This article explains the core I/O components of StarRocks—Tablet, Fragment, Pipeline, Morsel, ScanOperator, ChunkSource, ScanTask, and ChunkBuffer—showing how they work together to achieve high‑performance, low‑latency query execution in a compute‑storage separated architecture.
This article demonstrates how to integrate Apache Doris, a high‑performance MPP analytical database, into a Spring Boot application as a drop‑in replacement for MySQL, detailing environment setup, Maven dependencies, configuration, entity mapping, repository, service and controller code, and performance testing that shows Doris’s superior real‑time query speed.
This guide introduces StarRocks, a high‑performance MPP database, explains its architecture and typical use cases, walks through a Docker‑based quickstart, shows how to create databases and tables, load NYC crash and weather datasets via Stream Load, and demonstrates analytical SQL queries that reveal traffic‑accident patterns under different weather conditions.
This article details Haijing Technology's challenges with real‑time telecom data analysis, explains why traditional Hadoop and ClickHouse solutions fell short, and shows how ByConity's unified engine, multi‑table joins, and elastic scaling enable efficient, low‑latency analytics across complex B‑O‑M domains.
This article examines how StarRocks, a next‑generation MPP database, leverages AWS Graviton3 instances to achieve over 30% query speed improvement and 15% cost reduction compared with x86 C6i instances, detailing benchmark methodology, hardware specs, SIMD optimizations, and real‑world OLAP results.
StarRocks, an open‑source MPP analytical database, consolidates BI, interactive, and real‑time analytics into a single engine by evolving from version 1.0 to 3.x, introducing compute‑storage separation, unified catalog, generated columns, operator spill, and advanced materialized views, while outlining its cloud‑native lakehouse roadmap.
StarRocks introduces a Query Cache that stores intermediate aggregation results in memory, enabling reuse across semantically equivalent, partition‑overlapping, or append‑only queries, which can boost query performance by 3‑17× in high‑concurrency scenarios while reducing CPU and disk load.
This article explains how StarRocks’ new compute‑storage separation architecture reduces storage expenses and improves analytical performance by leveraging hot‑cold data segregation, elastic scaling, caching strategies, multi‑version storage, and optimized compaction, illustrated with real‑world log and e‑commerce workload examples.
This article explains the advantages of StarRocks as a next‑generation MPP database, detailing its simplified architecture, vectorized engine, storage layout, partitioning and bucketing strategies, and showcases two production case studies with performance comparisons, configuration tips, and future roadmap considerations.
StarRocks is a high‑performance MPP database whose simplified FE/BE architecture, fully vectorized engine, and CBO optimizer enable fast multi‑table joins, while its partition‑bucket‑tablet storage model supports real‑time metric services and dashboard migrations, accompanied by practical troubleshooting guidance and upcoming enhancements.
