Benchmarks and Concurrent Users for Typesense
TLDR A inquired about the number of concurrent users Typesense can support. Jason suggested benchmarking for better accuracy, while Kishore Nallan mentioned about an upcoming feature. Jason and Kishore Nallan both recommended tools and gave references for performing benchmarks.
May 19, 2021 (33 months ago)
Kishore Nallan04:29 AM
Kishore Nallan04:34 AM
Indexed 3015 threads (79% resolved)
Troubleshooting Typesense Document Import Error
Christopher had trouble importing 2.1M documents into Typesense due to memory errors. Jason clarified the system requirements, explaining the correlation between RAM and dataset size, and ways to tackle the issue. They both also discussed database-like query options.
Enhancing Vector Search Performance and Response Time using Multi-Search Feature
Bill faced performance issues with vector search using multi_search feature. Jason and Kishore Nallan suggested running models on a GPU and excluding large fields from the search. Through discussion, it was established that adding more CPUs and enabling server-side caching could enhance performance. The thread concluded with the user reaching a resolution.
Addressing Typesense Server Issues and Optimization Needs
Robert had an issue with a 'stuck' typesense server. Jason and Kishore Nallan gave advice on handling writes, configuration for high search volumes, and running multiple typesense instances. They also recommended monitoring CPU usage and updating the server version for bug fixes.
Understanding Indexing and Search-As-You-Type In Typesense
Steven had queries about indexing and search-as-you-type in Typesense. Jason clarified that bulk updates are faster and search-as-you-type is resource intensive but worth it. The discussion also included querying benchmarks and Typesense's drop_tokens_threshold parameter, with participation from bnfd.
Discussion on Performance and Scalability for Multiple Term Search
Bill asks the best way for multi-term searches in a recommendation system they developed. Kishore Nallan suggested using embeddings and remote embedder or storing and averaging vectors. Despite testing several suggested solutions, Bill continued to face performance issues, leading to unresolved discussions about scalability and recommendation system performance.