Troubleshoot Airflow Error – “dag_id could not be found”

How to fix “dag_id could not be found” error in Apache Airflow? What are best practices to avoid encountering this error and what alternative will make your life easier (SQLake)? Keep reading to learn more!

Explanation of “dag_id could not be found” Error

“dag_id could not be found” error in Apache Airflow means that the DAG with the specified id could not be located. To resolve the error, it is necessary to check these elements and determine the root cause of the issue. Here are few common cases that resulting with “dag_id could not be found” error.

A. the DAG Path Wasn’t Specified or Isn’t Correct

If Airflow is unable to locate and execute the DAG, which is required to run tasks, the primary suspect is a misplaced DAG file.


Ensure the DAG file ( in this case) is correctly placed in the specified DAG directory (/opt/airflow/dags/repo/ for example). It is also possible that the DAG file is present but has syntax errors or other issues that prevent it from being properly parsed by Airflow. In this case, it may be necessary to review the DAG file and make any necessary changes or corrections.

B. YAML file Misconfigured

Another possible issue is related to YAML’s configuration. The configuration could be causing the DAG to not be found if the DAG directory is specified incorrectly. Consequently, Airflow is looking for DAGs in the wrong location.


Check the configuration of the Airflow deployment. The text includes a YAML file with configuration values for deploying Airflow using a Helm chart.

C. Kubernetes Cluster and Pods Malfunction

If you are using Kubernetes, it is also worth checking the Kubernetes cluster and pods to ensure that they are running correctly and that there are no issues that could be causing the DAG to not be found. This could include checking the logs of the pods or debugging any issues with the cluster itself.

D. Metadata Database Instance Being Too Slow

If the metadata database instance being too slow it will cause a timeout when loading the DAGs. This suggests that there may be performance issues with the database that are causing problems with the Airflow deployment.


In this case, upgrading the database instance and increasing the “dagbag_import_timeout” parameter in the “airflow.cfg” file may help to resolve the issue.

This problem can be identified by adding the “–raw” parameter to the “airflow run” command which reveals the original exception.

Tired Of Debugging Airflow? Use SQLake

Although Airflow is a useful tool, it can be challenging to troubleshoot. SQLake is a good alternative that enables the automation of data pipeline orchestration.

With SQLake you can:

  • Build reliable, maintainable, and testable data ingestion.
  • Process pipelines for batch and streaming data, using familiar SQL syntax.
  • Jobs are executed once and continue to run until stopped.
  • There is no need for scheduling or orchestration.
  • The compute cluster scales up and down automatically, simplifying the deployment and management of your data pipelines.

Here is a code example of joining multiple S3 data sources into SQLake and applying simple enrichments to the data.

/* Ingest data into SQLake */
-- 1. Create a connection to SQLake sample data source.
CREATE S3 CONNECTION upsolver_s3_samples
    AWS_ROLE = 'arn:aws:iam::949275490180:role/upsolver_samples_role'
-- 2. Create empty tables to use as staging for orders and sales.
CREATE TABLE default_glue_catalog.database_a137bd.orders_raw_data()
    PARTITIONED BY $event_date;
CREATE TABLE default_glue_catalog.database_a137bd.sales_info_raw_data()
    PARTITIONED BY $event_date;
-- 3. Create streaming jobs to ingest raw orders and sales data into the staging tables..
CREATE SYNC JOB load_orders_raw_data_from_s3
   AS COPY FROM S3 upsolver_s3_samples 
      BUCKET = 'upsolver-samples' 
      PREFIX = 'orders/' 
   INTO default_glue_catalog.database_a137bd.orders_raw_data; 
CREATE SYNC JOB load_sales_info_raw_data_from_s3
   AS COPY FROM S3 upsolver_s3_samples 
      BUCKET = 'upsolver-samples' 
      PREFIX = 'sales_info/'
   INTO default_glue_catalog.database_a137bd.sales_info_raw_data;

Published in: Blog , Streaming Data
Upsolver Team
Upsolver Team

Upsolver enables any data engineer to build continuous SQL data pipelines for cloud data lake. Our team of expert solution architects is always available to chat about your next data project. Get in touch

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