haitianchen / knobstuningea

An Experimental Evaluation for Database Configuration Tuning

A Comprehensive Experimental Evaluation for Database Configuration Tuning

This is the source code to the paper "Facilitating Database Tuning with Hyper-ParameterOptimization: A Comprehensive Experimental Evaluation". Please refer to the paper for the experimental details.

Table of Content

• An Efficient Database Configuration Tuning Benchmark via Surrogate
• Experimental Evaluation
  • Environment Installation
  • Workload Preparation
  • Environment Variables
  • Experiments Design
    • Exp.1: Tuning improvement over knob set generated by different important measurements.
    • Exp.2: Performance improvement and tuning cost when increasing the number of tuned knobs.
    • Exp.3: Incremental Knob Selection.
    • Exp.4: Optimizer comparision on different configuration space.
    • Exp.5: Comparison experiment for knobs heterogeneity.
    • Exp.6: Algorithm overhead comparison.
    • Exp.7: Transfering methods comparison.

• Project Code Overview

An Efficient Database Configuration Tuning Benchmark via Surrogate

Through the benchmark, you can evaluate the tuning optimizers' performance with minimum overhead.

Quick installation & Run

1. Preparations: Python == 3.7

2. Install packages and download the surrogate model

   pip install -r requirements.txt
   pip install .

The surrogate models can be found in the Google drive. To easily run the tuning benchmark, you can download the surrogate models and place them in the fold autotune/tuning_benchmark/surrogate.

3. Run the benchmark for knob selection. We use selecting 5 knobs and tuning SYSBENCH via SMAC as an example.

bash experiment/compare_knob_selection.sh SYSBENCH SMAC 5

4. Run the benchmark for optimizer. We use optimization over the configuration space of SYSBENCH as an example.

python run_benchmark.py --method=VBO --knobs_config=experiment/gen_knobs/SYSBENCH_shap.json --knobs_num=5 --workload=sysbench  --lhs_log=result/sysbench_5knobs_vbo.res --model_path=../tuning_benchmark/surrogate/RF_SYSBENCH_5knob.joblib
python run_benchmark.py --method=MBO   --knobs_config=experiment/gen_knobs/SYSBENCH_shap.json --knobs_num=5 --workload=sysbench --lhs_log=result/sysbench_5knobs_mbo.res --model_path=../tuning_benchmark/surrogate/RF_SYSBENCH_5knob.joblib
python run_benchmark.py --method=SMAC  --knobs_config=experiment/gen_knobs/SYSBENCH_shap.json --knobs_num=5 --workload=sysbench   --lhs_log=result/sysbench_5knobs_smac.res --model_path=../tuning_benchmark/surrogate/RF_SYSBENCH_5knob.joblib
python run_benchmark.py --method=TPE --knobs_config=experiment/gen_knobs/SYSBENCH_shap.json --knobs_num=5 --workload=sysbench  --lhs_log=result/sysbench_5knobs_tpe.res --model_path=../tuning_benchmark/surrogate/RF_SYSBENCH_5knob.joblib
python run_benchmark.py --method=TURBO --knobs_config=experiment/gen_knobs/SYSBENCH_shap.json --knobs_num=5 --workload=sysbench --lhs_log=result/sysbench_5knobs_turbo.res --tr_init  --model_path=../tuning_benchmark/surrogate/RF_SYSBENCH_5knob.joblib
python run_benchmark.py --method=GA --knobs_config=experiment/gen_knobs/SYSBENCH_shap.json --knobs_num=5 --workload=sysbench --lhs_log=result/sysbench_5knobs_ga.res --model_path=../tuning_benchmark/surrogate/RF_SYSBENCH_5knob.joblib

Data Description

You can find all the training data for the tuning benchmark in autotune/tuning_benchmark/data.

Experimental Evaluation

Environment Installation

In our experiments, the operating system is Linux 4.9. We conduct experimets on MySQL 5.7.19.

1. Preparations: Python == 3.7

2. Install packages

   pip install -r requirements.txt
   pip install .

3. Download and install MySQL 5.7.19 and boost

   wget http://sourceforge.net/projects/boost/files/boost/1.59.0/boost_1_59_0.tar.gz
   wget https://dev.mysql.com/get/Downloads/MySQL-5.7/mysql-boost-5.7.19.tar.gz
   
   sudo cmake . -DCMAKE_INSTALL_PREFIX=PATH_TO_INSTALL -DMYSQL_DATADIR=PATH_TO_DATA -DDEFAULT_CHARSET=utf8 -DDEFAULT_COLLATION=utf8_general_ci -DMYSQL_TCP_PORT=3306 -DWITH_MYISAM_STORAGE_ENGINE=1 -DWITH_INNOBASE_STORAGE_ENGINE=1 -DWITH_ARCHIVE_STORAGE_ENGINE=1 -DWITH_BLACKHOLE_STORAGE_ENGINE=1 -DWITH_MEMORY_STORAGE_ENGINE=1 -DENABLE_DOWNLOADS=1 -DDOWNLOAD_BOOST=1 -DWITH_BOOST=PATH_TO_BOOST;
   sudo make -j 16;
   sudo make install;

