vectordb-benchmark

overview

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

1. Specify the dataset and parameters to calculate the Search Recall
2. Specify the search vectors and parameters, and calculate the QPS

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 search recall value of the server for the supported datasets and configuration parameters, so as to select index parameters and search parameters with a higher recall rate.

For parameter definitions, refer to the configuration file: ./configurations/milvus_recall.yaml

run help: python3 main.py recall --help

Usage: main.py recall [OPTIONS]

  :param host: server host

  :param engine: only support milvus

  :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

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

  :param config_name:     specify the name of the configuration file in the
  configurations directory,     and only use this configuration file;     if
  not specified, all milvus_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/milvus_concurrency.yaml

run help: python3 main.py concurrency --help

Usage: main.py concurrency [OPTIONS]

  :param host: server host

  :param engine: only support milvus

  :param config_name:     specify the name of the configuration file in the
  configurations directory,     and only use this configuration file;     if
  not specified, all milvus_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

INFO: [ParserResult] Starting sync report, interval:20s, intermediate state results are available for reference (parser_result.py:50)
INFO:  Name                            # reqs      # fails  |     Avg     Min     Max  Median    TP99  |   req/s failures/s (data_client.py:42)
INFO: --------------------------------------------------------------------------------------------------------------------- (data_client.py:46)
INFO:  Name                            # reqs      # fails  |     Avg     Min     Max  Median    TP99  |   req/s failures/s (data_client.py:42)
INFO:  search                            1467     0(0.00%)  |     131      43    1291      92     823  |   73.35    0.00 (data_client.py:44)
INFO:  Name                            # reqs      # fails  |     Avg     Min     Max  Median    TP99  |   req/s failures/s (data_client.py:42)
INFO:  search                            2706     0(0.00%)  |     154      47    1040     118     703  |   61.95    0.00 (data_client.py:44)
INFO:  Name                            # reqs      # fails  |     Avg     Min     Max  Median    TP99  |   req/s failures/s (data_client.py:42)
INFO:  search                            4209     0(0.00%)  |     137      44    1703      97    1167  |   75.15    0.00 (data_client.py:44)
INFO: [MultiProcessConcurrent] End concurrent pool (multi_process.py:49)
INFO: ------------------------------------------------- Print final status ------------------------------------------------ (data_client.py:49)
INFO:  Name                            # reqs      # fails  |     Avg     Min     Max  Median    TP99  |   req/s failures/s (data_client.py:50)
INFO:  search                            4279     0(0.00%)  |     139      43    1703     100     785  |   70.53    0.00 (data_client.py:52)

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 milvus_concurrency.yaml, and then conduct a concurrency test