Metadata-Version: 2.1
Name: dbt-duckdb
Version: 1.3.2
Summary: The duckdb adapter plugin for dbt (data build tool)
Home-page: https://github.com/jwills/dbt-duckdb
Author: Josh Wills
Author-email: joshwills+dbt@gmail.com
Description-Content-Type: text/markdown
Provides-Extra: glue
License-File: LICENSE

## dbt-duckdb

[DuckDB](http://duckdb.org) 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 and Parquet files directly, without requiring you to load
them into the database first.

[dbt](http://getdbt.com) 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](https://duckdb.org/2022/10/12/modern-data-stack-in-a-box.html) or a simple and powerful data lakehouse- no Java or Scala
required.

### 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` 1.3.x and `duckdb` version 0.5.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](https://aws.amazon.com/glue/), 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 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 standard profile settings including `threads` (to control how many concurrent models dbt will run at once) and
`schema` (to control the default schema that models will be materialized in.)

### DuckDB Extensions and Settings

As of version 1.2.3, you can load any supported [DuckDB extensions](https://duckdb.org/docs/extensions/overview) by listing them in
the `extensions` field in your profile. You can also set any additional [DuckDB configuration options](https://duckdb.org/docs/sql/configuration)
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
```

### External Materializations and Sources

One of DuckDB's most powerful features is its ability to read and write CSV and Parquet files directly, without needing to import/export
them from the database first. In `dbt-duckdb`, we support creating 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, either `parquet` or `csv`.
| delimiter | ,    | For CSV files, the delimiter to use for fields.
| 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 no `location` argument is 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 file format (either `.parquet` or `.csv`). By default, external materializations 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:

```
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') }}"
      external_root: "s3://my-bucket/my-prefix-path/"
  target: dev
```

`dbt-duckdb` also includes support for referencing external CSV and Parquet files as dbt `source`s via the `external_location`
meta option:

```
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](https://peps.python.org/pep-0498/) 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'])"
```

### Python Support

dbt added support for [Python models in version 1.3.0](https://docs.getdbt.com/docs/build/python-models). 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](https://realpython.com/python-exec/) call. The value of the `dbt.ref` and `dbt.source`
functions will be a [DuckDB Relation](https://duckdb.org/docs/api/python/reference/) object that can be easily converted into a
Pandas DataFrame or Arrow table, and the return value of the `def models` function can be either a DuckDB `Relation`, a Pandas DataFrame,
or an Arrow Table.

### 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
