January 2022 Summaries

QuestDB version 6.2, an open-source time-series database, introduces significant enhancements aimed at improving performance and functionality for demanding workloads. Key updates include JDK 17 support and the introduction of a Just-in-Time (JIT) compiler for the SQL engine, which notably speeds up query execution involving arithmetic expressions. The update also enhances SQL and ILP capabilities, optimizes queries with LIMIT clauses, and reduces the ILP commit timeout to 1 second, facilitating faster data availability in SQL queries. The version addresses memory footprint concerns by improving query cache handling and offers new settings to manage memory usage efficiency, especially when interfacing with Grafana. Enhancements in the Web Console include table autocomplete, and improvements in ILP stability through fuzz testing have resolved critical edge cases. Network configuration has been streamlined for better usability, with backward compatibility maintained. Future updates promise features like UPDATE support and further refinements to JIT-compiled filters, as the QuestDB team actively seeks user feedback through community forums and GitHub.

QuestDB, an open-source time-series database designed for high-performance workloads, has introduced a new Just-in-Time (JIT) compiler in its SQL engine as part of version 6.2.0. This addition aims to significantly speed up query execution, particularly for queries involving simple arithmetic expressions by improving CPU usage efficiency. The JIT compiler achieves this by compiling filter expressions into machine code, optimizing execution through vectorized processing using SIMD instructions, which allows processing multiple rows simultaneously. Currently, the JIT compiler is a beta feature and primarily supports x86-64 CPUs with AVX2 instructions, targeting filters with arithmetic expressions on fixed-size columns. Initial benchmarks indicate substantial performance improvements in CPU-bound scenarios where data is already cached, with execution times reduced by 76% compared to the previous Java implementation. QuestDB plans to expand the compiler's capabilities to support ARM64 CPUs, parallelize query execution, and address current limitations, inviting user feedback and contributions to further refine the feature.

QuestDB, an open-source time-series database known for its ultra-low latency and high ingestion throughput, embarked on a two-year journey to raise its Series A funding, ultimately securing over $15 million in venture capital. The company was co-founded by Vlad and his partner after identifying the need for a high-performance, open-source database capable of efficiently handling large datasets without code dependencies, accessible via SQL. Initial challenges in pitching the product stemmed from its open-source nature and lack of revenue, but support from investors who understood the potential of open-source community building helped secure initial funding. QuestDB joined Y Combinator in Summer 2020, focusing on product development and community engagement, which led to significant growth in developer interest and contributions. Throughout this period, they adhered to Y Combinator's advice of prioritizing product-market fit over rapid hiring, which facilitated a meaningful connection with early users. By Summer 2021, QuestDB had expanded its community significantly, prompting another fundraising round to enhance their database offerings and develop a scalable cloud-hosted service. Strategic advice from experienced open-source founders and selective investor choices played a critical role in this phase, emphasizing the importance of open-source traction and developer adoption over immediate revenue growth. The increased funding supports QuestDB's remote-first expansion, enabling the company to attract global talent and continue its innovative journey.