📌SmallPond
A lightweight data processing framework built on DuckDB and 3FS.
⭐ Popular: 5k stars, gaining about 7 a week
View on GitHub ↗repo profile
momentum
durability
since we covered it
why it's a big deal
- Gives teams a way to run petabyte-scale data processing without operating any long-running services, so jobs start and stop on demand rather than needing a standing cluster.
- Targets users who already work in Python and want DuckDB's query performance applied to datasets far larger than a single machine.
- Reports processing 110TB in about 30 minutes on 50 compute nodes and 25 storage nodes on the GraySort benchmark, giving a concrete reference point for large-scale throughput.
under the hood
- Uses DuckDB as the in-process SQL engine for query execution and DeepSeek's 3FS distributed storage system for scaling across nodes.
- Exposes a Python dataframe-style API with operations like read_parquet, repartition by a hash column and partition count, partial_sql for per-partition queries, and write_parquet.
- Runs on Python 3.8 through 3.12 and is released under the MIT license.
Radar summary, generated from the project's public sources
star history
- PR#3 2k 2025-03-05
- now 5k + 3k since first covered
curve is sampled from GitHub's star history, plus our own daily readings since we covered it; the dashed stretch is before we first covered it, the solid line since. figures at coverage are the numbers we printed then (approx.), current count is live.
understory
Better known than its recent output, coasting a little on attention.
- output, commits & releases
- clout, star velocity
output = commits & releases; clout = star velocity, both 0 to 100 monthly indices; the gap where output runs above clout is the understory. The understory →
covered in
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Scalable, Efficient LLM Fine-tuning
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