Seeds¶

Seeds are CSV files that smelt loads into your database as tables. They are useful for small reference datasets, lookup tables, and test data that you want to version-control alongside your models.

Directory structure¶

Place CSV files in the seeds/ directory (configurable via seed_paths in smelt.yml). Subdirectories map to schema names in the database.

my_project/
  seeds/
    raw/
      users.csv
      transactions.csv
      events.csv
      sessions.csv
  models/
    ...
  smelt.yml

In this example, seeds/raw/users.csv is loaded as the table raw.users.

CSV format¶

Seeds are standard CSV files with a header row:

user_id,user_name,signup_date
1,Alice,2025-01-01
2,Bob,2025-01-02
3,Charlie,2025-01-03
4,Diana,2025-01-04
5,Eve,2025-01-05

smelt infers column types from the data. The table is created (or replaced) each time you run the seed command.

Commands¶

Load all seeds¶

smelt seed

Load and display results¶

smelt seed --show-results

This prints the loaded data in a table format after seeding, which is helpful for verifying that the CSV was parsed correctly.

Load specific seeds¶

smelt seed --select users
smelt seed --select raw.users

Use --select to load only specific seed files by name or by schema.name.

Build (seed + run)¶

smelt build

The build command combines seeding and model execution in one step. It loads all seeds first, then runs all models. This is the most common command during development.

Tip

Use smelt build when starting fresh or resetting your development database. It ensures seeds are loaded before any models that depend on them run.

Configuration¶

The seed directory defaults to seeds/. Override it in smelt.yml:

seed_paths:
  - seeds
  - test_data

Multiple directories are supported. Each is scanned for CSV files, and subdirectories map to schema names as described above.

Target selection¶

Like model runs, seed loading respects the --target flag:

smelt seed --target dev
smelt seed --target spark

When to use seeds¶

Seeds work well for:

Reference data -- Country codes, status enums, category mappings
Test data -- Small datasets for development and testing
Static lookups -- Data that rarely changes and is small enough to version in git

Warning

Seeds are not designed for large datasets. CSV files are fully loaded into memory and inserted as a single batch. For datasets larger than a few thousand rows, use Sources instead and load the data with your ingestion pipeline.