January 2026 Summaries

Bollinger Bands are a widely used technical indicator in trading that visualize price volatility through a simple moving average (SMA) with upper and lower bands based on standard deviation, helping traders identify potential reversals and breakout opportunities when combined with other indicators. The text explains how to calculate Bollinger Bands using SQL with 15-minute OHLC candles over a 20-period window in QuestDB, and how to visualize them in Grafana by overlaying them on candlestick charts alongside other indicators like VWAP and RSI. This approach, which is utilized in a live FX order book dashboard, highlights the importance of Bollinger Bands in spotting volatility patterns and price movement in conjunction with additional indicators, using Grafana's features to customize and style different data sets for clearer analysis. The discussion emphasizes the adaptability of Bollinger Bands, as traders can adjust the period count and candle size to suit their trading timeframe, and the visualizations offer a comprehensive view of price action, volatility, and momentum.

In response to the lack of built-in or third-party solutions for market depth visualization in Grafana, a developer created a custom solution that queries QuestDB arrays at 250ms intervals to render real-time order book depth. The visualization, implemented using Plotly in Grafana, handles 50-100 price levels per side and highlights liquidity walls using a segmented algorithm to avoid visual clutter. The developer chose Plotly for its flexibility, allowing for precise control over chart rendering, including stair-step line visualization and efficient real-time updates. The implementation addresses challenges such as scale variance and visual clutter by using logarithmic scaling and a segmented wall detection algorithm, which highlights significant liquidity points without overwhelming the chart. The project demonstrates the effective integration of QuestDB, Grafana, and Plotly, achieving a practical and customizable market depth visualization for financial applications.

An intriguing exploration into recent OpenJDK commit changes reveals a significant performance improvement in measuring thread CPU time on Linux systems. The original method for obtaining user CPU time involved complex file operations and parsing, which was notably slower compared to a more efficient approach using the `clock_gettime()` function. The older method, which read from the `/proc` filesystem, was found to be 30x-400x slower than the `clock_gettime()` approach due to multiple system calls and kernel lock contention under concurrent loads. The new implementation leverages a clever bit manipulation trick to use a Linux-specific feature, allowing for direct and faster user CPU time retrieval. This change, which eliminates the need for file I/O and parsing, resulted in a significant reduction in latency, bringing about a 40-fold performance boost. This improvement is expected to be included in JDK 26, offering substantial benefits to developers using `ThreadMXBean.getCurrentThreadUserTime()`. The discussion also highlights the importance of revisiting and questioning old assumptions in code, as well as the insights that can be gained from understanding kernel source code beyond POSIX standards.

QuestDB 9.3 introduces significant enhancements to its open-source time-series database, catering to demanding workloads with ultra-low latency and high ingestion throughput. This release features four major additions: window joins, views, PIVOT functionality, and AI-assisted workflows. Window joins allow for efficient correlation of events with surrounding time data, while views offer reusable query logic with customizable parameters. The PIVOT keyword transforms rows into columns, facilitating multi-dimensional reporting and dashboard integration. AI assistance is integrated into the Web Console, enabling users to leverage language models for tasks like SQL writing and query debugging. Additionally, the update includes performance improvements such as column projection pushdown for Parquet, parallel sorting for ORDER BY clauses, and optimized min/max queries on timestamp columns, enhancing execution speed significantly. Overall, QuestDB 9.3 expands both the expressive power and user interaction capabilities of the database, while also enhancing its core performance.