slothdb 0.1.5

Fast in-process OLAP engine for Python — query Parquet, CSV, JSON, Arrow and SQLite with SQL. 1.1–8.6× faster than DuckDB.

SlothDB is a fast, in-process SQL database for your local data files.

A drop-in alternative to DuckDB with built-in readers for Parquet, CSV, JSON, Avro, Arrow and SQLite — 1.1×–8.6× faster on every benchmark query.

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Try it in 60 seconds

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.

Using your own files

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.

Why SlothDB?

Same embedded model as DuckDB and SQLite — link it into your process, 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.
• 1.1–8.6× faster than DuckDB on a 1M-row benchmark across 15 queries. JSON parse is 8.6×, Avro SUM is 5.4×, CSV COUNT(*) is 5.1×. Full numbers on GitHub →
• Stable C ABI — extensions don't break across releases.
• ~8 MB single binary, fully self-contained.

Quickstart

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()

What's not production-ready yet

• No multi-writer transactions (single-writer, crash-safe checkpoint).
• No distributed execution — single-node embedded engine.
• Some SQL corners still surprise you (open an issue).
• v0.1.4, ~6 months old. Treat as beta.

Performance

Format	Query	SlothDB	DuckDB	Speedup
Parquet	COUNT(*)	12 ms	34 ms	2.83×
CSV	COUNT(*)	33 ms	170 ms	5.08×
CSV	GROUP BY region	100 ms	191 ms	1.91×
JSON	SUM(revenue)	242 ms	314 ms	1.30×
Avro	SUM(revenue)	140 ms	760 ms	5.43×
Avro	GROUP BY region	170 ms	800 ms	4.71×

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

Links

• Source: https://github.com/SouravRoy-ETL/slothdb
• 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

License

MIT