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A Data Scientist’s Guide to Picking an Optimal Approximate Nearest-Neighbor Algorithm | by Braden Riggs | Aug, 2020

As evident by the graph above there were some clear winners and some clear losers. Focusing on the winners, we can see a few algorithms that really stand out, namely HNSWlib (yellow) and NGT-panng (red) both of which performed at a high accuracy and a high speed. Even though NGT never finished, the results do indicate it was performing exceptionally well prior to a memory-related failure.

So given these results, we now know which algorithms to pick for our next project right?

Unfortunately, this graph doesn’t depict the full story when it comes to the efficiency and accuracy of these ANN implementations. Whilst HNSWlib and NGT-panng can perform quickly and accurately, that is only after they have been built. “Build time” refers to the length of time that is required for the algorithm to construct its index and begin querying neighbors. Depending on the implementation of the algorithm, build time can be a few minutes or a few hours. Graphed below is the average algorithm build time for our benchmark excluding Faiss-HNSW which took 1491 minutes to build (about 24 hours):

Average build time, in minutes, for each algorithm tested excluding Faiss-HNSW which took 24 hours to build. Note how some of the algorithms that ran quickly took longer to build. Image by Author.

As we can see the picture changes substantially when we account for the time spend “building” the algorithm’s indexes. This index is essentially a roadmap for the algorithm to follow on its journey to find the nearest-neighbor. It allows the algorithm to take shortcuts, accelerating the time taken to find a solution. Depending on the size of the dataset and how intricate and comprehensive this roadmap is, build-time can be between a matter of seconds and a number of days. Although accuracy is always a top priority, depending on the circumstances it may be advantageous to choose between algorithms that build quickly or algorithms that run quickly:

  • Scenario #1: You have a dataset that updates regularly but isn’t queried often, such as a school’s student attendance record or a government’s record of birth certificates. In this case, you wouldn’t want an algorithm that builds slowly because each time more data is added to the set, the algorithm must rebuild it’s index to maintain a high accuracy. If your algorithm builds slowly this could waste valuable time and energy. Algorithms such as Faiss-IVF are perfect here because they build fast and are still very accurate.
  • Scenario #2: You have a static dataset that doesn’t change often but is regularly queried, like a list of words in a dictionary. In this case, it is more preferential to use an algorithm that is able to perform more queries per second, at the expense of built time. This is because we aren’t adding new data regularly and hence don’t need to rebuild the index regularly. Algorithms such as HNSWlib or NGT-panng are perfect for this because they are accurate and fast, once the build is completed.

There is a third scenario worth mentioning. In my experiments attempting to benchmark ANN algorithms on larger and larger portions of the deep1b dataset, available memory started to become a major limiting factor. Hence, picking an algorithm with efficient use of memory can be a major advantage. In this case, I would highly recommend the Faiss suite of algorithms which have been engineered to perform under some of the most memory starved conditions.

Regardless of the scenario, we almost always want high accuracy. In our case accuracy, or recall, is evaluated based on the algorithm’s ability to correctly determine the 10 nearest-neighbors of a given point. Hence the algorithm’s performance could change if we consider its 100 nearest-neighbors or its single nearest-neighbor.