title: hnswlib parameters for large datasets?
body: <p>I am using the library <code>hnswlib</code> (<code>https://github.com/nmslib/hnswlib</code> ) library in Python to implement a speedy KNN search. I am wondering about parameters for large datasets.</p>
  <p>I am using this benchmark from the official repository to test the libraries behavior on large datasets (vector dimension of 256+ with 1 million vectors+) <code>https://github.com/nmslib/hnswlib/blob/master/examples/example.py</code></p>
  <p>Testing with small datasets of a few 100k the recall results of this benchmark are quire good, usually around .9. Increasing to million this drops to .7</p>
  <p>The authors of the library outline some general properties of the lib's parameters (<code>https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md</code>) but finding a setup which leads to high recall on large data is time consuming as index build times take a few hours and increases further with choosing larger values for the parameters.</p>
  <p>Are there some best-practice values for certain data dimensionality or number of data points? I understood this library is quite popular but I couldn't find any value recommendations.</p>