ClickHouse vs StarRocks

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

Compare ClickHouse to Rockset here

Compare StarRocks to Rockset here

ClickHouse vs StarRocks Architecture

Architecture

ClickHouse

StarRocks

Deployment model

• Self-managed on-premises or on cloud infrastructure • Several managed cloud services available

PaaS or self managed

Use of storage hierarchy

• Designed to use hard disk drives for storage • Can also use SSD if available

Data is stored on disk and in memory

Isolation of ingest and query

No, but you can limit resources for ingestion and querying separately

Separation of compute and storage

No - although ClickHouse Cloud decouples compute and cloud storage

No, but StarRocks supports nodes that don't store data locally

Isolation for multiple applications

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.

StarRocks is a high-performance OLAP database that can be deployed on the cloud or self managed. StarRocks does not separate compute and storage and offers limited options for resource isolation. It offers a robust set of features and high performance but requires considerable expertise to operate and scale.

ClickHouse vs StarRocks Ingestion

Ingestion

ClickHouse

StarRocks

Data sources

• Core integrations for ingestion from Kafka, S3, Google Cloud Storage • Other partner and community integrations available

Streaming • Kafka • Flink Data lakes • HDFS compatible • Cloud storage

Semi structured data

• JSON Object type for handling nested JSON • Automatically infers schema from a subset of rows

Supports columns with JSON data • Does not support mixed-type columns • Support for star and snowflake schemas

Transformations and rollups

Yes - several storage engines can pre-aggregate data

Yes, via materialized views

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.

StarRocks ingests data from a variety of sources, including both batch and streaming data. StarRocks can ingest nested JSON data, but enforces type at the column level.

ClickHouse vs StarRocks Performance

Performance

ClickHouse

StarRocks

Updates

• Writes to immutable files • Updates rewrite and merge data files asynchronously • Frequent updates are not recommended due to potential for large rewrites

While StarRocks is mutable, the update rate is slow, which is why it is most often used for append-only workloads

Indexing

• Primary indexes use sparse indexing on data ordered by primary key • Secondary data skipping indexes

Columnar index, limited support for inverted indexes

Query latency

Sub-100ms to seconds, optimized for large-scale aggregations

50-1000ms queries on 100s of TB

Storage format

• Column-oriented • Heavily compressed to minimize storage footprint

• StarRocks is a columnstore that organizes data into prefix indexes, per-column data blocks, and per-column indexes • All data is replicated 3 times to achieve both fault-tolerance and concurrency

Streaming ingest

Recommends inserting in batches of >1000 rows and <1 insert per second

Data latency is typically 1-2 seconds

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.

StarRocks was purpose-built for high-performance ingest, low-latency queries, and high concurrency. Optimized performance requires significant manual tuning.

ClickHouse vs StarRocks Queries

Queries

ClickHouse

StarRocks

Joins

Yes

Multi-table join support

Query language

SQL

Developer tooling

• API for querying SQL via POST command • Python, Java, Node.js and Go language clients

Minimal

Visualization tools

Integrations with Metabase, Superset, Grafana, Tableau, Deepnote and Rocket BI

Compatibility with MySQL protocols enables StarRocks to work with BI 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.

StarRocks uses a high-performance vectorized SQL engine, a custom-built cost-based optimizer, and has support for materialized views.

ClickHouse vs StarRocks Scalability

Scalability

ClickHouse

StarRocks

Vertical scaling

Scale up single-node ClickHouse for vertical scaling

• Both frontend and backend nodes can be manually resized

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

• Both frontend and backend nodes can be manually scaled horizontally

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.

StarRocks can scale up or out, but its tightly coupled compute and storage scale together for performance. This often results in resource contention and overprovisioning. Scaling StarRocks often requires deep expertise as there are many levels of the system that need to be managed.