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BaikalDB, Baidu's distributed storage system, evolves from an OLTP‑focused engine to a hybrid HTAP architecture by introducing a vectorized query engine and a massively parallel processing (MPP) layer, addressing compute and resource bottlenecks for large‑scale analytical workloads while preserving transactional guarantees.
Shopee’s deployment of StarRocks across DataService, DataGo, and DataStudio demonstrates that its vectorized engine, cost‑based optimizer, and materialized‑view caching can query Hive, Iceberg, Delta Lake and Hudi up to 20,000× faster than Presto, cutting CPU usage and delivering consistently lower latency for complex analytics.
StarRocks, an open‑source, high‑performance MPP analytical database under the Linux Foundation, offers vectorized engines, CBO optimizer, materialized views, and storage‑compute separation, integrates with BI tools and data lakes, and demonstrates superior query speed in benchmark tests against ClickHouse, Druid, and Trino.
LibraDB, the column‑store replica of TDSQL MySQL, has evolved its execution engine from a simple scatter‑gather model to a vectorized SMP pipeline that integrates MPP parallelism, asynchronous I/O, SIMD‑accelerated aggregation and join operators, work‑stealing, and runtime filters, thereby fully exploiting CPU, memory, network and disk resources for both OLTP and analytical queries.
The article presents Turing Data Analysis (TDA), a self‑service BI platform that replaces fragile traditional pipelines with a unified DWD‑based data model, drag‑and‑drop analytics, multi‑engine query optimization and caching, delivering sub‑10‑second queries on billions of rows, fine‑grained permissions, and rapid dashboard creation, while reporting significant usage growth and outlining AI‑driven future enhancements.
Apache Doris is an open‑source MPP OLAP database that combines a minimalist architecture, ease of use, rich features such as partition‑bucket pruning, materialized views, and bitmap indexes, and provides high‑performance, scalable, and reliable data warehousing for big‑data analytics.
This article presents an in‑depth overview of the YouShu BI product, outlines the high‑concurrency performance challenges faced by enterprise BI, and details the multi‑layer performance architecture—including front‑end, back‑end, data engine, and data source layers—along with smart caching, MPP acceleration, materialized views, and the Data Doctor operations that together ensure low‑latency, reliable analytics for large‑scale users.
This article provides a comprehensive overview of Apache Doris, detailing its origins, MPP‑based analytical capabilities, data‑lake integration techniques, recent architectural enhancements, performance optimizations, community growth, and upcoming development plans, while also addressing common user questions.
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.
The article presents a comprehensive technical overview of MercsDB—formerly HermesDB—including its background, storage and indexing designs, native and Presto computation engines, vectorization optimizations, benchmark results, real‑world applications, and future development plans.
This article details Xiaomi's three‑year journey of adopting Apache Doris across dozens of internal services, describing the transition from a Spark‑SQL‑based Lambda architecture to a unified MPP database, performance benchmarks, data ingestion pipelines, compaction tuning, two‑phase commit, single‑replica writes, monitoring, and community contributions.
This article introduces Apache Doris 1.0, detailing its simplified architecture, high‑concurrency support, MPP execution engine, vectorized engine, memory‑controlled stability, multi‑source integration, upcoming lake‑house unification, storage‑compute separation, real‑time ingestion, and community growth.
The article traces the evolution of data platforms from early data warehouses—using schema‑on‑write, columnar storage, and MPP engines—to data lakes that retain raw data with schema‑on‑read, and finally to lakehouse architectures that merge storage and compute, offering unified metadata, versioning, and support for BI, big‑data, AI, and HPC workloads.
This article presents the cloud‑native redesign of ClickHouse, covering its current technical limitations in storage and computation, the proposed storage‑compute separation with DDL task management, multi‑replica and CommitLog mechanisms, and a new MPP query layer to meet future data‑warehouse demands such as real‑time analytics, flexibility, high throughput, low cost, and support for semi‑structured data.
This article explains TiDB's HTAP architecture, covering industry challenges, the row‑store TiKV and column‑store TiFlash design, MPP integration in TiDB 5.0, and a range of real‑time use cases such as dashboards, reporting, and data‑warehouse pipelines.
This article explains the design and implementation of a cloud‑native MPP query layer for ClickHouse, detailing its architecture, core features, execution flow, performance advantages, SQL compatibility, and future development plans to create a high‑performance, multi‑source OLAP data platform.
This article explains the various classifications of OLAP data warehouses—including MOLAP, ROLAP, HOLAP, and HTAP—based on data volume and modeling, reviews common open‑source ROLAP products, and details performance‑boosting techniques such as MPP architecture, cost‑based optimization, vectorized execution, and storage optimizations.
This article details how KuJiaLe's big data team replaced their legacy ADB and Presto clusters with a DorisDB MPP database, achieving sub‑second query latency, unified real‑time and offline analytics, simplified ETL pipelines, and significant cost savings while supporting billion‑row tables and high‑QPS workloads.
The article examines the challenges big data brings to traditional technologies and surveys major OLAP solutions—MPP, batch processing, and pre‑computation—including Greenplum, Druid, Kylin, and Hadoop‑based engines, then outlines Youku’s specific use‑case selections for real‑time APIs, BI reporting, and ad‑hoc analysis.
This article explores the challenges big data brings to traditional data technologies and reviews various OLAP solutions—including MPP, batch processing, pre‑computation, and Hadoop‑based engines—while detailing Youku’s specific business scenarios and how different OLAP engines are selected to meet performance, scalability, and real‑time analysis requirements.