---
id: 5c60cd57-96c4-46b3-a678-1c1ef208f532
---

# Spark driver failure incident.
---

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.

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

export MAX_CPU_USAGE_ALLOWED="PLACEHOLDER"

export MIN_FREE_MEM_REQUIRED="PLACEHOLDER"

export DRIVER_NODE_MOUNT_POINT="PLACEHOLDER"

export MIN_FREE_DISK_SPACE_REQUIRED="PLACEHOLDER"

export PATH_TO_SPARK_DEFAULTS_CONF="PLACEHOLDER"

export NEW_DRIVER_MEMORY="PLACEHOLDER"
```

## Debug

### Step 1: Check if Apache Spark is running
```shell
sudo systemctl status spark | grep -q "Active: active (running)" || echo "Apache Spark is not running"
```

### Step 2: Check the logs for error messages
```shell
sudo tail -n 100 /var/log/spark/spark-driver.log
```

### Step 4: Check the resource allocation of the driver
```shell
sudo ps -ef | grep ${DRIVER_PROCESS_ID}
```

### Step 5: Check the available resources on the cluster
```shell
sudo free -m
```

### Step 6: Check if there are any network issues
```shell
sudo netstat -tuln
```

### Step 3: Check the status of the Apache Spark driver
```shell
sudo jps | grep -q "Driver" || echo "Apache Spark driver is not running"
```

### Step 7: Check the configuration files for any errors
```shell
sudo cat /etc/spark/conf/spark-defaults.conf | grep -i error

sudo cat /etc/spark/conf/spark-env.sh | grep -i error
```

### Insufficient resources (RAM, disk space, CPU) available for Apache Spark driver.
```shell


#!/bin/bash



# Check available RAM on the driver node

free_mem=$(free -m | awk '/^Mem/ {print $4}')

if [[ $free_mem -lt ${MIN_FREE_MEM_REQUIRED} ]]; then

    echo "ERROR: Insufficient free memory available on driver node."

fi



# Check available disk space on the driver node

free_space=$(df -h ${DRIVER_NODE_MOUNT_POINT} | awk '/^\/dev/ {print $4}')

if [[ $free_space -lt ${MIN_FREE_DISK_SPACE_REQUIRED} ]]; then

    echo "ERROR: Insufficient free disk space available on driver node."

fi



# Check CPU usage on the driver node

cpu_usage=$(top -b -n 1 | grep "Cpu(s)" | awk '{print $2+$4}')

if [[ $cpu_usage -gt ${MAX_CPU_USAGE_ALLOWED} ]]; then

    echo "ERROR: High CPU usage detected on driver node."

fi


```

## Repair

### Increase the resources allocated to the Apache Spark driver to prevent future failures.
```shell


#!/bin/bash



# Set the path to the Spark configuration file

SPARK_CONF=${PATH_TO_SPARK_DEFAULTS_CONF}



# Set the new value for the Spark driver memory

NEW_DRIVER_MEMORY=${NEW_DRIVER_MEMORY}



# Update the spark.driver.memory configuration in spark-defaults.conf

sed -i "s/^spark.driver.memory\s*=.*$/spark.driver.memory $NEW_DRIVER_MEMORY/g" $SPARK_CONF


```


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.


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 DRIVER_PROCESS_ID="PLACEHOLDER"

export MAX_CPU_USAGE_ALLOWED="PLACEHOLDER"

export MIN_FREE_MEM_REQUIRED="PLACEHOLDER"

export DRIVER_NODE_MOUNT_POINT="PLACEHOLDER"

export MIN_FREE_DISK_SPACE_REQUIRED="PLACEHOLDER"

export PATH_TO_SPARK_DEFAULTS_CONF="PLACEHOLDER"

export NEW_DRIVER_MEMORY="PLACEHOLDER"
```


### Step 1: Check if Apache Spark is running

```shell
sudo systemctl status spark | grep -q "Active: active (running)" || echo "Apache Spark is not running"
```

### Step 2: Check the logs for error messages

```shell
sudo tail -n 100 /var/log/spark/spark-driver.log
```

### Step 4: Check the resource allocation of the driver

```shell
sudo ps -ef | grep ${DRIVER_PROCESS_ID}
```

### Step 5: Check the available resources on the cluster

```shell
sudo free -m
```

### Step 6: Check if there are any network issues

```shell
sudo netstat -tuln
```

### Step 3: Check the status of the Apache Spark driver

```shell
sudo jps | grep -q "Driver" || echo "Apache Spark driver is not running"
```

### Step 7: Check the configuration files for any errors

```shell
sudo cat /etc/spark/conf/spark-defaults.conf | grep -i error

sudo cat /etc/spark/conf/spark-env.sh | grep -i error
```

### Insufficient resources (RAM, disk space, CPU) available for Apache Spark driver.

```shell


#!/bin/bash



# Check available RAM on the driver node

free_mem=$(free -m | awk '/^Mem/ {print $4}')

if [[ $free_mem -lt ${MIN_FREE_MEM_REQUIRED} ]]; then

    echo "ERROR: Insufficient free memory available on driver node."

fi



# Check available disk space on the driver node

free_space=$(df -h ${DRIVER_NODE_MOUNT_POINT} | awk '/^\/dev/ {print $4}')

if [[ $free_space -lt ${MIN_FREE_DISK_SPACE_REQUIRED} ]]; then

    echo "ERROR: Insufficient free disk space available on driver node."

fi



# Check CPU usage on the driver node

cpu_usage=$(top -b -n 1 | grep "Cpu(s)" | awk '{print $2+$4}')

if [[ $cpu_usage -gt ${MAX_CPU_USAGE_ALLOWED} ]]; then

    echo "ERROR: High CPU usage detected on driver node."

fi


```


### Increase the resources allocated to the Apache Spark driver to prevent future failures.

```shell


#!/bin/bash



# Set the path to the Spark configuration file

SPARK_CONF=${PATH_TO_SPARK_DEFAULTS_CONF}



# Set the new value for the Spark driver memory

NEW_DRIVER_MEMORY=${NEW_DRIVER_MEMORY}



# Update the spark.driver.memory configuration in spark-defaults.conf

sed -i "s/^spark.driver.memory\s*=.*$/spark.driver.memory $NEW_DRIVER_MEMORY/g" $SPARK_CONF


```


Spark driver failure incident.

Overview

Parameters

Debug

Step 1: Check if Apache Spark is running

Step 2: Check the logs for error messages

Step 4: Check the resource allocation of the driver

Step 5: Check the available resources on the cluster

Step 6: Check if there are any network issues

Step 3: Check the status of the Apache Spark driver

Step 7: Check the configuration files for any errors

Insufficient resources (RAM, disk space, CPU) available for Apache Spark driver.

Repair

Increase the resources allocated to the Apache Spark driver to prevent future failures.

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.

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