0 views collected around this technical thread.
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.
The Tencent Cloud TVP series chronicles China’s fifty‑year database evolution, from early banking deposit‑withdrawal systems and the demanding 1990s railway ticket OLTP challenge to the influx of global vendors, highlighting rapid market growth driven by national projects and foreshadowing the internet‑era big‑data opportunities.
This article examines the trade‑offs between centralized and distributed OLTP databases, presents industry usage statistics, performance benchmarks, practical questions for migration, and detailed guidance on sharding, SQL design, and operational considerations to help decide when a distributed solution is truly needed.
This article introduces Shen Jiangwei, a MatrixOne storage engine engineer, and outlines his presentation on the design of MatrixOne's storage format, covering its motivation, data structures, performance and compatibility guarantees, and the system's cloud‑native OLTP/OLAP/Streaming capabilities.
This guide introduces Lealone, a Java open‑source high‑performance relational database for OLTP, outlines its key features, provides step‑by‑step instructions for downloading, starting, and using the server and client, shows JDBC CRUD examples, GUI client setup, and links to the author's technical blogs and source notes.
The article explains the fundamental differences between OLTP (online transaction processing) and OLAP (online analytical processing), describes how ETL bridges the two, and provides a detailed side‑by‑side comparison of their characteristics, purposes, and design considerations.
OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) are two distinct data processing systems—OLAP optimized for complex, multidimensional analysis and business intelligence, while OLTP handles high‑volume, real‑time transactional workloads—each serving different purposes, and often used together to drive data‑driven decision making.
The September 2022 China Database Industry Analysis report highlights a wave of hardware‑driven innovations—including multi‑core CPUs, heterogeneous GPUs/TPUs/DPU, programmable FPGAs, CXL‑DDR5, persistent memory, NVMe‑oF, and RDMA‑based storage—that enable massive data storage and high‑concurrency real‑time computing across a range of novel database architectures and products.
The paper describes iQIYI’s Content Platform Data Center (OLTP), a unified data hub that solves microservice data‑island problems by providing hundred‑billion‑scale storage, high‑QPS reads/writes, field‑level change notifications, final consistency between MongoDB and Elasticsearch, active‑active high availability, and a generic SDK, now serving 26 business lines with thousands of QPS and hundreds of millions of rows.
This article explains the characteristics of OLTP and OLAP workloads, compares their I/O patterns, and provides Oracle database performance best‑practice guidelines, including storage planning, SAN architecture, operating‑system queue‑depth settings, and SwingBench testing results for optimal configuration.
This article explains the core differences between OLTP and OLAP, evaluates whether traditional OLTP databases like MySQL can handle analytical workloads, introduces benchmark queries, and provides a comprehensive overview of data‑warehouse concepts such as data sources, fact and dimension tables, multi‑dimensional modeling, and common cube operations.
This article, the third in a five‑part series on OceanBase’s record‑breaking TPC‑C results, explains the SQL‑level challenges faced during the benchmark and details the optimizations—including stored procedures, LLVM‑based compilation, array binding, prepared statements, and updatable views—that enabled the performance gains.
This article explains fundamental big‑data concepts by comparing offline (batch) and real‑time (stream) computing, distinguishing real‑time queries from ad‑hoc queries, clarifying OLTP versus OLAP workloads, and outlining the differences between row‑based and column‑based storage architectures.
This article traces the development of China's database sector from the 1950s Soviet‑inspired mainframes through the rise of OLTP, foreign database dominance, government‑backed research projects, and the emergence of modern cloud‑native solutions like PolarDB, OceanBase and TiDB, highlighting technical, economic and policy factors that shaped each decade.
Through a step‑by‑step case study of a high‑traffic OLTP system, this article explains how evolving deletion logic—from simple full‑table scans to indexed intermediate tables and parallel processing—reduced daily expired‑data cleanup from several hours to under an hour, while addressing ORA‑01555 and clustering‑factor issues.
This article explains how Ant Group's financial‑grade distributed relational database OceanBase delivers extreme performance on ordinary servers by optimizing its storage engine, CPU usage, and system scalability, and it highlights the impressive throughput and latency gains demonstrated during the 2018 Double‑11 shopping festival.
This article examines the convergence of OLTP and OLAP by introducing Apache Phoenix, Apache Trafodion, and Splice Machine, compares their technical features, and describes how Baidu Waimai adopted a Phoenix‑based solution to address scalability and performance challenges in its operational data store.