vector-db-benchmark

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There are various vector search engines available, and each of them may offer a different set of features and efficiency. But how do we measure the performance? There is no clear definition and in a specific case you may worry about a specific thing, while not paying much attention to other aspects. This project is a general framework for benchmarking different engines under the same hardware constraints, so you can choose what works best for you.

Running any benchmark requires choosing an engine, a dataset and defining the scenario against which it should be tested. A specific scenario may assume running the server in a single or distributed mode, a different client implementation and the number of client instances.

How to run a benchmark?

Benchmarks are implemented in server-client mode, meaning that the server is running in a single machine, and the client is running on another.

Run the server

All engines are served using docker compose. The configuration is in the servers.

To launch the server instance, run the following command:

cd ./engine/servers/<engine-configuration-name>
docker compose up

Containers are expected to expose all necessary ports, so the client can connect to them.

Run the client

Install dependencies:

pip install poetry
poetry install

Run the benchmark:

$ poetry shell
$ python run.py --help

Usage: run.py [OPTIONS]

  Examples:

  python3 run.py --engines "qdrant-rps-m-*-ef-*" --datasets "dbpedia-openai-100K-1536-angular" # Qdrant RPS mode

  python3 run.py --engines "*-m-*-ef-*" --datasets "glove-*" # All engines and their configs for glove datasets

Options:
  --engines TEXT                  [default: *]
  --datasets TEXT                 [default: *]
  --host TEXT                     [default: localhost]
  --skip-upload / --no-skip-upload
                                  [default: no-skip-upload]
  --install-completion            Install completion for the current shell.
  --show-completion               Show completion for the current shell, to
                                  copy it or customize the installation.
  --help                          Show this message and exit.

Command allows you to specify wildcards for engines and datasets. Results of the benchmarks are stored in the ./results/ directory.

How to update benchmark parameters?

Each engine has a configuration file, which is used to define the parameters for the benchmark. Configuration files are located in the configuration directory.

Each step in the benchmark process is using a dedicated configuration's path:

• connection_params - passed to the client during the connection phase.
• collection_params - parameters, used to create the collection, indexing parameters are usually defined here.
• upload_params - parameters, used to upload the data to the server.
• search_params - passed to the client during the search phase. Framework allows multiple search configurations for the same experiment run.

Exact values of the parameters are individual for each engine.

How to register a dataset?

Datasets are configured in the datasets/datasets.json file. Framework will automatically download the dataset and store it in the datasets directory.

How to implement a new engine?

There are a few base classes that you can use to implement a new engine.

• BaseConfigurator - defines methods to create collections, setup indexing parameters.
• BaseUploader - defines methods to upload the data to the server.
• BaseSearcher - defines methods to search the data.

See the examples in the clients directory.

Once all the necessary classes are implemented, you can register the engine in the ClientFactory.