Based on Apache HugeGraph graph database ground-up practices and focusing on offline import and synchronous query performance optimization, as well as building logging and monitoring big picture around stability. We will also emphasize the importance of participating in the graph database community. In the talk, we will delve into the following aspects:

• Ground-up practices: We will share the actual application cases based on the HugeGraph graph database and discuss how to successfully apply the graph database to business scenarios. Involving graph modeling, graph data quality assurance and other aspects. Focus on the construction of online entity relationship graph to support real-time graph data query and analysis.
• Offline import performance optimization: We will discuss how to optimize the offline import process to achieve efficient large-scale graph data import. Covering key technologies such as data preprocessing and parallel import to improve import performance and efficiency.
• Synchronous query performance optimization: We will introduce the core functions developed based on HBase to optimize the performance and response time of synchronous queries.
• Logging and monitoring big picture construction: We will discuss how to establish a complete logging and monitoring system to achieve real-time monitoring and fault diagnosis of the graph database platform. Focus on aspects such as log recording, exception detection, and performance monitoring to ensure the stability and reliability of the system.
• Participate in community construction: We will emphasize the importance of graph database community construction and encourage the audience to actively participate. We will share community resources and cooperation opportunities, as well as how to work with other developers and users to jointly promote the development and innovation of the graph database platform.

Speakers:

Jiaqi Yang: Lalamove Big Data Engineer ,Apache Hugegraph Commiter. Currently, he is mainly responsible for graph database related work, providing efficient, stable, easy-to-use and accurate graph data services for the department. Engaged in big data related work for 8 years.