StarRocks Project Description

What is the project about?

StarRocks is a high-performance, open-source, distributed SQL query engine designed for real-time and ad-hoc analytics. It's built to deliver sub-second query responses, even on large datasets, and can operate both within a data lakehouse and on traditional data warehouses. It's a Linux Foundation project.

What problem does it solve?

StarRocks addresses the need for fast, interactive analytics on large datasets. It eliminates the performance bottlenecks often associated with traditional data warehouses and other query engines, which can struggle with complex queries and high concurrency. It also simplifies data pipelines by reducing or eliminating the need for data denormalization (creating wide, flat tables for performance), which can be complex and brittle. It allows for direct querying of data lakes, reducing the need for complex ETL processes.

What are the features of the project?

Native Vectorized SQL Engine: Utilizes vectorized execution for significant performance gains by leveraging CPU parallel processing.
Standard SQL Support: Supports ANSI SQL, including full TPC-H and TPC-DS compliance, and is compatible with the MySQL protocol.
Smart Query Optimization (CBO): Employs a Cost-Based Optimizer (CBO) to generate efficient query execution plans.
Real-time Updates: Supports UPSERT and DELETE operations based on primary keys, enabling efficient updates and queries concurrently.
Intelligent Materialized Views: Automatically updates and utilizes materialized views to accelerate queries.
Data Lake Querying: Directly queries data stored in Apache Hive, Apache Iceberg, Delta Lake, and Apache Hudi without data import.
Resource Management: Provides resource isolation and limits resource consumption for queries, enabling multi-tenancy within a cluster.
High Availability and Scalability: Features a simplified architecture (FE and BE nodes) with horizontal scalability and automatic data recovery.
Shared-Data Architecture (v3.0+): Offers improved scalability and cost-effectiveness through a shared-data architecture.

What are the technologies used in the project?

Core Languages: Java and C++
Data Lake Integrations: Apache Hive, Apache Iceberg, Delta Lake, Apache Hudi
Protocols: MySQL Protocol
Deployment: Can be deployed on-premises or in the cloud. Docker support is provided for development and deployment.

What are the benefits of the project?

Extreme Performance: Provides significantly faster query performance (often 3x or more) compared to other popular solutions.
Simplified Analytics: Reduces the need for complex data transformations and denormalization.
Real-time Insights: Enables real-time data analysis and decision-making.
Cost-Effectiveness: Optimizes resource utilization and can reduce infrastructure costs. The shared-data architecture further enhances cost efficiency.
Scalability and Reliability: Easily scales to handle growing data volumes and user demands.
Open Source: Benefits from community contributions and transparency (Apache License 2.0).
Easy to maintain: Simple architecture makes StarRocks easy to deploy, maintain and scale out.

What are the use cases of the project?

Real-time Dashboards and Reporting: Powering interactive dashboards and reports that require low-latency query responses.
Ad-hoc Querying and Exploration: Enabling analysts to quickly explore data and answer ad-hoc questions.
User-Facing Analytics: Providing fast analytics to end-users in applications.
Data Lake Analytics: Querying data directly in data lakes without complex ETL processes.
Log Analytics: Analyzing large volumes of log data for monitoring and troubleshooting.
Security Analytics: Detecting and responding to security threats in real-time.
Business Intelligence (BI): Supporting various BI tools and applications.
Multi-dimensional analysis
High-concurrency scenarios