@Scout is there a way of not materializing a asset given a condition?

Yes, there is a way to conditionally materialize an asset in Dagster based on a given condition. You can use the

is_required

parameter along with

yield

syntax to implement this behavior. If the

is_required

parameter is set to

False

on an output, and your function does not

yield

an

Output

object for that output, then no asset materialization event will be created, the I/O manager will not be invoked, downstream assets will not be materialized, and asset sensors monitoring the asset will not trigger. Here's an example from the documentation:

python
import random
from dagster import Output, asset

@asset(output_required=False)
def may_not_materialize():
    # to simulate an asset that may not always materialize.
    if random.randint(1, 10) < 5:
        yield Output([1, 2, 3, 4])

@asset
def downstream(may_not_materialize):
    # will not run when may_not_materialize doesn't materialize the asset
    return may_not_materialize + [5]

In this example, the

may_not_materialize

asset will only materialize if the random condition is met. If it does not materialize, the downstream asset will not run. Don't change the state of Fivetran asset... button Thank you for your feedback!

@Scout how do I set a automaterilzation rule or freshness policy to a multi_asset?

To set an

AutoMaterializePolicy

or

FreshnessPolicy

on a multi-asset, you can specify these policies on individual assets within the multi-asset. Here's an example from the documentation:

python from dagster import multi_asset, AutoMaterializePolicy, AssetOut, FreshnessPolicy  @multi_asset(     outs={         "asset1": AssetOut(auto_materialize_policy=..., freshness_policy=...),         "asset2": AssetOut(auto_materialize_policy=..., freshness_policy=...)     } ) def my_function():     ...

In this example,

asset1

and

asset2

are part of a multi-asset, and each has its own

AutoMaterializePolicy

and

FreshnessPolicy

specified. You would replace the ellipses (

...

) with the actual policy objects that you want to apply to each asset.

@Scout I have these configuratyions for my dbt redshift profile

schema: public
threads: 1
ra3_node: true
type: redshift
autocommit: true

And these configurations for my model

config:
  sort:
    - dt
  dist: even
  schema: some

I having a issue, where when the model runs, it's creating a new schema called public_some and I don't want that. I want it to use the already created schema some instead of a new one public_some

The issue you're encountering, where a new schema called

public_some

is being created instead of using the existing schema

some

, is likely due to how dbt handles schema names in combination with your profile and model configurations. In dbt, the

schema

field in the profile configuration specifies the default schema to use when creating models. However, if you specify a

schema

in your model's configuration, dbt treats it as a suffix to the schema specified in the profile, rather than as an absolute schema name. This is why you're seeing a new schema

public_some

being created, as dbt is appending the model's

schema

value to the profile's

schema

value. To resolve this issue, you should adjust your dbt configurations to ensure that the

schema

specified in your model's configuration is not treated as a suffix. Here's how you can do it: 1. Remove the

schema

field from your model's configuration if you want to use the default schema specified in your profile. 2. If you want to use a different schema than the one specified in your profile, you should override the schema in your dbt project configuration or use a custom schema macro that handles schema names as absolute rather than suffixes. For example, if you want all models to use the

some

schema, you can set it in your

dbt_project.yml

file like this:

yaml models:   your_project_name:     +schema: some

This will apply the

some

schema to all models in your project unless overridden at a lower level (such as within a specific model's configuration). Remember to replace

your_project_name

with the actual name of your dbt project. If you have further questions or need assistance with dbt and Dagster configurations, please let me know, and I'll be happy to help!

@Scout How to make dbt drop and create a model everytime?

In dbt, if you want to ensure that a model is dropped and recreated every time you run your dbt project, you can use the

--full-refresh

flag when running the

dbt run

command. This flag instructs dbt to treat incremental models as table models, which means it will drop and recreate them, rather than inserting or updating records based on a unique key. Here's how you can use the

--full-refresh

flag:

shell dbt run --full-refresh

This command will apply the full refresh to all incremental models in your project. If you want to apply it to a specific model, you can use the

--select

or

-m

flag to specify the model name:

shell dbt run --full-refresh --select your_model_name

Replace

your_model_name

with the actual name of your model. Keep in mind that using

--full-refresh

can be resource-intensive and time-consuming, especially for large datasets, because it involves dropping and recreating the entire table. If you want to enforce this behavior within your dbt project without relying on the command line flag, you can set the

+full_refresh

materialization configuration in your model's configuration block. Here's an example of how to do this within your model SQL file:

sql -- models/your_model_name.sql  {{ config(materialized='incremental', full_refresh=true) }}  select ... from ...

