Tracking issue: #10600
Some things to consider:
Although sources and sinks are inverse concepts, sources have a one-to-many relationship with downstream relations, while sinks have a one-to-one relationship with upstream relations. Relations have a zero-to-many relationship with downstream sinks, though, which gets in the way of implementing them as inverse dbt concepts (e.g. using pre- and post-hooks).
Something else to consider is that source and sink configuration might have different ownership than model development in the wild (e.g. data engineers vs. analytics engineers), so it'd be preferable not to tightly couple them.
From a developer perspective, this would require us to implement Materialize-specific versions of the following macros from dbt-external-tables:
-
create_external_table.sql -
get_external_build_plan.sql -
dropif.sql
Our implementation wouldn't need to live in the dbt-external-tables package, we could simply override the macros within dbt-materialize (for reference, see Firebolt's implementation).
and then:
- Deprecate the custom
sourcematerialization (codebase+documentation) - Adapt the dbt integration guide
- Adapt the dbt get started demo
- Consider adapting the MZ Hack Day demo
- Add a new section to the Materialize configurations page in the dbt documentation
From a user perspective, defining sources as external tables would have the following workflow:
1. Setting up dbt-external-tables
Add the dbt-external-tables package to packages.yml:
packages:
- package: dbt-labs/dbt_external_tables
version: <version>Modify dbt_project.yml to include:
dispatch:
- macro_namespace: dbt_external_tables
search_order: ['dbt', 'dbt_external_tables']Install the dbt-external-tables package dependency:
dbt deps
2. Defining a source
Define a table as external in dbt_project.yml:
sources:
- name: kafka_source
loader: kafka
tables:
- name: sometable
external:
host: 'kafka:9092'
topic: 'sometopic'
...Run stage_external_sources, the entrypoint macro of the dbt-external-tables package:
dbt run-operation stage_external_sources
The biggest downside is that this adds a bunch of overhead to what is the entrypoint of users to Materialize. It's not a straightforward workflow.
Option 2: pre-hook on models
From a developer perspective, this would require:
- Implement a
create_sourcemacro
This option sounds borked from the get-go, since it would tightly couple sources with models (when the relationship between them might not be one-to-one).
1. Defining a pre-hook in a(n entry?) model
{{
config({
"materialized":"materializedview",
"pre-hook": [
"{{ materialize.create_source(...
host='kafka:9092',
topic='sink_topic',
...) }}"
]
})
}}
Option 1: post-hook on models
From a developer perspective, this would require:
- Implement a
create_sinkmacro (similar to theunload_tablemacro indbt-redshift) - Consider (automatically) creating an
exposurefor lineage (see Option 2 👇)
and then:
- Deprecate the custom
sinkmaterialization (codebase+documentation) - Adapt the dbt integration guide
- Consider adapting the MZ Hack Day demo
- Add a new section to the Materialize configurations page in the dbt documentation
From a user perspective, defining sinks as post-hooks would have the following workflow:
1. Defining a post-hook in the model to sink
{{
config({
"materialized":"materializedview",
"post-hook": [
"{{ materialize.create_sink(...
this.materializedview,
host='kafka:9092',
topic='sink_topic',
...) }}"
]
})
}}
Option 2: custom metadata on exposures
From a developer perspective, it's a bit unclear how this could be implemented since exposures seem like...a purely metadata/documentation-based feature. According to Jeremy from dbt Labs, it might be possible to go this route using the meta configuration and some custom macros.
TBH, I'm not sure how this would work since exposures aren't compilable or executable, but maybe we can figure it out based on these two helpful threads:
It's also not possible to use a custom string as the exposure type (at least yet, see dbt #2835), so we'd have to go with one of the accepted values: dashboard, notebook, analysis, ml or application; this mainly dictates how exposures are surfaced in the dbt documentation, and having sinks listed under any of these options isn't ideal.
One of the benefits of using exposures would be having sinks as end nodes in the DAG. In contrast, with . Maybe there's a way to combine Option 1 and Option 2 (i.e. define a sink as a post-hooks we'd lose track of lineage information (AFAIU)post-hook and automatically create an exposure for lineage), so we get the best of both worlds?
1. Defining an exposure
Define an exposure with a custom meta configuration in dbt_project.yml:
exposures:
- name: kafka_sink
type: <exposure-type>
description: >
Some description.
depends_on:
- ref('mv_churn_prediction')
meta: ...
owner:
email: [email protected]For all cases, credentials should be handled as (secret) environment variables that are inherited from the development environment users are running dbt against.
Logging some interesting points from a conversation with a
dbt-materializeuser, for additional context:Sources
They're using
pre-hooksand a customcreate_sourcemacro to handle source creation. This is convenient to them as there is an internal practice of making staging views one-to-one with sources.Sinks
They're handling sink creation using analyses — SQL statements that get compiled but not executed at runtime. This allows them to delay sink creation until the upstream views are fully hydrated (~15-20 minutes after creation), and make sure there aren't any unexpected race conditions. From an old thread:
In addition, they're doing Blue-Green deployments and can't have dual publishers. Creating the sinks manually at a later stage also allows them to wait until the Green deployment is ready to take over.
Takeaway:
So far, the only thing I'm 💯 sold on is that we need to provide
create_sourceandcreate_sinkmacros that encapsulate the different SQL grammar variations to 1) reduce boilerplate code and 2) avoid that each user has to implement (and therefore maintain) their own.