April 2026 Summaries

QuestDB, an open-source time-series database, excels in handling demanding workloads by offering ultra-low latency and high ingestion throughput with its multi-tier storage engine. A recent community-driven enhancement focused on optimizing QuestDB's integration with HdrHistogram by rewriting it as an off-heap, flyweight class. This change aimed to improve the approx_percentile() function's scalability across worker threads without incurring garbage collection or allocator costs on the data path. Initial benchmarks showed a parallel speedup of 3x but revealed a single-threaded regression. After a review and optimization, which involved hoisting configuration-dependent computations out of the hot path, the rewrite achieved a significant improvement, with the single-threaded regression eliminated and parallel performance reaching up to 8.9x. This effort highlighted the importance of community contributions, the trade-offs of off-heap memory management, and the value of collaborative reviews in optimizing performance-critical components.

QuestDB is an open-source time-series database designed for high-demand environments, offering ultra-low latency and high ingestion throughput. The latest updates, QuestDB 9.3.5 and QuestDB Enterprise 3.2.5, introduce features such as lateral joins for dynamic filtering and top-N per group patterns, UNNEST for expanding arrays and JSON into rows, and new statistical window functions for detailed risk analytics. Enhancements include support for multi-table HORIZON JOIN, bloom filter configuration for optimized data retrieval, and performance improvements in hash joins and query execution. These updates aim to make data processing more efficient, portable, and AI-ready, while reducing vendor lock-in. The release also addresses various bug fixes and introduces breaking changes in timezone handling for SAMPLE BY queries, urging users to review documentation for potential impacts. QuestDB Enterprise customers benefit from additional fixes and support, with options for self-managed and BYOC configurations.

QuestDB, an open-source time-series database, is leveraged for high-demand workloads like trading and mission control due to its ultra-low latency and high ingestion throughput. This tutorial explores three methods to load data from QuestDB into Python for use with machine learning tools, focusing on data engineering patterns rather than the model itself. The methods include direct SQL queries using REST API or ConnectorX, exporting data to Parquet for reproducible training pipelines, and accessing Parquet partitions directly for a lakehouse-style approach. The tutorial uses Google's TimesFM, a foundation model for time-series forecasting, to predict cryptocurrency trading volume and volatility using 1-minute BTC-USDT bars, demonstrating how QuestDB's data can be seamlessly integrated into Python ML workflows. This approach emphasizes the importance of efficient data movement from databases to ML models, highlighting QuestDB's capabilities for modern analytics and forecasting tasks.

QuestDB, an open-source time-series database designed for high-performance workloads, has released versions 9.3.4 and Enterprise 3.2.4, introducing several enhancements. Key updates include dynamic window functions in SQL, which allow for per-row lookback intervals, enhancing query flexibility and performance. The new release also adds bloom filter pruning for Parquet files, optimizing data retrieval by skipping irrelevant data and improving I/O efficiency. Additionally, QuestDB now supports element-wise array functions for more granular order book analytics and enables per-column Parquet encoding and compression, providing customizable storage solutions. The Enterprise edition simplifies access management with COPY PERMISSIONS for easy permission duplication and automatic permission clean-up when users or objects are removed. Performance improvements across various operations, including ASOF and WINDOW JOIN, and enhanced parallel execution, further boost the database's efficiency and speed.