StarRocks is an open‑source project under the Linux Foundation, focused on building a high‑performance, scalable analytical database that enables enterprises to create an efficient, unified lake‑house paradigm. It is widely used across many industries worldwide, helping numerous companies enhance their data analytics capabilities.
This article details Qunar's transition to StarRocks as a unified OLAP engine, covering the business background, engine evaluation, architecture redesign, observability, high‑availability strategies, query‑performance optimizations, real‑world application cases, community contributions, and future plans.
This article explores the evolution of business intelligence toward intelligent BI, detailing traditional BI limitations, agile BI improvements, and how StarRocks' MPP lakehouse engine combined with large language models enables natural‑language analytics, real‑time performance, AI‑driven insights, and scalable enterprise deployments.
This step‑by‑step tutorial shows how to deploy StarRocks and MinIO in a decoupled storage‑compute architecture using Docker Compose and Kubernetes, configure local caching, create storage volumes, load public datasets, and run SQL queries to explore the combined data.
Shopee combined the Paimon data lake with StarRocks and Flink to create a quasi‑real‑time warehouse, enabling fast task diagnostics and a high‑performance financial reconciliation system while dramatically reducing storage costs and latency through innovative ODS, snapshot, and branch table techniques.
Suixingfu rebuilt its payment data pipeline by replacing a fragmented Lambda stack with a unified Porter CDC + StarRocks + Elasticsearch architecture, achieving three‑fold query speed, ten‑fold analytics efficiency, 20% storage reduction, and sub‑second data‑capture latency across high‑concurrency, ad‑hoc, and batch workloads.
StarRocks 3.5 introduces a cluster‑level Snapshot backup for fast recovery, a bulk‑load optimization that reduces small files and compaction cost, smarter partition management with time‑based merging and TTL, multi‑statement transactions with full ACID guarantees, low‑cardinality dictionary support for lake tables, and several security and performance upgrades.
This guide outlines the StarRocks SRCA certification exam, highlights essential study resources, breaks down the core topics such as architecture, data import/export, SQL optimization and performance tuning, and offers practical tips, mock‑exam details, and personal experience to help candidates succeed.
StarRocks MCP Server provides a universal adapter that lets large language models like Claude, OpenAI, and Gemini execute SQL queries directly against StarRocks, simplifying data Q&A, intelligent analysis, and automated reporting by eliminating the need for bespoke plugins or complex prompt engineering.
Grab replaced its fragmented Grafana‑Superset stack with a StarRocks‑backed Iris platform, achieving over ten‑fold query speedups, 40% lower resource usage, and a unified real‑time and historical data store for Spark observability across its Southeast Asian super‑app ecosystem.
This article provides a comprehensive technical overview of StarRocks' query optimizer, covering its evolution, core architecture, multi‑stage optimization pipeline, key optimizations such as multi‑join colocate, low‑cardinality global dictionary, MV union rewrite, and advanced mechanisms like cost‑estimation fixes, query feedback, adaptive execution, runtime filters, join‑reorder strategies, and SQL plan management.
