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Kumar Shivenduandgenerall
80f2b6f978
* feat: Update benchmarks * fix: Improve blog * feat: Add result files with 1 and 100 parallel clients * fix: Add elasticsearch benchmark numbers from dbpedia 1M openai embeddings * feat: Make mean time (latency) the default metric for the 2nd plot * feat: Improve conclusions based on es results * fix: Make charts work on changing dataset * fix: Typo in table * feat: Update results with quantization * fix: Show a single graph to keep things simple * fix: Improve words * review fixes * add link to open-source * feat: Improve units to make results more readable * fix: Small grammatical mistake * fix: Make search threads value constant based on plot metric * feat: Make dataset num vectors more readable * fix: Spacing in table * feat: Add results from the latest Redis benchmarks * feat: Update benchmarks page * feat: Update date and some of the points * feat: Update benchmarks and content * chores: Improve observations * feat: Improve observations and put it after the graph --------- Co-authored-by: generall <andrey@vasnetsov.com>
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draft, id, title, weight
| draft | id | title | weight |
|---|---|---|---|
| false | 2 | How vector search should be benchmarked? | 1 |
Benchmarking Vector Databases
At Qdrant, performance is the top-most priority. We always make sure that we use system resources efficiently so you get the fastest and most accurate results at the cheapest cloud costs. So all of our decisions from choosing Rust, io optimisations, serverless support, binary quantization, to our fastembed library are all based on our principle. In this article, we will compare how Qdrant performs against the other vector search engines.
Here are the principles we followed while designing these benchmarks:
- We do comparative benchmarks, which means we focus on relative numbers rather than absolute numbers.
- We use affordable hardware, so that you can reproduce the results easily.
- We run benchmarks on the same exact machines to avoid any possible hardware bias.
- All the benchmarks are open-sourced, so you can contribute and improve them.
Scenarios we tested
Some of our experiment design decisions are described in the F.A.Q Section. Reach out to us on our Discord channel if you want to discuss anything related Qdrant or these benchmarks.