---
id: 65b3a344-70f8-11ee-b962-0242ac120002
---

# Apache Airflow Task Deadlocks Causing Execution Failure
---

This incident type involves the Apache Airflow task deadlocking frequently, leading to the failure of task execution. Deadlocking occurs when two or more tasks are waiting for each other to finish before they can proceed. When this happens, the tasks become stuck, and the workflow stops. This can cause significant downtime and impact on the overall performance of the system. It is crucial to identify the root cause of the deadlocking and resolve it promptly to prevent further incidents.

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

export AIRFLOW_WORKER_POD_NAME="PLACEHOLDER"

export TASK_NAME="PLACEHOLDER"

export NODE_NAME="PLACEHOLDER"

```

## Debug

### Check if the task is in a deadlock state
```shell
kubectl exec -it -n ${NAMESPACE} ${AIRFLOW_WORKER_POD_NAME} ps aux | grep ${TASK_NAME}
```

### Check if the task is stuck waiting for another task to finish
```shell
kubectl exec -it -n ${NAMESPACE} ${AIRFLOW_WORKER_POD_NAME} ps aux | grep ${TASK_NAME} | grep "waiting on"
```

### Check if the resource limits for the Airflow worker pod are set correctly
```shell
kubectl describe pod -n ${NAMESPACE} ${AIRFLOW_WORKER_POD_NAME} | grep "Limits"
```

### Check if there are any resource constraints on the node where the Airflow worker pod is running
```shell
kubectl describe node ${NODE_NAME} | grep "Capacity" -A 5
```

## Repair

### Modify the task code to handle the deadlocking situation. This may involve adding a timeout function or implementing a retry mechanism.
```shell
bash

#!/bin/bash



# Set variables

TASK_NAME=${TASK_NAME}

COMMAND=${COMMAND}

TIMEOUT=${TIMEOUT_VALUE}


# Update task code to include timeout function

kubectl edit tasks $TASK_NAME --namespace=${NAMESPACE} --output yaml | \

sed 's/command:/command: ["/bin/sh", "-c", "timeout '$TIMEOUT' ${COMMAND}"]/g' | \

kubectl apply --namespace=${NAMESPACE} -f -


```


This incident type involves the Apache Airflow task deadlocking frequently, leading to the failure of task execution. Deadlocking occurs when two or more tasks are waiting for each other to finish before they can proceed. When this happens, the tasks become stuck, and the workflow stops. This can cause significant downtime and impact on the overall performance of the system. It is crucial to identify the root cause of the deadlocking and resolve it promptly to prevent further incidents.


```shell
export NAMESPACE="PLACEHOLDER"

export AIRFLOW_WORKER_POD_NAME="PLACEHOLDER"

export TASK_NAME="PLACEHOLDER"

export NODE_NAME="PLACEHOLDER"

```


### Check if the task is in a deadlock state

```shell
kubectl exec -it -n ${NAMESPACE} ${AIRFLOW_WORKER_POD_NAME} ps aux | grep ${TASK_NAME}
```

### Check if the task is stuck waiting for another task to finish

```shell
kubectl exec -it -n ${NAMESPACE} ${AIRFLOW_WORKER_POD_NAME} ps aux | grep ${TASK_NAME} | grep "waiting on"
```

### Check if the resource limits for the Airflow worker pod are set correctly

```shell
kubectl describe pod -n ${NAMESPACE} ${AIRFLOW_WORKER_POD_NAME} | grep "Limits"
```

### Check if there are any resource constraints on the node where the Airflow worker pod is running

```shell
kubectl describe node ${NODE_NAME} | grep "Capacity" -A 5
```


### Modify the task code to handle the deadlocking situation. This may involve adding a timeout function or implementing a retry mechanism.

```shell
bash

#!/bin/bash



# Set variables

TASK_NAME=${TASK_NAME}

COMMAND=${COMMAND}

TIMEOUT=${TIMEOUT_VALUE}


# Update task code to include timeout function

kubectl edit tasks $TASK_NAME --namespace=${NAMESPACE} --output yaml | \

sed 's/command:/command: ["/bin/sh", "-c", "timeout '$TIMEOUT' ${COMMAND}"]/g' | \

kubectl apply --namespace=${NAMESPACE} -f -


```


Apache Airflow Task Deadlocks Causing Execution Failure

Overview

Parameters

Debug

Check if the task is in a deadlock state

Check if the task is stuck waiting for another task to finish

Check if the resource limits for the Airflow worker pod are set correctly

Check if there are any resource constraints on the node where the Airflow worker pod is running

Repair

Modify the task code to handle the deadlocking situation. This may involve adding a timeout function or implementing a retry mechanism.

Learn more

Related Runbooks

Support