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SingleStore vs ClickHouse

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

Compare SingleStore to Rockset here

Compare ClickHouse to Rockset here

SingleStore vs ClickHouse Architecture

Architecture
SingleStore
ClickHouse
Deployment model
Self managed and SaaS deployment options
• Self-managed on-premises or on cloud infrastructure • Several managed cloud services available
Use of storage hierarchy
• 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
• Designed to use hard disk drives for storage • Can also use SSD if available
Isolation of ingest and query
No - databases share ingest and queries
No
Separation of compute and storage
Yes - Singlestore Cloud uses cloud object storage for separation of compute and storage
No - although ClickHouse Cloud decouples compute and cloud storage
Isolation for multiple applications
No
No

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 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 vs ClickHouse Ingestion

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

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.

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 vs ClickHouse Performance

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

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 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 vs ClickHouse Queries

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

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

SingleStore vs ClickHouse Scalability

Scalability
SingleStore
ClickHouse
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.
Scale up single-node ClickHouse for vertical scaling
Horizontal scaling
Self-managed offering: Increase or decrease the number of nodes in the cluster. Rebalancing required
• Compute and storage scaled in lockstep • Data rebalanced to populate newly added nodes • Cloud offerings automate some of the scaling and rebalancing effort

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

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.