This article discusses the implementation of Apache Kylin as an OLAP engine for logistics data, focusing on optimizing cube building and query performance to handle large-scale, high-dimensional data analytics.
Apache Kylin leverages pre‑computed OLAP cubes on Hadoop/Spark/Flink to deliver sub‑second query responses for massive datasets, detailing its architecture, integration with BI platforms, user security, cube building, monitoring, and storage using HBase, illustrating how it overcomes big‑data analytical challenges.
The article explains the challenges of big‑data analytics, introduces pre‑computation OLAP concepts, and details how Apache Kylin together with Spark, Flink, Presto and other components can be integrated into a BI platform to achieve near‑real‑time query performance on massive datasets.
The 3rd Youzan Big Data Technology Salon, held online for over 200 participants, showcased Apache Kylin 4’s performance boost, GeTui’s five‑step AI method, Kwai’s sustainable data‑governance system, and Youzan’s intelligent copy algorithms, highlighting data governance’s evolution into a core business priority and the shift toward intelligent discovery.
This article provides an in‑depth overview of Apache Kylin, covering its history, mission, core MOLAP principles, technical architecture, step‑by‑step installation (Docker and Hadoop), performance tuning, advanced cube settings, and detailed case studies from major companies such as Baidu, Lianjia, and Didi.
This article explains the background, challenges, and architectural design of a big‑data OLAP platform that integrates Apache Kylin with a BI system, detailing pre‑computation strategies, cube construction, user authentication, storage engines, and query mechanisms to achieve sub‑second analytics on massive datasets.
This article presents a detailed case study of how Meituan's in‑store dining sales team identified severe efficiency issues in their Apache Kylin‑based OLAP system, dissected the construction process, and applied a step‑by‑step optimization roadmap—including engine migration, dimension pruning, resource configuration, and Spark‑based layered building—to boost query performance and achieve near‑perfect SLA.
This article explains Apache Kylin’s core principles and technical architecture, then details how major Chinese companies such as Baidu Maps, Lianjia, and Didi have deployed Kylin for large‑scale OLAP, describing their system designs, performance results, and the challenges they encountered.
This article compares Apache Kylin and ClickHouse, two popular big‑data OLAP engines, by examining their technical principles, storage structures, optimization techniques, and ideal application scenarios to help readers make an informed technology selection.
Meituan’s sales system “Qingtian” boosted OLAP performance by migrating Apache Kylin’s build engine from MapReduce to Spark, consolidating Hive files, refining dictionary creation, applying a By‑layer algorithm, and bulk‑loading cuboid files to HBase, cutting resource consumption and halving build time, ultimately reaching a 100 % SLA.
This article details how Beike's engineering team scaled Apache Kylin to handle tens of millions of daily queries by optimizing HBase configurations, reducing region count, improving data locality, addressing IO and JVM GC bottlenecks, and implementing comprehensive slow‑query detection and active‑defense mechanisms.
This article details ZTO Express's journey of adopting Apache Kylin for OLAP, comparing it with Presto, describing platform architecture, performance gains, integration with scheduling and monitoring systems, and the practical optimizations and future plans that enabled sub‑second query responses on massive daily data volumes.
This article describes Meituan's massive OLAP workloads, the specific challenges of data scale, complex schemas, and precise counting, explains how Apache Kylin was integrated using wide tables and bitmap deduplication, compares its performance and features with Presto, Druid and other engines, and outlines future improvements.
Beike’s OLAP platform has progressed from a basic Hive‑MySQL batch pipeline to a Kylin‑based single‑engine solution, and now to a flexible multi‑engine architecture that uses a query‑engine layer to route metrics across Kylin, Druid, ClickHouse and Doris, dramatically cutting cube‑build times, supporting real‑time ingestion, and paving the way for further engine consolidation and automated performance routing.
The article details iQIYI’s migration from a Hive + MySQL OLAP stack to Apache Kylin, describing the system’s architecture, typical use cases, performance gains, independent HBase deployment, service platform for monitoring, and future plans such as automated cube building and clustering.
This article provides a comprehensive overview of Apache Kylin, covering its background, core OLAP concepts, technical architecture, installation steps, cube-building methods, real‑world enterprise deployments, and resources for further learning, illustrating how it enables sub‑second query performance on massive datasets.
This article details AutoHome's use of Apache Kylin as its core OLAP engine, covering its development timeline, architecture, large‑scale cube deployment, performance optimizations, cluster upgrade experiences, and future directions such as real‑time OLAP and cloud‑native deployment.
This article provides a comprehensive step‑by‑step guide for installing Apache Kylin on a single machine, covering required software versions, environment variable configuration, Spark dependency handling, main Kylin properties, verification steps, and detailed solutions to common errors such as Zookeeper host issues, HTTP 404, Jackson conflicts, MapReduce jobhistory problems, missing Spark classes, HiveConf errors, and YARN shuffle service configuration.
