June 2026 Summaries

QuestDB, an open-source time-series database known for its ultra-low latency and high ingestion throughput, emphasizes the complexities and limitations of database benchmarking, using ClickBench as a case study. ClickBench, a widely recognized benchmark for analytical databases, measures performance by running a set of 43 queries on a large dataset and evaluating each system based on "hot" and "cold" run times. However, the post highlights that these benchmarks can be misleading due to inherent asymmetries, such as the different conditions for self-hosted versus managed cloud services, which can affect cold-run rankings. Furthermore, the study reveals how seemingly minor changes, like increasing the number of query iterations or keeping a database process alive, can significantly alter benchmark outcomes, suggesting that benchmarks should be treated with skepticism and tailored to reflect real-world use cases. QuestDB acknowledges that while benchmarks can spotlight areas for improvement, they should not be the sole criterion for database selection, advocating instead for personalized testing based on specific workloads. The overarching message is to scrutinize benchmark scenarios closely and to recognize that real-world performance may differ from the controlled conditions of a benchmark.

QuestDB, an open-source time-series database designed for high-demand workloads, has released its Enterprise 3.3.1 version, which builds on the QuestDB 9.4.2 engine and includes features from the 3.3.0 line, such as a new storage policy engine for tiering partitions to Parquet and posting indexes. This version introduces a custom root CA option for replication object stores, column-level GRANT/REVOKE with EXCLUDE, and various improvements in correctness and resilience. The storage policy engine allows partitions to be automatically converted to Parquet on a schedule, optimizing data compression and query efficiency, while posting indexes offer a compact, faster-to-query index type. Additional updates include stricter object-store configuration parsing and an upgrade to JDK 25, alongside enhancements in replication, backup, and SQL functionalities. The release emphasizes hardening and bug fixes, ensuring improved reliability and performance.

QuestDB 9.4.2 is an open-source time-series database optimized for demanding workloads that emphasizes ultra-low latency, high ingestion throughput, and a multi-tier storage engine, with native support for Parquet and SQL to ensure data portability and AI readiness. The release is primarily focused on hardening efforts through fuzz testing and stricter query-result assertions, addressing edge cases related to Parquet tables and new posting indexes, and includes improvements from version 9.4.1 such as new aggregate functions, DECIMAL window functions, and enhanced materialized view refresh speeds. The Web Console has been updated to allow the sharing of queries via runnable links, facilitating collaboration without the need for SQL copy-pasting, while also enhancing the visibility of storage policy settings and refining query validation. The update introduces two new aggregate functions, array_agg() and regr_r2(), which are useful for grouping values and assessing linear regression trends, respectively, and expands window function support to all DECIMAL sub-types. Additionally, the release addresses several bug fixes and introduces breaking changes such as modifications to index types and commands related to views, making this release a comprehensive enhancement to the database's functionality and robustness.

QuestDB is an open-source time-series database designed for demanding workloads, such as those found in trading floors and mission control, offering ultra-low latency, high ingestion throughput, and a multi-tier storage engine. It integrates with Aeron, a high-performance stack for low-latency applications, to support real-time market data and time-series analytics, crucial for pre-trade, post-trade, and surveillance tasks in capital markets. This integration allows firms to handle both the immediate processing of millions of messages with microsecond latency and the long-term storage of queryable historical data, supporting regulatory compliance and advanced analytics. The architectural design separates the "hot path" for immediate data processing from the "history path" for long-term data storage and querying, facilitating exactly-once semantics and enabling detailed analysis, backtesting, and reproducibility of trading policies. The use of open-source infrastructure, such as QuestDB and Aeron, aligns with regulatory demands and supports AI and machine learning applications by providing open, machine-readable formats and ensuring data portability with no vendor lock-in. This approach is increasingly adopted in capital markets as firms seek to rebuild their tech stacks on open foundations, enhancing both the speed of operations and the analytical capabilities while maintaining high availability and resilience.