December 2025 Summaries

In 2025, QuestDB underwent significant enhancements across 16 open-source and 15 enterprise releases, focusing predominantly on capital markets while also gaining traction in other sectors like energy, aerospace, and fintech. Key developments included advanced data types such as arrays for order book analysis, nanosecond precision timestamps for high-frequency trading, and DECIMAL for exact financial computations, which are crucial for sectors requiring high precision like crypto. The introduction of ASOF JOINs and optimized JOINs, including RIGHT and FULL OUTER JOINs, improved temporal query performance for trading workflows. Materialized views were enhanced to manage vast amounts of market data efficiently, while symbol auto-scaling and various query optimizations addressed high-cardinality challenges. The year also saw a focus on open formats with Parquet support, enabling seamless data interoperability without duplication, and the introduction of enterprise-level features like OAuth2/OIDC authentication and robust replication across storage mediums, ensuring high availability and security for financial institutions. Looking forward to 2026, QuestDB plans to enhance integration capabilities further with features like window JOINs, SQL views, and LLM-assisted analytics, continuing its mission to meet the demands of quantitative finance and beyond.

Airtel XStream Play, a subsidiary of Bharti Airtel, is a rapidly growing OTT media provider in India, offering a diverse library of Indian and Western content from partners like Sony LIV and Lionsgate Play. With over 100 million users, the platform has faced challenges in managing its massive data throughput, which exceeds 1 billion records daily. To address these issues, the company transitioned from an underperforming analytical database to QuestDB, known for its rapid ingestion and high-performance querying capabilities. This switch has enhanced their ability to analyze user engagement and content performance, essential for planning and delivering efficient streaming services. QuestDB's support for deduplication ensures data integrity across their distributed systems, significantly improving data processing and reducing latency issues. Ajay Pilaniya, a Senior Engineer at XStream Play, highlights that QuestDB has transformed their analytics capabilities, allowing them to sustain and scale their operations effectively while handling the platform's high-volume workloads.

Aquis Exchange, part of SIX Group, has implemented QuestDB for its exchange-wide surveillance to analyze orders, trades, and infrastructure metrics in real time, driven by the need for a high-throughput, low-latency system that can capture every market event promptly. QuestDB's architecture, which supports fast ingestion and consistently fast queries, enables Aquis to handle real-time monitoring and operational metrics efficiently, meeting their stringent latency requirements. This integration allows Aquis to restructure their domain model for live order-book aggregation and transaction analysis, which was previously unattainable with other tested alternatives. Compatibility with SQL and PostgreSQL wire-protocol facilitates seamless onboarding and integration with tools like Grafana for real-time visualization of metrics and market activities. As their dataset grows, Aquis continues to benefit from QuestDB's performance improvements, ensuring the ongoing efficiency of their operational monitoring and market-data processing needs.

The Inclusive Financial Technology Foundation, formerly known as the XRP Ledger Foundation, adopted QuestDB to manage the XRP Ledger's substantial transaction data, a decision driven by the need for a robust, cost-effective database after their previous engine was discontinued due to high operational costs. QuestDB was chosen for its superior query speed, data throughput, and open-source architecture, which aligns with the foundation's operational philosophy. This modern database infrastructure enables the XRP Ledger to process extensive historical datasets in milliseconds, facilitating real-time data retrieval and analysis across the blockchain's growing ecosystem. By leveraging QuestDB, the foundation has significantly reduced its total cost of ownership by over 90% compared to legacy cloud platforms, allowing them to efficiently handle complex queries and deliver timely, accurate data to user-facing applications within the ecosystem.