The article introduces Kyligence's Kylin on Parquet solution, explains its plug‑in architecture, reasons for replacing HBase with Parquet, details the new Spark‑based build and query engines, auto‑tuning, global dictionary, fault‑tolerance features, and presents performance comparisons with Kylin 3.0.
The article chronicles Apache Kylin's evolution from an Apache incubator OLAP engine to a comprehensive analytical data warehouse, highlighting its five‑year growth, extensive enterprise adoption, core data‑warehouse features, and the community’s rebranding to better reflect its big‑data capabilities.
This article introduces Apache Kylin, details its deployment at Tongcheng Yilong, explains the design of a large‑scale trajectory model, and provides step‑by‑step optimization techniques—including cube dimension reduction, HBase rowkey tuning, build parameter tweaks, high‑cardinality handling, and query compression disabling—to achieve sub‑second OLAP queries on multi‑terabyte data.
Apache Kylin is an open‑source, Hadoop‑based OLAP engine that provides sub‑second, multi‑dimensional SQL queries on massive datasets, with features such as cube pre‑computation, real‑time analytics, and seamless BI tool integration, and its latest v2.6.4 release adds numerous fixes and improvements.
This article explains how Apache Kylin, an open‑source distributed analytics engine built on Hadoop/Spark, achieves sub‑second OLAP query performance through pre‑computed cubes, a layered cuboid generation algorithm, bitmap‑based distinct counting, dimension optimization techniques, and tight integration with HBase for storage and fast SQL querying.
This article describes Meituan’s large‑scale OLAP requirements, how Apache Kylin was integrated to meet them, the architectural solutions, performance benchmarks against other engines, and future work, providing practical insights for building stable, precise, and high‑performance analytics platforms.
This article provides an in‑depth overview of Apache Kylin’s pre‑computation architecture, data‑warehouse concepts, step‑by‑step cube creation from Hive tables, and advanced optimization techniques such as derived dimensions, aggregation groups, and HBase row‑key encoding to achieve sub‑second OLAP queries on massive datasets.
This article provides an in‑depth technical comparison of Apache Kylin and Apache Doris, covering their system architectures, aggregation and detail data models, storage engines, data import processes, query execution, deduplication, metadata handling, performance, high availability, maintainability, usability, schema‑change capabilities, features, and community ecosystems.
At Meitu’s Technology Salon, senior big‑data experts detailed the end‑to‑end architecture and stability measures of Meitu’s large‑scale data platform, introduced the high‑performance distributed bitmap solution Naix, showcased the evolution of Meizu’s user‑insight system, and highlighted Apache Kylin’s OLAP capabilities and Superset integration for scalable, real‑time analytics.
This article details Chain Home's OLAP platform architecture and Kylin usage, covering the evolution from early ROLAP to MOLAP multi-dimensional engine, Kylin's basic principles, platform structure, application scenarios, usage specifications, capability extensions, and middleware development.
This article shares a data engineer's three‑year experience using Apache Kylin for real‑time OLAP on petabyte‑scale data, describing the business background, challenges of pre‑computation, cube modeling, dimension reduction, and various optimization techniques such as hierarchy, mandatory, and joint dimensions, as well as precise count‑distinct handling.
This article presents a detailed case study of Baidu Waimai's traffic analysis platform, outlining the data challenges of high dimensionality and volume, the evaluation of OLAP engines, the adoption of Apache Kylin for pre‑computation, the end‑to‑end data modeling, cube construction, incremental builds, and integration with Saiku‑Mondrian reporting, while sharing practical lessons and performance gains.
Apache Kylin is an open‑source, distributed OLAP engine built on Hadoop that uses pre‑computed cubes to deliver sub‑second, high‑concurrency SQL queries on massive datasets, integrates with popular BI tools, offers a modular architecture, recent 1.5.x enhancements, and extensive deployment options.
This article presents a comprehensive overview of the Qdata session on OLAP engine exploration, detailing the limitations of traditional MySQL‑based solutions, the requirements for large‑scale analytics, the architecture and theoretical foundations of Apache Kylin, its cube construction process, storage in HBase, query rewriting, real‑world flight‑ticket data applications, and the encountered challenges with corresponding optimization practices.
This article presents Qunar's experience transitioning from MySQL‑based OLAP to Apache Kylin, detailing the performance challenges, required features, Kylin's architecture and theory, cube construction process, storage mechanisms, real‑world applications, and the pitfalls and optimization practices discovered along the way.
This article explains how Baidu Maps’ open platform handles massive daily location data through real‑time and offline pipelines, Hadoop‑based offline computing, stream processing, and query engines built on MySQL, Redis, and Apache Kylin, while outlining future big‑data enhancements.
Baidu Maps’ Data Intelligence team built a large‑scale OLAP platform using Apache Kylin, detailing the challenges of multi‑dimensional analysis on billions of rows, the architecture, custom extensions for task, resource, and monitoring management, and performance optimizations that achieve millisecond‑level SQL responses.
