QueryBot 5000 (QB5000) is a robust forecasting framework that allows a DBMS to predict the expected arrival rate of queries in the future based on historical data. This is the source code for our SIGMOD paper: Query-based Workload Forecasting for Self-Driving Database Management Systems.
./run.sh
We provide an example of the workload forecasting for a sample subset of BusTracker workload. The prediction specified in the script is on 1 hour interval and 3 day horizon. The predicted arrival rates with different models for each cluster are in the prediction-results folder. All the query templates of the workload can be found at templates.txt.
The default experimental setting is to run under CPU. If you have a GPU, you can change this parameter to True to enable GPU training.
python>=3.5 scikit-learn>=0.18.1 sortedcontainers>=1.5.7 statsmodels>=0.8.0 scipy>=0.19.0 numpy>=1.14.2 matplotlib>=2.0.2 pytorch>=0.2.0_1 (you need to install the GPU version if you want to use GPU)
We first anonymize all the queries from the real-world traces used in our experiments for privacy purposes. The components below use the anonymization results from this step as their input.
cd anonymizer
./log-anonymizer.py --help
This component extracts the templates from the anonymized queries and records the arrival rate history for each template.
cd pre-processor
./templatizer.py --help
This component groups query templates with similar arrival rate patterns into clusters.
cd clusterer
./online_clustering.py --help
generate-cluster-coverage.py generates the time series for the largest MAX_CLUSTER_NUM clusters on each day, which are used in the forecasting evaluation.
This component uses a combination of linear regression, recurrent neural network, and kernel regression to predict the arrival rate pattern of each query cluster on different prediction horizons and intervals.
cd forecaster
./exp_multi_online_continuous.py --help
This simulator populates a synthetic database with a given schema file, removes all the secondary indexes, replays the query trace of the workload, and builds appropriate indexes with the real-time workload forecasting results.
cd workload-simulator
./workload-simulator.py --help
Due to legal and privacy constraints, unfortunately we cannot publish the full datasets that we used in the experiments for the publication (especially for the two student-related Admissions and MOOC workloads). To the best of our effort, we managed to publish a subset (2% random sampling) of the BusTracker workload trace and the schema file.
We use this script to generate the sample subset of the original workload trace.
This repo does not have an end-to-end running framework. We build different components separately and pass the results through a workload simulator that connects to MySQL/PostgreSQL for experimental purposes. We are integrating the full framework into Peloton Self-Driving DBMS. Please check out our source code there for more reference.
Copyright 2018, Carnegie Mellon University
Licensed under the Apache License, Version 2.0 (the "License");
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