OKX, a major global cryptocurrency exchange, faced challenges with its previous data management systems as they struggled to handle the high-frequency market data demands of their quantitative trading division. To address these issues, OKX adopted QuestDB as its core time series database due to its high performance, low-latency data ingestion, and operational simplicity. The transition to QuestDB allowed OKX to overcome bottlenecks and inefficiencies experienced with their former system, InfluxDB, by providing faster and more predictable query latencies, increased ingestion capacity, and reduced operational overhead. Key features that facilitated this improvement include integration with Apache Kafka for data streaming, the use of ILP for rapid data ingestion, and the implementation of Grafana dashboards for real-time analytics. QuestDB's deployment in OKX's cloud environment allowed for enhanced flexibility and compliance. As a result, OKX achieved a more stable and efficient data processing architecture, enabling real-time visibility into trading metrics and supporting the exchange's extensive operational requirements. Looking ahead, OKX plans to expand its use of QuestDB by implementing features such as high availability, enhanced cold storage capabilities, data compression, and improved system integrations.

Copenhagen Atomics, a company focused on addressing energy resource limitations, employs QuestDB to monitor and manage their next-generation molten salt thorium reactors in real time. These reactors, designed to fit in containers, aim to safely and economically reduce nuclear waste radioactivity while generating high energy output. QuestDB, chosen for its high performance and open-source nature, supports anomaly detection through real-time monitoring of over 100,000 sensors, with data being processed and visualized through customized plot systems. The company deploys its architecture on Azure and utilizes Kubernetes for scalable, cloud-native operations. Copenhagen Atomics plans to scale its reactor production, offering Energy as a Service, and continues to collaborate with QuestDB to integrate new features like cold storage and user access control, facilitating seamless data management and security.

Energetech leverages QuestDB to enhance its commodity trading strategies by efficiently managing real-time energy market prices and forecasts. The company deals with significant market volatility and high data volume, requiring robust data ingestion and processing capabilities, which QuestDB provides through its powerful deduplication, compression, and time-series data handling features. This architecture allows Energetech to achieve high ingestion rates of over 10 million messages per minute while maintaining minimal database growth, leading to significant cost savings. The database's built-in deduplication ensures forecast accuracy, and its materialized views facilitate instant analytics for energy and commodity prices. After a successful proof of concept, Energetech transitioned to a Bring Your Own Cloud deployment, adopting features like read-replicas and materialized views to enhance system availability and query efficiency.

Reflexivity, a SaaS company specializing in AI-powered investment insights, transitioned from using InfluxDB to QuestDB to handle its extensive time series data more efficiently and cost-effectively. Initially, Reflexivity relied on InfluxDB, but as their data volumes grew, InfluxDB's infrastructure demands became unsustainable. QuestDB was selected for its ability to seamlessly migrate existing data, maintain fast query response times, and ingest new data without disruption, all while reducing infrastructure costs. Upon switching, Reflexivity experienced an impressive improvement in query speeds, with response times dropping from over 5 seconds to approximately 15 milliseconds, allowing the company to operate on fewer machines and alleviating the need for extensive infrastructure management. This transition enabled Reflexivity to focus more on enhancing its AI models and features, supported by proactive assistance from the QuestDB team throughout the migration process.

Virtual Global Trading has transitioned from using MongoDB to QuestDB to manage time-series data related to energy production and consumption, facilitating dynamic pricing and efficient distribution across smart meters, power plants, and grid infrastructure. QuestDB's capabilities allow for real-time data processing, efficient aggregation, and precise time-zone alignment, enhancing the company's ability to generate reliable forecasts and support predictive energy analytics at scale. The system's use of SQL and time-series extensions such as SAMPLE BY improves data handling and visualization, while deduplication ensures the cleanliness and accuracy of the data. The elastic deployment on Azure supports scalability in line with demand, enabling Virtual Global Trading to overcome previous limitations in time-based aggregations and bottlenecks in data ingestion and analytics. The transition has provided a robust framework for energy data management, offering instant grid visibility and real-time insights crucial for both customer information and internal applications.

