dbslice

Extract minimal, referentially-intact database subsets for local development and debugging.

The Problem

Copying an entire production database to your machine is infeasible. But reproducing a bug often requires having the exact data that caused it. dbslice solves this by extracting only the records you need, following foreign key relationships to ensure referential integrity.

Quick Start

# Install globally
uv tool install dbslice   # or: pip install dbslice

# Extract an order and all related records
dbslice extract postgres://localhost/myapp --seed "orders.id=12345" > subset.sql

# Import into local database
psql -d localdb < subset.sql

Features

• Zero-config start -- Introspects schema automatically, no data model file required
• Single command -- Extract complete data subsets with one CLI invocation
• Safe by default -- Auto-detects and anonymizes sensitive fields (emails, phones, SSNs, etc.)
• Compliance profiles -- Built-in GDPR, HIPAA Safe Harbor, and PCI-DSS profiles with two-phase PII scanning
• Column mapping UI -- Local browser UI to visually map columns, apply compliance profiles, and export config
• Multiple output formats -- SQL, JSON, and CSV
• Streaming -- Memory-efficient extraction for large datasets (100K+ rows)
• Virtual foreign keys -- Support for Django GenericForeignKeys and implicit relationships via config
• Config files -- YAML-based configuration for repeatable extractions
• Validation -- Checks referential integrity of extracted data

Database Support

Installation

# Install with uv (recommended)
uv add dbslice

# Try without installing
uvx dbslice --help

# Or with pip
pip install dbslice

Usage

Basic Extraction

# Extract by primary key
dbslice extract postgres://user:pass@host:5432/db --seed "orders.id=12345"

# Extract with WHERE clause
dbslice extract postgres://localhost/db --seed "orders:status='failed' AND created_at > '2024-01-01'"

# Multiple seeds
dbslice extract postgres://localhost/db \
  --seed "orders.id=100" \
  --seed "orders.id=101"

Control Traversal

# Limit depth (default: 3)
dbslice extract postgres://... --seed "orders.id=1" --depth 2

# Direction: up (parents only), down (children only), both (default)
dbslice extract postgres://... --seed "orders.id=1" --direction up

Anonymization

# Auto-anonymize detected sensitive fields
dbslice extract postgres://... --seed "users.id=1" --anonymize

# Redact additional fields
dbslice extract postgres://... --seed "users.id=1" --anonymize --redact "audit_logs.ip_address"

Column Mapping UI

Map columns visually, apply compliance profiles, and generate a ready-to-use config — all from a local browser UI.

dbslice map postgres://localhost/myapp

# Custom port
dbslice map postgres://localhost/myapp --port 8888

# Also works with uvx (no install needed)
uvx dbslice map postgres://localhost/myapp

Map columns to anonymization rules	Generate and export config

Runs on 127.0.0.1:9473 with a one-time session token — no data leaves your machine. Apply GDPR, HIPAA, or PCI-DSS profiles with one click, review what gets masked, then download the YAML.

Compliance Profiles

# HIPAA Safe Harbor — auto-masks all 18 identifier types
dbslice extract postgres://... --seed "patients.id=1" --compliance hipaa --compliance-strict

# Multiple profiles + audit manifest
dbslice extract postgres://... --seed "users.id=1" --compliance gdpr --compliance pci-dss -f subset.sql
# Produces subset.sql + subset.manifest.json

Output Formats

# SQL (default)
dbslice extract postgres://... --seed "orders.id=1" --output sql

# JSON fixtures
dbslice extract postgres://... --seed "orders.id=1" --output json --out-file fixtures/

# CSV
dbslice extract postgres://... --seed "orders.id=1" --output csv --out-file data/

Virtual Foreign Keys

For relationships not defined in the database schema (Django GenericForeignKeys, implicit relationships):

# dbslice.yaml
database:
  url: postgres://localhost:5432/myapp

virtual_foreign_keys:
  - source_table: notifications
    source_columns: [object_id]
    target_table: orders
    description: "Generic FK to orders via ContentType"

  - source_table: audit_log
    source_columns: [user_id]
    target_table: users
    description: "Implicit FK without DB constraint"

dbslice extract --config dbslice.yaml --seed "users.id=1"

Inspect Schema

dbslice inspect postgres://localhost/myapp

Configuration File

# Generate config from database
dbslice init postgres://localhost/myapp --out-file dbslice.yaml

# Use config
dbslice extract --config dbslice.yaml --seed "orders.id=12345"

How It Works

1. Introspect -- Reads database schema to discover tables and foreign key relationships
2. Traverse -- Starting from seed record(s), follows FK relationships via BFS
3. Extract -- Fetches all identified records
4. Sort -- Topologically sorts tables for correct INSERT order
5. Output -- Generates SQL/JSON/CSV with proper escaping

Comparison

dbslice is the lightweight, zero-config Python option: install and extract in under a minute.

Development

git clone https://github.com/nabroleonx/dbslice.git
cd dbslice
uv sync --dev
uv run pytest

License

MIT