Note that the link of the new actively maintained repository is:
https://github.com/Beliefuture/Index_EAB/
Please "STAR" the new repository if you find this project useful!
This is the code respository of the testbed proposed in the Index Advisor (EA&B) paper, which conducts a comprehensive assessment of the heuristic-based and the learning-based index advisors.
*Note that we have released a python package about this testbed at the official open-source website: index-eab · PyPI. Therefore, you can use the follwoing command to download it with little effort!
pip install index-eab==0.1.0Specifically, the testbed is comprised of three modules.
- (1) Configuration Loader: initializes a series of evaluation settings, including the benchmark, the index advisor, and the database;
- (2) Workload Generator: supports three methods for generating workloads with diverse features (e.g., query changes due to typical workload drifts) to simulate the requirements posed by various scenarios;
- (3) Index Advisor Selector: implements existing index advisors, including seven heuristic-based index advisors and ten learning-based index advisors.
The overall code structure of our Index Advisor (EA&B) project, where the critical files are marked with additional comments.
Index_EAB/
├── configuration_loader # Module 1: the evaluation settings
│ ├── becnhmark
│ ├── index_advisor
│ │ ├── heu_run_conf
│ │ │ ├──xxxx_config.json # configurations of heuristic-based index advisors
│ │ │ └── ...
│ │ └── rl_run_conf
│ │ │ ├──xxxx_config.json # configurations of learning-based index advisors
│ │ │ └── ...
│ └── database
│ │ ├── db_con.conf # configurations of database connection
│ │ └── ...
├── workload_generator # Module 2: the testing workloads
│ ├── template_based # workload from template-based generation
│ ├── perturbation_based # workload from perturbation-based generation
│ │ ├── perturb_utils
│ │ └── ...
│ ├── random # workload from random generation
│ └── gen_workload.py
├── index_advisor_selector # Module 3: the implemented index advisors
│ ├── index_candidate_generation
│ │ └── distill_model
│ │ ├── distill_utils
│ │ │ ├── distill_com.py # parameters of learned filter model
│ │ │ └── ...
│ │ └── ...
│ ├── index_selection
│ │ ├── heu_selection
│ │ │ ├── heu_utils
│ │ │ │ ├── heu_com.py # parameters of heuristic-based index ad
8000
visors
│ │ │ │ └── ...
│ │ │ ├── heu_algos
│ │ │ ├── heu_run.py # entrance of heuristic-based index advisors
│ │ │ └── ...
│ │ ├── dqn_selection
│ │ │ └── dqn_utils
│ │ │ ├── dqn_run.py # inference entrance of learning-based index advisors
│ │ │ └── ...
│ │ ├── swirl_selection
│ │ │ ├── swirl_utils
│ │ │ │ ├── swirl_com.py # parameters of learning-based index advisors
│ │ │ │ └── ...
│ │ │ ├── gym_db
│ │ │ ├── stable_baselines
│ │ │ ├── swirl_main.py # training entrance of learning-based index advisors
│ │ │ ├── swirl_run.py # inference entrance of learning-based index advisors
│ │ │ └── ...
│ │ ├── mab_selection
│ │ │ ├── bandits
│ │ │ ├── simualtion
│ │ │ ├── shared
│ │ │ │ ├── mab_com.py # parameters of learning-based index advisors
│ │ │ │ └── ...
│ │ │ ├── database
│ │ │ ├── mab_run.py # inference entrance of learning-based index advisors
│ │ │ └── ...
│ │ └── mcts_selection
│ │ ├── mcts_utils
│ │ │ ├── mcts_com.py # parameters of learning-based index advisors
│ │ │ └── ...
│ │ ├── mcts_run.py # inference entrance of learning-based index advisors
│ │ └── ...
└── ├── index_benefit_estimation
└── ├── benefit_utils
├── optmizer_cost
│ ├── optimizer_utils
│ │ ├── optimizer_com.py # parameters of statistic-based method
│ │ └── ...
│ ├── optimizer_train.py # training entrance of statistic-based method
│ ├── optimizer_infer.py # inference entrance of statistic-based method
│ └── ...
├── tree_model
│ ├── tree_cost_utils
│ │ ├── tree_cost_com.py # parameters of learned estimation model
│ │ └── ...
│ ├── tree_cost_main.py # training entrance of learned estimation model
│ ├── tree_cost_infer.py # inference entrance of learned estimation model
│ └── ...
├── index_cost_lib
│ ├── lib_train.py # training entrance of learned estimation model
│ ├── lib_infer.py # inference entrance of learned estimation model
│ └── ...
└── query_former
├── former_train.py # training entrance of learned estimation model
├── former_infer.py # inference entrance of learned estimation model
└── ...
We introduce the indispensable step, i.e., experiment setup for the experiment evaluations, you should check the following things:
- Create the database instance according to the provided toolkit;
- Create the HypoPG extension on the database instance for the usage of hypothetical index according to HypoPG/hypopg: Hypothetical Indexes for PostgreSQL (github.com);
- Create the python virtual environment. Specifically, you can utilize the following script and the corresponding file
requirements.txtis provided under the main directory. Please check the packages required are properly installed.
