BOSS Zhipin —— How to save 70% storage cost through OceanBase with an archive database of 1 billion rows per day?
Author: Zhang Yujie, Database Engineer with BOSS Zhipin
8 docs tagged with "User Case"
View All TagsDMALL —— a summary of database selection experience in SaaS scenarios
Feng Guangpu, head of the Dmall database team, is responsible for the stability of OceanBase, TiDB, MySQL, Redis, and other databases of Dmall and the construction of its database platform as a service (PaaS) model. Feng has a wealth of experience in multi-active database architecture and data synchronization schemes.
From MySQL to OceanBase: how does E-mind manage the analysis of humongous data
Background
Handling over 10 million daily API calls with an ultra-low query latency: A real-time analytical solution of KYE based on Flink and OceanBase
About the author: Bao Guiming, Senior Big Data Engineer at KUAYUE EXPRESS (KYE), works on the research and introduction of real-time data R&D platforms and data lakes. Focusing on advancing the best practices and solutions of real-time/offline data warehouse architectures, Bao is committed to providing his company with accessible and efficient big data infrastructure systems, such as real-time computing systems and data lakes.
KUAYUE EXPRESS —— In the real-time warehouse scenario, utilizing OceanBase to make up for the shortcomings of MySQL and StarRocks
Introduction: This article is based on the speech made by Zhang Jie, Big Data Architect of KUAYUE EXPRESS (hereinafter referred to as KYE), at the DTCC conference. The speech introduced the pain points faced by KYE in data analysis and the company's ideas and practices in the development of its query engine solution.
Momo —— Exploration and practice of persistent cache based on OceanBase KV storage
About the author: Ji Haodong, head of the database division at Momo, part of the Hello Group. He is now responsible for the database teams of Momo and Tantan and the database storage and operation throughout the Hello Group. He has a wealth of professional experience and practical know-how in the fields of large-scale data source stability construction, team building, cost optimization, and IDC migration.
OceanBase provides stable support for massive clusters of Kwai
Kwai is a short video app boasting more than 10 million daily active users. How does it efficiently process highly concurrent user requests? Kwai once deployed multiple MySQL clusters in the backend to support high traffic with large data storage and satisfactory performance. What are the weak points of this conventional sharding solution? What pushed Kwai to select distributed databases and eventually deploy OceanBase Database? In this post, Xiaochong, the head of Kwai's O&M team, shared the team’s reflection and experience in implementing the OceanBase Database solution.
Sichuan Hwadee —— The practice of lightweight data warehouse construction of health big data based on OceanBase
Introduction: This article introduces Sichuan Hwadee's practice of migrating its data computing platform from Hadoop to OceanBase Database. This case demonstrates the advantages of OceanBase Database in terms of performance and storage costs. With OceanBase Database, hardware costs are reduced by 60% and the O&M work is significantly cut down, relieving maintenance personnel from responsibility for the many Hadoop components. OceanBase Database enables Hwadee to meet the needs of hybrid transaction/analytical processing (HTAP) scenarios with just one system, simplifying O&M for the company.