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Standardizing models

Project description

Bollhav

A lightweight config class for defining data pipeline models.

Usage

Basic table

model = Model(
    name="orders",
    source_table="raw.orders",
    destination_table="orders",
    destination_schema="public",
    write_mode=WriteMode.TRUNCATE_INSERT,
    destination_db=Database.POSTGRES,
    columns=[
        PostgresColumn(name="id", data_type=PostgresType.BIGINT, primary_key=True, nullable=False),
        PostgresColumn(name="customer_id", data_type=PostgresType.BIGINT),
        PostgresColumn(name="amount", data_type=PostgresType.NUMERIC, precision=10, scale=2),
        PostgresColumn(name="created_at", data_type=PostgresType.TIMESTAMPTZ),
    ],
)

View

model = Model(
    name="orders_view",
    source_table="raw.orders",
    destination_table="orders_view",
    destination_schema="public",
    model_type=ModelType.VIEW,
    write_mode=WriteMode.VIEW,
    destination_db=Database.POSTGRES,
    columns=[
        PostgresColumn(name="id", data_type=PostgresType.BIGINT),
        PostgresColumn(name="amount", data_type=PostgresType.NUMERIC, precision=10, scale=2),
    ],
)

With a schedule

model = Model(
    name="orders",
    source_table="raw.orders",
    destination_db=Database.POSTGRES,
    columns=[
        PostgresColumn(name="id", data_type=PostgresType.BIGINT),
        PostgresColumn(name="amount", data_type=PostgresType.NUMERIC, precision=10, scale=2),
    ],
    cron="0 3 * * *",
)
model.batch_size  # BatchSize.DAILY

batch_size is inferred from the cron expression and is read-only.

BatchSize Example cron
YEARLY 0 0 1 1 *
MONTHLY 0 0 1 * *
WEEKLY 0 0 * * 0
DAILY 0 3 * * *
HOURLY 0 * * * *

With dynamic kwargs

def my_ddl(table_name: str, schema: str, **kwargs) -> str:
    return f"CREATE TABLE {schema}.{table_name} (id SERIAL PRIMARY KEY);"

model = Model(
    name="orders",
    source_table="raw.orders",
    destination_db=Database.POSTGRES,
    columns=[
        PostgresColumn(name="id", data_type=PostgresType.BIGINT, primary_key=True, nullable=False),
    ],
    destination_ddl=my_ddl,
    table_name="orders",
    schema="public",
)
model.extra["destination_ddl"]  # resolved DDL string

Callables in **kwargs are resolved at init using the non-callable kwargs as arguments.

Write modes

WriteMode ModelType Description
APPEND TABLE Insert without truncating
TRUNCATE_INSERT TABLE Truncate then insert
OVERWRITE_INSERT TABLE Overwrite matching rows
MERGE TABLE Upsert based on keys
VIEW VIEW Create or replace view

ModelType and WriteMode are validated against each other at init.

PostgresColumn

Field Type Default Notes
name str required
data_type PostgresType required
nullable bool True
order int | None None
primary_key bool False Implies nullable=False
unique bool False
precision int | None None For NUMERIC, DECIMAL
scale int | None None For NUMERIC, DECIMAL
length int | None None For VARCHAR, CHAR, BIT

primary_key=True with nullable=True raises at init.

Tags

Optional and freeform.

model = Model(
    name="orders",
    source_table="raw.orders",
    destination_db=Database.POSTGRES,
    columns=[
        PostgresColumn(name="id", data_type=PostgresType.BIGINT),
    ],
    tags=["finance", "critical"],
)

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