---
id: f4dfab0b-7b90-4679-b499-ddfdbde1c7f8
---

# Airflow DAG Execution Failure
---

Apache Airflow is an open-source platform used to programmatically schedule, monitor, and manage workflows. A DAG (Directed Acyclic Graph) is a collection of tasks with dependencies that can be automated with Airflow. An execution failure of a DAG means that one or more tasks within the workflow failed to execute successfully. This can be caused by various factors such as invalid input data, network issues, software bugs, or resource constraints. The failure of a DAG execution can impact the timely delivery of critical business processes and can cause disruption to the overall workflow management system.

### Parameters
```shell
export DAG_ID="PLACEHOLDER"

export TASK_ID="PLACEHOLDER"

export EXECUTION_DATE="PLACEHOLDER"

export DAG_FILE_PATH="PLACEHOLDER"

export PATH_TO_DEPENDENCIES_FILE="PLACEHOLDER"

export PATH_TO_VIRTUALENV="PLACEHOLDER"

export NAME_OF_THE_DAG="PLACEHOLDER"
```

## Debug

### 1. Check the Airflow webserver status:
```shell
systemctl status airflow-webserver.service
```

### 2. Check the Airflow scheduler status:
```shell
systemctl status airflow-scheduler.service
```

### 3. Check the DAGs folder for any syntax errors:
```shell
airflow dags list

airflow dags show ${DAG_ID}
```

### 4. Check the logs of the failed DAG run:
```shell
airflow logs ${DAG_ID} ${TASK_ID} ${EXECUTION_DATE}
```

### 5. Check the Airflow metadata database for any issues:
```shell
airflow db check
```

### 6. Check the Airflow connections for any issues:
```shell
airflow connections --list
```

### 7. Check the Airflow variables for any issues:
```shell
airflow variables --list
```

### 8. Check the system logs for any relevant errors or warnings:
```shell
journalctl -u airflow-webserver.service

journalctl -u airflow-scheduler.service
```

### Configuration errors in the Apache Airflow DAG (Directed Acyclic Graph) file.
```shell
bash

#!/bin/bash



# Check if Airflow is installed

if ! command -v airflow &> /dev/null

then

    echo "Airflow is not installed. Please install Airflow and try again."

    exit 1

fi



# Check if DAG file exists

if [ ! -f "${DAG_FILE_PATH}" ]

then

    echo "DAG file not found. Please provide the correct path to the DAG file."

    exit 1

fi



# Check DAG file for syntax errors

if ! airflow list_dags | grep -q "${DAG_ID}"

then

    echo "DAG not found. Please provide the correct DAG ID."

    exit 1

fi



# Check DAG file for configuration errors

if ! airflow test "${DAG_ID}" "${TASK_ID}" "${EXECUTION_DATE}"

then

    echo "There are configuration errors in the DAG file. Please check the logs for more details."

    exit 1

fi



echo "DAG file is configured correctly."


```

## Repair

### Ensure that all dependencies for the DAG are installed and updated. This includes any packages, libraries, or plugins used in the DAG.
```shell
bash

#!/bin/bash



# define variables

DAG_NAME=${NAME_OF_THE_DAG}

DEPENDENCIES=${PATH_TO_DEPENDENCIES_FILE}



# activate virtual environment if applicable

source ${PATH_TO_VIRTUALENV}/bin/activate



# install dependencies

pip install -r $DEPENDENCIES



# update dependencies

pip install --upgrade -r $DEPENDENCIES



# restart Airflow scheduler and webserver

systemctl restart airflow-scheduler

systemctl restart airflow-webserver



# run the DAG

airflow dags trigger $DAG_NAME


```


Apache Airflow is an open-source platform used to programmatically schedule, monitor, and manage workflows. A DAG (Directed Acyclic Graph) is a collection of tasks with dependencies that can be automated with Airflow. An execution failure of a DAG means that one or more tasks within the workflow failed to execute successfully. This can be caused by various factors such as invalid input data, network issues, software bugs, or resource constraints. The failure of a DAG execution can impact the timely delivery of critical business processes and can cause disruption to the overall workflow management system.


This incident type refers to a failure in one or more Spark executors during the execution of a job. Spark executors are worker processes that run computations and store data in memory or on disk. When an executor fails, it can cause the entire job to fail or result in degraded performance. This type of incident can occur for a variety of reasons, such as hardware or network issues, memory errors, or software bugs.


Spark executor failure during job execution.

Apache Spark driver failure refers to an incident where the driver program in an Apache Spark cluster fails to execute or crashes during runtime. This can happen due to a variety of reasons such as hardware failure, software bugs, resource constraints, or programming errors. As the driver program is responsible for coordinating the execution of tasks across the cluster, any failure in the driver can result in the entire Spark job failing. This can lead to data loss, processing delays, and impact the overall performance of the Spark cluster.


Spark driver failure incident.

