Can I read On Enhancing Root Cause Analysis with SQL Summaries for Failures in Database Workload Replays at SAP HANA on EtoBox?

On Enhancing Root Cause Analysis with SQL Summaries for Failures in Database Workload Replays at SAP HANA by Jambigi, Neetha; Hammesfahr, Joshua; Mueller, Moritz; Bach, Thomas; Felderer, Michael is a scholarly article available to read on EtoBox.

What is On Enhancing Root Cause Analysis with SQL Summaries for Failures in Database Workload Replays at SAP HANA about?

Capturing the workload of a database and replaying this workload for a new version of the database can be an effective approach for regression testing. However, false positive errors caused by many factors such as data privacy limitations, time dependency or non-determinism in multi-threaded environment can negatively impact the effectiveness. Therefore, we employ a machine learning based framework to automate the root cause analysis of failures found during replays. However, handling unseen novel issues not found in the training data is one general challenge of machine learning approaches with respect to generalizability of the learned model. We describe how we continue to address this challenge for more robust long-term solutions. From our experience, retraining with new failures is inadequate due to features overlapping across distinct root causes. Hence, we leverage a large language model (LLM) to analyze failed SQL statements and extract concise failure summaries as an additional feature to enhance the classification process. Our experiments show the F1-Macro score improved by 4.77% for our data. We consider our approach beneficial for providing end users with additional infor

Author

Jambigi, Neetha; Hammesfahr, Joshua; Mueller, Moritz; Bach, Thomas; Felderer, Michael

Published

2024

Language

EN