dbt-duckdb

DuckDB is an embedded database, similar to SQLite, but designed for OLAP-style analytics. It is crazy fast and allows you to read and write data stored in CSV, JSON, and Parquet files directly, without requiring you to load them into the database first.

dbt is the best way to manage a collection of data transformations written in SQL or Python for analytics and data science. dbt-duckdb is the project that ties DuckDB and dbt together, allowing you to create a Modern Data Stack In A Box or a simple and powerful data lakehouse with Python.

Installation

This project is hosted on PyPI, so you should be able to install it and the necessary dependencies via:

pip3 install dbt-duckdb

The latest supported version targets dbt-core 1.4.x and duckdb version 0.7.x, but we work hard to ensure that newer versions of DuckDB will continue to work with the adapter as they are released. If you would like to use our new (and experimental!) support for persisting the tables that DuckDB creates to the AWS Glue Catalog, you should install dbt-duckdb[glue] in order to get the AWS dependencies as well.

Configuring Your Profile

A minimal dbt-duckdb profile only needs two settings, type and path:

default:
  outputs:
   dev:
     type: duckdb
     path: /tmp/dbt.duckdb
  target: dev

The path field should normally be the path to a local DuckDB file on your filesystem, but it can also be set equal to :memory: if you would like to run an in-memory only version of dbt-duckdb. Keep in mind that if you are using the in-memory mode, any models that you want to keep from the dbt run will need to be persisted using one of the external materialization strategies described below.

dbt-duckdb also supports common profile fields like schema and threads, but the database property is special: it's value is automatically set to the basename of the file in the path argument with the suffix removed. For example, if the path is /tmp/a/dbfile.duckdb, the database field will be set to dbfile. If you are running with the path equal to :memory:, then the name of the database will be memory.

DuckDB Extensions, Settings, and Filesystems

You can load any supported DuckDB extensions by listing them in the extensions field in your profile. You can also set any additional DuckDB configuration options via the settings field, including options that are supported in any loaded extensions. For example, to be able to connect to S3 and read/write Parquet files using an AWS access key and secret, your profile would look something like this:

default:
  outputs:
    dev:
      type: duckdb
      path: /tmp/dbt.duckdb
      extensions:
        - httpfs
        - parquet
      settings:
        s3_region: my-aws-region
        s3_access_key_id: "{{ env_var('S3_ACCESS_KEY_ID') }}"
        s3_secret_access_key: "{{ env_var('S3_SECRET_ACCESS_KEY') }}"
  target: dev

As of verion 1.4.1, we have added (experimental!) support for DuckDB's (experimental!) support for filesystems implemented via fsspec. The fsspec library provides support for reading and writing files from a variety of cloud data storage systems including S3, GCS, and Azure Blob Storage. You can configure a list of fsspec-compatible implementations for use with your dbt-duckdb project by installing the relevant Python modules and configuring your profile like so:

default:
  outputs:
    dev:
      type: duckdb
      path: /tmp/dbt.duckdb
      filesystems:
        - fs: s3
          anon: false
          key: "{{ env_var('S3_ACCESS_KEY_ID') }}"
          secret: "{{ env_var('S3_SECRET_ACCESS_KEY') }}"
          client_kwargs:
            endpoint_url: "http://localhost:4566"
  target: dev

Here, the filesystems property takes a list of configurations, where each entry must have a property named fs that indicates which fsspec protocol to load (so s3, gcs, abfs, etc.) and then an arbitrary set of other key-value pairs that are used to configure the fsspec implementation. You can see a simple example project that illustrates the usage of this feature to connect to a Localstack instance running S3 from dbt-duckdb here.

Fetching credentials from context

Instead of specifying the credentials through the settings block, you can also use the use_credential_provider property. If you set this to aws (currently the only supported implementation) and you have boto3 installed in your python environment, we will fetch your AWS credentials using the credential provider chain as described here. This means that you can use any supported mechanism from AWS to obtain credentials (e.g., web identity tokens).

Attaching Additional Databases

DuckDB version 0.7.0 and dbt-duckdb version 1.4.0 support attaching additional databases to your dbt-duckdb run so that you can read and write from multiple databases. Additional databases may be configured using dbt run hooks or via the attach argument in your profile:

default:
  outputs:
    dev:
      type: duckdb
      path: /tmp/dbt.duckdb
      attach:
        - path: /tmp/other.duckdb
        - path: ./yet/another.duckdb
          alias: yet_another
        - path: s3://yep/even/this/works.duckdb
          read_only: true
        - path: sqlite.db
          type: sqlite

The attached databases may be referred to in your dbt sources and models by either the basename of the database file minus its suffix (e.g., /tmp/other.duckdb is the other database and s3://yep/even/this/works.duckdb is the works database) or by an alias that you specify (so the ./yet/another.duckdb database in the above configuration is referred to as yet_another instead of another.) Note that these additional databases do not necessarily have to be DuckDB files: DuckDB's storage and catalog engines are pluggable, and DuckDB 0.7.0 ships with support for reading and writing from attached SQLite databases. You can indicate the type of the database you are connecting to via the type argument, which currently supports duckdb and sqlite.

