Alluxio Day XV September 15, 2022 For more on Alluxio Day: https://www.alluxio.io/alluxio-day/ For more Alluxio events: https://alluxio.io/events/ Speakers: Chunxu Tang (Senior Software Engineer, Twitter) Beinan Wang (Senior Software Engineer, Alluxio & PrestoDB Committer) With the advent of the Big Data era, it is usually computationally expensive to calculate the resource usages of a SQL query. Can we estimate the resource usages of SQL queries more efficiently without any computation in a SQL engine kernel? In this session, Chunxu and Beinan would like to introduce how Twitter’s data platform leverages a machine learning-based approach in Presto and BigQuery to estimate query utilization with 90%+ accuracy.

1. ML-Based SQL Query
Resource Usage Prediction
Chunxu Tang (Twitter)
Beinan Wang (Alluxio)
September 2022

2. 2
1 2 3
Presto System Forecasting SQL Query
Resource Usage
Conclusion & Takeaways
Roadmap

3. Presto System
A long path pursuing high scalability and availability

4. 4
Use Cases
❏ Interactive analytics
❏ Business Intelligence (BI)
❏ Dashboard reporting
❏ Program Debugging
❏ ETL/Scheduled jobs

5. 5
Hybrid-Cloud SQL Federation System

6. 6
Issues Without Query Utilization Prediction
❏ Query scheduling requires an estimate of the immediate workload
in the SQL system.
❏ Data system customers would like to know the resource
consumption estimation of their queries.
❏ Elastic scaling needs query resource usage forecasting.
Pre-existing DBMS approaches usually use query plans generated from
SQL engines.

7. Forecasting SQL Query Cost
How to use machine learning to predict the resource
utilization of a SQL query ?

8. 8
Design

9. 9
Query Resource Usage Prediction System
❏ Request logs
❏ ~1.2M records in 3 months
❏ Training cluster
❏ CPU time model
❏ Peak memory bytes model
❏ Model repository
❏ Serving cluster

10. 10
Data Preprocessing

11. 11
Data Discretization
Transforming the continuous data to discrete data
❏ Customers do not care about the accurately predicted value
❏ Query scheduling/scaling does not require an accurate value

12. 12
Feature Extraction
Word frequency vs. TF-IDF

13. 13
Model Training & Evaluation
❏ Random Forest
❏ XGBoost
❏ Logistic Regression

14. 14
Model Evaluation (Accuracy)

15. 15
Model Evaluation (Accuracy)

16. 16
Model Evaluation (Precision & Recall)
XGBoost + TF-IDF

17. 17
Model Serving
❏ Real-time model serving for CPU time
and peak memory bytes prediction
❏ Model re-training may be required
because of concept drift

18. 18
Open-Source Project
❏ Codebase
❏ https://github.com/prestodb/presto-query-predictor
❏ Docs
❏ https://chunxutang.github.io/presto-query-predictor-docs/ (temporary)
❏ https://github.com/prestodb/presto-query-predictor/tree/main/docs
❏ Paper
❏ Published at IC2E 2021 https://arxiv.org/pdf/2204.05529.pdf

19. Can we apple the ML-based
approach to other query systems?

20. 20
BigQuery Slots Prediction
❏ A BigQuery slot is a virtual CPU used by BigQuery to execute SQL
queries.
❏ The processing of each SQL query generates a log record containing a
field “total_slot_ms”.

21. 21
Evaluation
❏ Dataset
❏ ~400k BigQuery SQL records in one Google Cloud project
❏ Training cluster
❏ Total slot time model
❏ XGBoost + TF-IDF
❏ Overall accuracy
❏ 92.4%
Total slot time Precision Recall
[0, 1h) 0.94 0.80
[1h, ) 0.92 0.98

22. Conclusion & Takeaways
What we have learned

23. 23
Conclusion & Takeaways
Machine learning
can be useful
Some basic models can
give quite good results
Logs are reliable
starting points
Machine learning
requires data for training
Be careful!
Models should be
evaluated on various
metrics

24. 24
Future Work
❏ More sophisticated data discretization
❏ More state-of-art classification algorithms
❏ Model decay optimization
❏ Model interpretation
❏ ...

25. Q & A
Alluxio Slack: @Chunxu Tang @Beinan Wang
PrestoDB Slack: @Chunxu Tang @beinan