This incident type refers to a problem in a Spark application where it fails during the checkpointing process. Checkpointing is an important feature of Spark applications that allows for fault tolerance and recovery. When a checkpoint fails, it can cause data loss and potentially lead to application failure. This type of incident requires investigation to determine the root cause and implement a solution to prevent it from happening again.


Spark application failure during checkpointing.

Spark YARN container failure is an incident type that occurs when a container running on the YARN (Yet Another Resource Negotiator) resource manager in a Spark cluster fails to complete its assigned task. This can be caused by a variety of factors such as resource constraints, hardware failures, or software bugs. When a container fails, it can cause delays or failures in the overall Spark job, leading to reduced performance or data loss.


Spark YARN Container Failure

Apache Spark Out of Memory Error is a type of incident that occurs when the Apache Spark application running on a system is unable to allocate enough memory to perform its tasks. This can happen when the system's memory is insufficient or when there is a memory leak in the application's code. When this incident occurs, the application may crash, become unresponsive, or produce incorrect results. It is important to diagnose and resolve this issue as soon as possible to ensure the smooth functioning of the application.


Apache Spark Out of Memory Error incident.

```shell
export DAG_ID="PLACEHOLDER"

export TASK_ID="PLACEHOLDER"

export EXECUTION_DATE="PLACEHOLDER"

export DAG_FILE_PATH="PLACEHOLDER"

export PATH_TO_DEPENDENCIES_FILE="PLACEHOLDER"

export PATH_TO_VIRTUALENV="PLACEHOLDER"

export NAME_OF_THE_DAG="PLACEHOLDER"
```


### 1. Check the Airflow webserver status:

```shell
systemctl status airflow-webserver.service
```

### 2. Check the Airflow scheduler status:

```shell
systemctl status airflow-scheduler.service
```

### 3. Check the DAGs folder for any syntax errors:

```shell
airflow dags list

airflow dags show ${DAG_ID}
```

### 4. Check the logs of the failed DAG run:

```shell
airflow logs ${DAG_ID} ${TASK_ID} ${EXECUTION_DATE}
```

### 5. Check the Airflow metadata database for any issues:

```shell
airflow db check
```

### 6. Check the Airflow connections for any issues:

```shell
airflow connections --list
```

### 7. Check the Airflow variables for any issues:

```shell
airflow variables --list
```

### 8. Check the system logs for any relevant errors or warnings:

```shell
journalctl -u airflow-webserver.service

journalctl -u airflow-scheduler.service
```

### Configuration errors in the Apache Airflow DAG (Directed Acyclic Graph) file.

```shell
bash

#!/bin/bash



# Check if Airflow is installed

if ! command -v airflow &> /dev/null

then

    echo "Airflow is not installed. Please install Airflow and try again."

    exit 1

fi



# Check if DAG file exists

if [ ! -f "${DAG_FILE_PATH}" ]

then

    echo "DAG file not found. Please provide the correct path to the DAG file."

    exit 1

fi



# Check DAG file for syntax errors

if ! airflow list_dags | grep -q "${DAG_ID}"

then

    echo "DAG not found. Please provide the correct DAG ID."

    exit 1

fi



# Check DAG file for configuration errors

if ! airflow test "${DAG_ID}" "${TASK_ID}" "${EXECUTION_DATE}"

then

    echo "There are configuration errors in the DAG file. Please check the logs for more details."

    exit 1

fi



echo "DAG file is configured correctly."


```


### Ensure that all dependencies for the DAG are installed and updated. This includes any packages, libraries, or plugins used in the DAG.

```shell
bash

#!/bin/bash



# define variables

DAG_NAME=${NAME_OF_THE_DAG}

DEPENDENCIES=${PATH_TO_DEPENDENCIES_FILE}



# activate virtual environment if applicable

source ${PATH_TO_VIRTUALENV}/bin/activate



# install dependencies

pip install -r $DEPENDENCIES



# update dependencies

pip install --upgrade -r $DEPENDENCIES



# restart Airflow scheduler and webserver

systemctl restart airflow-scheduler

systemctl restart airflow-webserver



# run the DAG

airflow dags trigger $DAG_NAME


```


Airflow DAG Execution Failure

Overview

Parameters

Debug

1. Check the Airflow webserver status:

2. Check the Airflow scheduler status:

3. Check the DAGs folder for any syntax errors:

4. Check the logs of the failed DAG run:

5. Check the Airflow metadata database for any issues:

6. Check the Airflow connections for any issues:

7. Check the Airflow variables for any issues:

8. Check the system logs for any relevant errors or warnings:

Configuration errors in the Apache Airflow DAG (Directed Acyclic Graph) file.

Repair

Ensure that all dependencies for the DAG are installed and updated. This includes any packages, libraries, or plugins used in the DAG.

Learn more

Related Runbooks

Spark executor failure during job execution.

Spark driver failure incident.

Spark application failure during checkpointing.

Spark YARN Container Failure

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