# Create the virtualenv `TRAP`
conda create -n Index_EAB python=3.7
# Activate the virtualenv `TRAP`
conda activate Index_EAB
# Install requirements with pip
while read requirement; do pip install $requirement; done < requirements.txt Please Specify the configuration about the benchmark, the index advisor, and the database.
-
Benchmark: set the vocabulary (already provided) to generate workloads of Query Perturbation
-
Index Advisor:
- set the configurations for the heuristic-based index advisors at
/configuration_loader/heu_run_conf - set the configurations for the learning-based index advisors at
/configuration_loader/rl_run_conf
- set the configurations for the heuristic-based index advisors at
| Parameter | Description |
|---|---|
| constraint | The constraint of the budget type (storage by default) |
| budget_MB | The constraint of the storage budget (MB) (500 by default, valid when constraint = storage) |
| max_indexes | The constraint of the maximum allowable number (5 by default, valid when constraint = number) |
| max_index_width | The constraint of the index width over the considered index candidates (2 by default) |
The parameters above are the basic configurations of index advisors. More illustrations about the fine-grained parameters (e.g., the method utilized in each underlying building block) are presented in the running script in Step 3. Index Advisor Evaluation.
- Database: set the configurations at
configuration_loader/databse/db_con.conffor the connection to your own database instance
host = -- your host --
port = -- your port --
user = -- your user --
password = -- your password --
database = -- your database --
Apart from that, we provide some files including the statistics of the database / benchmark in the directory. For example, the file configuration_loader/database/schema_tpch.json stores the schema information of the TPC-H benchmark.
The workload data provided in /workload_generator has already been preprocessed, which involves three types of the workloads, i.e., (1) template-based, (2) perturbation-based, and (3) random. These data can be utilized for direct evaluation and you can generate your own workload data organized in the following format.
[
[
1, # query ID
"SELECT MIN(mc.note) AS production_note, MIN(t.title) AS movie_title ...", # query frequency
666 # query frequency
],
...
]For example, you can generate your own perturbation-based workload, i.e., conduct query changes (e.g., add a new selection predicate) over the given workloads using the file /workload_generator/gen_workload.py. It currently supports three perturbation manners with different amplitudes that simulate the typical workload drifts introduced in our paper:
- Value Only Perturbation: modifications on the predicate values of the query templates with placeholders;
- Column Consistent Perturbation: modifications on the values and the set same of columns (e.g., change the column order in GROUP BY clause);
- Shared Table Perturbation: modifications on the SQL tokens of the same table (e.g., add a new selection predicate).
With the specified configurations in Step 1. Configuration Setup and the prepared data in Step 2. Data Preparation, we next proceed to the evaluation of different index advisors.
For example, we can evaluate heuristic-based index advisors with the following script:
| Parameter | Descritpiton |
|---|---|
| cand_gen | The methods utilized in Index Candidate Generation building block ("permutation" / "dqn_rule" / "openGauss") |
| est_model | The methods utilized in Index Benefit Estimation building block ("optimizer" / "tree" / "lib" / "queryformer") |
| process | The ddetails of the overall process of Index Selection building block |
| overhead | The time overhead spent on each building block |
python heu_run.py
--res_save /index_advisor_selector/index_selection/heu_res.json
--process --overhead
--sel_params parameters
--exp_conf_file /configuration_loader/index_advisor/heu_run_conf/{}_config.json
--constraint storage --budget_MB 500
--cand_gen permutation --est_model optimizer
--work_file /workload_generator/template_based/tpch_work_temp_multi.json
--db_conf_file /configuration_loader/database/db_con.conf
--schema_file /configuration_loader/database/schema_tpch.jsonBesides, we can evaluate learning-based index advisors with the following script:
| Parameter | Descritpiton |
|---|---|
| exp_id | The experiment ID specified to store the result under /index_advisor_selector/index_selection/swirl_selection/exp_res |
| algo | The learning-based index advisor to be assessed, i.e., "swirl", "drlinda", "dqn" |
| workload_embedder | The class of the workload representation of the learning-based index advisors |
| observation_manager | The class of the state representation of the learning-based index advisors |
| action_manager | The class of the action space of the learning-based index advisors |
| reward_calculator | The class of the reward function of the learning-based index advisors |
| rl_exp_load | The configurations of the trained learning-based index advisors |
| rl_model_load | The agent of the trained learning-based index advisors |
| rl_env_load | The environment of the trained learning-based index advisors |
# Training
python swirl_main.py
--algo swirl --exp_id swirl_tpch_v1
--timesteps 100000 --seed 666
--constraint storage --max_budgets 500
--exp_conf_file /configuration_loader/index_advisor/rl_run_conf/swirl_config.json
--work_size 18 --work_gen load
--work_type not_template --temp_num 22
--training_instances 80 --validation_testing_instances 5
--work_file /workload_generator/template_based/tpch_work_temp_multi.json
--eval_file /workload_generator/template_based/tpch_work_temp_multi.json
--db_conf_file /configuration_loader/database/db_con.conf
--schema_file /configuration_loader/database/schema_tpch.json
--colinfo_load /configuration_loader/database/colinfo_tpch.json
-------------------------------------------------------------------------------------------
# Inference
python swirl_run.py
--algo swirl --seed 666
--constraint storage --max_budgets 500
--rl_exp_load /index_advisor_selector/index_selection/swirl_selection/exp_res/swirl_tpch_v1/experiment_object.pickle
--rl_model_load /index_advisor_selector/index_selection/swirl_selection/exp_res/swirl_tpch_v1/best_mean_reward_model.zip
--rl_env_load /index_advisor_selector/index_selection/swirl_selection/exp_res/swirl_tpch_v1/vec_normalize.pkl
--work_file /workload_generator/template_based/tpch_work_temp_multi.json
--db_conf_file /configuration_loader/database/db_con.conf
We sincerely appreciate the authors of the following work for their efforts over the research of index advisors assessed in our work !
