vectordb-benchmark

Overview

This is an open-source benchmark for evaluating the performance of vector databases, the main functions are as follows:

Specify the dataset and parameters to calculate the Search Recall
Specify the search vectors and parameters, and calculate the RPS

Run benchmark server

You can use your existing server, or deploy the server through the docker-compose provided by the tool, using the following command:

cd .server/<engine>/; docker-compose up -d

Run benchmark client

Logs of the benchmarks are stored in the ./results/result.*
Datasets of the benchmarks are stored in the ./datasets/dataset_files/
Configs of the benchmarks are stored in the ./configurations/*.yaml

install dependencies:

python3 (>=3.8)

pip install -r requirements.txt

run recall benchmark

This method mainly provides the calculation of the server's search recall value for the supported datasets and configuration parameters, thus selecting index parameters and search parameters with a higher recall rate.

For parameter definitions, refer to the configuration file: ./configurations/<engine>_recall.yaml

run help: python3 main.py recall --help

Usage: main.py recall [OPTIONS]

  :param host: server host

  :param engine: only supports milvus / elasticsearch

  :param dataset_name: four datasets are available to choose from as follows:
  glove-25-angular / glove-100-angular / gist-960-euclidean / deep-image-96-angular /
  sift-128-euclidean

  :param prepare: search an existing collection without skipping data
  preparation

  :param config_name:     specify the name of the configuration file in the
  configurations directory by prefix matching;     if not specified, all
  <engine>_recall*.yaml in the configuration directory will be used.

Options:
  --host TEXT               [default: localhost]
  --engine TEXT             [default: milvus]
  --dataset-name TEXT       [default: glove-25-angular]
  --prepare / --no-prepare  [default: prepare]
  --config-name TEXT
  --help                    Show this message and exit.

example: python3 main.py recall --host localhost --engine milvus --dataset-name glove-25-angular

run concurrency benchmark

This method is used to perform concurrent search operations on an existing collection and given concurrency parameters, and print concurrency test results such as RPS.

For parameter definitions, refer to the configuration file: ./configurations/<engine>_concurrency.yaml

run help: python3 main.py concurrency --help

Usage: main.py concurrency [OPTIONS]

  :param host: server host

  :param engine: only supports milvus / elasticsearch

  :param config_name:     specify the name of the configuration file in the
  configurations directory by prefix matching;     if not specified, all
  <engine>_concurrency*.yaml in the configuration directory will be used.

Options:
  --host TEXT         [default: localhost]
  --engine TEXT       [default: milvus]
  --config-name TEXT
  --help              Show this message and exit.

example: python3 main.py concurrency --host localhost --engine milvus

reqs: the total number of api requests
fails: the total number of api failed requests
Avg: average response time of interface within statistical interval
Min: minimum response time of interface within statistical interval
Max: maximum response time of interface within statistical interval
Median: median response time of interface within statistical interval
TP99: TP99 response time of interface within statistical interval
req/s: the number of requests per second for the api in the statistical interval
failures/s: the number of failed requests per second of the api within the statistical interval

[ParserResult] Starting sync report, interval:20s, intermediate state results are available for reference
 Name                            # reqs      # fails  |     Avg     Min     Max  Median    TP99  |   req/s failures/s
---------------------------------------------------------------------------------------------------------------------
 Name                            # reqs      # fails  |     Avg     Min     Max  Median    TP99  |   req/s failures/s
 search                            4339     0(0.00%)  |      41      29     441      38      72  |  216.95    0.00
 Name                            # reqs      # fails  |     Avg     Min     Max  Median    TP99  |   req/s failures/s
 search                            9034     0(0.00%)  |      42      29     307      39      74  |  234.75    0.00
 Name                            # reqs      # fails  |     Avg     Min     Max  Median    TP99  |   req/s failures/s
 search                           13587     0(0.00%)  |      43      29     433      39     199  |  227.65    0.00
[MultiProcessConcurrent] End concurrent pool
------------------------------------------------- Print final status ------------------------------------------------
 Name                            # reqs      # fails  |     Avg     Min     Max  Median    TP99  |   req/s failures/s
 search                           13966     0(0.00%)  |      42      29     441      38     170  |  225.73    0.00
------------------------ Print the status without start and end warmup time:0s as a reference -----------------------
 Name                            # reqs      # fails  |     Avg     Min     Max  Median    TP99  |   req/s failures/s
 search                           13966     0(0.00%)  |      42      29     441      38     170  |  225.73    0.00
[ParserResult] Completed sync report

If you want to perform a concurrency test based on the search parameter with the most appropriate recall value, you can update the search parameters of the recall scene to <engine>_concurrency.yaml, and then conduct a concurrency test

Contributing

Contributions to vectordb-benchmark are welcome from everyone. See Guidelines for Contributing for details on adding a new engine and the contribution workflow.

threaddao / vectordb-benchmark Goto Github PK