This article explains how Ping An Bank transformed its retail finance model from product‑centric to customer‑centric using a five‑in‑one data‑driven approach, the KYC/KYP/KYATO methodology, and the StarRocks analytics platform to build the Smart Bank 3.0 architecture, CDP, and real‑time metric layers.
By replacing part of its Spark‑based offline pipeline with StarRocks, Xiaohongshu’s data‑warehouse team cut job execution from hours to minutes, reduced resource usage over 90 %, lowered back‑fill cost by 99 %, and accelerated daily data production by 1.5 hours.
This article presents Tencent Game's comprehensive lakehouse integration practice, detailing the project background, storage‑compute separation, data layering, unified DDL/DML operations, performance optimizations, and future plans, illustrating how StarRocks, Iceberg, and Spark are combined to achieve scalable, cost‑effective analytics for massive game data.
This guide walks you through using StarRocks—covering effortless expression‑based partitioning, streamlined data loading with INSERT FROM FILES and PIPE, powerful in‑flight data transformation using SELECT/JOIN/UNNEST, and flexible table structure tweaks via ALTER TABLE to boost query performance.
In this interview, graduate student Sun Yinzhen shares how he selected, designed, and implemented an ORC Chunk Writer for the StarRocks database during the Open Source Summer program, detailing the technical challenges, learning outcomes, and his perspective on open‑source contributions for computer science students.
Didi leverages StarRocks materialized views with a global dictionary and bitmap aggregation to perform real‑time, high‑cardinality precise deduplication, automatically rewriting queries and refreshing views, cutting query latency by ~80%, reducing resource use ~95%, and boosting concurrent QPS up to 100‑fold, while planning further automation and bitmap optimizations.
StarRocks’ materialized view feature lets Didi replace costly fuzzy deduplication with precise, high‑concurrency deduplication for real‑time dashboards, using global dictionary mapping, layered ODS/DWD/ADS views, synchronous and asynchronous refreshes, and transparent query rewrite to cut query latency by 80% and boost QPS dramatically.
This article introduces a new lakehouse analytics paradigm by combining StarRocks and Paimon, covering the evolution of data lake technologies, key integration scenarios, core technical mechanisms such as JNI connectors, materialized views, and future roadmap for enhanced lakehouse capabilities.
This article provides a comprehensive overview of open‑source OLAP technologies, examines various business scenarios and data‑lake architectures, and details StarRocks' core features, performance optimizations, and future development plans within the EMR ecosystem.
This article explains the storage‑compute separation architecture introduced in StarRocks 3.0, presents industry case studies, details the design of StarOS and compute nodes, discusses technical challenges and key techniques, and evaluates cost, reliability, elasticity, and performance through benchmarks and user feedback.
This article details Didi's evolution from a multi‑engine OLAP stack to a unified StarRocks solution, explains the design of global dictionaries and materialized views for real‑time dashboard acceleration, and shares performance results, challenges, and future optimization directions.
This article explains how Apache InLong provides automatic, secure, high‑performance real‑time data transfer to StarRocks, detailing the transactional Stream Load API, the two‑phase commit process, Flink‑based ingestion architecture, exactly‑once guarantees, and performance test results across different parallelism levels.
This article details Didi's evolution of OLAP systems, the adoption of StarRocks for high‑performance MPP analytics, and how materialized views, global dictionary mapping, and transparent acceleration were engineered to boost real‑time dashboard queries while outlining performance gains, challenges, and future optimization plans.
This article presents Didi's practical experience with StarRocks materialized views, covering the evolution of its OLAP architecture, the challenges of previous engines, the adoption of StarRocks, the design of materialized view acceleration for real‑time dashboards, and future optimization directions.
This article introduces the self‑service data extraction platform developed by the Government Procurement Cloud, detailing its architecture, core modules such as self‑service extraction, data push, resource management, operation audit, permission controls, performance optimizations, and future development plans.
In 2022 Tencent's A/B Test team created the overseas SaaS product ABetterChoice, abstracting internal experiment capabilities, adapting to multi‑cloud compliance, and unifying computation with StarRocks, enabling game titles like Honor of Kings, PUBG Mobile, and Ubisoft to run scalable, compliant A/B experiments worldwide.
Xiaohongshu’s OLAP team migrated from Presto to StarRocks, doubling cluster count to 30, boosting query speed by 6‑7 times, cutting latency to 200 ms, and achieving up to 35% cost reduction through gray‑scale migration and AWS Spot‑based elastic scaling.
This article details practical methods for testing BI reports built on Hive and StarRocks, covering the report creation workflow, testing characteristics, SQL writing techniques, impact analysis, data warehouse simplification, and the application of data quality tools to ensure accurate and efficient reporting.
This article presents a comprehensive case study of building a self‑service OLAP analytics platform at ZhaiZhai using Quick BI and StarRocks, covering background motivations, technical architecture, implementation details, performance‑optimizing case studies, and the resulting business impact.
