Scripts

Benchmarking on sift1m

Download dataset

wget http://ann-benchmarks.com/sift-128-euclidean.hdf5

Run storage backends

docker-compose up -d

Run benchmarking script

Install dependencies:

pip install "docarray[benchmark,qdrant,annlite,weaviate,elasticsearch,redis]"

Run the benchmark on sift1m or random dataset:

# sif1M
python benchmarking_sift1m.py [-h] [--exclude-backends EXCLUDE_BACKENDS] 

# random
python benchmarking.py [-h] [--fixed-hnsw] [--exclude-backends EXCLUDE_BACKENDS] 

options:
  -h, --help            show this help message and exit
  --fixed-hnsw          Whether to use default HNSW configurations for random dataset
  --exclude-backends EXCLUDE_BACKENDS
                        List of comma-separated backends to exclude from the benchmarks

Available storage backends include: memory, sqlite, annlite, qdrant, weaviate, elasticsearch, and redis.

The results of the benchmarks are stored in the current directory:

• benchmark-seconds-{storage}.csv
• benchmark-qps-{n}.csv
• benchmark.png, a figure of of Recall@10/QPS for all storage backends

Generate the chart of Recall/QPS on sift1m dataset benchmarking results:

python benchmarking_html.py

Benchmark HNSW parameters

Current HNSW parameters are written in the function get_configuration_storage_backends of benchmarking_utils.py. Parameters include:

• ef_construction/ef_construct: Number of maximum allowed potential outgoing edges candidates for each node in the graph, during the graph building.
• max_connection/max_connections/m: Number of maximum allowed outgoing edges for each node in the graph in each layer.
• ef_search/hnsw_ef/ef/num_candidates/ef_runtime: Number of maximum top candidates to hold during the KNN search.