[1] Rainer Schlosser, Jan Kossmann, and Martin Boissier. 2019. Efficient Scalable Multi-attribute Index Selection Using Recursive Strategies. In ICDE. 1238–1249.
[2] Gary Valentin, Michael Zuliani, Daniel C. Zilio, Guy M. Lohman, and Alan Skelley. 2000. DB2 Advisor: An Optimizer Smart Enough to Recommend Its Own Indexes. In ICDE. 101–110.
[3] Surajit Chaudhuri and Vivek R. Narasayya. 1997. An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server. In VLDB. 146–155.
[4] Kyu-Young Whang. 1987. Index Selection in Relational Databases. Foundations of Data Organization (1987), 487–500.
[5] Nicolas Bruno and Surajit Chaudhuri. 2005. Automatic Physical Database Tuning: A Relaxation-based Approach. In SIGMOD Conference. 227–238.
[6] S. Chaudhuri and V. Narasayya. 2020. Anytime Algorithm of Database Tuning Advisor for Microsoft SQL Server. https://www.microsoft.com/en-us/research/publication/.
[7] Alberto Caprara, Matteo Fischetti, and Dario Maio. 1995. Exact and Approximate Algorithms for the Index Selection Problem in Physical Database Design. IEEE Trans. Knowl. Data Eng. 7, 6 (1995), 955–967.
[8] Debabrata Dash, Neoklis Polyzotis, and Anastasia Ailamaki. 2011. CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads. Proc. VLDB Endow. 4, 6 (2011), 362–372.
[9] Jan Kossmann, Alexander Kastius, and Rainer Schlosser. 2022. SWIRL: Selection of Workload-aware Indexes using Reinforcement Learning. In EDBT. 2:155–2:168.
[10] Zahra Sadri, Le Gruenwald, and Eleazar Leal. 2020. DRLindex: deep reinforcement learning index advisor for a cluster database. In IDEAS. 11:1–11:8.
[11] Hai Lan, Zhifeng Bao, and Yuwei Peng. 2020. An Index Advisor Using Deep Reinforcement Learning. In CIKM. 2105–2108.
[12] R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein, and Renata Borovica-Gajic. 2021. DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees. In ICDE. 600–611.
[13] Xuanhe Zhou, Luyang Liu, Wenbo Li, Lianyuan Jin, Shifu Li, Tianqing Wang, and Jianhua Feng. 2022. AutoIndex: An Incremental Index Management System for Dynamic Workloads. In ICDE. 2196–2208.
[14] Wentao Wu, Chi Wang, Tarique Siddiqui, Junxiong Wang, Vivek R. Narasayya, Surajit Chaudhuri, and Philip A. Bernstein. 2022. Budget-aware Index Tuning with Reinforcement Learning. In SIGMOD Conference. 1528–1541.
[15] Bailu Ding, Sudipto Das, Ryan Marcus, Wentao Wu, Surajit Chaudhuri, and Vivek R. Narasayya. 2019. AI Meets AI: Leveraging Query Executions to Improve Index Recommendations. In SIGMOD Conference. 1241–1258.
[16] Tarique Siddiqui, Wentao Wu, Vivek R. Narasayya, and Surajit Chaudhuri. 2022. DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning. Proc. VLDB Endow. 15, 10 (2022), 2019–2031.
[17] Jiachen Shi, Gao Cong, and Xiaoli Li. 2022. Learned Index Benefits: Machine Learning Based Index Performance Estimation. Proc. VLDB Endow. 15, 13 (2022), 3950–3962.
[18] Yue Zhao, Gao Cong, Jiachen Shi, and Chunyan Miao. 2022. QueryFormer: A Tree Transformer Model for Query Plan Representation. Proc. VLDB Endow. 15, 8 (2022), 1658–1670.