Troubleshooting Airflow ValueError

Airflow ValueError is one of the most common errors users get when using Apache Airflow. In this article, we will discuss the meaning of this error message, the common reasons it occurs, and how to handle each case. Finally, we will discuss a solution, SQLake, for automating data pipeline orchestration for data engineers.

Why are you getting ValueError in Apache Airflow?

A ValueError in Airflow typically indicates an issue with the input or parameters being passed to a function or operation.

This could be due to:

  • Invalid data types
  • Missing required values
  • Other issues with the input data.

Here are some common cases resulting from Apache Airflow ValueError message.

A. Airflow Is Unable to Configure the ‘file.processor’ Handler

The root cause, in this case, is that the user encounters a ValueError when trying to set up and run Apache Airflow. The specific error message is “Unable to configure handler ‘file.processor’: ‘FileProcessorHandler’ object has no attribute ‘log'”. This error occurs when Airflow cannot configure the ‘file.processor’ handler, which is responsible for reading and processing log files.

There are several potential causes for this issue. One possibility is an issue with the configuration of the ‘file.processor’ handler in the Airflow configuration file (airflow.cfg).
This file specifies various parameters and settings for Airflow, including the location and settings for the log files. It is possible that the ‘file.processor’ handler is not correctly configured in the airflow.cfg file, or that the file itself is not correctly formatted.

B. Corrupted Log Files

Another potential cause of this issue is a problem with the log files themselves. The log files may be corrupted, missing, or otherwise inaccessible. This can prevent the ‘file.processor’ handler from being able to read and process the logs, resulting in an error message.

To troubleshoot this issue, first check the configuration of the ‘file.processor’ handler in the airflow.cfg file. Ensure the handler is correctly configured and the file is correctly formatted. The user should also check the log files to ensure they are not corrupted or missing. If necessary, the user may need to delete and recreate the log files to fix any issues.

If these steps do not resolve the issue, the user may need to try restarting the Airflow web server or restarting the machine on which Airflow is running. Sometimes the web server or machine may become unresponsive, which can cause issues with the ‘file.processor’ handler and other parts of the Airflow system. Restarting the web server or machine may resolve these issues and allow the ‘file.processor’ handler to function properly.

C. The ‘wasb_task_handler’ Module in The ‘airflow.utils.log’ Package Is Not Found

The problem, in this case, is that Airflow is unable to configure the handler ‘processor’ and is encountering a ValueError. This error occurs because the ‘wasb_task_handler’ module in the ‘airflow.utils.log’ package is not found.

There are a few potential solutions to this problem:

  • Check the $PYTHONPATH environment variable to ensure that it includes the correct directories for Airflow. Make sure that the ‘$AIRFLOW_HOME/config/’ directory is included in the $PYTHONPATH so that Airflow can find the necessary modules and packages.
  • Make sure that the ‘wasb_task_handler’ module is located in the correct directory. In this case, it should be located in ‘$AIRFLOW_HOME/venv/lib/python3.6/site-packages/airflow/utils/log/’.
  • Check the ‘’ file to make sure that it is properly configured. Make sure that the REMOTE_BASE_LOG_FOLDER is set to the correct directory and that the ‘logging_config_class’ in the ‘airflow.cfg’ file is set to ‘log_config.LOGGING_CONFIG’.
  • Check the ‘wasb_hook’ module to make sure that it is imported correctly and that it is located in the correct directory. This module should be located in ‘$AIRFLOW_HOME/venv/lib/python3.6/site-packages/airflow/contrib/hooks/’.
  • Make sure that the user defined in the Airflow blob connection has the necessary permissions to access the REMOTE_BASE_LOG_FOLDER.

D. Syntax Misconfiguration

In this case, the problem is a syntax error in the logging configuration file. This error is causing the Airflow scheduler to crash when it tries to start up, resulting in the “ValueError: Unable to configure handler ‘processor’: expected token ‘:’, got ‘}'” message being displayed.

To fix this problem, locate the logging configuration file and check it for syntax errors. This may involve looking for unbalanced curly braces or issues with the file’s formatting. Once the syntax error has been identified and corrected, the Airflow scheduler should be able to start up correctly.

E. Missing Dependency or Old Version

It is also possible that an issue could cause the problem with the version of Airflow being used or with a missing dependency. In this case, the user may need to upgrade to a newer Airflow version or install any missing dependencies to resolve the issue.

Alternative Approach – Automated Orchestration:

Airflow is a great tool, but difficult to debug. SQLake is a great alternative that allows you to automate 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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