StarRocks: Bridging Lakehouse and OLAP for High-Performance Analytics
May 5, 2025
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Sida Shen, a product manager at CelerData and a contributor to StarRocks, dives into the innovative world of high-performance analytical databases. He shares the origins of StarRocks, illustrating its evolution from Apache Doris into a robust Lakehouse query engine. Topics include handling high concurrency and low latency queries, bridging traditional OLAP with lakehouse architecture, and the importance of integration with formats like Apache Iceberg. Sida also emphasizes the challenges of denormalization and real-time data processing in modern analytics.
StarRocks is designed as a high-performance analytical database, utilizing both shared-nothing and shared-data architectures for optimized query performance.
The unique architecture of StarRocks separates front-end query management from back-end execution, enhancing scalability and maintaining low latency under heavy loads.
Integration with open data formats like Apache Iceberg allows StarRocks to streamline workflows, facilitating near-instantaneous access to aggregated data without extensive pre-processing.
Deep dives
The Challenge of Data Migrations
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Overview of StarRocks
StarRocks is a high-performance analytical database that was developed to address the current challenges in data analytics. Initially forked from the Doris project, StarRocks aims to optimize query performance through various innovative features like a cost-based optimizer and vectorized operators. It supports both shared-nothing and shared-data architectures, allowing it to efficiently handle diverse workloads while providing fast, real-time analytics. This dual capability enables organizations to conduct complex queries without the need for extensive pre-computation, promoting simplified data management.
Performance and Scalability
StarRocks operates on a unique architecture that separates its front-end and back-end processes to enhance scalability and performance. The front-end handles query management while the back-end focuses on executing those queries, both written in languages optimized for their specific tasks. This design allows StarRocks to efficiently process large volumes of data and high concurrency, sustaining low latency even under heavy loads. The architectural choices facilitate parallel query processing, enabling quick data retrieval for customer-facing applications.
Integration with Lakehouse Architecture
StarRocks successfully integrates with modern lakehouse architectures, marrying the benefits of high-performance OLAP systems with open data formats like Apache Iceberg. This integration allows users to store data in a unified manner without the need to duplicate or pre-process it significantly, streamlining their data workflows. Users can also leverage materialized views to optimize query performance, providing near-instantaneous access to aggregated data. By enabling a single interface for querying across different storage systems, StarRocks simplifies complex data operations and enhances overall efficiency.
Emerging Use Cases and Future Directions
The adoption of StarRocks by various industries is revealing novel applications and use cases for real-time analytics. For instance, organizations like Tencent Games are utilizing StarRocks to achieve sub-second data freshness while simultaneously managing vast datasets in open formats. As more companies move toward using StarRocks for customer-facing analytics, the focus will remain on enhancing query predictability and reliability. Future development plans aim to further increase support for open data formats, enabling deeper integrations within the evolving landscape of data management technologies.
Summary In this episode of the Data Engineering Podcast Sida Shen, product manager at CelerData, talks about StarRocks, a high-performance analytical database. Sida discusses the inception of StarRocks, which was forked from Apache Doris in 2020 and evolved into a high-performance Lakehouse query engine. He explains the architectural design of StarRocks, highlighting its capabilities in handling high concurrency and low latency queries, and its integration with open table formats like Apache Iceberg, Delta Lake, and Apache Hudi. Sida also discusses how StarRocks differentiates itself from other query engines by supporting on-the-fly joins and eliminating the need for denormalization pipelines, and shares insights into its use cases, such as customer-facing analytics and real-time data processing, as well as future directions for the platform.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'm interviewing Sida Shen about StarRocks, a high performance analytical database supporting shared nothing and shared data patterns
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what StarRocks is and the story behind it?
There are numerous analytical databases on the market. What are the attributes of StarRocks that differentiate it from other options?
Can you describe the architecture of StarRocks?
What are the "-ilities" that are foundational to the design of the system?
How have the design and focus of the project evolved since it was first created?
What are the tradeoffs involved in separating the communication layer from the data layers?
The tiered architecture enables the shared nothing and shared data behaviors, which allows for the implementation of lakehouse patterns. What are some of the patterns that are possible due to the single interface/dual pattern nature of StarRocks?
The shared data implementation has cacheing built in to accelerate interaction with datasets. What are some of the limitations/edge cases that operators and consumers should be aware of?
StarRocks supports management of lakehouse tables (Iceberg, Delta, Hudi, etc.), which overlaps with use cases for Trino/Presto/Dremio/etc. What are the cases where StarRocks acts as a replacement for those systems vs. a supplement to them?
The other major category of engines that StarRocks overlaps with is OLAP databases (e.g. Clickhouse, Firebolt, etc.). Why might someone use StarRocks in addition to or in place of those techologies?
We would be remiss if we ignored the dominating trend of AI and the systems that support it. What is the role of StarRocks in the context of an AI application?
What are the most interesting, innovative, or unexpected ways that you have seen StarRocks used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on StarRocks?
When is StarRocks the wrong choice?
What do you have planned for the future of StarRocks?
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
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