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DDL Generator

Infers SQL DDL (Data Definition Language) from table data.

Use at command line:

$ ddlgenerator -i postgresql '[{"Name": "Alfred", "species": "wart hog", "kg": 22}]'

DROP TABLE generated_table;
CREATE TABLE generated_table (
        name VARCHAR(6) NOT NULL,
        kg INTEGER NOT NULL,
        species VARCHAR(8) NOT NULL
)
;
INSERT INTO generated_table (kg, Name, species) VALUES (22, 'Alfred', 'wart hog');

Reads data from files:

$ ddlgenerator postgresql mydata.yaml > mytable.sql

Enables one-line creation of tables with their data

$ ddlgenerator --inserts postgresql mydata.json | psql

To use in Python:

>>> from ddlgenerator.ddlgenerator import Table
>>> table = Table([{"Name": "Alfred", "species": "wart hog", "kg": 22}])
>>> sql = table.sql('postgresql', inserts=True)

Supported data formats

  • Pure Python
  • YAML
  • JSON
  • CSV
  • Pickle
  • HTML

Features

  • Supports all SQL dialects supported by SQLAlchemy
  • Coerces data into most specific data type valid on all column's values
  • Takes table name from file name
  • Guesses format of input data if unspecified by file extension
  • with -i/--inserts flag, adds INSERT statements
  • with -u/--uniques flag, surmises UNIQUE constraints from data
  • Handles nested data, creating child tables as needed
  • Reads HTML tables, including those embedded in noisy websites

Options

-h, --help            show this help message and exit
-k KEY, --key KEY     Field to use as primary key
-r, --reorder         Reorder fields alphabetically, ``key`` first
-u, --uniques         Include UNIQUE constraints where data is unique
-t, --text            Use variable-length TEXT columns instead of VARCHAR
-d, --drops           Include DROP TABLE statements
-i, --inserts         Include INSERT statements
--no-creates          Do not include CREATE TABLE statements
--save-metadata-to FILENAME
                      Save table definition in FILENAME for later --use-
                      saved-metadata run
--use-metadata-from FILENAME
                      Use metadata saved in FROM for table definition, do
                      not re-analyze table structure
-l LOG, --log LOG     log level (CRITICAL, FATAL, ERROR, DEBUG, INFO, WARN)

Generate SQLAlchemy models

Use sqlalchemy as the model to generate Python for defining SQLAlchemy models:

$ ddlgenerator sqlalchemy '[{"Name": "Alfred", "species": "wart hog", "kg": 22}]'

Table0 = Table('Table0', metadata,
  Column('species', Unicode(length=8), nullable=False),
  Column('kg', Integer(), nullable=False),
  Column('name', Unicode(length=6), nullable=False),
  schema=None)

Generate Django models

If Django is installed on the path then using django as the model will run the generated ddl through Django's inspectdb management command to produce a model file:

$ ddlgenerator django '[{"Name": "Alfred", "species": "wart hog", "kg": 22}]'

# This is an auto-generated Django model module.
# You'll have to do the following manually to clean this up:
#   * Rearrange models' order
#   * Make sure each model has one field with primary_key=True
#   * Remove `managed = False` lines if you wish to allow Django to create and delete the table
# Feel free to rename the models, but don't rename db_table values or field names.
#
# Also note: You'll have to insert the output of 'django-admin.py sqlcustom [appname]'
# into your database.
from __future__ import unicode_literals

from django.db import models

class Table0(models.Model):
    species = models.CharField(max_length=8)
    kg = models.IntegerField()
    name = models.CharField(max_length=6)
    class Meta:
        managed = False
        db_table = 'Table0'

Large tables

As of now, ddlgenerator is not well-designed for table sizes approaching your system's available memory.

One approach to save time and memory for large tables is to break your input data into multiple files, then run ddlgenerator with --save-metadata against a small but representative sample. Then run with --no-creates and -use-saved-metadata to generate INSERTs from the remaining files without needing to re-determine the column types each time.

Installing

Requires Python3.

From PyPI:

pip3 install ddlgenerator

From source:

git clone https://github.com/catherinedevlin/ddl-generator.git
cd ddl-generator
pip3 install .

Alternatives

Credits

  • Mike Bayer for sqlalchemy
  • coldfix and Mark Ransom for their StackOverflow answers
  • Audrey Roy for cookiecutter
  • Brandon Lorenz for Django model generation