This article defines TPS versus QPS, illustrates a multi-SQL transaction example, and provides a concrete reference table mapping MySQL server configurations (4-core/8GB through 64-core+) to typical OLTP TPS ranges (1,000–50,000+), concluding with architectural guidance for scaling beyond 10,000 TPS.
This article explains why online transaction databases (OLTP) should not be used for multi‑year analytics, outlines the four core characteristics of a data warehouse, details the ETL process, compares star and snowflake schemas, contrasts data warehouses with data lakes, and guides you through selecting Lambda or Kappa architectures using a concrete retail‑store scenario.
The article compares four emerging data‑lake‑for‑PostgreSQL solutions—Databricks LTAP, EDB Fusion Analytics, Snowflake pg_lake, and pgEdge's ColdFront—highlighting ColdFront's unique transparent Iceberg layer, writable cold data, DuckDB integration, and the strategic trade‑offs developers must weigh when choosing a modern OLTP/OLAP/AI architecture.
The article examines why MySQL’s transaction layer remains robust while its analytical capabilities lag, outlines enterprise demands for simultaneous OLTP and OLAP workloads, introduces DuckDB’s lightweight yet powerful engine, describes AliSQL’s integration of DuckDB for full MySQL compatibility, and presents TPCH benchmark results showing a dramatic performance boost for analytical queries.
OLTP handles real‑time transactional workloads with low latency and high concurrency, OLAP is optimized for large‑scale analytical queries on historical data, and HTAP combines both to support simultaneous transaction processing and analytics; the article compares their characteristics, typical use cases, and popular implementations to guide database selection.
Despite five years of hype around HTAP databases, most enterprises continue to run OLTP and OLAP on separate systems because of performance trade‑offs, data consistency challenges, higher operational complexity, migration costs, and the fact that real‑time analytics is needed only in a small fraction of use cases.
This article explains what OLTP (Online Transaction Processing) is, outlines its key characteristics, and details how MySQL—through ACID‑compliant transactions, the InnoDB storage engine, various indexing strategies, fast locking mechanisms, query optimization, and high‑availability features—effectively supports high‑concurrency, low‑latency transactional workloads.
This article walks through using MySQL to pivot rows into columns, covering storage concepts, practical CASE‑WHEN aggregation techniques, and detailed SQL examples for sum, count, average and max calculations, while also discussing performance considerations for medium‑scale and large‑scale data workloads.
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 Baidu Search's content storage team's HTAP table storage system, detailing the challenges of supporting massive OLAP workloads on an OLTP‑oriented backend, the architectural split into Neptune and Saturn, storage‑engine optimizations such as row partitioning and dynamic columns, and a SQL‑like KQL framework for compute and scheduling.
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.
This article defines distributed databases, examines their external traits such as write‑heavy, low‑latency, massive concurrency, massive storage and high reliability, explores internal architectures like client‑side sharding, proxy middleware and unit‑based designs, compares them with Amazon Aurora, and summarizes key takeaways.
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.
This article explains why database selection is critical, outlines the evolution and types of databases, presents a methodology for evaluating requirements, and reviews Volcengine's cloud database offerings to help you pick the most suitable solution for OLTP workloads.
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.
This article explains the fundamental differences between transactional (OLTP) and analytical (OLAP) database workloads, describes how data warehouses isolate analytical queries, and introduces star and snowflake schema designs for efficient reporting and business intelligence.
This article details iQIYI's design and implementation of a unified OLTP data center that consolidates data across business lines, solves data‑island issues, ensures strong consistency between MongoDB and Elasticsearch, and provides high‑availability, massive‑scale storage for billions of records.
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 why MySQL's default InnoDB storage engine uses B+ trees instead of B trees or hash indexes, covering storage engine basics, query performance, I/O considerations, and the structural advantages that make B+ trees ideal for OLTP workloads.
This article explains how to evaluate business architecture, data volume, data types, and team expertise to select the most suitable database—whether relational with strong ACID guarantees for OLTP, analytical OLAP systems, or various NoSQL options—while highlighting Huawei Cloud offerings for different scenarios.
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 explains the TPC‑C benchmark, covering its purpose, data model, database schema, the five transaction types, and a detailed step‑by‑step walkthrough of the New‑Order operation with accompanying SQL illustrations, while also summarizing workload characteristics and providing reference links.
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.
The article explains the two main types of data processing—OLTP for real‑time transactional workloads and OLAP for analytical data‑warehousing tasks—detailing their distinct characteristics, resource demands, typical features, major vendor products, and a five‑point comparison of their design and usage.
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.
This article explains the concepts of OLTP, OLAP and HTAP, describes the DRDS HTAP architecture—including its engine and storage layers, Fireworks Spark‑based engine, optimizer stages, and streaming capabilities—and demonstrates cross‑database MPP queries and streaming joins while outlining suitable use cases and limitations.
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.
The 2018 NDBC conference in Dalian gathered leading academia and industry experts to showcase cutting‑edge database technologies such as Alibaba's POLARDB, POLARDB X, SolarDB, Huawei Gauss, and AnalyticDB, while discussing cloud‑native challenges, large‑scale deployment, AI integration, and future collaborative research directions.
Facing billions of rows spread across 1024 tables in 64 databases, a Chinese gaming platform replaced its custom sharding framework with TiDB, achieving real‑time OLTP consistency, elastic scaling, robust backup, high availability, and lower migration costs while preserving MySQL compatibility.
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.
An in‑depth walkthrough of Oracle 12c R2 Sharding, covering its core concepts, architecture, environment setup across three virtual machines, detailed GSM and shard catalog configuration commands, test model, results, limitations, and troubleshooting tips for real‑world OLTP deployments.
The article compares PostgreSQL and MySQL, highlighting PostgreSQL’s strengths in analytics and Oracle compatibility but criticizing its OLTP performance, while emphasizing MySQL’s superior scalability, feature set, and growing ecosystem, and discusses the broader trend of moving away from Oracle‑centric architectures.
Before migrating enterprise OLTP workloads to open‑source MySQL, this guide evaluates three high‑availability architectures—Percona XtraDB Cluster, MHA, and native dual‑master—by outlining key pre‑migration questions, their advantages, drawbacks, and practical deployment considerations.
