An Embedded SQL Engine For Files That Is Honest About Its Speed
SlothDB is an experimental embedded SQL engine in C++20 that queries Parquet, CSV, JSON, Arrow, Avro, SQLite, and Excel files directly in-process, reaching 476 GitHub stars from data engineers who want a DuckDB-style tool for ad hoc file querying, and its issues expose the gap between a benchmark demo and a dependable engine: reading Parquet with zstd compression fails, Avro queries return invalid results, Python wheels ship without the required native library, and a pointed top-voted issue argues the speed claims are exaggerated because the planner pattern-matches known benchmark query shapes rather than generalizing. People want fast, embeddable SQL over the messy file formats they actually have. The wedge is an embedded SQL engine whose format support is correct and whose performance is honest on real, non-benchmark queries.
Problem Statement
A data engineer reaches for an embedded SQL engine to query the Parquet, Avro, and Excel files they already have, but Parquet files with zstd compression fail to read, Avro queries return invalid responses, the Python wheel is missing its native library, and the headline speed turns out to come from a planner that pattern-matches known benchmark queries rather than generalizing. The promise of fast in-process SQL over any file is compelling, but an engine that fails on common compression and formats and is fast only on benchmark shapes cannot be trusted with real pipelines.
The Idea
An embedded, in-process SQL engine that correctly queries real-world file formats like Parquet, Avro, and Excel with honest, generalizable performance instead of benchmark-tuned shortcuts.
Why Now
DuckDB proved that in-process SQL over files is how analysts and apps want to work in 2026, and SlothDB's traction shows appetite for alternatives, but its zstd Parquet failures, broken Avro reads, missing native libraries, and a community calling out benchmark-shaped optimizations show that correctness across formats and honest general-case performance, not flashy ClickBench numbers, are what stand between a demo engine and one teams embed in production.
Target User
Data engineers and app developers needing in-process SQL over file formats
Target Market
Embedded analytical SQL engines
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