Market depth charts are essential tools for understanding financial markets by visualizing the layers of buy and sell orders at various price levels. This article explains the process of efficiently storing and transforming order book data into these visualizations using QuestDB and SQL. Order books, likened to a continuous two-sided auction, capture the live supply and demand of a financial instrument, with price levels organized for buyers (bid side) and sellers (ask side). The best bid and ask prices form the bid-ask spread, a critical liquidity indicator. Market makers play a pivotal role by quoting buy and sell prices to maintain liquidity, placing orders at multiple price levels to create market depth. The article details how to store order book data as 2D arrays in QuestDB, maximizing efficiency in terms of storage and retrieval, and demonstrates how to compute cumulative order volumes with SQL functions like array_cum_sum(). Visualization of this data using tools like Grafana and Plotly can reveal insights into liquidity, market sentiment, and execution costs, providing a dynamic view of market conditions. The use of QuestDB's advanced features, including array operations and nanosecond precision, enables high-frequency querying and real-time dashboard updates, offering powerful solutions for financial data analysis and trading infrastructure development.

QuestDB is an open-source time-series database designed for high-performance workloads, offering features like ultra-low latency and high ingestion throughput, along with support for Parquet and SQL to maintain data portability. A developer, experienced in Linux systems, encountered a kernel bug when using the async-profiler to capture CPU heatmaps, which caused their machine to freeze due to a deadlock in the Linux kernel's handling of the cpu-clock event. The issue was traced to a recent update in the Ubuntu distribution using kernel version 6.17, which led to a deadlock during the hrtimer cancellation process in the perf_events subsystem. The developer explored the issue using QEMU for kernel debugging and identified the problem within the kernel's source code, eventually finding a workaround by using the -e ctimer option in async-profiler to avoid the problematic kernel feature. Despite this workaround, the developer delved deeper into the kernel's internals, using GDB to debug and manipulate the kernel state in a virtual environment, ultimately managing to unfreeze the machine by forcing the kernel to kill a process, showcasing both the complexity and intricacies of kernel-level debugging and the resilience required to solve such technical challenges.

QuestDB, a next-generation database optimized for market data, offers high ingestion throughput and efficient SQL analytics, making it ideal for managing complex financial data streams. The text discusses how Databento, a market data aggregator, simplifies the process of accessing and normalizing data from multiple financial exchanges, facilitating integration with QuestDB for comprehensive analysis. Databento maintains latency, convenience, and integrity in its offerings, covering major exchanges and supporting multiple clients. The document provides a detailed guide on setting up a data ingestion pipeline using Databento's Python client with QuestDB, enabling users to subscribe to market data, process it, and visualize it in Grafana. It also explores the financial concept of "rolls" and the cost of carry in futures contracts, illustrating how to calculate implicit annualized costs using SQL queries. The post concludes by highlighting the ease of setting up market data ingestion and analysis pipelines with these tools, inviting readers to engage further on their community forum for more information.

QuestDB is an open-source, high-performance database tailored for market data, particularly crypto market data, offering excellent ingestion throughput, SQL analytics, and hardware efficiency. The tutorial outlines three methods for ingesting crypto market data into QuestDB: using the Cryptofeed library for easy integration with various exchanges, building a custom data pipeline for unsupported exchanges or more control over data, and employing Change Data Capture (CDC) for streaming data from external sources like Kafka. Cryptofeed simplifies data ingestion with its preconfigured integrations, but custom pipelines allow for greater customization and preprocessing, using the InfluxDB Line Protocol via QuestDB's Python SDK. CDC is highlighted as an efficient method for integrating existing data feeds from sources such as Kafka, minimizing infrastructure burden.

QuestDB is an open-source time-series database designed for high-performance workloads, offering features like ultra-low latency, high ingestion throughput, and a multi-tier storage engine, while supporting Parquet and SQL for portability and AI readiness. A common application of time-series databases is in data visualization for business decisions, and this tutorial demonstrates how to connect QuestDB with Apache Superset, an open-source data exploration and visualization platform, to create dashboards. The process involves setting up QuestDB and Apache Superset using Docker, importing datasets on New York taxi rides and European energy consumption into QuestDB, and configuring the connection in Superset. Users can create datasets, construct charts, and build dashboards by selecting visualization types and defining data parameters. The tutorial further explores creating datasets from SQL statements and using the Mixed Chart type in Superset to visualize energy consumption data, encouraging users to explore Superset's capabilities and contribute to open-source projects.

