scitex-db 0.1.9

Database utilities for scientific computing with SQLite3 and PostgreSQL

scitex-db

Database utilities for scientific computing — SQLite3 + PostgreSQL with NumPy-aware storage.

Full Documentation · uv pip install scitex-db[all]

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Problem and Solution

#	Problem	Solution
1	Storing ndarrays in SQLite means pickle.dumps → BLOB — no compression, no dtype/shape, no deterministic hashing	db.save_array(table, arr) / load_array(...) — typed compressed BLOBs round-trip with dtype, shape, is_compressed, _hash columns
2	sqlite3 API is low-level — every project re-writes connect / transaction / execute boilerplate	with db.transaction(): ... — context-managed transactions, health checks, dedup, schema inspection built-in
3	Switching SQLite ↔ Postgres rewrites every call site	Mixin composition — SQLite3 and PostgreSQL share _BaseMixins/; the same call site works against either backend

Installation

pip install scitex-db                 # SQLite3 only
pip install scitex-db[postgresql]     # add psycopg2 driver
pip install scitex-db[all]            # everything

Configuration

Defaults work out of the box. To override, drop a config.yaml next to your script, or point SCITEX_DB_CONFIG at one — see .env.example for the full env-var list and resolution order.

Quick Start

from scitex_db import SQLite3
import numpy as np

db = SQLite3("experiments.db")

db.create_table("results", {
    "id": "INTEGER PRIMARY KEY",
    "experiment": "TEXT",
    "accuracy": "REAL",
})
db.insert_many("results", [
    {"experiment": "exp1", "accuracy": 0.95},
    {"experiment": "exp2", "accuracy": 0.92},
])

# NumPy arrays round-trip with dtype/shape preserved
db.save_array("features", np.random.rand(1000, 50), column="embeddings",
              additional_columns={"model": "bert"})
features = db.load_array("features", "embeddings", where="model = 'bert'")

2 Interfaces

Python API ⭐⭐⭐  _{primary surface}

from scitex_db import SQLite3, PostgreSQL, check_health, inspect

# Backends
db = SQLite3("experiments.db")
db = PostgreSQL(host=..., user=..., dbname=...)

# CRUD
db.insert("results", {"experiment": "exp1", "accuracy": 0.95})
db.insert_many("results", rows, batch_size=1000)
rows = db.get_rows("results", where="accuracy > 0.9")
db.update("results", {"accuracy": 0.97}, where="id = 1")
db.delete("results", where="id = 1")

# Arrays / Blobs
db.save_array(table, arr, column="data")
db.load_array(table, "data", where=...)
db.save_blob(table, obj, column="checkpoint")
db.load_blob(table, "checkpoint", where=...)

# Transactions / maintenance
with db.transaction():
    db.insert("a", {...}); db.insert("b", {...})
db.summary                # schema + row counts
inspect("experiments.db") # standalone helper
check_health("experiments.db", fix_issues=True)

CLI ⭐⭐  _{scitex-db <subcommand>}

scitex-db --help-recursive            # all subcommands at once
scitex-db inspect-db experiments.db   # schema + row counts
scitex-db inspect-db experiments.db --tables results --json
scitex-db check-health experiments.db --fix --yes
scitex-db check-health experiments.db --dry-run
scitex-db list-python-apis            # introspect public Python surface

Every subcommand supports -h/--help, --json, and the safety pair --dry-run / --yes where it mutates state.

Architecture

scitex_db/
├── __init__.py            ← public API (SQLite3, PostgreSQL, check_health, inspect)
├── __main__.py            ← `scitex-db` CLI entry
├── _BaseMixins/           ← backend-agnostic mixins (CRUD, schema, batch, ...)
├── _sqlite3/              ← SQLite3 driver
│   └── _SQLite3Mixins/    ← SQLite3-specific mixin overrides
├── _postgresql/           ← PostgreSQL driver
│   └── _PostgreSQLMixins/ ← PostgreSQL-specific mixin overrides
├── _check_health.py       ← `scitex-db check-health`
├── _inspect.py            ← `scitex-db inspect-db`
├── _inspect_optimized.py  ← faster path for large DBs
├── _delete_duplicates.py  ← duplicate-row cleanup
├── _utils.py              ← shared helpers
└── _skills/               ← agent-facing skill files

Each backend composes its _*Mixins/ folder onto _BaseMixins/, so swapping SQLite3 ↔ PostgreSQL does not change call sites.

Demo

flowchart LR
    U["user code"] --> A["SQLite3('exp.db')"]
    U --> B["PostgreSQL(host=..., user=...)"]
    A --> M["_BaseMixins (CRUD · schema · batch · maintenance)"]
    B --> M
    A -.-> SM["_SQLite3Mixins<br/>(backend overrides)"]
    B -.-> PM["_PostgreSQLMixins<br/>(backend overrides)"]
    M --> H["scitex-db check-health<br/>(fix orphans, vacuum)"]
    M --> I["scitex-db inspect-db<br/>(schema + row counts)"]

Part of SciTeX

scitex-db is part of SciTeX. Install via the umbrella with pip install scitex[db], then import as scitex.db or invoke scitex db <subcommand> — the standalone scitex-db package remains the source of truth.

Four Freedoms for Research

0. The freedom to run your research anywhere — your machine, your terms.
1. The freedom to study how every step works — from raw data to final manuscript.
2. The freedom to redistribute your workflows, not just your papers.
3. The freedom to modify any module and share improvements with the community.

AGPL-3.0 — because we believe research infrastructure deserves the same freedoms as the software it runs on.

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