I. Introduction
In the big family of data science, vector databases play an important role. They provide efficient data processing capabilities for complex machine learning tasks through a unique vector operation mechanism. However, how to make these databases run stably in the production environment has become an important challenge for the operation and maintenance team. This article will delve into the operability of vector databases and share some interesting cases.
2. Dynamic configuration: database optimization without restarting
When faced with performance problems, the operation and maintenance team often needs to optimize the database in real time. Starting from version 2.3.0, the MIlvus Cloud database introduces the feature of dynamic configuration. This means that you can adjust settings on the fly, such as adjusting memory allocation, disk storage, etc., without restarting the database. This is of great convenience for solving unexpected problems in the production environment.
For example, one day, the operation and maintenance team discovers that the performance of MIlvus Cloud suddenly drops. Through dynamic configuration, they can immediately increase memory allocation to increase query speed. There is no need to restart the database, saving time and avoiding possible downtime.
3. Tracing support: transparent operation and maintenance of the database
Tracing is one of the important tools for database operation and maintenance. MIlvus Cloud supports the Opentelemetry tracing protocol from version 2.3.0. Through this protocol, we can clearly see the execution path of each request to identify potential performance bottlenecks.
For example, an operation and maintenance team found that the query speed was abnormally slow. Through Tracing, they found that it was an index scanning problem. They can then perform targeted optimizations, such as rebuilding indexes, to improve query speed.
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