ClickHouse vs Rockset
Compare and contrast ClickHouse and Rockset by architecture, ingestion, queries, performance, and scalability.
ClickHouse vs Rockset Architecture
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.
Rockset is built to be a cloud-only database and does not have a self-managed option. It disaggregates compute from both hot storage and cloud storage, allowing multiple isolated compute clusters to run on the same shared data.
ClickHouse vs Rockset Ingestion
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.
Rockset has built-in connectors that manage streaming ingestion from common data sources. It has native support for semi-structured data, so that nested JSON and XML can be ingested and queried as is.
ClickHouse vs Rockset 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.
Rockset is designed to make streaming data queryable as quickly as possible by avoiding the need to batch data. It also updates documents efficiently by only reindexing fields that are part of an update request. Rockset indexes all data by default, which results in storage amplification but also enables low-latency queries that require less compute.
ClickHouse vs Rockset Queries
ClickHouse uses SQL for querying, with support for SQL joins. ClickHouse integrates with some common tools for visual analytics, including Superset, Grafana and Tableau.
Rockset supports SQL as its native query language and can perform SQL joins. Users can create data APIs by storing SQL queries in Rockset that are executed from dedicated REST endpoints. Rockset integrates with some common visualization tools, but BI is not Rockset’s primary use case.
ClickHouse vs Rockset Scalability
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.
Rockset Virtual Instances are distributed compute clusters that can be scaled up for faster queries or scaled out for practically unlimited concurrency or if compute isolation is needed. Rockset has shared storage that scales automatically and independently, so no rebalancing is required.