May 2025 Summaries

QuestDB is an open-source time-series database known for its ultra-low latency and high ingestion throughput, making it suitable for demanding workloads. It offers native support for Parquet and SQL, ensuring data portability and AI readiness without vendor lock-in. Arroyo, a new stream processing engine written in Rust, complements QuestDB by enabling low-latency, SQL-first stream processing and simplifying the creation of streaming data pipelines. Although Arroyo lacks a native QuestDB connector, a workaround using Arroyo's webhook connector in combination with QuestDB's HTTP endpoint for InfluxDB Line Protocol (ILP) provides an efficient, dependency-free pipeline for real-time analytics. This approach eliminates the need for Kafka and JSON encoding, resulting in a straightforward integration that leverages both systems' SQL capabilities.

QuestDB is an open-source time-series database designed for high-performance workloads, offering ultra-low latency, high ingestion throughput, and a multi-tier storage engine with native support for Parquet and SQL, ensuring data portability and AI readiness without vendor lock-in. A common usage involves the SAMPLE BY query for computing Open High Low Close (OHLC) prices on dashboards, though high-scale data ingestion and multiple users can strain server capacity. Materialized views are proposed as a solution to optimize aggregation queries by pre-computing and storing query results, thus reducing latency and avoiding repetitive computations. These views refresh automatically with base table updates, making them ideal for real-time dashboards, though they introduce complexities in data freshness and maintenance, especially when base tables change. The text explores creating materialized views, verifying their status, and utilizing advanced patterns like cascading views for performance optimization, while considering trade-offs such as refresh time, storage impact, and query complexity.