The 7th Tongcheng Travel Big Data Technology Salon in Beijing featured a series of expert talks covering StarRocks architecture evolution, lake‑house solutions with Paimon, Iceberg real‑time upsert, Data+AI for travel recommendation, vector retrieval in AI, JD Logistics real‑time computing governance, and multi‑task hotel ranking modeling, providing deep technical insights and future roadmaps.
StarRocks 3.2, released on December 21, 2023, introduces major usability upgrades—including optimized random bucketing, fast schema evolution, PIPE import, HTTP SQL API, runtime profiling, enhanced storage‑compute separation, data lake analysis, and advanced materialized view capabilities—while refining existing features such as indexing, catalog support, and export syntax.
WeChat transformed its data platform from Hadoop and ClickHouse to a StarRocks‑based lakehouse, tackling massive data volume, ultra‑low latency, and storage fragmentation by deploying lake‑on‑warehouse and warehouse‑lake fusion architectures, real‑time incremental materialized views, and unified SQL access, resulting in dramatic cost cuts and performance gains.
The article explains why analytical databases struggle with real‑time data changes due to columnar storage, complex indexes and distributed processing, and then details StarRocks' primary‑key model, adaptive update mode, bitmap indexes, row/column partial updates, and practical SQL upsert techniques to achieve low‑latency updates without sacrificing query performance.
This article presents Bilibili's big‑data metadata warehouse, covering its background, technology selection between data‑lake and data‑warehouse solutions, the architecture built on Prometheus, StarRocks, Flink and Routine Load, performance comparisons, diagnostic system design, and future development plans.
This article summarizes Li Yu's presentation on Alibaba Cloud EMR's deep collaboration with the StarRocks community, detailing major contributions across versions, the serverless StarRocks product’s core capabilities, and future plans to enhance OLAP‑lakehouse integration, performance, and cloud‑native elasticity.
This article describes how a payment reconciliation system progressed from a simple MySQL‑based solution through a Hive‑based big‑data approach to a high‑performance StarRocks architecture, detailing the integration of Flink, DataX, and SQL adaptations that dramatically improved query speed, cost, and operational efficiency.
This guide explains how to deploy the high‑performance MPP database StarRocks 3 on ARM servers by using Docker images, configuring FE and BE nodes, adjusting Java versions, integrating with Hadoop/Hive, and setting up monitoring and log‑cleanup scripts.
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.
This article details a Chinese tech company's migration of its internal big‑data analytics platform to StarRocks’ compute‑storage separation architecture, describing the original multi‑component setup, the pain points encountered, the evaluation methodology, performance and cost benchmarks, operational optimizations, migration steps, and future roadmap.
StarRocks introduces a spill-to-disk mechanism that writes intermediate results of heavy operators to disk, freeing memory and enabling stable execution of ETL and ad‑hoc queries, while combined with materialized views it dramatically improves query success rates and delivers up to 4.35× faster performance than Spark.
Ctrip migrated its Artnova reporting platform from Hive‑based queries to StarRocks, first loading data into OLAP tables and then using StarRocks as a lakehouse with Hive catalog, Data Cache and materialized views, achieving average query latency reductions from 20 seconds to 1.5 seconds, over 7× speed‑up versus Trino and up to 40× acceleration for complex workloads.
This article explains how to design efficient StarRocks tables with proper field types, partitioning and bucketing, compares update and primary‑key models, presents real‑world cases of memory and tablet issues, provides a complete table‑creation example, and outlines comprehensive monitoring metrics to keep the analytical data warehouse performant and stable.
The article provides a deep technical overview of StarRocks 3.0’s data‑lake analysis capabilities, its unified Lakehouse architecture, Catalog integration, Trino compatibility, extensive I/O optimizations, materialized view features, resource isolation techniques, real‑world use cases, and future development directions.
This article explains how StarRocks 3.0 extends real‑time data‑warehouse capabilities to support data‑lake analysis, external catalog integration, Trino compatibility, extensive I/O optimizations, and powerful materialized‑view features that together enable a unified, cloud‑native Lakehouse solution with high performance and flexible resource isolation.
This article explains why lake‑warehouse fusion is needed, describes the challenges of integrating real‑time data warehouses with data lakes, introduces a new StarRocks‑based architecture that supports real‑time ingestion, cooling, offline loading, and adaptive hot‑cold query rewriting, and outlines future plans and Q&A.
This article reviews traditional Lambda and Kappa data‑warehouse architectures, then details four Paimon‑StarRocks lakehouse solutions—including a data‑lake center, accelerated query with materialized views, hot‑cold data separation, and the JNI connector—while also outlining StarRocks’ future roadmap for lakehouse analytics.
This article details a Chinese bank’s journey to real‑time OLAP, covering the challenges of full‑link real‑time, the evolution from Kafka‑based ETL to Flink‑driven ELT, architecture components, practical deployment statistics, and future directions with Flink Table Store.
