StarRocks vs ClickHouse

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

Compare StarRocks to Rockset here

Compare ClickHouse to Rockset here

StarRocks vs ClickHouse Architecture

Architecture

StarRocks

ClickHouse

Deployment model

PaaS or self managed

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

Use of storage hierarchy

Data is stored on disk and in memory

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

Isolation of ingest and query

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

Separation of compute and storage

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

No - although ClickHouse Cloud decouples compute and cloud storage

Isolation for multiple applications

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 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 vs ClickHouse Ingestion

Ingestion

StarRocks

ClickHouse

Data sources

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

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

Semi structured data

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

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

Transformations and rollups

Yes, via materialized views

Yes - several storage engines can pre-aggregate data

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 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 vs ClickHouse Performance

Performance

StarRocks

ClickHouse

Updates

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

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

Indexing

Columnar index, limited support for inverted indexes

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

Query latency

50-1000ms queries on 100s of TB

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

Storage format

• 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

• Column-oriented • Heavily compressed to minimize storage footprint

Streaming ingest

Data latency is typically 1-2 seconds

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

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

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 vs ClickHouse Queries

Queries

StarRocks

ClickHouse

Joins

Multi-table join support

Yes

Query language

SQL

Developer tooling

Minimal

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

Visualization tools

Compatibility with MySQL protocols enables StarRocks to work with BI tools

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

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

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 vs ClickHouse Scalability

Scalability

StarRocks

ClickHouse

Vertical scaling

• Both frontend and backend nodes can be manually resized

Scale up single-node ClickHouse for vertical scaling

Horizontal scaling

• Both frontend and backend nodes can be manually scaled horizontally

• Compute and storage scaled in lockstep • Data rebalanced to populate newly added nodes • Cloud offerings automate some of the scaling and rebalancing effort

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.

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.