Show HN: Reckoner – A query workbench for domain experts
Reckoner is a semantic query workbench that allows users to query structured datasets using plain language based on meaning rather than technical schema details. It supports fast querying with real-time feedback and execution traces, working across diverse domains like music collections, legal databases, and film archives. The tool can be quickly set up using demo datasets or extended to custom data through a browser-based Model Builder.
- ▪Reckoner enables users to query structured data using semantic terms like who, what, when, where, why, and how without needing to know database schema details.
- ▪It provides results in milliseconds with a full execution trace showing how each answer was derived.
- ▪The system supports CSV, Excel, and Postgres data sources and uses Semantic Normalized Form (SNF) and the Portolan planner for query processing.
- ▪Users can try Reckoner with built-in demo datasets such as discogsv1 and disney without any setup.
- ▪Custom data can be integrated using the separate snf-model-builder tool, which maps raw data to semantic dimensions and compiles it into a DuckDB file.
Opening excerpt (first ~120 words) tap to expand
Reckoner A semantic query workbench for structured data. Reckoner lets you query any dataset by meaning — not by column name, table structure, or JOIN logic. Drop in a CSV or connect a database. Ask questions in plain semantic terms. Get results in milliseconds with a full execution trace showing exactly how the answer was found. Built on SNF (Semantic Normalized Form) and the Portolan planner. See it running A record collection, a data quality catch, a diff between two named sets, and a substrate switch. 5 minutes. No setup required to watch. What it looks like You have a record collection. You want everything by Miles Davis released between 1955 and 1965 on Blue Note.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.