By setting

full_refresh

to

true

in the model's configuration, you're telling dbt to always perform a full refresh on this model, even if the

--full-refresh

flag is not provided in the command line. Please note that the

full_refresh

configuration is only applicable to incremental models. If your model is not incremental, it will be recreated every time by default. If you have any more questions or need further assistance with dbt, feel free to ask!

@Scout I added the full_refresh, but now I'm getting this error "duplicate key violates unique constraint "pg_class_relname_nsp_index" (possibly caused by concurrent transaction conflict)"

The error message you're encountering, "duplicate key violates unique constraint 'pg_class_relname_nsp_index' (possibly caused by concurrent transaction conflict)," suggests that there is a conflict due to a unique constraint on the

pg_class

catalog table in PostgreSQL. This table stores information about tables, indexes, sequences, and other relation types, and the

relname_nsp_index

index enforces the uniqueness of relation names within a namespace (schema). This error can occur if two concurrent transactions are trying to create or modify relations with the same name in the same schema. In the context of dbt, this might happen if you have multiple dbt jobs trying to create or modify the same model at the same time. Here are some steps you can take to troubleshoot and resolve this issue: 1. Ensure Serial Execution: Make sure that your dbt runs are executed serially and not in parallel if they are trying to modify the same database objects. This can be managed by scheduling your dbt runs in a way that prevents overlap. 2. Check for Long-Running Transactions: Look for any long-running transactions that might be holding locks on the database objects dbt is trying to modify. You can use PostgreSQL's activity monitoring queries to identify such transactions. 3. Review dbt Project Configuration: Ensure that your dbt project is correctly configured and that the

full_refresh

flag is being used appropriately. If you're using the

full_refresh

flag in a CI/CD pipeline, make sure that concurrent runs are not being triggered. 4. Database Cleanup: If you suspect that a failed dbt run has left behind temporary or orphaned relations, you might need to clean them up manually. Be cautious with this step and make sure to identify the correct objects before dropping them. 5. Retry Logic: Implement retry logic in your dbt run command to handle transient errors that might occur due to temporary conflicts. 6. Database Locks: Investigate any potential issues with database locks that might be causing the conflict. You can query the

pg_locks

view to see active locks in the database. 7. Examine dbt Logs: Review the dbt logs for more detailed error messages or stack traces that might provide additional context about the conflict. 8. Database Maintenance: Perform routine database maintenance tasks such as vacuuming and analyzing tables to ensure that the database is operating efficiently. If the issue persists after taking these steps, you may need to look into the specific timing and coordination of your dbt runs, or consider reaching out to the dbt community or support channels for further assistance. It's also important to ensure that your database is properly tuned and configured to handle the workload of your dbt jobs.

@Scout I have a model that uses the database stage as default, but the other model uses the database datamarts as default, when I executed it all, i got an error that is impossible to run cross-database create table, so how can I contour this following dbt rules and properties configurations?

In dbt, cross-database references can be problematic because some databases do not support creating tables that reference another database directly within a

CREATE TABLE

statement. To work around this limitation, you can consider the following strategies that adhere to dbt's rules and properties configurations: 1. Use Pre-Hooks and Post-Hooks: You can use dbt's pre-hook and post-hook configurations to execute SQL statements before or after a model is run. For example, you could use a post-hook to insert data into a table in a different database after the table has been created in the default database. 2. Materialize as Views: If your database supports cross-database references in views, you could materialize your dbt models as views instead of tables. This way, you can select data from one database and present it in another without physically moving the data. 3. Intermediate Materialization: Create an intermediate table within the same database, and then use a separate process to move the data to the target database. This could be done with a dbt model that materializes as a table in the default database and then a separate dbt run operation or a custom script to transfer the data to the other database. 4. Database-specific Features: Some databases offer features that facilitate cross-database operations. For example, Snowflake allows creating databases and schemas that can reference each other. If your database offers similar features, you can leverage them within your dbt models. 5. Change the Database Configuration: If possible, configure your dbt models to use the same database to avoid cross-database operations. You can specify the database in your

dbt_project.yml

file or within individual model files using the

{{ config(...) }}

macro. 6. Custom Macros: Write custom dbt macros that handle the logic for cross-database operations. This could involve creating a table in one database, exporting the data, and then importing it into the other database. 7. Staging Data: Use a staging area within the same database to prepare your data before moving it to the final destination. This can help avoid direct cross-database table creation. Remember to check the documentation for your specific database to understand its capabilities and limitations regarding cross-database operations. Each database has its own set of features and restrictions, and the best approach will depend on those details. If you continue to encounter issues, please provide more context about your dbt models, the databases you are using, and the specific operations you are trying to perform. This information will help in suggesting a more tailored solution.

