibility with MySQL and MariaDB to support additional drivers
Visualization tools
Integrations with Metabase, Superset, Grafana, Tableau, Deepnote and Rocket BI
Integrations with Cognos Analytics, Dremio, Looker, Microstrategy, Power BI, Sisense, Tableau and Tibco Spotfire

ClickHouse uses SQL for querying, with support for SQL joins. ClickHouse integrates with some common tools for visual analytics, including Superset, Grafana and Tableau.

SingleStore supports SQL as its native query language and can perform SQL joins. It is designed for querying structured data with static schemas. Users can create data APIs to execute SQL statements against the database over an HTTP connection. Common SingleStore use cases include business intelligence and analytics, and the database offers a number of integrations to visualization tools.


ClickHouse Performance vs SingleStore

Performance
ClickHouse
SingleStore
Streaming ingest
Recommends inserting in batches of >1000 rows and <1 insert per second
• Columnnar store/universal storage ingests on a batch basis • Data latency is typically seconds by relying on memory
Updates
• Writes to immutable files • Updates rewrite and merge data files asynchronously • Frequent updates are not recommended due to potential for large rewrites
SingleStore columnar store/universal storage is immutable. Updates are fast when the data still resides in memory
Storage format
• Column-oriented • Heavily compressed to minimize storage footprint
Two table formats-either use the rowstore or columnstore/universal storage
Indexing
• Primary indexes use sparse indexing on data ordered by primary key • Secondary data skipping indexes
Indexes can be manually configured: Skiplist index, hash index, full-text index, geospatial index
Query latency
Sub-100ms to seconds, optimized for large-scale aggregations
50-1000ms queries when the working set is contained in memory

ClickHouse leverages column orientation and heavy compression for better performance on analytics workloads. It also uses indexing to accelerate queries as well. While ClickHouse use cases often involve streaming data from Kafka, batching data is recommended for efficient ingestion.

SingleStore has two storage formats: a rowstore and a columnar store referred to as universal storage. The columnar store is used for analytical workloads, loading data in batch and relying on memory to achieve seconds of data latency. The columnar store can also execute queries in seconds when the working set is contained in memory. SingleStore provides the ability to configure and manage additional indexes on the data for faster performance.


ClickHouse Scalability vs SingleStore

Scalability
ClickHouse
SingleStore
Vertical scaling
Scale up single-node ClickHouse for vertical scaling
• Cloud offering: Resize compute workspaces in the UI or using the Management API • Self-managed offering: Change cluster configuration by updating command-line arguments or to the cluster directly.
Horizontal scaling
• Compute and storage scaled in lockstep • Data rebalanced to populate newly added nodes • Cloud offerings automate some of the scaling and rebalancing effort
Self-managed offering: Increase or decrease the number of nodes in the cluster. Rebalancing required

ClickHouse can be used in both single-node and distributed modes. Tight coupling of compute and storage and the need to rebalance data make scaling out more complex, but cloud versions of ClickHouse help automate this process.

SingleStore Cloud can be sized up or down using the UI or the Management API. There is no ability to scale out by increasing or decreasing the leaf and aggregator nodes in the cloud offering. In the self-managed offering, horizontal and vertical scaling can occur by updating command-line arguments or the cluster directly. Horizontal scaling does require rebalancing

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