Reading and Writing External Files

One of DuckDB's most powerful features is its ability to read and write CSV, JSON, and Parquet files directly, without needing to import/export them from the database first.

Reading from external files

You may reference external files in your dbt model's either directly or as dbt sources by configuring the external_location meta option on the source:

sources:
  - name: external_source
    meta:
      external_location: "s3://my-bucket/my-sources/{name}.parquet"
    tables:
      - name: source1
      - name: source2

Here, the meta options on external_source defines external_location as an f-string that allows us to express a pattern that indicates the location of any of the tables defined for that source. So a dbt model like:

SELECT *
FROM {{ source('external_source', 'source1') }}

will be compiled as:

SELECT *
FROM 's3://my-bucket/my-sources/source1.parquet'

If one of the source tables deviates from the pattern or needs some other special handling, then the external_location can also be set on the meta options for the table itself, for example:

sources:
  - name: external_source
    meta:
      external_location: "s3://my-bucket/my-sources/{name}.parquet"
    tables:
      - name: source1
      - name: source2
        meta:
          external_location: "read_parquet(['s3://my-bucket/my-sources/source2a.parquet', 's3://my-bucket/my-sources/source2b.parquet'])"

In this situation, the external_location setting on the source2 table will take precedence, so a dbt model like:

SELECT *
FROM {{ source('external_source', 'source2') }}

will be compiled to the SQL query:

SELECT *
FROM read_parquet(['s3://my-bucket/my-sources/source2a.parquet', 's3://my-bucket/my-sources/source2b.parquet'])

Note that the value of the external_location property does not need to be a path-like string; it can also be a function call, which is helpful in the case that you have an external source that is a CSV file which requires special handling for DuckDB to load it correctly:

sources:
  - name: flights_source
    tables:
      - name: flights
        meta:
          external_location: "read_csv('flights.csv', types={'FlightDate': 'DATE'}, names=['FlightDate', 'UniqueCarrier'])"

Writing to external files

We support creating dbt models that are backed by external files via the external materialization strategy:

{{ config(materialized='external', location='local/directory/file.parquet') }}
SELECT m.*, s.id IS NOT NULL as has_source_id
FROM {{ ref('upstream_model') }} m
LEFT JOIN {{ source('upstream', 'source') }} s USING (id)

Option	Default	Description
location	{{ name }}.{{ format }}	The path to write the external materialization to. See below for more details.
format	parquet	The format of the external file (parquet, csv, or json)
delimiter	,	For CSV files, the delimiter to use for fields.
options	None	Any other options to pass to DuckDB's COPY operation (e.g., partition_by, codec, etc.)
glue_register	false	If true, try to register the file created by this model with the AWS Glue Catalog.
glue_database	default	The name of the AWS Glue database to register the model with.

If the location argument is specified, it must be a filename (or S3 bucket/path), and dbt-duckdb will attempt to infer the format argument from the file extension of the location if the format argument is unspecified (this functionality was added in version 1.4.1.)

If the location argument is not specified, then the external file will be named after the model.sql (or model.py) file that defined it with an extension that matches the format argument (parquet, csv, or json). By default, the external files are created relative to the current working directory, but you can change the default directory (or S3 bucket/prefix) by specifying the external_root setting in your DuckDB profile.

Re-running external models with an in-memory version of dbt-duckdb

When using :memory: as the DuckDB database, subsequent dbt runs can fail when selecting a subset of models that depend on external tables. This is because external files are only registered as DuckDB views when they are created, not when they are referenced. To overcome this issue we have provided the register_upstream_external_models macro that can be triggered at the beginning of a run. To enable this automatic registration, place the following in your dbt_project.yml file:

on-run-start:
  - "{{ register_upstream_external_models() }}"

Python Support

dbt added support for Python models in version 1.3.0. For most data platforms, dbt will package up the Python code defined in a .py file and ship it off to be executed in whatever Python environment that data platform supports. However, in dbt-duckdb, the local machine is the data platform, and so we support executing any Python code that will run on your machine via an exec call. The value of the dbt.ref and dbt.source functions will be a DuckDB Relation object that can be easily converted into a Pandas DataFrame or Arrow table, and the return value of the def models function can be any Python object that DuckDB knows how to turn into a relation, including a Pandas or Polars DataFrame, a DuckDB Relation, or an Arrow Table, Dataset, RecordBatchReader, or Scanner.

Roadmap

Things that we would like to add in the near future:

• Support for Delta and Iceberg external table formats (both as sources and destinations)
• Make dbt's incremental models and snapshots work with external materializations
• Make AWS Glue registration a first-class concept and add support for Snowflake/BigQuery registrations