February 2026 Summaries

QuestDB 9.3.3 introduces several new features and performance enhancements designed for high-demand time-series data applications, emphasizing ultra-low latency and high throughput. The release highlights the HORIZON JOIN, a new join type tailored for markout analysis, which simplifies the measurement of price evolution at specific time offsets without the need for complex self-joins or window functions. The update also introduces the twap() function for computing time-weighted averages, SQL-standard WINDOW definitions for cleaner query syntax, and JIT-compiled filters on ARM64 for improved performance on Apple Silicon and AWS Graviton systems. Significant performance improvements include optimized Parquet I/O, enhanced parallel GROUP BY operations, and better latency predictability. Additionally, security updates and bug fixes accompany these new SQL features, enhancing the overall robustness and efficiency of the QuestDB platform.

QuestDB, an open-source time-series database, faced a complex issue involving a sporadic hang in its Windows Continuous Integration (CI) pipeline due to a deadlock, which was uncovered after an in-depth investigation into various system components, including the Java Virtual Machine's (JVM) garbage collection, Rust's thread-local storage, the Java Native Interface (JNI), and Windows' Loader Lock. The deadlock arose when the operating system held the Loader Lock during thread termination, blocking Rust threads at the safepoint barrier due to garbage collection, and simultaneously preventing new Java threads from initializing due to their need for the Loader Lock. This issue was further complicated by interactions between Java's safepoint mechanism, Rust's TLS destructors, JNI's thread attachment, and Windows' loader lock, revealing a critical lock inversion problem. The resolution involved explicitly detaching threads before TLS destructors ran, using tools like ProcDump, WinDbg, and safepoint timeout logging to diagnose and fix the problem, and opening upstream issues to address the library design flaw in jni-rs. This experience underscored the complexities of debugging and the need for explicit thread management when using JNI with Rust on Windows.

QuestDB 9.3.2 introduces significant advancements in the realm of time-series databases, notably with the introduction of TICK, a new temporal interval syntax that simplifies complex time-range filters into concise expressions. This release enhances SQL capabilities with new aggregate and window functions, including arg_min, arg_max, and geomean, alongside EMA and VWEMA for time-series analytics. It offers a 6x speedup on Parquet queries, improved large language model (LLM) integration, and enhanced AI assistant functionality in the web console. Additionally, QuestDB Enterprise 3.2.1 supports scheduled incremental backups to object storage, optimizing both speed and bandwidth efficiency. The comprehensive refresh of documentation facilitates easier navigation, while performance improvements ensure faster query execution and reduced file mapping contention. Overall, QuestDB 9.3.2 empowers users with a robust toolkit for efficient and effective data analysis, encouraging experimentation with its innovative TICK feature.