Hosting database services on cloud systems has become a common practice. This has led to the increasing volume of database workloads, which provides the opportunity for pattern analysis. Discovering workload patterns from a business logic perspective is conducive to better understanding the trends and characteristics of the database system. However, existing workload pattern discovery systems are not suitable for large-scale cloud databases which are commonly employed by the industry. This is because the workload patterns of large-scale cloud databases are generally far more complicated than those of ordinary databases. In this paper, we propose Alibaba Workload Miner (AWM), a real-time system for discovering workload patterns in complicated large-scale workloads. AW M encodes and discovers the SQL query patterns logged from user requests and optimizes the querying processing based on the discovered patterns. First, Data Collection & Preprocessing Module collects streaming query logs and encodes them into high-dimensional feature embeddings with rich semantic contexts and execution features. Next, Online Workload Mining Module separates encoded query by business groups and discovers the workload patterns for each group. Meanwhile, Offline Training Module collects labels and trains the classification model using the labels. Finally, Pattern-based Optimizing Module optimizes query processing in cloud databases by exploiting discovered patterns. Extensive experimental results on one synthetic dataset and two real-life datasets (extracted from Alibaba Cloud databases) show that AW M enhances the accuracy of pattern discovery by 66% and reduce the latency of online inference by 22%, compared with the state-of-the-arts.

Time series forecasting, that predicts events through a sequence of time, has received increasing attention in past decades. The diverse range of time series forecasting models presents a challenge for selecting the most suitable model for a given dataset. As such, the Alibaba Cloud database monitoring system must address the issue of selecting an optimal forecasting model for a single time series data. While several model selection frameworks, including AutoAI-TS, have been developed to predict a dataset, their effectiveness may be limited as they may not adapt well to all types of time series, resulting in reduced prediction accuracy. Alternatively, models such as AutoForecast, which train on individual data points, may offer better adaptability but are limited by longer training time required. In this paper, we introduce SimpleTS, a versatile framework for time series forecasting that exhibits high efficiency and accuracy across all types of time series data. When performing an online prediction task, SimpleTS first classifies input time series into one type, and then efficiently selects the most suitable prediction model for this type. To optimize performance, SimpleTS (i) clusters models with similar performance to improve the efficiency of classification; (ii) uses soft labeling and weighted representation learning to achieve higher classification accuracy for different time series types. Extensive experiments on 3 private datasets and 52 public datasets show that SimpleTS outperforms the state-of-the-art toolkits in terms of both training time and prediction accuracy.