---
id: 622c5c8f-995a-4b66-8f2b-288998cd4a40
---

# Spark application failure during checkpointing.
---

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.

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

export SPARK_APPLICATION_LOG_FILE="PLACEHOLDER"

export SPARK_APPLICATION_CONFIG_FILE="PLACEHOLDER"

export CHECKPOINTING_DIRECTORY="PLACEHOLDER"

export EXECUTOR_MEMORY="PLACEHOLDER"

export PATH_TO_SPARK_APP="PLACEHOLDER"

export DRIVER_MEMORY="PLACEHOLDER"

export AMOUNT_OF_RESOURCES_TO_ALLOCATE="PLACEHOLDER"
```

## Debug

### Check if Spark application is running
```shell
ps aux | grep ${SPARK_APPLICATION_NAME}
```

### Check the status of Spark application
```shell
sudo systemctl status ${SPARK_APPLICATION_NAME}
```

### Check if there are any logs generated by Spark
```shell
sudo tail -f /var/log/${SPARK_APPLICATION_LOG_FILE}
```

### Check if Spark is using the correct checkpointing directory
```shell
grep "spark.checkpoint.dir" ${SPARK_APPLICATION_CONFIG_FILE}
```

### Check if there is enough disk space in the checkpointing directory
```shell
df -h ${CHECKPOINTING_DIRECTORY}
```

### Check if the Spark checkpointing directory has the correct permissions
```shell
ls -l ${CHECKPOINTING_DIRECTORY}
```

### Check if the Spark application is configured to use enough memory
```shell
grep "spark.driver.memory" ${SPARK_APPLICATION_CONFIG_FILE}
```

### Check if the Spark application is configured to use enough cores
```shell
grep "spark.driver.cores" ${SPARK_APPLICATION_CONFIG_FILE}
```

### Check if the Spark application is using the correct version of Java
```shell
java -version
```

### Insufficient memory allocation for the Spark application, leading to checkpointing failures.
```shell


#!/bin/bash



# Set the Spark application name and memory allocation

SPARK_APP_NAME=${SPARK_APPLICATION_NAME}

SPARK_DRIVER_MEMORY=${DRIVER_MEMORY}

SPARK_EXECUTOR_MEMORY=${EXECUTOR_MEMORY}



# Check the available system memory

TOTAL_MEMORY=$(free -m | awk '/^Mem:/{print $2}')

USED_MEMORY=$(free -m | awk '/^Mem:/{print $3}')

AVAILABLE_MEMORY=$((TOTAL_MEMORY - USED_MEMORY))

echo "Total memory: ${TOTAL_MEMORY}MB"

echo "Used memory: ${USED_MEMORY}MB"

echo "Available memory: ${AVAILABLE_MEMORY}MB"



# Check if the requested memory allocation exceeds the available memory

if [[ $((SPARK_DRIVER_MEMORY + SPARK_EXECUTOR_MEMORY)) -gt $AVAILABLE_MEMORY ]]; then

    echo "Insufficient memory allocation for the Spark application"

    exit 1

fi



# Run the Spark application with the specified memory allocation

spark-submit --name ${SPARK_APP_NAME} --driver-memory ${SPARK_DRIVER_MEMORY} --executor-memory ${SPARK_EXECUTOR_MEMORY} ${PATH_TO_SPARK_APP}


```

## Repair

### Increase the resources allocated to the Spark application to mitigate potential resource contention issues.
```shell
bash

#!/bin/bash



# Set the variables

SPARK_APP_NAME=${SPARK_APPLICATION_NAME}

SPARK_APP_RESOURCES=${AMOUNT_OF_RESOURCES_TO_ALLOCATE}



# Stop the Spark application

sudo systemctl stop ${SPARK_APP_NAME}



# Update the resources allocated to the Spark application

sudo sed -i "s/$SPARK_EXECUTOR_MEMORY=$.*/\1$SPARK_APP_RESOURCES/" /etc/${SPARK_APPLICATION_CONFIG_FILE}



# Start the Spark application

sudo systemctl start ${SPARK_APP_NAME}


```

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.


This incident type involves the failure of the YARN ResourceManager, which can impact the performance of Spark jobs. The ResourceManager is responsible for managing resources in a Hadoop cluster, and when it fails, it can prevent Spark jobs from running properly. This incident requires investigation to determine the root cause of the failure, and recovery steps to restore the ResourceManager and prevent similar failures from occurring in the future.


YARN ResourceManager Failure Impacting Spark Jobs.

This incident type refers to a situation where Spark tasks are failing due to out of memory errors. Spark is a distributed computing system used for big data processing. When the data volume exceeds the allocated memory, the Spark tasks fail, and the system generates an out of memory error. This type of incident can cause data processing delays or even system downtime, which can impact the overall performance of the application.


Spark tasks failing due to out of memory errors.

This incident type typically occurs in distributed computing systems, where Spark tasks are experiencing high disk I/O and shuffle spills. Spark is a popular distributed computing engine that uses shuffle operations to move data between nodes in a cluster, which can sometimes result in performance issues due to spills. The spills occur when the data being shuffled exceeds the memory capacity allocated for the shuffle operations. This incident requires optimization of the shuffle operations to reduce spills and improve overall performance.


Spark tasks experiencing shuffle spills and high disk I/O.

This incident type refers to a situation where Spark jobs are failing due to resource contentions in the cluster. When multiple Spark jobs are trying to access the same resources or data at the same time, it can cause a bottleneck that leads to job failures. This can happen when the resources in the cluster are not properly allocated or when the number of jobs running simultaneously exceeds the cluster's capacity to handle them. The result is that Spark jobs fail, leading to disruptions in data processing and analysis.


Spark job failures due to cluster resource contentions.

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.