Workload Preparation

SYSBENCH

Download and install

git clone https://github.com/akopytov/sysbench.git
./autogen.sh
./configure
make && make install

Load data

sysbench --db-driver=mysql --mysql-host=$HOST --mysql-socket=$SOCK --mysql-port=$MYSQL_PORT --mysql-user=root --mysql-password=$PASSWD --mysql-db=sbtest --table_size=800000 --tables=150 --events=0 --threads=32 oltp_read_write prepare > sysbench_prepare.out

OLTP-Bench

We install OLTP-Bench to use the following workload: TPC-C, SEATS, Smallbank, TATP, Voter, Twitter, SIBench.

• Download

git clone https://github.com/oltpbenchmark/oltpbench.git

• To run oltpbenchmark outside the folder, modify the following file:

  • ./src/com/oltpbenchmark/DBWorkload.java (Line 85)

    pluginConfig = new XMLConfiguration("PATH_TO_OLTPBENCH/config/plugin.xml"); # modify this
    

  • ./oltpbenchmark

    
    #!/bin/bash
    
    java -Xmx8G -cp `$OLTPBENCH_HOME/classpath.sh bin` -Dlog4j.configuration=$OLTPBENCH_HOME/log4j.properties com.oltpbenchmark.DBWorkload $@
    
    

  • ./classpath.sh

    #!/bin/bash
    
    echo -ne "$OLTPBENCH_HOME/build"
    
    for i in `ls $OLTPBENCH_HOME/lib/*.jar`; do
    
        # IMPORTANT: Make sure that we do not include hsqldb v1
    
        if [[ $i =~ .*hsqldb-1.* ]]; then
    
            continue
    
        fi
    
        echo -ne ":$i"
    
    done
    

• Install

  ant bootstrap
  ant resolve
  ant build

Join-Order-Benchmark (JOB)

Download IMDB Data Set from http://homepages.cwi.nl/~boncz/job/imdb.tgz.

Follow the instructions of https://github.com/winkyao/join-order-benchmark to load data into MySQL.

Environment Variables

Before running the experiments, the following environment variables require to be set.

export SYSBENCH_BIN=PATH_TO_sysbench/src/sysbench
export OLTPBENCH_BIN=PATH_TO_oltpbench/oltpbenchmark
export MYSQLD=PATH_TO_mysqlInstall/bin/mysqld
export MYSQL_SOCK=PATH_TO_mysql/base/mysql.sock
export MYCNF=PATH_TO_autotune/template/experiment_normandy.cnf
export DATADST=PATH_TO_mysql/data
export DATASRC=PATH_TO_mysql/data_copy

Experiments Design

All optimization methods are listed as follows:

Method	String of ${METHOD}
Vanilla BO	VBO
Mixed-Kernel BO	MBO
Sequential Model-based Algorithm Configuration	SMAC
Tree-structured Parzen Estimator	TPE
Trust-Region BO	TURBO
Deep Deterministic Policy Gradient	DDPG
Genetic Algorithm	GA

Exp.1: Tuning improvement over knob set generated by different important measurements.

Compared importance measurements: lasso, gini, fanova, ablation, shap.

To conduct the experiment shown in Figure 3(a), the script is as follows. Please specify ${lhs_log}.

python train.py --knobs_config=experiment/gen_knobs/JOB_lasso.json    --knobs_num=5 --method=VBO --workload=job --dbname=imdboload --y_variable=lat --lhs_num=10 --lhs_log=${lhs_log}
python train.py --knobs_config=experiment/gen_knobs/JOB_gini.json     --knobs_num=5 --method=VBO --workload=job --dbname=imdboload --y_variable=lat --lhs_num=10 --lhs_log=${lhs_log}
python train.py --knobs_config=experiment/gen_knobs/JOB_fanova.json   --knobs_num=5 --method=VBO --workload=job --dbname=imdboload --y_variable=lat --lhs_num=10 --lhs_log=${lhs_log}
python train.py --knobs_config=experiment/gen_knobs/JOB_ablation.json --knobs_num=5 --method=VBO --workload=job --dbname=imdboload --y_variable=lat --lhs_num=10 --lhs_log=${lhs_log}
python train.py --knobs_config=experiment/gen_knobs/JOB_shap.jso      --knobs_num=5 --method=VBO --workload=job --dbname=imdboload --y_variable=lat --lhs_num=10 --lhs_log=${lhs_log}

