Efficient and consistent feature computation is crucial for a wide range of online ML applications. Typically, feature computation is divided into two distinct phases, i.e., offline stage for model training and online stage for model serving. These phases often rely on execution engines with different interface languages and function implementations, causing significant inconsistencies. Moreover, many online ML features involve complex time-series computations (e.g., functions over varied-length table windows) that differ from standard streaming and analytical queries. Existing data processing systems (e.g., Spark, Flink, DuckDB) often incur multi-second latencies for these computations, making them unsuitable for real-time online ML applications that demand timely feature updates. This paper presents OpenMLDB, a feature computation system deployed in 4Paradigm's SageOne platform and over 100 real scenarios. Technically, OpenMLDB first employs a unified query plan generator for consistent computation results across the offline and online stages, significantly reducing feature deployment overhead. Second, OpenMLDB provides an online execution engine that resolves performance bottlenecks caused by long window computations (via pre-aggregation) and multi-table window unions (via data self-adjusting). It also provides a high-performance offline execution engine with window parallel optimization and time-aware data skew resolving. Third, OpenMLDB features a compact data format and stream-focused indexing to maximize memory usage and accelerate data access. Evaluations in testing and real workloads reveal significant performance improvements and resource savings compared to the baseline systems. The open community of OpenMLDB now has over 150 contributors and gained 1.6k stars on GitHub.

Real-time feature computation, which calculates features from raw data on demand, is a crucial component in the machine learning (ML) application process. These real-time features are vital for various real-world ML applications, such as anti-fraud management, risk control, and personalized recommendations. In these cases, low latency (milliseconds) in computing fresh data features is crucial for accurate and high-quality online inference. As illustrated in the accompanying figure, a data scientist typically begins an ML application by developing feature computation scripts (for example, using Python or SparkSQL) for offline training. However, these scripts cannot meet the demands of online serving, including low latency, high throughput, and high availability. Hence, it is necessary to transform these scripts into performance-optimized code (for example, using C) that can be developed by an engineering team with system and production knowledge.