Summary
In this chapter, you delved into the nuances of optimizing vector search performance in Elasticsearch. We explored various tuning techniques, highlighting the intricacies of ML model deployment, node scaling, and configuration tuning. Tools such as Rally were introduced to aid in load testing specific use cases. Moreover, the focus on troubleshooting – bolstered by insights into monitoring cluster metrics and the hot threads API – has empowered you with the skills to tackle slow queries effectively.
In the next chapter, we will shift from text-focused semantic search to image-focused semantic search and explore the history and practical uses for these models, expanding our vector search capabilities.
