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The article details how Apache Paimon, integrated as an external table format in Alibaba’s Dolphin OLAP engine, achieves millisecond‑level query latency and up to 10k QPS through ORC push‑down, manifest conversion, caching, concurrency, and encoding optimizations, outperforming StarRocks and Hologres.
ClickHouse is an open‑source, massively parallel, column‑oriented OLAP database that integrates its own columnar storage, vectorized batch processing, pre‑sorted data, diverse table engines, extensive data types, sharding with replication, sparse primary‑key and skip indexes, and a multithreaded query engine, delivering high‑throughput real‑time analytics on massive datasets.
This article explains how ByteHouse integrates high‑performance GIS capabilities into its OLAP engine, describes its spatial indexing architecture, showcases benchmark results against ClickHouse, StarRocks, PostGIS and DuckDB using the NYC Taxi dataset, and outlines when to choose ByteHouse versus other spatial database solutions.
The article explains ByteHouse's GIS capabilities, describing its R‑Tree and Google S2 spatial index implementation, OGC‑compatible data types and functions, and presents benchmark results that show ByteHouse outperforming ClickHouse, StarRocks, PostGIS, and DuckDB on key geospatial queries.
This article explains how ClickHouse’s columnar storage, bitmap functions, and distributed architecture can be leveraged to store billions of tag bitmaps, combine them efficiently, and support fast group calculations for customer data platforms, while addressing data‑warehouse integration, storage format, and performance challenges.
This article introduces Easy-Query, a Java ORM that offers strong‑typed OLTP and OLAP query capabilities, demonstrates how to define entity classes with many‑to‑many and one‑to‑one relationships, and provides multiple code examples for complex queries, DTO generation, and automatic inclusion of related data.
This article examines a fast‑growing company's sales data platform, outlines the data‑access pain points caused by ACL permissions, describes the migration from ClickHouse to ByteHouse, details the optimizer’s rule‑based, cost‑based, and distributed‑plan enhancements, and presents benchmark results showing query speedups of up to sixteen times.
This article presents Kuaishou's experience of designing and implementing a Doris‑driven lakehouse integrated analytics system, covering the current OLAP landscape, challenges of data duplication and governance, the new architecture with caching and auto‑materialization, implementation details, performance impact, and future work.
To achieve sub‑second, multi‑dimensional analytics on Bilibili’s hundred‑million‑row datasets, the team built a ClickHouse‑based acceleration layer that combines grouping‑set pre‑aggregation, bitmap (RBM) distinct handling, and a dense dictionary encoding service, dramatically cutting CPU, memory and query latency versus traditional OLAP pipelines.
This article analyzes why data engineers face endless data‑extraction requests, identifies common missteps in data‑construction practices, and proposes a comprehensive solution based on dimensional modeling, OLAP multidimensional reporting, self‑service tools, and knowledge empowerment to dramatically improve efficiency and scalability.
This article introduces Apache SeaTunnel, a modern data integration platform designed for the EtLT era, detailing its architecture, core connector APIs, checkpoint mechanism, model inference, multi‑table synchronization, the high‑performance SeaTunnel Zeta engine, OLAP use cases, community roadmap, and the commercial WhaleTunnel product.
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.
This article presents a configurable OLAP framework that streamlines multi‑dimensional, multi‑metric analysis by separating core and interaction domains, using metadata‑driven modeling, runtime request handling, and Maven‑based integration to accelerate development and improve query performance.
The ByteHouse performance whitepaper details the cloud‑native data warehouse’s architecture, rule‑based and cost‑based optimizer enhancements, exchange runtime, runtime filters, parallelism and wide‑table optimizations, and presents benchmark comparisons on TPC‑DS, TPC‑H and SSB datasets demonstrating orders‑of‑magnitude query speed improvements.
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 analyzes why data engineers face endless data‑retrieval requests, identifies common missteps in data‑construction such as demand‑driven development, lack of modeling and OLAP concepts, and proposes a dimension‑model‑based data warehouse with OLAP reporting, tooling, and knowledge‑empowerment to break the cycle.
This article explains how Douyin rebuilt its interest‑circle platform by replacing MySQL with the columnar OLAP engine ByteHouse, achieving roughly a hundred‑fold improvement in query speed, lower hardware costs, and seamless horizontal scalability for massive daily data volumes.
This article details Xiaomi’s practical deployment of Trino, covering its architectural role, core and extended capabilities, performance comparisons, integration with Iceberg and Spark, operational enhancements, multi‑cluster and ad‑hoc query scenarios, future cloud‑storage plans, and a Q&A session.
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.
This article presents Jushuitan's cloud‑native OLAP architecture, detailing its evolution, current big‑data stack—including DataWorks, MaxCompute, Flink, Hologres, and Aerospike—along with logistics warning workflows, rule‑matching mechanisms, real‑time processing challenges, and future scalability plans.