StarRocks Architecture: StarRocks as a Data Warehouse & StarRocks as a Lakehouse Query Engine

Описание: 00:00 StarRocks can function both as a data warehouse and as a lakehouse query engine, offering a versatile solution for managing and querying data. Below is a summary of how StarRocks is structured and utilized in these two roles:

00:20 StarRocks as a Data Warehouse

🌟Simple Architecture: Compared to similar solutions, StarRocks has a streamlined architecture with no external dependencies, consisting of two main types of processes: FE (Frontend) and CN (Compute Node).

Frontend (FE): Acts as the catalog manager, handling metadata management and query plan generation.

Compute Node (CN): Serves as the workhorse, responsible for scanning data from external storages, caching data, and executing queries.

🌟Data Persistence: StarRocks maintains its data using its own file and table formats, designed for high-performance workloads. These formats support real-time mutable data, allowing for updates and sub-ten-second data freshness.

🌟Storage and Compute Separation: Utilizes a shared data design, where data is stored in cloud object storage (e.g., AWS S3) in StarRocks' file format. This setup facilitates memory and disk-based caching on CNs for query acceleration, resembling the performance of a shared-nothing architecture.

🌟Scalability and Efficiency: The shared data architecture enables independent scaling of compute and storage, optimizing resource usage and allowing for easy node eviction without data loss during low traffic periods.

🌟SQL Compliance and Trino Dialects Support: StarRocks is compatible with standard SQL and supports Trino dialects, ensuring compatibility with various BI tools that adhere to these standards.

03:12 StarRocks as a Lakehouse Query Engine

🌊 External Data Persistence: Unlike its role as a data warehouse, when functioning as a lakehouse query engine, StarRocks queries data persisted in external data lakes or lakehouse systems using open lake table formats (e.g., Delta Lake, Apache Iceberg, Apache Hudi) and standardized file formats (e.g., Parquet, ORC, CSV).

🌊 Data Warehouse-like Performance on Data Lakes: StarRocks is engineered to provide data warehouse-level performance for querying data lakes, allowing for the unification of demanding workloads on data lake platforms.

In both use cases, StarRocks offers a robust solution for managing and querying large datasets, whether stored internally in its optimized formats or externally in a data lake. Its architecture emphasizes performance, scalability, and compatibility, catering to a wide range of data management and analytics needs.

🎥 This video is part of our "What Is StarRocks: Features and Use Cases" webinar. To watch in full, visit:    • What Is StarRocks: Features and Use Cases  

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