The benchmark comparison between InfluxDB 3 Core and QuestDB 9.2.2 reveals that QuestDB significantly outperforms InfluxDB 3 Core in both data ingestion and query performance. InfluxDB 3 Core, a major architectural overhaul using Apache Arrow and DataFusion, shows a consistent ingestion rate of approximately 320,000 rows per second regardless of data cardinality. In contrast, QuestDB ingests data 12 to 36 times faster, peaking at 11.4 million rows per second, and executes analytical queries 17 to 418 times faster depending on the query type. Despite InfluxDB 3 Core's slower performance compared to its predecessors, it aims to eliminate the cardinality-based performance issues observed in earlier versions. QuestDB's purpose-built columnar engine and SIMD optimizations ensure superior performance, making it a preferable choice for workloads requiring high-throughput ingestion and low-latency queries.

QuestDB is an open-source time-series database designed for high-demand scenarios, offering ultra-low latency and high ingestion throughput, making it ideal for applications ranging from trading floors to mission control. It supports data portability with native Parquet and SQL, ensuring no vendor lock-in. While Grafana, a popular tool for visualizing data, limits its default dashboard refresh rate to once every 5 seconds to ease server load, this rate may not suffice for real-time applications like financial market data. QuestDB can accommodate higher refresh rates, allowing for more current data visualization and smoother updates. The process involves adjusting Grafana's server settings to allow for refresh intervals below the standard 5 seconds, which can be especially beneficial in scenarios requiring immediate feedback, such as algorithmic trading or real-time monitoring systems. By configuring Grafana's minimum refresh interval setting, users can achieve more frequent updates, enhancing the real-time data experience in various use cases.

This tutorial guides users through creating K-line, or candlestick, charts that visualize crypto asset price movements over time using QuestDB and Grafana. By streaming real-time trade data from Polygon.io, the tutorial demonstrates how to efficiently aggregate open, high, low, and close (OHLC) data points using QuestDB's materialized views, which offer performance benefits by persisting data to disk and providing incremental refresh capabilities. The tutorial includes setting up QuestDB and Grafana via Docker Compose, ingesting data with Python scripts, and constructing visualizations in Grafana using its built-in Candlestick plugin. It emphasizes the flexibility and performance of materialized views and offers insights into using different data insertion protocols for varying throughput requirements.

QuestDB, an open-source time-series database known for its ultra-low latency and high ingestion throughput, is ideal for demanding workloads and supports Parquet and SQL for data portability. This tutorial guides users through creating a real-time Grafana dashboard using the QuestDB Grafana plugin, featuring line charts that leverage aggregate SQL functions and global variables for data sampling. Grafana, an open-source tool for data visualization and dashboards, connects to data sources such as QuestDB to visualize data in real-time. The tutorial includes steps for setting up Grafana and QuestDB using Docker, loading datasets, and creating visualizations with SQL queries. By configuring data sources and dashboards, users can generate panels displaying average trip distances from taxi data, separate data based on payment types, and explore the relationship between taxi fares and rainfall using ASOF JOIN, with dual Y-axis graphs for comparative analysis. This approach allows for detailed exploration of time-series data, offering insights into potential correlations between different datasets.