```shell
export SPARK_APPLICATION_NAME="PLACEHOLDER"

export SPARK_APPLICATION_LOG_FILE="PLACEHOLDER"

export SPARK_APPLICATION_CONFIG_FILE="PLACEHOLDER"

export CHECKPOINTING_DIRECTORY="PLACEHOLDER"

export EXECUTOR_MEMORY="PLACEHOLDER"

export PATH_TO_SPARK_APP="PLACEHOLDER"

export DRIVER_MEMORY="PLACEHOLDER"

export AMOUNT_OF_RESOURCES_TO_ALLOCATE="PLACEHOLDER"
```


### Check if Spark application is running

```shell
ps aux | grep ${SPARK_APPLICATION_NAME}
```

### Check the status of Spark application

```shell
sudo systemctl status ${SPARK_APPLICATION_NAME}
```

### Check if there are any logs generated by Spark

```shell
sudo tail -f /var/log/${SPARK_APPLICATION_LOG_FILE}
```

### Check if Spark is using the correct checkpointing directory

```shell
grep "spark.checkpoint.dir" ${SPARK_APPLICATION_CONFIG_FILE}
```

### Check if there is enough disk space in the checkpointing directory

```shell
df -h ${CHECKPOINTING_DIRECTORY}
```

### Check if the Spark checkpointing directory has the correct permissions

```shell
ls -l ${CHECKPOINTING_DIRECTORY}
```

### Check if the Spark application is configured to use enough memory

```shell
grep "spark.driver.memory" ${SPARK_APPLICATION_CONFIG_FILE}
```

### Check if the Spark application is configured to use enough cores

```shell
grep "spark.driver.cores" ${SPARK_APPLICATION_CONFIG_FILE}
```

### Check if the Spark application is using the correct version of Java

```shell
java -version
```

### Insufficient memory allocation for the Spark application, leading to checkpointing failures.

```shell


#!/bin/bash



# Set the Spark application name and memory allocation

SPARK_APP_NAME=${SPARK_APPLICATION_NAME}

SPARK_DRIVER_MEMORY=${DRIVER_MEMORY}

SPARK_EXECUTOR_MEMORY=${EXECUTOR_MEMORY}



# Check the available system memory

TOTAL_MEMORY=$(free -m | awk '/^Mem:/{print $2}')

USED_MEMORY=$(free -m | awk '/^Mem:/{print $3}')

AVAILABLE_MEMORY=$((TOTAL_MEMORY - USED_MEMORY))

echo "Total memory: ${TOTAL_MEMORY}MB"

echo "Used memory: ${USED_MEMORY}MB"

echo "Available memory: ${AVAILABLE_MEMORY}MB"



# Check if the requested memory allocation exceeds the available memory

if [[ $((SPARK_DRIVER_MEMORY + SPARK_EXECUTOR_MEMORY)) -gt $AVAILABLE_MEMORY ]]; then

    echo "Insufficient memory allocation for the Spark application"

    exit 1

fi



# Run the Spark application with the specified memory allocation

spark-submit --name ${SPARK_APP_NAME} --driver-memory ${SPARK_DRIVER_MEMORY} --executor-memory ${SPARK_EXECUTOR_MEMORY} ${PATH_TO_SPARK_APP}


```


### Increase the resources allocated to the Spark application to mitigate potential resource contention issues.

```shell
bash

#!/bin/bash



# Set the variables

SPARK_APP_NAME=${SPARK_APPLICATION_NAME}

SPARK_APP_RESOURCES=${AMOUNT_OF_RESOURCES_TO_ALLOCATE}



# Stop the Spark application

sudo systemctl stop ${SPARK_APP_NAME}



# Update the resources allocated to the Spark application

sudo sed -i "s/$SPARK_EXECUTOR_MEMORY=$.*/\1$SPARK_APP_RESOURCES/" /etc/${SPARK_APPLICATION_CONFIG_FILE}



# Start the Spark application

sudo systemctl start ${SPARK_APP_NAME}


```


Spark application failure during checkpointing.

Overview

Parameters

Debug

Check if Spark application is running

Check the status of Spark application

Check if there are any logs generated by Spark

Check if Spark is using the correct checkpointing directory

Check if there is enough disk space in the checkpointing directory

Check if the Spark checkpointing directory has the correct permissions

Check if the Spark application is configured to use enough memory

Check if the Spark application is configured to use enough cores

Check if the Spark application is using the correct version of Java

Insufficient memory allocation for the Spark application, leading to checkpointing failures.

Repair

Increase the resources allocated to the Spark application to mitigate potential resource contention issues.

Learn more

Related Runbooks

YARN ResourceManager Failure Impacting Spark Jobs.

Spark tasks failing due to out of memory errors.

Spark tasks experiencing shuffle spills and high disk I/O.

Spark job failures due to cluster resource contentions.

Support