📄️ Overview
These best practices are written by experienced database engineers. Designing for efficiency does more than improve query speed, it decreases costs by reducing storage, CPU, and object storage (e.g., S3) API costs.
📄️ Partitioning
Fast analytics in StarRocks begin with a table layout that matches your query patterns. This guide distills hands‑on experience into clear rules for partitioning, helping you:
📄️ Table clustering
A thoughtful sort‑key is the highest‑leverage physical‑design knob in StarRocks. This guide explains how the sort key works under the hood, the systemic benefits it unlocks, and a concrete playbook for picking an effective key for your own workload.
📄️ Bucketing
A concise field guide to choosing between Hash Bucketing and Random Bucketing in StarRocks, including their mechanics, trade‑offs, and recommended use cases.
📄️ Primary Key tables
The Primary Key table uses a new storage engine designed by StarRocks. Its main advantage lies in supporting real-time data updates while ensuring efficient performance for complex ad-hoc queries. In real-time business analytics, decision-making can benefit from Primary Key tables, which use the newest data to analyze results in real-time, which can mitigate data latency in data analysis.
📄️ Authentication and Authorization
This topic aims to provide a coherent guide for best practices on developing your own authentication and authorization workflow.
📄️ Audit Log-based Resource Group Configuration
In StarRocks, Resource Groups provide an effective mechanism for resource isolation by allocating CPU, memory, and concurrency limits based on classifiers such as user identity and query type. This feature is essential for achieving efficient resource utilization in multi-tenant environments.
🗃️ Query Tuning
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