QuestDB, an open-source time-series database known for its ultra-low latency and high ingestion throughput, is utilized in a tutorial to set up a monitoring system for IoT device data using various tools such as MQTT, Telegraf, and Grafana. The tutorial guides users through the process of simulating data collection by using a script to gather electricity consumption data from Open Power System Data, which is then sent to an MQTT-compatible message broker, Mosquitto. This data is subsequently channeled into QuestDB via Telegraf, allowing for seamless data transfer. Grafana is then connected to QuestDB to create dashboards that visualize the data, providing insights into electricity consumption patterns and enabling a comparison between actual and forecasted energy loads. The tutorial highlights the efficiency and accessibility of open-source tools in handling and visualizing time-series data, ultimately empowering users to optimize performance and make informed, data-driven decisions.

QuestDB and InfluxDB are compared in detail regarding performance, architecture, and usability, focusing on their functionalities as time-series databases. QuestDB, an open-source database written in Java and C++, offers significant performance advantages, especially in data ingestion and complex query handling, being up to 36 times faster in data ingestion and up to 130 times faster in certain analytical queries compared to InfluxDB. In contrast, InfluxDB, developed by InfluxData and primarily written in Go, excels slightly in specific simple aggregation queries. QuestDB uses a columnar storage model that efficiently handles high cardinality by storing all data in a single table structure, which contrasts with InfluxDB's measurement-based model that can suffer from performance degradation due to its per-series storage overhead. While both databases support SQL querying, QuestDB extends standard SQL for enhanced time-series analysis, offering better integration with PostgreSQL tools and compatibility with numerous programming languages, whereas InfluxDB has broader native integrations and a larger ecosystem due to its longer presence in the market. The choice between the two databases depends on specific use cases, such as QuestDB's suitability for high-cardinality workloads and real-time analytics versus InfluxDB's advantages for simple monitoring tasks and existing ecosystem investments.

QuestDB and TimescaleDB are two open-source time-series databases designed to handle demanding workloads, with QuestDB prioritizing raw performance and TimescaleDB enhancing PostgreSQL for faster analytics. QuestDB demonstrates superior performance in both ingestion and complex query benchmarks, consistently surpassing TimescaleDB by a significant margin, particularly in high cardinality scenarios. QuestDB's architecture is built for high throughput and low latency, utilizing a three-tier columnar storage system and supporting SQL-based queries, while TimescaleDB extends PostgreSQL's row-based architecture with a hybrid storage engine and hypertables for time-series data management. Despite its performance advantages, QuestDB has limitations in ecosystem maturity and PostgreSQL compatibility compared to TimescaleDB, which benefits from its integration with the broader PostgreSQL ecosystem. Both databases have unique strengths and limitations, making the choice between them dependent on specific business requirements and use cases.

QuestDB, an open-source time-series database designed for high-performance workloads, offers ultra-low latency, high ingestion throughput, and a versatile storage engine with native support for Parquet and SQL, ensuring data portability without vendor lock-in. This tutorial explores how to utilize Grafana's Geomap panel with QuestDB to visualize real-time data, using Sydney, Australia's bus data as a case study. The process involves importing CSV data into QuestDB, configuring a Geomap panel in Grafana, and customizing map views and markers to display dynamic information such as bus position, speed, and direction. Through various configurations and queries, the tutorial demonstrates how to manage and enhance map visuals, addressing Grafana's current limitations in plotting multiple routes while suggesting adjustments to achieve desired outcomes.

QuestDB, an open-source time-series database known for its high ingestion throughput and low latency, was compared to InfluxDB 3.0 Core Alpha in a performance benchmark. The test revealed that QuestDB significantly outperformed InfluxDB, with ingestion speeds approximately 5.5 times faster and much quicker query response times. InfluxDB 3.0 Core, described as a "recent-data engine," is still in its Alpha stage and not production-ready, exhibiting limitations such as a 72-hour query time span, data loss under concurrent loads, and performance issues with ingestion. Although it introduces a modernized engine with SQL support, the current version falls short in reliability and efficiency compared to QuestDB, which shows robust performance and compatibility with the Influx Line Protocol. The evaluation suggests that InfluxDB's open-source version may serve more as a precursor to its commercial cloud offering, prompting users seeking a fully capable open-source solution to consider alternatives like QuestDB.