Discussing Denormalization and Performance in Typesense Collections
TLDR Viji inquired about the performance implications of denormalizing Typesense collections, and also self-hosting. Jason explained that denormalized data is more performant and clarified how typesense handles queries, latency, and provided an insight about their index structure.
Jun 03, 2022 (19 months ago)
re: self-hosted vs Cloud, performance-wise there should be no difference. In fact we run the same open source version of Typesense to power Typesense Cloud.
The performance issue I was referring to is to do with latency rather than of Typesense Cloud
If you're sending queries from your frontend to your backend and then to Typesense, then running Typesense in the same network as your backend will have the lowest latency. You could pick a region that's closest to your backend, and that will add may be 10-20ms of latency.
we only store a given field's value once in the index
Indexed 3005 threads (79% resolved)
Optimizing Typesense Implementation for Large Collections
Oskar faced performance issues with his document collection in Typesense due to filter additions. Jason suggested trying a newer Typesense build and potentially partitioning the data into country-wise collections. They also discussed reducing network latency with CDN solutions.
Revisiting Typesense for Efficient DB Indexing and Querying
kopach experienced slow indexing and crashes with Typesense. The community suggested to use batch import and check the server's resources. Improvements were made but additional support was needed for special characters and multi-search queries.
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.
Improving Typesense Query Performance
Jonathan queried about slower than expected typesense query performance. Jason and Kishore Nallan offered solutions and explanations. After a series of tests, Jonathan found other queries returned results quickly, indicating the issue was specific to the original query.
Discussing Document Indexing Speeds and Typesense Features
Thomas asks about the speed of indexing and associated factors. The conversation reveals that larger batch sizes and NVMe disk usage can improve speed, but the index size is limited by RAM. Jason shares plans on supporting nested fields, and they explore a solution for products in multiple categories and catalogs.