StarRocks introduces user‑space scheduling with resource groups and classifiers to provide hard memory isolation, soft CPU/IO isolation, short‑query groups, concurrency limits, and large‑query circuit breaking, balancing isolation and utilization while supporting multi‑tenant workloads and future serverless scenarios.
This article presents Youzu Network’s adoption of StarRocks for multi-dimensional analytics, detailing the historical OLAP challenges, StarRocks’ features and advantages, its application scenarios, data modeling choices, ingestion methods, performance benchmarks, and future roadmap for unified analytics.
This article shares practical experience comparing ClickHouse and StarRocks as real‑time data warehouses, outlines the project requirements, evaluates each system's suitability for log‑type and business‑type data, and describes CDC‑based synchronization methods from MySQL to both platforms.
This article introduces the UData platform, explains its data‑integration architecture, details the StarRocks‑based query engine workflow from SQL parsing to distributed execution, and describes recent optimizations such as computation push‑down, support for JSF/HTTP/ClickHouse external tables, and a proxy‑based federated query framework.
The article explains why enterprises need real-time data warehouses, compares Lambda and Kappa architectures, describes Ctrip Hotel's Lambda‑plus‑OLAP variant built with Flink and StarRocks, and details practical solutions for ordering, wide‑table generation, and data validation that enable billion‑row, low‑latency analytics.
This article presents a comprehensive overview of StarRocks within the modern data stack, covering the evolution of MPP architectures, typical industry use cases, core features, performance benchmark comparisons, real‑time data‑warehouse construction methods, CDP and lakehouse analytics, as well as short‑term roadmap plans and a brief Q&A.
This article explains how Alibaba Cloud EMR StarRocks extends data lake analytics to support Hive, Iceberg, and Hudi, detailing its architecture, Iceberg integration, performance gains over Trino, IO merging, lazy materialization, intelligent caching, and elastic compute capabilities for faster, unified, and cost‑effective queries.
This article explains how Alibaba Cloud EMR‑StarRocks integrates with Flink CDC, outlines common real‑time ingestion pain points, and introduces the CTAS/CDAS and Connector‑V2 features that streamline table creation, schema evolution, and resource‑efficient streaming for large‑scale analytics.
The article describes how TCL leveraged StarRocks to create a unified data analytics platform, detailing the company’s background, OLAP evolution, typical StarRocks use cases such as real‑time dashboards, HR analytics, and email alerts, and outlines future plans for further integration and performance improvements.
This article reviews the practical applications and experimental explorations of StarRocks at 360, describing the cloud‑native lake‑warehouse product Yunzhou, its three‑tier architecture, performance comparisons with Trino using TPCH 100 GB, challenges of Kubernetes integration, and future directions for storage‑compute separation.
Cross‑Express consolidated multiple big‑data engines into a unified, high‑performance analytics platform using StarRocks, achieving millisecond‑level query latency, real‑time data warehousing, significant cost savings, and improved multi‑scenario business applications; the initiative also simplified BI development, reduced hardware requirements, and set a roadmap for future engine enhancements.
This article analyzes the requirements, design choices, and implementation details of materialized views in StarRocks, covering demand analysis, synchronous and asynchronous refresh solutions, partition binding, task scheduling, partition‑refresh maintenance, Insert‑Overwrite mechanics, view invalidation handling, and the upcoming features planned for version 2.5.
This article details why 360 selected StarRocks as its OLAP engine, compares its performance and resource usage against MySQL, Hive, Spark, Druid, ClickHouse and Doris, and describes the concrete deployment scenarios and data products built on StarRocks within the company.
This article outlines the evolution of StarRocks’ storage engine—from its real‑time update capabilities and primary‑key model challenges to recent optimizations like persistent indexes, partial column updates, conditional updates, high‑frequency import improvements, DML support, and future plans for separating primary and sort keys, introducing row‑store, and enhancing materialized view support.
This article explains user path analysis as a method to visualize and optimize user flow, describes its productization in the Hunyi analytics platform, details the underlying computation logic, presents a complex StarRocks SQL solution, discusses performance challenges, and suggests future improvements and recruitment opportunities.
This article explains how to design and implement a cloud‑native Lakehouse using StarRocks and Tencent Cloud EMR, covering core technical requirements, a five‑layer architecture, data ingestion with Iceberg/Hudi, performance tricks like Z‑order clustering, cost‑control through elastic scaling, and the key product features of EMR StarRocks.
This article describes how Meituan Food Service SaaS built a high‑quality, large‑scale data middle platform using StarRocks, covering business overview, technical selection, multi‑layer architecture, virtual views, intelligent tiered querying, multi‑active hot standby, and the performance gains achieved.
This article provides a comprehensive, step‑by‑step guide to StarRocks join optimization, covering join types, logical rewrite rules, predicate push‑down, join reorder algorithms, cost modeling, distributed join strategies, and runtime filters, while offering practical tips for achieving high‑performance query execution.
This article explains the core concepts, architecture, and source‑code details of StarRocks’ Pipeline execution framework, covering BE initialization, query lifecycle management, operator splitting, PipelineBuilder processing, and the scheduling logic of PipelineDriver, with concrete code examples and diagrams to illustrate each step.
This article details Baicaowei’s journey from a Hadoop‑based data platform to a modern StarRocks‑driven architecture, illustrating how digitalization, evolving business needs, and streamlined data pipelines empower the fast‑moving consumer goods sector through efficient data collection, modeling, and analytics.
