cozip 2026.5.15

Python bindings to libcozip, the reference writer for the Cloud-Optimized ZIP (cozip) format.

---

Open a ZIP like a table. Still a ZIP, now queryable.

cozip glues a Parquet manifest onto an ordinary ZIP. The manifest has one row per entry (name, offset, size, plus any columns you tag onto it). Fetch the index, fetch the manifest, query it locally, then range-request just the bytes you actually want. A 20 GB archive becomes a queryable dataset in two reads.

It works because nothing about the ZIP changes. unzip works. zipfile.ZipFile works. Your OS preview pane works. The manifest is just the first entry, and any conforming ZIP reader walks right past it.

Two functions

write packs files plus metadata into a cozip. read returns the manifest. That is the whole surface area in every binding.

The write manifest reserves two columns. path is where each file lives on disk, consumed at write time and dropped from the manifest. name is how it is stored inside the archive. write then adds two more columns to the manifest, offset and size, holding the byte offset and length of each file in the ZIP.

Everything else rides along and is queryable on read. Local file or remote URL, same call.

Python

import cozip
import pyarrow as pa

table = pa.table({
    "path":  ["local/tile_001.tif", "local/tile_002.tif", "local/tile_003.tif"],
    "name":  ["tile_001.tif", "tile_002.tif", "tile_003.tif"],
    "split": ["train", "val", "train"],
    "label": ["cloud", "water", "forest"],
})
cozip.write("dataset.zip", table)

manifest = cozip.read("https://example.com/dataset.zip")
train = manifest.filter(pa.compute.equal(manifest["split"], "train"))

R

library(cozip)
library(arrow)

tbl <- arrow_table(
  path  = c("local/tile_001.tif", "local/tile_002.tif", "local/tile_003.tif"),
  name  = c("tile_001.tif", "tile_002.tif", "tile_003.tif"),
  split = c("train", "val", "train"),
  label = c("cloud", "water", "forest")
)
cozip_write("dataset.zip", tbl)

manifest <- cozip_read("https://example.com/dataset.zip")
train <- manifest |> dplyr::filter(split == "train")

Julia

using Cozip
using DataFrames

df = DataFrame(
    path  = ["local/tile_001.tif", "local/tile_002.tif", "local/tile_003.tif"],
    name  = ["tile_001.tif", "tile_002.tif", "tile_003.tif"],
    split = ["train", "val", "train"],
    label = ["cloud", "water", "forest"],
)
Cozip.write("dataset.zip", df)

manifest = Cozip.read("https://example.com/dataset.zip")
train = filter(:split => ==("train"), manifest)

Bindings

Language	Read	Write	Install
Python	✓	✓	pip install cozip
R	✓	✓	install.packages("cozip", repos = "https://asterisk-labs.r-universe.dev")
Julia	✓	✓	Pkg.Registry.add("https://github.com/asterisk-labs/AsteriskRegistry"); Pkg.add("Cozip")

Every binding wraps the same C core, so a cozip written by R reads byte for byte identically in Julia, in Python, in C. The high-level API is uniform across runtimes. Python and R speak Apache Arrow tables; Julia speaks Tables.jl-compatible DataFrames.

Spec

See SPEC.md. The format is short and stable. Any conforming reader handles any conforming writer.

License

MIT.

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