Facing massive daily query loads, Wanwu Newborn’s Watcher reporting platform migrated from MySQL, Greenplum, and Trino to StarRocks, cutting compute nodes by half while achieving 3‑10× faster query performance, higher success rates, and lower cost, as demonstrated by TPC‑DS and real‑world business query benchmarks.
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.
StarRocks 3.0 introduces a lake‑warehouse unified architecture where materialized views reduce data processing complexity, boost query performance, and improve timeliness, offering declarative modeling, transparent acceleration, and incremental computation for enterprise‑scale analytics.
StarRocks 3.1 introduces a cloud‑native, lakehouse‑oriented architecture with enhanced storage‑compute separation, up to 3‑6× faster data‑lake queries than Trino/Presto, expanded Iceberg and Paimon support, richer materialized view capabilities, new random bucketing, expression partitioning, generated columns, and spill‑to‑disk stability, all backed by extensive performance optimizations and open‑source contributions.
This guide explains how to set up a StarRocks cluster using Docker for rapid testing and the StarRocks Kubernetes Operator for production‑grade deployments, covering architecture basics, required tools, command‑line steps, YAML configuration, and connection methods for both internal and external access.
This guide walks you through creating a fully managed EMR Serverless StarRocks instance, loading historical World Cup data, and running OLAP SQL queries to analyze championship counts and host‑nation performance, all using a free trial of compute and storage resources.
Mango TV replaced its fragmented EMR‑Hive‑Kudu‑Presto stack with a unified StarRocks lakehouse, simplifying architecture, cutting operational costs, and achieving more than a ten‑fold increase in query speed while supporting real‑time analytics, materialized views, bitmap indexing, and store‑compute separation.
The article details how Ctrip's train ticket business revamped its multi‑engine OLAP metrics platform by consolidating to the StarRocks MPP database, describing the new architecture, query workflow, data synchronization strategies, practical lessons, and the resulting dramatic improvement in query latency and reliability.
Tongcheng Travel migrated its multi‑stage OLAP platform from Druid/Kylin and ClickHouse‑Greenplum to a unified StarRocks solution, dramatically improving real‑time query latency, offline report performance, and CDP data processing while reducing operational complexity and enabling cloud‑native deployment.
The DataFunCon conference showcases leading experts from Ctrip, Didi, Bilibili, and ByteDance presenting next‑generation OLAP technologies such as StarRocks, ClickHouse, and ByteHouse, covering architecture, materialized views, ELT practices, and performance optimization to guide practitioners in big‑data platform selection and implementation.
This article explains SeaTunnel’s architecture and its StarRocks connector, detailing source and sink features such as field projection, predicate push‑down, parallel reading, state recovery, data type mapping, Stream Load writes, CDC support, configuration examples, and future roadmap for exactly‑once semantics.
This article details Tongcheng Travel’s production deployment of the StarRocks OLAP database, covering background, business scenarios, technical evaluation against ClickHouse and Greenplum, implementation with Flink SQL, real‑time analytics, offline reporting, CDP use cases, performance optimizations, and future cloud‑native plans.
This article details Waterdrop's transition from ClickHouse and TiDB to a unified StarRocks‑based OLAP platform on Alibaba Cloud EMR, covering architecture evolution, performance benchmarks, best‑practice recommendations, and future plans for large‑scale real‑time analytics.
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 guide explains the importance of query performance optimization for database products and engineers, outlines latency and throughput goals, shows how to locate bottlenecks with observability tools and Linux profilers, and provides practical high‑level and low‑level optimization techniques along with testing best practices.
This article explains why lake‑warehouse integration is needed, outlines its challenges, describes StarRocks' four integration paradigms—including query acceleration, layered modeling, real‑time warehouse‑lake fusion, and the cloud‑native 3.0 solution—and previews the upcoming StarRocks 3.0 release.
This article details Autohome's transition from an hourly offline ad data warehouse to a Flink‑StarRocks real‑time architecture, covering background, engine and storage selection, multi‑layer design, implementation steps, encountered issues, monitoring strategies, and future roadmap to achieve second‑level data freshness and high accuracy.
StarRocks 3.0 introduces a storage‑compute separation architecture, a full‑featured RBAC permission framework, enhanced materialized views, Trino‑compatible query dialect, richer Primary‑Key update/delete syntax, automatic partition creation, and numerous performance optimizations, marking a major step from OLAP to lakehouse analytics.
Yidian Tianxia tackled massive daily data volumes and complex analytics by defining a five‑layer data‑warehouse standard, comparing ClickHouse and StarRocks performance, and implementing a unified real‑time/offline architecture with StarRocks, DataPlus, and EasyJob, achieving multi‑fold query speedups and lower operational costs.
This article presents ZhongAn Insurance's experience building real-time multi-dimensional analytics, covering application scenarios, technical challenges, the evolution of their architecture from offline to Flink‑ClickHouse and finally to StarRocks, and the principles guiding their technology selection.
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.
