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ClickHouse vs StarRocks

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

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Compare StarRocks to Rockset here

ClickHouse vs StarRocks Architecture

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

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

• 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
• 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

Multi-table join support
Query language
Developer tooling
• API for querying SQL via POST command • Python, Java, Node.js and Go language clients
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

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.