queryen

Queryen is a distributed data query engine designed for interactive analytics on large-scale datasets. It provides a SQL-like query language and an experimental natural-language interface that translates plain language questions into executable queries. The project emphasizes low-latency response times, pluggable storage backends, and ease of integration with existing data infrastructures.

Origin and governance: The project originated as a research initiative at the Center for Data Systems Research and was first released to the public in 2020. It has since been maintained by an international community of contributors under an informal governance model and a rotating maintainership.

Architecture: Queryen's architecture is modular, comprising a front-end parser, a cost-based optimizer/planner, a distributed execution engine, and storage connectors. It supports SQL-like syntax and a high-level QueryenQL for expressing analytics tasks. It includes a columnar in-memory engine with vectorized operators, a cache layer, and fault-tolerant execution with data replication. Security features include authentication, authorization, and audit logging. It can ingest data from Parquet, ORC, CSV, and streaming sources like Kafka.

Usage and performance: Typical deployments are cloud-based clusters or on-premises data centers. It is used for interactive dashboards, ad-hoc analysis, and data exploration. Example query: SELECT region, AVG(sales) FROM sales WHERE year = 2024 GROUP BY region. Through connectors, it can join historical data with real-time streams for near-real-time insights.

Reception and limitations: While popular for rapid prototyping and exploratory analytics, Queryen's natural-language interface remains experimental and depends on training data. Performance can vary with data layout and statistics accuracy, and the ecosystem of tools and documentation is uneven across environments.