A collection of wrappers and functions to make SQLAlchemy core easier to use in an ETL application. The package is used only for database abstraction and not as an ORM, allowing users to write extraction scripts that can work with multiple database backends. Functions include:
- Self-expanding schema. If a column is written that does not exist on the table, it will be created automatically.
- Upserts. Records are either created or updated, depdending on whether an existing version can be found.
- Query helpers for simple queries such as all rows in a table or all distinct values of a set of columns.
A typical use case for sqlaload may include code like this::
from sqlaload import connect, get_table, distinct, update
engine = connect('sqlite:///customers.db')
table = get_table('customers')
for entry in distinct(engine, table, 'post_code', 'city')
lon, lat = geocode(entry['post_code'], entry['city'])
update(entry, {'lon': lon, 'lat': lat})
In this example, we selected all distinct post codes and city names from an imaginary customers database, sent them through our geocoding routine and finally updated all matching rows with our geo information.
Another example, updating data in a datastore, might look like this::
from sqlaload import connect, get_table, upsert
engine = connect('sqlite:///things.db')
table = get_table('data')
for item in magic_data_source_that_produces_entries():
assert 'key1' in item
assert 'key2' in item
# this will either insert or update, depending on
# whether an entry with the matching values for
# 'key1' and 'key2' already exists:
upsert(engine, table, item, ['key1', 'key2'])
Please feel free create issues on the GitHub bug tracker at: