An inquiry into machine learning-based automatic configuration tuning services on real-world database management systems

Modern database management systems (DBMS) expose dozens of configurable knobs that control their runtime behavior. Setting these knobs correctly for an application's workload can improve the performance and efficiency of the DBMS. But because of their complexity, tuning a DBMS often requires considerable effort from experienced database administrators (DBAs). Recent work on automated tuning methods using machine learning (ML) have shown to achieve better performance compared with expert DBAs. These ML-based methods, however, were evaluated on synthetic workloads with limited tuning opportunities, and thus it is unknown whether they provide the same benefit in a production environment. To better understand ML-based tuning, we conducted a thorough evaluation of ML-based DBMS knob tuning methods on an enterprise database application. We use the OtterTune tuning service to compare three state-of-the-art ML algorithms on an Oracle installation with a real workload trace. Our results with OtterTune show that these algorithms generate knob configurations that improve performance by 45% over enterprise-grade configurations. We also identify deployment and measurement issues that were overlooked by previous research in automated DBMS tuning services.

An Examination of Test Score Trajectories Around School Switching Due to Grade Configuration

Do grade configurations affect student academic performance? To bring new evidence to this question, I use recent district-by-grade data for nearly the entire United States which contain measures of test score achievement and rates of school switching induced by grade configuration. Past research has found that student performance is on average relatively low following switches due to grade configuration, but in fact students perform relatively better in the grades just prior to these switches. In the national data, I find that this so-called “top dog/bottom dog” pattern appears for all terminal grade choices among grades 3 through 8, is geographically widespread, and is robust to controlling for grade-specific effects of a rich set of covariates. Thus I establish that the top dog/bottom dog pattern is a very pervasive phenomenon in American education. I explore potential mechanisms and discuss policy and research implications.

The Weaknesses and Shortcomings of the Junior High School in Israel: Some Insights into Grade Configurations of Educational Systems

The current study aimed to explore the weaknesses of the junior high school system in Israel, a country whose first junior high schools (Grades 7–9) were established in the early 1970s and whose educational system differs from the systems of Europe and North America in terms of structure, ideology, and control. Based on semistructured interviews with role incumbents in the junior high school system, the study found major difficulties in areas such as student behavior, student achievements, personal attention, interschool transition, resources, clear policy, and so forth. Implications for the planning of grade configurations worldwide are suggested.