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)
- Pure Python
- YAML
- JSON
- CSV
- Pickle
- HTML
- 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/--insertsflag, adds INSERT statements - with
-u/--uniquesflag, surmises UNIQUE constraints from data - Handles nested data, creating child tables as needed
- Reads HTML tables, including those embedded in noisy websites
-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)
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)
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'
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.
Requires Python3.
From PyPI:
pip3 install ddlgenerator
From source:
git clone https://github.com/catherinedevlin/ddl-generator.git cd ddl-generator pip3 install .
- csvkit.csvsql
- pandas.read_* methods
- prequel for SQLite
- Mike Bayer for sqlalchemy
- coldfix and Mark Ransom for their StackOverflow answers
- Audrey Roy for cookiecutter
- Brandon Lorenz for Django model generation
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