This article presents a technical case study of ZhongAn's Jizhi analytics platform, detailing how switching from ClickHouse to the MPP OLAP engine StarRocks resolved multi‑concurrency and join performance bottlenecks, improved real‑time query speed, and enabled near‑billion‑row data handling for insurance business operations.
This article explains how StarRocks manages petabyte‑level user behavior logs, ads and orders through a shared‑nothing architecture, tablet‑based data distribution, MPP compute, high‑availability metadata, real‑time mini‑batch ingestion, and online schema changes, enabling 24/7 analytical services for diverse internet companies.
This article explains how Sohu Smart Media built a high‑performance tracing system for microservices by integrating Zipkin for data collection with StarRocks for storage and analytics, covering architecture, data models, SQL queries, Flink processing, and real‑world results that boost observability and engineering efficiency.
This article describes how Sohu Intelligent Media built a microservice tracing system using Zipkin for data collection and StarRocks for storage and analysis, covering architecture, data model, ingestion pipeline, SQL analytics, performance monitoring, and future improvements.
Facing inflexible point‑lookup limits and slow query times on HBase, Zepp Health redesigned its massive event‑tracking data pipeline—migrating ingestion through Kafka, Flink, and Hudi to a StarRocks‑based OLAP layer—achieving sub‑100 ms average query latency, 20 % storage savings, and dramatically faster multi‑dimensional analytics.
This article explains why data lakes are essential for today’s analytics, outlines the three main user demands, defines data lakes, compares rule‑based and cost‑based optimizers, explores record‑oriented versus block‑oriented processing, and details StarRocks’ frontend‑backend architecture and benchmark results.
Community testing of StarRocks 2.0 revealed that, across multiple benchmarks—including low‑cardinality queries, SSB workloads, and high‑concurrency scenarios—StarRocks consistently outperformed ClickHouse and Druid, delivering performance gains ranging from 2‑3× to over 7×.
This article explains the fundamentals of data lake analytics, compares optimization strategies such as rule‑based vs cost‑based and record‑oriented vs block‑oriented processing, describes StarRocks' lightweight frontend/backend architecture, and presents benchmark results that demonstrate its performance advantages over competing engines.
This article presents a comprehensive overview of StarRocks, covering its key features, deployment challenges, backup and synchronization methods, cluster configuration and upgrade procedures, as well as monitoring and alerting solutions, followed by practical lessons learned from real‑world usage.
This article explains the evolution, challenges, and technical solutions for building an end‑to‑end real‑time data warehouse by combining Apache Flink's stream processing with StarRocks' ultra‑fast OLAP engine, covering architecture, data models, integration methods, best‑practice cases, and future roadmap.
This article presents JD Daojia's data platform built on StarRocks, detailing business background, challenges such as pagination inconsistency, case‑when performance, and array‑type issues, and provides concrete SQL solutions, best‑practice tips, and future optimization directions.
Autohome replaced Flink and Kylin with StarRocks to power sub‑second real‑time OLAP analytics, detailing data sources, pain points, benchmark comparisons against Apache Kylin, ClickHouse, Presto, Spark, and Doris, integration with Flink‑connector, broker‑load scripts, monitoring setup, and lessons learned from large‑scale deployments.
This article describes AutoHome's real‑time data analysis architecture, the challenges of existing OLAP solutions, the reasons for choosing StarRocks, detailed performance comparisons with Kylin, ClickHouse, Doris, Presto and Spark, and the practical integration of StarRocks with Flink, broker‑load scripts, and monitoring tools.
SF Express’s data engineering team details how they migrated from a mixed‑component OLAP stack to a unified StarRocks platform, describing the evaluation criteria, performance‑critical design choices, import and query optimizations, and future roadmap for a high‑availability, low‑cost big‑data analytics solution.
The first StarRocks Hacker Meetup recapped essential techniques for building high‑performance databases, covering CPU vectorization basics, key optimization strategies, and a preview of the upcoming session on real‑time updates and storage engine redesign, all presented by community experts.
StarRocks 1.19 introduces a Global Runtime Filter that speeds up joins up to six‑fold, adds BE‑level disk balancing, launches an experimental Primary Key table type with near‑duplicate‑key read performance, enables external‑table writes between clusters, and includes a host of CPU‑cache and query‑optimizations.
This article describes how Ctrip's hotel data platform migrated from ClickHouse to StarRocks, detailing the platform's current state, pain points, the evaluation and selection of StarRocks, its architecture, performance benchmarks, data ingestion models, high‑availability design, and future migration plans.
StarRocks 1.18 introduces Lateral Join + Unnest for elegant row‑to‑multiple‑row conversion, Fast Decimal with up to 38‑digit precision and 4× faster arithmetic, extensive Bitmap function enhancements, and a suite of import, storage, operator, and ecosystem optimizations that dramatically boost query performance and scalability.
Didi's data architecture team migrated high‑volume, real‑time funnel analysis from ClickHouse to StarRocks, built a multi‑layer pipeline with Kafka, Flink/Spark, Hive, and materialized views, and achieved sub‑3‑second query times on billions of rows, while outlining future enhancements.
