openGemini: Batch Query Optimization
This article introduces what is batch query and the performance problems caused by serial execution of batch query. openGemini makes full use of the powerful concurrent processing capability of Go language to carry out concurrent transformation of serial query, and proposes an adaptive parallel query task scheduler to solve the performance problems of serial query.
openGemini Optimization Example: Query a Plan Template
Database performance tuning is a process that requires patience and perseverance. It is necessary to continuously analyze and optimize, find out potential performance problems, and take appropriate measures to optimize. Only through continuous efforts and practice can we really improve the performance of the database system and provide better support for the business. This article presents an example of how to optimize openGemini based on flame graph analysis, and also describes how to add a query template to help readers.
openGemini: Full-Text Index Parsing
This article introduces the function of openGemini full-text index, including the basic principle, index creation, full-text query and filtering. Compared with the traditional full-text index, the CLV dynamic segmentation algorithm adopted by openGemini has great advantages in the memory resource consumption and matching efficiency of inverted index.
The Application Scenarios and Constraints of the Multistage Downsampling Function
This article mainly introduces the openGemini multilevel downsampling function, including application scenarios, task creation, view and deletion, and usage constraints. Multilevel downsampling can greatly reduce data storage costs and system costs, but it does not retain the original data details, you must be clear when using.