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With the growing market of cloud databases, careful detection and elimination of slow queries are of great importance to service stability. Previous studies focus on optimizing the slow queries that result from internal reasons (e.g., poorly-written SQLs). In this work, we discover a different set of slow queries which might be more hazardous to database users than other slow queries. We name such queries Intermittent Slow Queries (iSQs), because they usually result from intermittent performance issues that are external (e.g., at database or machine levels). Diagnosing root causes of iSQs is a tough but very valuable task. This paper presents iSQUAD, Intermittent Slow QUery Anomaly Diagnoser, a framework that can diagnose the root causes of iSQs with a loose requirement for human intervention. Due to the complexity of this issue, a machine learning approach comes to light naturally to draw the interconnection between iSQs and root causes, but it faces challenges in terms of versatility, labeling overhead and interpretability. To tackle these challenges, we design four components, i.e., Anomaly Extraction, Dependency Cleansing, Type-Oriented Pattern Integration Clustering (TOPIC) and Bayesian Case Model. iSQUAD consists of an offline clustering & explanation stage and an online root cause diagnosis & update stage. DBAs need to label each iSQ cluster only once at the offline stage unless a new type of iSQs emerges at the online stage. Our evaluations on real-world datasets from Alibaba OLTP Database show that iSQUAD achieves an iSQ root cause diagnosis average F1-score of 80.4%, and outperforms existing diagnostic tools in terms of accuracy and efficiency.

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Objective. To investigate the intra- and inter-rater reliability of three measurements on painful and pain-free sides in participants with chronic low back pain (CLBP) at different ages. Methods. We recruited 60 participants with CLBP and divided them equally into a group of younger participants with chronic low back pain (18 ≤ age ≤ 35, Y-CLBP) and a group of older participants with chronic low back pain (36 ≤ age ≤ 65, O-CLBP). Participants were assessed by two testers within the same day (10 min interval), and one of the testers repeated the assessment program 24 h later. The intraclass correlation coefficient (ICC) was used to assess reliability. The Pearson correlation coefficient was used to analyze the correlation between tactile acuity and age, waistline, and pain-related variables. Results. In the Y-CLBP group, the intra-rater reliability of two-point discrimination (TPD), point-to-point test (PTP), and two-point estimation (TPE) on the painful and pain-free sides was good (ICC range: 0.74–0.85), whereas the inter-rater reliability of TPD, PTP, and TPE on the painful and pain-free sides was moderate to good (ICC range: 0.65–0.76). In the O-CLBP group, the intra-rater reliability of TPD, PTP, and TPE on the painful and pain-free sides was good (ICC range: 0.75–0.85), whereas the inter-rater reliability of TPD, PTP, and TPE on the painful and pain-free sides was moderate to good (ICC range: 0.70–0.85). Age, waistline, duration of pain, maximum pain, general pain, and unpleasant score caused by pain were positively correlated with tactile acuity thresholds (D-TPD, A-TPD, PTP, and TPE) (r > 0.365, p < 0.05 ). When BMI was controlled, age, waistline, and pain-related variables were positively correlated with tactile acuity thresholds (r > 0.388; p < 0.05 ). Conclusion. In the participants of Y-CLBP and O-CLBP groups, TPD, PTP, and TPE have moderate-to-good intra- and inter-rater reliability on the painful and pain-free sides of the fifth lumbar vertebrae.

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