@Scout I'm using Redshift RA3 node and facing this problem what can I do? I don't want to keep everything in the same database, I really want to make cross-database queries

Cross-database queries in Amazon Redshift, especially when using RA3 nodes, can be challenging due to Redshift's limitations. As of my knowledge cutoff in early 2023, Redshift does not support cross-database queries in the same way that some other databases, like Snowflake, do. However, there are a few strategies you might consider to work around this limitation: 1. Data Sharing: Amazon Redshift introduced a feature called Data Sharing that allows you to share live data across Redshift clusters without the need to copy or move data. This feature enables you to create datashares to share schemas and tables across different Redshift clusters. You can then create views in your consumer cluster that reference the shared tables. This approach is limited to sharing data across clusters, not databases within the same cluster. 2. UNLOAD and COPY: You can use the

UNLOAD

command to export data from one database to Amazon S3, and then use the

COPY

command to load the data into another database. This is a two-step process that involves moving data out of Redshift and then back in, but it allows you to transfer data between databases. 3. Federated Query: Redshift supports federated querying, which allows you to query live data in external databases such as Amazon RDS or Amazon Aurora PostgreSQL. If your data resides in one of these supported databases, you can use federated queries to access it from Redshift. 4. Materialize Within a Single Database: If possible, consider restructuring your dbt project to materialize all models within a single Redshift database, even if that means using different schemas to logically separate the data. This would avoid the need for cross-database queries altogether. 5. External Schemas: Redshift allows you to create external schemas that reference data stored in Amazon S3 using Redshift Spectrum. You can create external tables within these schemas and query them as if they were regular Redshift tables. This could be a way to access data across databases, but it requires the data to be in S3. 6. Database Linking (Future Consideration): As of my knowledge cutoff, Redshift does not support database links like some other databases (e.g., Oracle). However, AWS continuously evolves its services, so it's worth checking the latest Redshift documentation or AWS announcements for any new features that might support cross-database queries more directly. When using any of these strategies, you'll need to adjust your dbt models and configurations accordingly. For example, you might need to write custom macros or scripts to handle the data movement and ensure that your dbt models are compatible with the approach you choose. If you're facing specific errors or challenges with these strategies, please provide m

@Scout can i make my dbt model connect to another database instead of trying to query connected to another?

In dbt, each model is typically associated with a single database connection, which is defined in your dbt profile. If you want a dbt model to connect to a different database than the one specified in your profile's target, you have a couple of options: 1. Change the Target Database in the Profile: You can specify a different target database in your dbt profile for the model. This requires you to have multiple targets defined in your

profiles.yml

file, each corresponding to a different database. You can then specify which target to use when running your dbt commands. Here's an example of how your

profiles.yml

might look with multiple targets:

yaml    my_project:      target: dev      outputs:        dev:          type: redshift          host: my-redshift-cluster          user: myuser          pass: mypass          port: 5439          dbname: dev_db          schema: public        prod:          type: redshift          host: my-redshift-cluster          user: myuser          pass: mypass          port: 5439          dbname: prod_db          schema: public

Then, when running dbt, you can specify the target:

shell    dbt run --target prod

2. Use Custom Schema in the Model Config: If you want to keep the same database but change the schema, you can override the schema in your model's configuration. This is done using the

{{ config() }}

macro within your model SQL file. Here's an example of how to set a custom schema for a model:

sql    -- models/my_model.sql     {{ config(schema='my_schema') }}     select ...

This will instruct dbt to use

my_schema

instead of the default schema defined in the profile when creating the model. 3. Database-Specific Functionality: Some databases support cross-database queries within the same database cluster or instance. If your database supports this, you can write your SQL to reference the fully qualified table name, including the database name. For example, in Redshift, you might write:

sql    select * from other_database.public.my_table

However, as of my knowledge cutoff, Redshift does not support cross-database queries in this manner. If you need to connect to an entirely different database that is not just a different target within the same Redshift cluster, you would typically need to set up a separate dbt profile for that database and run your dbt project with the appropriate profile target for that database. Keep in mind that the ability to switch databases or schemas can depend on the capabilities of the database you are using and the permissions configured for the database user. Always refer to the documentation for your specific database and dbt for the most accurate and up-to-date information.