ZeroBucket

Your database. Your images. Zero buckets.

ZeroBucket is a database-native image storage library. It lets you store and retrieve images using the PostgreSQL database you already have, instead of standing up a separate object-storage service like S3.

from zerobucket import ZeroBucket

images = ZeroBucket(database_url="postgresql://...")

image_id = images.put("avatar.jpg")

image = images.get(image_id)
print(image.mime_type)   # "image/jpeg"
print(image.size_bytes)  # 1116478
print(image.data)        # raw bytes, ready to serve

Full documentation, architecture notes, and benchmark results live in the GitHub repository.

Installation

pip install zerobucket

Requires Python 3.10+ and PostgreSQL 13+ (uses gen_random_uuid(), built in since Postgres 13).

Quick reference

from zerobucket import ZeroBucket, ImageNotFoundError, ImageValidationError

images = ZeroBucket(
    database_url="postgresql://user:pass@localhost/mydb",
    max_bytes=8 * 1024 * 1024,  # default: 8MB
)

image_id = images.put("photo.jpg")
image_id = images.put(open("photo.jpg", "rb"))
image_id = images.put(request.files["avatar"])  # framework upload objects

image = images.get(image_id)
info = images.metadata(image_id)

try:
    images.get("nonexistent-id")
except ImageNotFoundError:
    ...

try:
    images.put("not-actually-an-image.txt")
except ImageValidationError:
    ...

Serving from a web API

from flask import Flask, Response

app = Flask(__name__)
images = ZeroBucket(database_url=DATABASE_URL)

@app.route("/images/<image_id>")
def serve_image(image_id):
    image = images.get(image_id)
    return Response(image.data, mimetype=image.mime_type)

Optimizing images (compression)

Off by default -- put() stores your exact input bytes unless you opt in:

image_id = images.put(
    "photo.jpg",
    optimize=True,
    max_width=1600,      # downscale if wider, aspect ratio preserved
    format="webp",       # optional re-encode target: "jpeg", "png", "webp"
    quality=90,           # 1-100, JPEG/WebP only; omit for data-backed defaults
)

Quality defaults (JPEG=90, WebP=88) are backed by measured SSIM data across multiple content types -- typical photos see 70-95% size reduction with no visible quality loss. One thing this data caught: don't target format="jpeg" for flat/graphic content (screenshots, logos) -- it can make them larger, not smaller. See COMPRESSION_RESULTS.md on GitHub for the full methodology.

What it validates

• Format: JPEG, PNG, WebP built in, plus HEIC/HEIF (iPhone photos) via the optional pip install zerobucket[heic] extra -- detected from actual file content, never from filename extension or a client-supplied Content-Type header.
• Corruption: truncated or malformed images are decoded and rejected before they reach the database.
• Decompression bombs: a tiny compressed file that decodes to an enormous pixel grid is rejected, not silently allocated.
• Size: configurable via max_bytes (default 8MB) -- see the benchmark results for why.

Transactions

By default, put()/get()/delete() each use their own independent database connection -- not your application's own transaction, even against the same database. Pass your own open psycopg connection via connection= to make a write participate in your transaction (e.g. "user

• avatar, atomically, or neither"). See the Transactions section on GitHub for a worked example -- this was verified by direct experiment during development, not assumed.

Batch operations

results = images.put_many([open("a.jpg", "rb"), open("b.jpg", "rb")])
fetched = images.get_many([id1, id2])
deleted = images.delete_many([id1, id2])

Best-effort, not all-or-nothing -- check .success/.error per item. get_many/delete_many are genuine single-query batch operations; see the full docs on GitHub for what's actually batched vs. still per-item.

Retry behavior

Transient errors (connection drops, deadlocks, serialization failures) are automatically retried with exponential backoff (max_retries=3 by default). Important: passing your own connection= disables automatic retry for that call -- see the full explanation on GitHub for why that's a deliberate safety rule, not an oversight.

Custom content types (PDFs and beyond)

from zerobucket.validators.pdf import PDFValidator

doc_id = images.put(pdf_bytes, validator=PDFValidator())
doc = images.get(doc_id)  # no special handling needed -- ever

Everything (transactions, retry, batch ops) works identically regardless of which validator produced a row. See the full explanation on GitHub for why this is a pluggable hook rather than native PDF support built into the core.

CLI

zerobucket init      # create the schema if missing
zerobucket info      # image count, total size, breakdown by format
zerobucket verify    # re-checksum every image to detect corruption

Takes --database-url or reads ZEROBUCKET_DATABASE_URL from the environment. verify exits non-zero on any mismatch, so it's usable in cron/CI. See the full CLI docs on GitHub.

Deduplication

images = ZeroBucket(database_url=DATABASE_URL, dedup=True)
id1 = images.put("photo.jpg")
id2 = images.put("photo.jpg")  # identical content -- stored exactly once, referenced twice

Opt-in (dedup=True), uses separate tables from classic mode, so it's safe to add later without touching existing data. See the full explanation on GitHub, including the migration path for existing classic-mode data.

Limitations (read before using in production)

• Not built for large files or high-volume media. Full images are read into memory on both ends of every request -- no streaming, no range requests, no CDN.
• Deduplication is opt-in, not automatic. Default dedup=False stores every upload as a separate row; pass dedup=True for content-addressed, reference-counted storage (see above).
• PostgreSQL only, for now. The storage layer is abstracted for future adapters, but only Postgres exists today.

See the full README and roadmap on GitHub for more detail.

License

MIT -- see LICENSE.