This tool allows you to
meta
config in your dbt model docs. Currently it relies on Mermaid to render the output. You can choose to render your output inside your dbt docs directly, or store it as a separate SVG image.mermaid
will be rendered.[!TIP] Read the blog post for more details
[!TIP] Demo Jaffle Shop docs pages
As dbt-diagrams
is just a Python package. Install it using your favourite Python package manager (e.g. pip install dbt-diagrams
). In case you want to render your ERD to a SVG image, you will have to install the dbt-diagrams[svg]
extras package as well.
Simply run dbt-diagrams docs generate
instead of dbt docs generate
. Any Markdown code block tagged with mermaid
will now be picked up and rendered as an image.
meta
blocks and render in dbt docsThis will achieve the same functionality as (1), plus the following: let's say you have the following models defined in your dbt project
version: 2
models:
- name: customers
description: >
This table has basic information about a customer, as well as some derived facts based on a customer's orders
```mermaid[erd="customer_erd"]```
config:
meta:
erd:
connections:
- diagram: customer_erd
target: orders
source_cardinality: one
target_cardinality: one_or_more
label: creates
columns:
- name: customer_id
description: This is a unique identifier for a customer
- name: first_name
description: Customer's first name. PII.
- name: last_name
description: Customer's last name. PII.
- name: orders
description: >
This table has basic information about orders, as well as some derived facts based on payments
```mermaid[erd="customer_erd"]```
columns:
- name: order_id
- name: customer_id
description: Foreign key to the customers table
- name: order_date
description: Date (UTC) that the order was placed
- name: status
description: '{{ doc("orders_status") }}'
Using the meta
section of a model, you can define ERD connections to other models. Based on these connections and other table attributes the ERD can be generated. The target
attribute is another dbt model name. Accepted relation cardinalities are one
, zero_or_one
, zero_or_more
or one_or_more
. Use the label
attribute to specify a human readable interpretation to a relation. The diagram
is optional and allows you to add a name to your ERD. This is useful in case you want to define multiple ERDs and reference them in dbt docs directly.
Notice the mermaid[erd="cusomer_erd"]
expressions in the customers
and orders
model descriptions. When running dbt-diagrams docs generate
, this will be replaced by the ERD Mermaid definition so that your ERD can be rendered in any dbt docs page.
erDiagram
customers ||--|{ orders : creates
customers {
STRING customer_id
STRING first_name
STRING last_name
}
orders {
STRING order_id
STRING customer_id
DATE order_date
STRING status
}
meta
blocks and render as SVGGiven the same setup as above, you can also render your output to SVG:
dbt-diagrams[svg]
extras. This will install a headless browser in which Mermaid can run.dbt-diagrams render-erds -dbt-target-dir target --format svg --output ./out
. This will use the manifest
and catalog
files from ./target
to render all defined ERDs as SVG. All detected diagrams will be stored as SVG files in the ./out
folder.Every erd
section inside a meta
block of a model will be picked up. It should look like the following:
erd:
connections:
- diagram: <Optional. This connection will be added to a diagram of this name>
target: <Required. Other model name>
source_cardinality: <Required. One of {zero_or_one, one, zero_or_more, one_or_more}>
target_cardinality: <Required. One of {zero_or_one, one, zero_or_more, one_or_more}>
label: <Optional. Any string that describes the relation from this model to target model.>