SlothDB is an embedded SQL database that runs everywhere: on your laptop, on a server, and in the browser. Built from scratch as a DuckDB alternative. Up to 5x faster on real workloads (138 ms vs 540 ms on a 5-query warm JOIN batch; 5.43x peak on Avro SUM; 16-query suite median 1.70x). Built-in readers for Parquet, CSV, JSON, Avro, Arrow, Excel, and SQLite.

pip install slothdb
python -c "import slothdb; slothdb.demo()"

Generates a 100 000-row CSV, runs three queries, and prints the side-by-side with DuckDB shown above. No files to find, no setup.

import slothdb
db = slothdb.connect()
df = db.sql("SELECT region, SUM(revenue) FROM 'sales.parquet' GROUP BY region").fetchdf()

No server. No import step. No CREATE TABLE. Point SQL at files on disk.

• Nested aggregates work everywhere. ROUND(AVG(x)), AVG(x) + 1, SUM(x) / COUNT(*), CAST(SUM(y) AS DOUBLE) and similar shapes that wrap an aggregate inside a scalar function or arithmetic used to throw "Function execution for: AVG". Fixed.
• ORDER BY by aggregate alias works. SELECT region, COUNT(*) AS cnt ... ORDER BY cnt DESC no longer silently sorts by column 0.
• Arithmetic type promotion fixed. AVG(x) + 1 no longer drops the +1 and AVG(x) / COUNT(*) no longer returns inf.
• 408 unit tests, 131,537 assertions, green on Windows, Linux, macOS.

Same embedded model as DuckDB and SQLite. You link it into your process and point SQL at files. Different defaults:

• 7 file formats built in - Parquet, CSV, JSON, Avro, Arrow, SQLite, Excel. DuckDB needs extensions for Avro and SQLite.
• Faster than DuckDB on real workloads. 5-query warm JOIN batch: 138 ms vs 540 ms (3.9x). Peak speedups: 5.43x on Avro SUM, 5.08x on CSV COUNT(*), 2.83x on Parquet COUNT(*). Median across the 16-query suite: 1.70x. Full numbers on GitHub.
• Stable C ABI. Numeric error codes don't shift between releases. Bindings built against 0.1.x keep working.
• ~1-4 MB single binary, fully self-contained.

import slothdb

# In-memory
db = slothdb.connect()

# Query files directly
db.sql("SELECT * FROM 'data.csv' WHERE score > 90").show()
db.sql("SELECT COUNT(*) FROM 'logs.parquet'").show()
db.sql("SELECT * FROM read_json('events.json') LIMIT 5").show()
db.sql("SELECT * FROM sqlite_scan('app.db', 'users')").show()

# Persistent database
db = slothdb.connect("analytics.slothdb")

# DataFrame integration
df = db.sql("SELECT region, SUM(revenue) FROM 'sales.csv' GROUP BY region").fetchdf()

• No multi-writer transactions (single writer, crash-safe checkpoint).
• No distributed execution. Single-node embedded engine.
• No secondary indexes. Scan-based execution; zone-map pruning helps on sorted data, but no B-tree / hash index for point lookups.
• Window-function coverage is partial. Plain OVER / PARTITION BY works; ROWS BETWEEN ... frames and cumulative SUM OVER (ORDER BY) shapes have known gaps.
• Authenticated S3 not implemented. s3:// URLs work for anonymous public-bucket reads only.
• Some SQL corners still surprise you. Open an issue with a repro.
• 0.2.x, about a year old. Treat as beta.

1M-row dataset, warm cache, 5-run median. Full 15-query table + methodology →

There's a Discord: discord.gg/XJWyGmX5G. Bug reports, install help, weird query plans, "is this slower than it should be", feature ideas - any of it. The maintainer reads everything. GitHub issues are still the canonical tracker; the server is for the questions that come before you file one.

• Source: https://github.com/SouravRoy-ETL/slothdb
• Discord: https://discord.gg/XJWyGmX5G
• Changelog: https://github.com/SouravRoy-ETL/slothdb/blob/main/CHANGELOG.md
• Issues: https://github.com/SouravRoy-ETL/slothdb/issues
• SQL reference: https://github.com/SouravRoy-ETL/slothdb/blob/main/docs/DOCUMENTATION.md

MIT