This article explains the core concepts, architecture, and code logic of StarRocks' Pipeline execution framework, covering ExecPlan, PlanFragment, Fragment Instance, ExecNode, SourceOperator, SinkOperator, PipelineDriver scheduling, asynchronous handling of blocking operations, and the roles of FE and BE in MPP scheduling.
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.
This article details why 360 chose StarRocks as its OLAP engine, presents performance and operational comparisons with MySQL, Hive, Spark, Druid, Doris and ClickHouse, describes three major production use cases, and outlines ongoing explorations such as cloud‑native integration and Kubernetes support.
The article details how 37 Mobile Games built and continuously evolved a multi-dimensional analytics platform—covering business background, data challenges, the migration from MySQL through Druid, Impala, ClickHouse to StarRocks, self‑service data tools, monitoring, and future roadmap—highlighting technical decisions and lessons learned.
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.
This article details ZhongAn Insurance's digital transformation through its 4633 data‑centric framework, the architecture of its JiZhi data platform, the challenges of its original ClickHouse‑based real‑time warehouse, and how migrating to StarRocks improved performance, scalability, and operational efficiency across advertising and insurance use cases.
This article explains how CPU‑centric vectorization, especially SIMD, reduces instruction count and CPI, addresses the four major CPU bottlenecks, and how StarRocks systematically applies automatic and manual SIMD techniques, verification methods, and a suite of engineering optimizations to achieve multi‑fold query speedups.
This article explains how StarRocks uses a global dictionary to transform low‑cardinality string columns into integer codes, dramatically improving query performance across scan, filter, aggregation, join, shuffle, and sort phases, and details the construction, maintenance, and practical impact of this optimization.
This article provides a comprehensive technical overview of StarRocks' query optimizer, covering its cascades/ORCA-inspired architecture, logical and physical plan transformations, cost modeling, statistics derivation, memo structure, task scheduling, and practical examples of join optimization in a distributed OLAP engine.
JD Logistics leverages StarRocks to create the Udata unified query engine, addressing data silos, low performance, and high maintenance costs by integrating data services and analytics, enabling low‑code data service generation, high‑speed federated queries, real‑time updates, and future data‑lake and resource isolation capabilities.
This talk by Zhongan Insurance’s Data Senior Director Shi Xingtian outlines the company’s digital transformation, detailing the 4633 framework, the real-time data warehouse architecture, the migration from ClickHouse to StarRocks, and how these technologies support fine‑grained, intelligent financial operations and advertising analytics.
37 Mobile Games, a leading mobile game publisher, migrated its user‑profile analytics from a Hadoop‑Hudi‑Kafka‑Hive‑Flink stack to StarRocks, achieving sub‑second query latency on billion‑row tables, simplifying operations, reducing storage costs, and enabling real‑time data sync, as detailed in this technical case study.
This article presents a comprehensive case study of building a real‑time advertising data warehouse at Auto Home, detailing the evaluation of streaming engines and storage solutions, the layered architecture design, implementation steps with Flink and StarRocks, monitoring practices, encountered issues, and future roadmap, demonstrating how second‑level data freshness and high accuracy were achieved.
Songguo Mobility’s data‑center team migrated from a fragmented Impala‑Kudu‑ClickHouse stack to a unified StarRocks‑based real‑time OLAP architecture, iterating through three versions to solve scalability, latency, and maintenance challenges while supporting minute‑level dashboards for orders and vehicle analytics.
The Qunhe Technology data team replaced their legacy Hadoop and Presto clusters with a StarRocks MPP database, achieving up to three times faster queries, supporting billion‑row tables and sub‑second latency for both real‑time and analytical workloads on a daily 1TB data influx.
This article describes how the gaming company Boke City rebuilt its comprehensive data service platform by replacing a CDH‑based Impala solution with StarRocks, detailing the architectural changes, performance benchmark results, and the resulting improvements in query speed, real‑time data updates, and operational simplicity.
This article explains how to construct a real‑time data warehouse by combining Flink CDC for end‑to‑end change data capture with StarRocks' high‑performance OLAP engine, detailing the architectural challenges, optimization techniques, and a practical e‑commerce case study.
This article explores how combining Flink CDC with StarRocks can streamline real‑time data pipelines, reduce component complexity, support both full and incremental synchronization, and enable efficient OLAP queries and updates for fast, scalable analytics across diverse business scenarios.
This article explains the design and implementation of StarRocks 2.x Primary Key tables, covering real‑time update mechanisms, write and commit workflows, in‑memory primary indexing, compaction, read‑path optimizations, performance benchmarks, and upcoming features such as partial and conditional updates.
Facing massive data storage and query challenges, 360 upgraded its analytics architecture by adopting StarRocks, achieving multi‑dimensional, high‑concurrency analysis, simplified data pipelines, and significant performance and cost improvements across its radar and user‑portrait platforms.
This article explains how StarRocks converts a SQL statement into an optimal distributed physical execution plan, schedules the plan across compute nodes, and runs it using MPP, pipeline parallelism, and vectorized execution to achieve near‑linear performance scaling.
This article explains StarRocks' streamlined architecture, cost‑based optimizer, massively parallel processing and vectorized engine, and how they enable high‑performance queries over data stored in Hive, Iceberg, Hudi and other lake formats, backed by benchmark results and future roadmap details.
This article describes how ZhongAn Insurance’s JiZhi data‑analysis platform migrated from ClickHouse to the MPP OLAP engine StarRocks, detailing the business requirements, architectural challenges, benchmark results across single‑table and multi‑table queries, and the resulting improvements in latency, concurrency, and operational simplicity for real‑time analytics.
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.
