My hourly partitioned dbt asset materialized the entire table because it didn't already exist (so it wasn't an incremental run), however Dagster only recorded the latest partition as being materialized. How do I configure Dagster to recognize when a run is incremental and correctly log the partition materialization events

When working with dbt assets in Dagster, especially when dealing with partitioned assets, it's important to ensure that Dagster correctly recognizes and logs partition materialization events. The documentation provides guidance on how to work with partitioned assets and how to use partitions with dbt assets. To configure Dagster to recognize when a run is incremental and to correctly log the partition materialization events, you can use the partitioning APIs provided by Dagster. For dbt assets, you can pass the partition information to the dbt command as a variable, which then gets used as part of the dbt incremental materialization policy. This allows you to specify which partition of the data should be processed during the dbt run. Here's an example from the documentation that illustrates how you can pass partition information to dbt:

python from dagster_dbt import dbt_cli_resource  @asset(required_resource_keys={"dbt"}) def my_dbt_asset(context):     # Assuming 'date' is the partition key     partition_key = context.asset_partition_key     context.resources.dbt.run(         vars={"partition_date": partition_key}     )

In this example, the

partition_key

is passed to the dbt command using the

vars

argument, which allows dbt to use this variable in its incremental materialization logic. Additionally, if you want to ensure that Dagster recognizes all partitions that have been materialized, you can use the

DagsterInstance

API to report asset materializations. This can be done programmatically or through the Dagit UI by reporting materialization events for the relevant partitions. If you find that Dagster is not recording all partition materializations as expected, you may need to review your dbt models to ensure they are configured correctly for incremental runs and that the partition keys are being passed and used correctly. You may also want to check the logs for the dbt run to see if there are any indications of why only the latest partition was recorded. If you continue to experience issues with partition materialization logging, it may be helpful to consult the Dagster community or support channels for further assistance, as the specific details of your setup and the behavior you're observing may require a more in-depth investigation.

@Scout The issue was that Dagster provided the partition information to dbt as variables, but dbt did not execute an incremental run, thus the variables were never used.

If dbt did not execute an incremental run and instead materialized the entire table, this could be due to the dbt model's configuration not being set up for incremental loading, or because the

--full-refresh

flag was inadvertently passed to the dbt command, which forces a full refresh of the model. To ensure that dbt executes an incremental run, you need to: 1. Set up your dbt models with the correct materialization strategy. In your

dbt_project.yml

or within the model SQL files, you should specify the materialization type as

incremental

and provide the logic for how dbt should handle incremental updates. 2. Make sure that the dbt command executed by Dagster does not include the

--full-refresh

flag unless a full refresh is desired. 3. Pass the partition information correctly to dbt using variables, as shown in the previous example, and ensure that your dbt models use these variables to filter the data for the incremental run. Here's an example of how you might configure an incremental dbt model:

sql -- models/my_incremental_model.sql  {{ config(materialized='incremental') }}  select ... from ...  {% if is_incremental() %}    -- this filter will only be applied on incremental runs   where timestamp > (select max(timestamp) from {{ this }})  {% endif %}

In this example, the

is_incremental()

function checks if the run is incremental, and if so, applies a filter to only select new or updated records based on a

timestamp

column. If you've confirmed that your dbt models are set up correctly for incremental runs and you're still encountering issues with Dagster not recognizing the run as incremental, you may need to debug further: - Check the dbt logs within the Dagster run to see if there are any messages indicating why a full refresh was performed instead of an incremental run. - Review the Dagster asset definitions and the dbt CLI resource configuration to ensure that the partition variables are being passed correctly and that no additional flags are causing a full refresh. - Ensure that the dbt profiles or target configurations do not override the incremental behavior. If the problem persists after these checks, it may be beneficial to reach out to the Dagster community or file an issue on the Dagster GitHub repository with details of your setup and the behavior you're observing for further assistance.

@Scout in the Dagster docs, it uses the below code as an example for setting up partitioned dbt incremental models

-- Use the Dagster partition variables to filter rows on an incremental run
{% if is_incremental() %}
where order_date >= '{{ var('min_date') }}' and order_date <= '{{ var('max_date') }}'
{% endif %}

This seems to suggest to me that there would be overlap between the partitions because the filter for max_date is inclusive. This means that the max_date of one partition could equal the min_date of the next. Couldn't this result in always processing more partitions than intended?

The code snippet you provided from the Dagster documentation indeed uses an inclusive filter for `max_date`:

sql -- Use the Dagster partition variables to filter rows on an incremental run {% if is_incremental() %} where order_date >= '{{ var('min_date') }}' and order_date <= '{{ var('max_date') }}' {% endif %}

This SQL snippet is intended to be used within a dbt model to filter the data for incremental processing based on the partition variables

min_date

and

max_date

. If

max_date

is inclusive, as shown, there is indeed a potential for overlap between partitions where the

max_date

of one partition could be the same as the

min_date

of the next partition. To avoid processing the same data in multiple partitions, you typically want to ensure that the range is half-open, meaning that the end of one partition is the beginning of the next, but not included in the previous partition. This can be achieved by using a strict inequality for the upper bound of the date range:

sql -- Use the Dagster partition variables to filter rows on an incremental run {% if is_incremental() %} where order_date >= '{{ var('min_date') }}' and order_date < '{{ var('max_date') }}' {% endif %}

By changing

<=

to

<

for the

max_date

comparison, you ensure that each partition is exclusive of the upper bound, thus preventing overlap. It's important to align the partitioning logic in dbt with the partitioning scheme in Dagster to ensure that each partition is processed once and only once. If the documentation suggests an inclusive filter for

max_date

, it might be assuming a particular partitioning scheme where the

max_date

is the last moment of the partition period (e.g.,

23:59:59

of the day) and the

min_date

of the next partition starts immediately after (e.g.,

00:00:00

of the following day). However, for clarity and to avoid any ambiguity, it's generally safer to use a half-open range as described above. If you find that the partitioning logic is not behaving as expected, you may need to adjust the dbt model's incremental logic to match your specific partitioning scheme and ensure that there is no overlap between partitions.