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Fixing links again (#697)

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* use relative links instead of absolute

* add trailing slashes to avoid 301 redirect

* add trailing slashes to avoid 301 redirect

* make link checker unhappy with local redirects

* test if ci fails (should fail)

* rollback: test if ci fails (should fail)
This commit is contained in:
Andrey Vasnetsov
2024-03-07 20:31:05 +01:00
committed by GitHub
parent 2285373bc8
commit cae8456ce1
72 changed files with 142 additions and 140 deletions
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qdrant-landing/content/benchmarks/benchmarks-intro.md
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# 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](/articles/why-rust), [io optimisations](/articles/io_uring), [serverless support](/articles/serverless), [binary quantization](/articles/binary-quantization), to our [fastembed library](/articles/fastembed) are all based on our principle. In this article, we will compare how Qdrant performs against the other vector search engines.
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](/articles/why-rust/), [io optimisations](/articles/io_uring/), [serverless support](/articles/serverless/), [binary quantization](/articles/binary-quantization/), to our [fastembed library](/articles/fastembed/) 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:
qdrant-landing/content/benchmarks/single-node-speed-benchmark.md
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## Observations
Most of the engines have improved since [our last run](/benchmarks/single-node-speed-benchmark-2022). Both life and software have trade-offs but some clearly do better:
Most of the engines have improved since [our last run](/benchmarks/single-node-speed-benchmark-2022/). Both life and software have trade-offs but some clearly do better:
* **`Qdrant` achives highest RPS and lowest latencies in almost all the scenarios, no matter the precision threshold and the metric we choose.** It has also shown 4x RPS gains on one of the datasets.
* `Elasticsearch` has become considerably fast for many cases but it's very slow in terms of indexing time. It can be 10x slower when storing 10M+ vectors of 96 dimensions! (32mins vs 5.5 hrs)