VDTuner

VDTuner is tested on a server configured with CentOS 7.9.2009 (Linux 5.5.0) and Python 3.11. The evaluated Vector Database Management System and benchamark is Mivlus (version 2.3.1) and vector-db-benchmark.

See the preprint at http://arxiv.org/abs/2404.10413

Dependencies

1. Make sure Milvus (2.3.1 version with docker-compose) is deployed on your server.
2. Install benchmark vector-db-benchmark from github source.
3. Install Python 3.11 and necessary package BoTorch.

Preparations

Modify the defualt engine in benchmark.

• According to Milvus configuration document, modify (or add) the file docker-compose.yml (Milvus startup file), milvus.yaml (Milvus configuration file for utilizing) and milvus.yaml.backup (Milvus configuration file for copying and modifying) in your benchmark path vector-db-benchmark-master/engine/servers/milvus-single-node.
• Copy the modified run_engine.sh to vector-db-benchmark-master/run_engine.sh.
• After that, you can test if Milvus deployed successfully on your server by testing a relatively small dataset. Go to vector-db-benchmark-master and run: sudo ./run_engine.sh "" "" random-100.

Download dataset.

• Take GloVe as an example, download it to vector-db-benchmark-master/datasets/glove-100-angular/glove-100-angular.hdf5.

Specify the similarity search requests.

• Modify the file vector-db-benchmark-master/experiments/configurations/milvus-single-node.json to a defualt index configuration as follow. The parameter parallel can be modified according to your server specifications.

   [
     {
       "name": "milvus-p10",
       "engine": "milvus",
       "connection_params": {},
       "collection_params": {},
       "search_params": [
         {
           "parallel": 10,
           "params": {}
         }
       ],
       "upload_params": {
         "parallel": 10,
         "index_type": "AUTOINDEX",
         "index_params": {}
       }
     }
   ]

• Specify your dataset and timeout limit. In file auto-configure/vdtuner/utils.py (line 117), assume we test dataset GloVe, with a maximum of 15 minutes for each workload replay:

   result = sp.run(f'sudo timeout 900 {RUN_ENGINE_PATH} "" "" glove-100-angular', shell=True, stdout=sp.PIPE)

Specify your config file path.

• To run VDTuner, you need to specify the configuration file of tuning parameters and benchmark path. Here is an example.
  In file auto-configure/configure.py, line 4-9:

   with open('/home/ytn/milvusTuning/auto-configure/index_param.json', 'r') as f:
       INDEX_PARAM_DICT = json.load(f)
  
   CONF_PATH = r'/home/ytn/milvusTuning/vector-db-benchmark-master/experiments/configurations/milvus-single-node.json'
   ORIGIN_PATH = r'/home/ytn/milvusTuning/vector-db-benchmark-master/engine/servers/milvus-single-node/milvus.yaml.backup'
   ADJUST_PATH = r'/home/ytn/milvusTuning/vector-db-benchmark-master/engine/servers/milvus-single-node/milvus.yaml'

  In file auto-configure/vdtuner/utils.py, line 13-14:

   KNOB_PATH = r'/home/ytn/milvusTuning/auto-configure/whole_param.json'
   RUN_ENGINE_PATH = r'/home/ytn/milvusTuning/vector-db-benchmark-master/run_engine.sh'

Run VDTuner

Start auto-tuning

• Congratulations! You can run VDTuner to optimize your vector database now! Go to auto-configure/vdtuner/ and run:

   python3.11 main_tuner.py

  Note that this would take very long time (about 30000s for 200 iterations with dataset GloVe), because VDTuner iteratively performs workload replay and configuration recommendation. You can change the number of iterations as desired in main_tuner.py.

• Results of sampled configurations and optimizer's internal information will be logged to record.log and pobo_record.log in real time. Here is an example output in record.log:

   [1] 125 {index_type: FLAT, nlist: 128, nprobe: 10, m: 10, nbits: 8, M: 32, efConstruction: 256, ef: 500, reorder_k: 500} {dataCoord*segment*maxSize: 512, dataCoord*segment*sealProportion: 0.23, queryCoord*autoHandoff: True, queryCoord*autoBalance: True, common*gracefulTime: 5000, dataNode*segment*insertBufSize: 16777216, rootCoord*minSegmentSizeToEnableIndex: 1024} 230.5802223315391 0.9999830000000002 125
   [2] 214 {index_type: IVF_FLAT, nlist: 128, nprobe: 10, m: 10, nbits: 8, M: 32, efConstruction: 256, ef: 500, reorder_k: 500} {dataCoord*segment*maxSize: 512, dataCoord*segment*sealProportion: 0.23, queryCoord*autoHandoff: True, queryCoord*autoBalance: True, common*gracefulTime: 5000, dataNode*segment*insertBufSize: 16777216, rootCoord*minSegmentSizeToEnableIndex: 1024} 1086.9213657571365 0.8496440000000001 88
   [3] 302 {index_type: IVF_SQ8, nlist: 128, nprobe: 10, m: 10, nbits: 8, M: 32, efConstruction: 256, ef: 500, reorder_k: 500} {dataCoord*segment*maxSize: 512, dataCoord*segment*sealProportion: 0.23, queryCoord*autoHandoff: True, queryCoord*autoBalance: True, common*gracefulTime: 5000, dataNode*segment*insertBufSize: 16777216, rootCoord*minSegmentSizeToEnableIndex: 1024} 908.6127610863159 0.8461550000000001 88
  ...

Citation

If you use VDTuner in your scientific article, please cite our ICDE 2024 paper:

@inproceedings{yang2024vdtuner,
     title={VDTuner: Automated Performance Tuning for Vector Data Management Systems},
     author={Yang, Tiannuo and Hu, Wen and Peng, Wangqi and Li, Yusen and Li, Jianguo and Wang, Gang and Liu, Xiaoguang},
     booktitle={2024 IEEE 40th International Conference on Data Engineering (ICDE)},
     year={2024}
}

Contributors

Tiannuo Yang [email protected]
Wangqi Peng [email protected]
-- From Lab NBJL and Ant Group