gs1-gpc 0.3.3

A tool for importing GS1 Global Product Classification (GPC) data into SQL databases

GS1 GPC

A tool for importing GS1 Global Product Classification (GPC) data into SQL databases.

Features

• Import GS1 GPC XML data into SQLite or PostgreSQL databases
• Download the latest GPC data directly from GS1 API using the gpcc library
• Automatically use the newest cached version if download is not available
• Export database tables to SQL file for backup or migration
• Path handling relative to script location for reliable execution from any directory
• Command-line interface with Click
• Pip installable package

Installation

Development Installation

# Clone the repository
git clone https://github.com/mcgarrah/gs1_gpc_python.git
cd gs1_gpc_python

# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install the package in development mode
pip install -e .

Using requirements.txt

For convenience, you can install all dependencies including gpcc from GitHub:

pip install -r requirements.txt
pip install -e .

PostgreSQL Support

To use PostgreSQL instead of SQLite, install the PostgreSQL extra:

pip install -e ".[postgresql]"

Directory Structure

• /data/imports - Directory for XML files (downloaded or manually placed)
• /data/instances - Directory for SQLite database files
• /data/exports - Directory for SQL dump files

Usage

Basic Import

gpc import-gpc

This will:

1. Look for the latest cached XML file in the imports directory
2. If none found, use the fallback file
3. Import the data into the default SQLite database

Download Latest Data

gpc import-gpc --download

This will:

1. Download the latest GPC data from the GS1 API
2. Save it to the imports directory with standard naming convention: {language_code}-{version}.xml
3. Import the data into the default SQLite database

Specify Language

gpc import-gpc --download --language nl

This will download and import the Dutch version of the GPC data.

Custom Files

gpc import-gpc --xml-file ./my_custom_file.xml --db-file ./my_database.sqlite3

Export Database to SQL

gpc import-gpc --dump-sql

This will:

1. Import data as usual
2. Export all GPC tables to a SQL file in the exports directory
3. The SQL file will follow the naming convention: {language_code}-v{date}.sql

Export Only (No Import)

gpc export-sql --db-file ./data/instances/gpc_data_xml.sqlite3

PostgreSQL Support

gpc import-gpc --db-type postgresql --db-file "postgresql://user:password@localhost/dbname"

Other Options

gpc import-gpc --help

Database Schema

The database uses the following schema with all tables prefixed with "gpc_":

CREATE TABLE gpc_segments (
    segment_code TEXT PRIMARY KEY,
    description TEXT
);

CREATE TABLE gpc_families (
    family_code TEXT PRIMARY KEY,
    description TEXT,
    segment_code TEXT,
    FOREIGN KEY (segment_code) REFERENCES gpc_segments (segment_code)
);

CREATE TABLE gpc_classes (
    class_code TEXT PRIMARY KEY,
    description TEXT,
    family_code TEXT,
    FOREIGN KEY (family_code) REFERENCES gpc_families (family_code)
);

CREATE TABLE gpc_bricks (
    brick_code TEXT PRIMARY KEY,
    description TEXT,
    class_code TEXT,
    FOREIGN KEY (class_code) REFERENCES gpc_classes (class_code)
);

CREATE TABLE gpc_attribute_types (
    att_type_code TEXT PRIMARY KEY,
    att_type_text TEXT,
    brick_code TEXT,
    FOREIGN KEY (brick_code) REFERENCES gpc_bricks (brick_code)
);

CREATE TABLE gpc_attribute_values (
    att_value_code TEXT PRIMARY KEY,
    att_value_text TEXT,
    att_type_code TEXT,
    FOREIGN KEY (att_type_code) REFERENCES gpc_attribute_types (att_type_code)
);

Example Queries

List all segments and families

SELECT 
    gpc_segments.segment_code, 
    gpc_families.family_code, 
    gpc_segments.description AS segment_text, 
    gpc_families.description AS family_text 
FROM gpc_segments 
JOIN gpc_families ON gpc_segments.segment_code = gpc_families.segment_code;

List all hierarchy levels with limit

SELECT 
    gpc_segments.segment_code, 
    gpc_families.family_code, 
    gpc_classes.class_code, 
    gpc_bricks.brick_code,
    gpc_segments.description AS segment_text, 
    gpc_families.description AS family_text, 
    gpc_classes.description AS class_text, 
    gpc_bricks.description AS brick_text
FROM gpc_segments 
JOIN gpc_families ON gpc_segments.segment_code = gpc_families.segment_code
JOIN gpc_classes ON gpc_families.family_code = gpc_classes.family_code
JOIN gpc_bricks ON gpc_classes.class_code = gpc_bricks.class_code
LIMIT 16;

Filter by segment

SELECT 
    gpc_segments.segment_code, 
    gpc_families.family_code, 
    gpc_classes.class_code, 
    gpc_bricks.brick_code,
    gpc_segments.description AS segment_text, 
    gpc_families.description AS family_text, 
    gpc_classes.description AS class_text, 
    gpc_bricks.description AS brick_text
FROM gpc_segments 
JOIN gpc_families ON gpc_segments.segment_code = gpc_families.segment_code
JOIN gpc_classes ON gpc_families.family_code = gpc_classes.family_code
JOIN gpc_bricks ON gpc_classes.class_code = gpc_bricks.class_code
WHERE gpc_segments.segment_code = '50000000' 
LIMIT 16;

Development

Running Tests

pip install -e ".[dev]"
pytest gs1_gpc/tests/