python train.py --knobs_config=experiment/gen_knobs/JOB_lasso.json    --knobs_num=20 --method=VBO --workload=job --dbname=imdboload --y_variable=lat --lhs_num=10 --lhs_log=${lhs_log}
python train.py --knobs_config=experiment/gen_knobs/JOB_gini.json     --knobs_num=20 --method=VBO --workload=job --dbname=imdboload --y_variable=lat --lhs_num=10 --lhs_log=${lhs_log}
python train.py --knobs_config=experiment/gen_knobs/JOB_fanova.json   --knobs_num=20 --method=VBO --workload=job --dbname=imdboload --y_variable=lat --lhs_num=10 --lhs_log=${lhs_log}
python train.py --knobs_config=experiment/gen_knobs/JOB_ablation.json --knobs_num=20 --method=VBO --workload=job --dbname=imdboload --y_variable=lat --lhs_num=10 --lhs_log=${lhs_log}
python train.py --knobs_config=experiment/gen_knobs/JOB_shap.jso      --knobs_num=20 --method=VBO --workload=job --dbname=imdboload --y_variable=lat --lhs_num=10 --lhs_log=${lhs_log}

To conduct the experiments in (b), (c), and (d), modify ${knobs_num},${method},${workload}, ${dbname}, and ${y_variable}, where

• ${knobs_num} = 5, 20

• ${method} = VBO, DDPG

• ${workload} = job, sysbench

  • if ${workload} == job, then ${dbname} = imdbload, ${y_variable}=lat
  • if ${workload} == sysbench, then ${dbname} =sbtest , ${y_variable}=tps

Note${knobs_config} indicates the configuration file where knobs are ranked by importance.

• We provide the configuration file generated on our VM: experiment/gen_knobs/${workload}_${measure}.json.
• You can also generate new configuration file with samples in your environment.

Exp.2: Performance improvement and tuning cost when increasing the number of tuned knobs.

To conduct the experiment shown in Figure 5 (a) and 5 (b), the script is as follows.

python train.py --method=VBO --workload=job --dbname=imdbload --y_variable=lat --lhs_num=10 --knobs_num=${knobs_num} --knobs_config=experiment/gen_knobs/JOB_shap.json --lhs_log=${lhs_log}
python train.py --method=VBO --workload=sysbench --dbname=sbtest --y_variable=tps --lhs_num=10 --knobs_num=${knobs_num} --knobs_config=experiment/gen_knobs/SYSBENCH_shap.json --lhs_log=${lhs_log}

Please specify ${knobs_num} and ${lhs_log}, where

• ${knobs_num} = 5, 10, 15, 20, 30, 50, 70, 90, 197

Exp.3: Incremental Knob Selection.

Compared methods: 5 Knobs, 20 Knobs, increase, decrease.

To conduct the experiment shown in Figure 6(a), the script is as follows. Please specify ${lhs_log}.

python train.py --method=VBO       --knobs_num=5  --workload=job --y_variable=lat --dbname=imdbload --knobs_config=experiment/gen_knobs/JOB_shap.json --lhs_log=${lhs_log}
python train.py --method=VBO       --knobs_num=20 --workload=job --y_variable=lat --dbname=imdbload --knobs_config=experiment/gen_knobs/JOB_shap.json --lhs_log=${lhs_log}
python train.py --method=increase --knobs_num=-1 --workload=job --y_variable=lat --dbname=imdbload --knobs_config=experiment/gen_knobs/JOB_shap.json --lhs_log=${lhs_log}
python train.py --method=decrease   --knobs_num=-1 --workload=job --y_variable=lat --dbname=imdbload --knobs_config=experiment/gen_knobs/JOB_shap.json --lhs_log=${lhs_log}

To conduct the experiment shown in (b), you can

• replace --workload=JOB --y_variable=lat with --workload=sysbench --y_variable=tps

Exp.4: Optimizer comparision on different configuration space.

Compared optimizers: VBO, MBO, SMAC, TPE, TURBO, DDPG, GA.

To conduct the experiment shown in Figure 7(a), the script is as follows. Please specify ${lhs_log}.

python train.py --method=VBO   --knobs_num=5 --workload=job --y_variable=lat --dbname=imdbload --knobs_config=experiment/gen_knobs/JOB_shap.json --lhs_log=${lhs_log}
python train.py --method=MBO   --knobs_num=5 --workload=job --y_variable=lat --dbname=imdbload --knobs_config=experiment/gen_knobs/JOB_shap.json --lhs_log=${lhs_log}
python train.py --method=SMAC  --knobs_num=5 --workload=job --y_variable=lat --dbname=imdbload --knobs_config=experiment/gen_knobs/JOB_shap.json --lhs_log=${lhs_log}
python train.py --method=TPE   --knobs_num=5 --workload=job --y_variable=lat --dbname=imdbload --knobs_config=experiment/gen_knobs/JOB_shap.json --lhs_log=${lhs_log}
python train.py --method=TURBO --knobs_num=5 --workload=job --y_variable=lat --dbname=imdbload --knobs_config=experiment/gen_knobs/JOB_shap.json --lhs_log=${lhs_log}
python train.py --method=DDPG  --knobs_num=5 --workload=job --y_variable=lat --dbname=imdbload --knobs_config=experiment/gen_knobs/JOB_shap.json --lhs_log=${lhs_log}
python train.py --method=GA    --knobs_num=5 --workload=job --y_variable=lat --dbname=imdbload --knobs_config=experiment/gen_knobs/JOB_shap.json --lhs_log=${lhs_log}

