dosubot[bot]
commented
1 month ago To address the performance degradation and intermittent connection errors with your Airflow DAGs using cosmos for dbt tasks, consider the following targeted steps:
-
Optimize Dependency Installation: If dependencies are installed with each run, this can slow down execution. After the initial run, set
install_depstoFalseto reuse the environment, as shown in the cosmos_manifest_example.py. -
Parallel Model Execution: Ensure your Airflow configuration supports parallel task execution to fully utilize dbt's parallel model execution capabilities.
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Resource Allocation: Check that your Airflow and dbt tasks have sufficient memory and CPU resources.
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Data Volume and Complexity: For large or complex data transformations, optimize your dbt models or break down tasks into smaller segments.
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Network Latency: If Airflow and the database are not in the same network, reduce network latency by optimizing your network configuration.
For the Snowflake connection issues:
- Ensure Correct Configuration: Verify that your Airflow Snowflake connection is correctly configured, especially for key-pair authentication using environment variables for sensitive information like
private_keyandprivate_key_passphrase. - Review Airflow and dbt Logs: Detailed error messages in these logs can provide insights into the specific issues.
- Network and Snowflake Warehouse Performance: Assess network setup and Snowflake warehouse configuration to ensure they are optimized for your workload.
- Update Dependencies: Ensure all related dependencies are up to date to benefit from performance improvements and bug fixes.
These steps are derived from analyzing the provided configuration and issues. Implementing these recommendations should help mitigate the performance and connectivity issues you're experiencing.
#### Details
**Sources** - [How can I Improve on performance](https://github.com/astronomer/astronomer-cosmos/issues/840) - [cosmos/profiles/snowflake/user_pass.py](https://github.com/astronomer/astronomer-cosmos/blob/main/cosmos/profiles/snowflake/user_pass.py) - [cosmos/profiles/snowflake/__init__.py](https://github.com/astronomer/astronomer-cosmos/blob/main/cosmos/profiles/snowflake/__init__.py) - [cosmos/profiles/snowflake/user_encrypted_privatekey_env_variable.py](https://github.com/astronomer/astronomer-cosmos/blob/main/cosmos/profiles/snowflake/user_encrypted_privatekey_env_variable.py) - [dev/dags/performance_dag.py](https://github.com/astronomer/astronomer-cosmos/blob/main/dev/dags/performance_dag.py) - [cosmos/dbt/graph.py](https://github.com/astronomer/astronomer-cosmos/blob/main/cosmos/dbt/graph.py) - [cosmos/config.py](https://github.com/astronomer/astronomer-cosmos/blob/main/cosmos/config.py) - [cosmos/operators/local.py](https://github.com/astronomer/astronomer-cosmos/blob/main/cosmos/operators/local.py) - [cosmos/operators/kubernetes.py](https://github.com/astronomer/astronomer-cosmos/blob/main/cosmos/operators/kubernetes.py)**About Dosu** This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.
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tatiana
liranc1
Before using cosmos Airflow dag was running for about 15 min for a certain dbt command. After the change to cosmos, the same dbt command is much more volatile, often taking 20-30 min. All Airflow's resources stayed the same for the dag, and there was no change in the dbt connection details.
I also encountered some tasks randomly failing due to connection error (snowflake), that was successful on the next run. This issue did not occur without cosmos.
cosmos configurations used:
ExecutionConfig(dbt_executable_path=DBT_EXECUTABLE_PATH)
RenderConfig( select=["models"], test_behavior=TestBehavior.NONE, load_method=LoadMode.DBT_LS, dbt_deps=False )
ProjectConfig(os.environ["DBT_PROJECT_PATH"], dbt_vars=dbt_vars)
dbt version: Core: