---
id: 117fdc27-6127-405b-8f43-4610b4a64624
---

# Spark driver program crash during job runtime.
---

This incident type refers to an unexpected termination of the Spark driver program during the runtime of a job. The driver program is responsible for coordinating the execution of a Spark job and if it crashes, the entire job is affected. This can result in data loss and downtime, and requires investigation and troubleshooting to identify the root cause of the issue.

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

export PATH_TO_DRIVER_LOGS="PLACEHOLDER"

export PATH_TO_SPARK_CONF="PLACEHOLDER"

export NEW_MEMORY_SIZE="PLACEHOLDER"

export MEMORY_SIZE="PLACEHOLDER"
```

## Debug

### Check if Spark is running
```shell
ps aux | grep spark
```

### Check if the driver program is running
```shell
ps aux | grep ${DRIVER_PROGRAM_NAME}
```

### Check for any logs or error messages related to the driver program crash
```shell
grep -r "error" ${PATH_TO_DRIVER_LOGS}
```

### Check for any system-level errors or warnings
```shell
dmesg | tail
```

### Check for any resource issues, such as memory usage or CPU utilization
```shell
top
```

### Check for any network-related issues
```shell
netstat -a
```

### Check for any firewall or security-related issues
```shell
iptables -L
```

### Insufficient memory resources allocated to the Spark driver program.
```shell
bash

#!/bin/bash



# Define parameters

SPARK_DRIVER_MEMORY="${MEMORY_SIZE}"



# Get current memory usage of the Spark driver

SPARK_DRIVER_PID=$(ps aux | grep spark.driver.memory | grep -v grep | awk '{print $2}')

SPARK_DRIVER_MEMORY_USAGE=$(pmap -x "$SPARK_DRIVER_PID" | tail -n 1 | awk '{print $4}')



# Check if current memory usage exceeds allocated memory

if (( $(echo "$SPARK_DRIVER_MEMORY_USAGE / 1024 / 1024 / 1024" | bc) > $SPARK_DRIVER_MEMORY )); then

    echo "Spark driver program is using more memory than allocated: $SPARK_DRIVER_MEMORY_USAGE bytes"

    echo "Allocated memory: $SPARK_DRIVER_MEMORY GB"

    echo "Please increase the value of spark.driver.memory configuration parameter and restart the Spark driver program."

else

    echo "Spark driver program is using memory within allocated limits: $SPARK_DRIVER_MEMORY_USAGE bytes"

fi


```

## Repair

### Increase the resources allocated to the Spark driver program, such as increasing the memory or the number of CPU cores.
```shell


#!/bin/bash



# Set the path to the Spark configuration file

SPARK_CONF=${PATH_TO_SPARK_CONF}



# Increase the driver program's memory allocation

sed -i 's/spark.driver.memory ${MEMORY_SIZE}/spark.driver.memory ${NEW_MEMORY_SIZE}/g' $SPARK_CONF



# Restart the Spark cluster to apply the changes

systemctl restart spark


```

This incident type refers to an unexpected termination of the Spark driver program during the runtime of a job. The driver program is responsible for coordinating the execution of a Spark job and if it crashes, the entire job is affected. This can result in data loss and downtime, and requires investigation and troubleshooting to identify the root cause of the issue.


This incident type refers to a situation where the Tomcat server's JDBC connection pool usage surpasses its configured limits. This can lead to a shortage of available connections for incoming requests, resulting in slow response times or even server crashes. It is important to monitor and manage the JDBC connection pool usage to prevent such incidents from occurring.


Tomcat High JDBC Connection Pool Usage Incident.

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.

```shell
export DRIVER_PROGRAM_NAME="PLACEHOLDER"

export PATH_TO_DRIVER_LOGS="PLACEHOLDER"

export PATH_TO_SPARK_CONF="PLACEHOLDER"

export NEW_MEMORY_SIZE="PLACEHOLDER"

export MEMORY_SIZE="PLACEHOLDER"
```


### Check if Spark is running

```shell
ps aux | grep spark
```

### Check if the driver program is running

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

### Check for any logs or error messages related to the driver program crash

```shell
grep -r "error" ${PATH_TO_DRIVER_LOGS}
```

### Check for any system-level errors or warnings

```shell
dmesg | tail
```

### Check for any resource issues, such as memory usage or CPU utilization

```shell
top
```

### Check for any network-related issues

```shell
netstat -a
```

### Check for any firewall or security-related issues

```shell
iptables -L
```

### Insufficient memory resources allocated to the Spark driver program.

```shell
bash

#!/bin/bash



# Define parameters

SPARK_DRIVER_MEMORY="${MEMORY_SIZE}"



# Get current memory usage of the Spark driver

SPARK_DRIVER_PID=$(ps aux | grep spark.driver.memory | grep -v grep | awk '{print $2}')

SPARK_DRIVER_MEMORY_USAGE=$(pmap -x "$SPARK_DRIVER_PID" | tail -n 1 | awk '{print $4}')



# Check if current memory usage exceeds allocated memory

if (( $(echo "$SPARK_DRIVER_MEMORY_USAGE / 1024 / 1024 / 1024" | bc) > $SPARK_DRIVER_MEMORY )); then

    echo "Spark driver program is using more memory than allocated: $SPARK_DRIVER_MEMORY_USAGE bytes"

    echo "Allocated memory: $SPARK_DRIVER_MEMORY GB"

    echo "Please increase the value of spark.driver.memory configuration parameter and restart the Spark driver program."

else

    echo "Spark driver program is using memory within allocated limits: $SPARK_DRIVER_MEMORY_USAGE bytes"

fi


```


### Increase the resources allocated to the Spark driver program, such as increasing the memory or the number of CPU cores.

```shell


#!/bin/bash



# Set the path to the Spark configuration file

SPARK_CONF=${PATH_TO_SPARK_CONF}



# Increase the driver program's memory allocation

sed -i 's/spark.driver.memory ${MEMORY_SIZE}/spark.driver.memory ${NEW_MEMORY_SIZE}/g' $SPARK_CONF



# Restart the Spark cluster to apply the changes

systemctl restart spark


```


Spark driver program crash during job runtime.

Overview

Parameters

Debug

Check if Spark is running

Check if the driver program is running

Check for any system-level errors or warnings

Check for any resource issues, such as memory usage or CPU utilization

Insufficient memory resources allocated to the Spark driver program.

Repair

Increase the resources allocated to the Spark driver program, such as increasing the memory or the number of CPU cores.

Learn more

Related Runbooks

Tomcat High JDBC Connection Pool Usage Incident.

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.

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