To conduct the experiment shown in (b), (c), (d), (e), (f), and (g), you can

• replace --knobs_num=5 with--knobs_num=20 or --knobs_num=197
• replace --workload=JOB --y_variable=lat --dbname=imdbload with --workload=sysbench --y_variable=tps --dbname=sbtest

Exp.5: Comparison experiment for knobs heterogeneity.

Compared optimizers: VBO, MBO, SMAC, DDPG.

To conduct the experiment shown in Figure 8(a) and (b), the script is as follows.

python train.py --method=${method} --knobs_num=20 --workload=job --y_variable=lat --dbname=${dbname}   --knobs_config=experiment/gen_knobs/JOB_continuous.json --lhs_log=${lhs_log} --lhs_num=10
python train.py --method=${method} --knobs_num=20 --workload=job --y_variable=lat --dbname=${dbname}   --knobs_config=experiment/gen_knobs/JOB_heterogeneous.json --lhs_log=${lhs_log} --lhs_num=10

Please specify ${method}, ${dbname} and ${lhs_log}, where

• ${method} is one of VBO, MBO, SMAC, DDPG.

Exp.6: Algorithm overhead comparison.

Compared optimizers: MBO, SMAC, TPE, TURBO, DDPG, GA.

To conduct the experiment shown in Figure 8(a) and (b), the script is as follows.

python train.py --method=${method} --knobs_num=20 --workload=job --y_variable=lat --dbname=${dbname}   --knobs_config=experiment/gen_knobs/job_shap.json --lhs_log=${lhs_log} --lhs_num=10

Please specify ${method}, ${dbname} and ${lhs_log}, where

• ${method} is one of MBO, SMAC, TPE, TURBO, DDPG, GA.

Note if you have already done Exp.4, you can skip running the above script and analyze log files in script/log/.

Exp.7: Transfering methods comparison.

Compared methods: RGPE-MBO, RGPE-SMAC, MAP-MBO, MAP-SMAC, FineTune-DDPG

To conduct the experiment shown in Table 9, there are two steps:

• Pre-train on source workloads (Smallbank, SIBench, Voter, Seats, TATP);
• Validate on target workloads (TPCC, SYSBENCH, Twitter).

Scripts for pre-trains is similar to the ones for Exp.4

To validate on target workloads, the scripts are as follows.

python train.py --method=MBO  --RGPE --source_repo=${repo}         --knobs_num=20 --workload=job --y_variable=lat --dbname=tpcc   --knobs_config=experiment/gen_knobs/oltp.json --lhs_log=${lhs_log} --lhs_num=10 
python train.py --method=SMAC --RGPE --source_repo=${repo}         --knobs_num=20 --workload=job --y_variable=lat --dbname=tpcc   --knobs_config=experiment/gen_knobs/oltp.json --lhs_log=${lhs_log} --lhs_num=10  
python train.py --method=MBO  --workload_map --source_repo=${repo} --knobs_num=20 --workload=job --y_variable=lat --dbname=tpcc   --knobs_config=experiment/gen_knobs/oltp.json --lhs_log=${lhs_log} --lhs_num=10 
python train.py --method=SMAC --workload_map --source_repo=${repo} --knobs_num=20 --workload=job --y_variable=lat --dbname=tpcc   --knobs_config=experiment/gen_knobs/oltp.json --lhs_log=${lhs_log} --lhs_num=10 
python train.py --method=DDPG --params=model_params/${ddpg_params} --knobs_num=20 --workload=job --y_variable=lat --dbname=tpcc   --knobs_config=experiment/gen_knobs/oltp.json --lhs_log=${lhs_log} --lhs_num=10 

Note that

• for RGPE- methods, you should specify --RGPE --source_repo=${repo}
• for MAP- methods, you should specify --workload_map --source_repo=${repo}
• for FineTune-DDPG, you should specify --params=model_params/${ddpg_params}

Project Code Overview

• autotune/tuner.py : the implemented optimization methods.
• autotune/dbenv.py : the interacting functions with database.
• script/train.py : the python script to start an experiment.
• script/experiment/gen_knob : the knob importance ranking files generated by different methods.