---
id: 68c83dc1-e9a0-42bd-b575-0ff12badb55f
---

# Spark executor failure during job execution.
---

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.

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

export MASTER_URL="PLACEHOLDER"

export SPARK_HOME="PLACEHOLDER"

export EXECUTOR_ID="PLACEHOLDER"

export DRIVER_ID="PLACEHOLDER"

export EXECUTOR_MEMORY="PLACEHOLDER"

export EXECUTOR_CORES="PLACEHOLDER"

export DISK_LIMIT="PLACEHOLDER"
```

## Debug

### Check the status of the Spark application
```shell
${SPARK_HOME}/bin/spark-submit --master ${MASTER_URL} --status ${APPLICATION_ID}
```

### View the logs for the failed executor
```shell
${SPARK_HOME}/bin/spark-submit --master ${MASTER_URL} --logs ${APPLICATION_ID} ${EXECUTOR_ID}
```

### Check the resource usage of the executor
```shell
${SPARK_HOME}/bin/spark-submit --master ${MASTER_URL} --metrics ${APPLICATION_ID} --executor-id ${EXECUTOR_ID} --driver-id ${DRIVER_ID}
```

### Check the system logs for any relevant error messages
```shell
journalctl -u spark.service | grep ERROR
```

### Insufficient resources allocated to the Spark executor leading to failure during job execution.
```shell


#!/bin/bash



# Define parameters

SPARK_EXECUTOR_MEMORY=${EXECUTOR_MEMORY}

SPARK_EXECUTOR_CORES=${EXECUTOR_CORES}



# Check available system memory

SYS_MEM=$(free -m | awk 'NR==2{print $2}')

if (( $SYS_MEM < $SPARK_EXECUTOR_MEMORY )); then

  echo "Insufficient system memory available for Spark executor"

fi



# Check available CPU cores

CORES=$(nproc)

if (( $CORES < $SPARK_EXECUTOR_CORES )); then

  echo "Insufficient CPU cores available for Spark executor"

fi


```

## Repair

### Check if the executor has sufficient resources such as memory, CPU cores, and disk space. Increase the resources if necessary.
```shell


#!/bin/bash



# Set the resource limits

MEMORY=${EXECUTOR_MEMORY}

CPU=${EXECUTOR_CORES}

DISK=${DISK_LIMIT}



# Get the current executor resources

CURRENT_MEMORY=$(grep -oP 'spark\.executor\.memory\s*\K\S+' $SPARK_HOME/conf/spark-defaults.conf)

CURRENT_CPU=$(grep -oP 'spark\.executor\.cores\s*\K\S+' $SPARK_HOME/conf/spark-defaults.conf)

CURRENT_DISK=$(grep -oP 'spark\.executor\.dir\s*\K\S+' $SPARK_HOME/conf/spark-defaults.conf)



# Check if the current resources are less than the desired limits

if [ $CURRENT_MEMORY -lt $MEMORY ] || [ $CURRENT_CPU -lt $CPU ] || [ $CURRENT_DISK -lt $DISK ]; then

  # Increase the resources

  sed -i "s/spark\.executor\.memory.*/spark\.executor\.memory $MEMORY/" $SPARK_HOME/conf/spark-defaults.conf

  sed -i "s/spark\.executor\.cores.*/spark\.executor\.cores $CPU/" $SPARK_HOME/conf/spark-defaults.conf

  sed -i "s/spark\.executor\.dir.*/spark\.executor\.dir $DISK/" $SPARK_HOME/conf/spark-defaults.conf

  echo "Executor resources updated"

else

  echo "Executor resources are sufficient"

fi


```

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.


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 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.


Spark driver program crash during job runtime.

```shell
export APPLICATION_ID="PLACEHOLDER"

export MASTER_URL="PLACEHOLDER"

export SPARK_HOME="PLACEHOLDER"

export EXECUTOR_ID="PLACEHOLDER"

export DRIVER_ID="PLACEHOLDER"

export EXECUTOR_MEMORY="PLACEHOLDER"

export EXECUTOR_CORES="PLACEHOLDER"

export DISK_LIMIT="PLACEHOLDER"
```


### Check the status of the Spark application

```shell
${SPARK_HOME}/bin/spark-submit --master ${MASTER_URL} --status ${APPLICATION_ID}
```

### View the logs for the failed executor

```shell
${SPARK_HOME}/bin/spark-submit --master ${MASTER_URL} --logs ${APPLICATION_ID} ${EXECUTOR_ID}
```

### Check the resource usage of the executor

```shell
${SPARK_HOME}/bin/spark-submit --master ${MASTER_URL} --metrics ${APPLICATION_ID} --executor-id ${EXECUTOR_ID} --driver-id ${DRIVER_ID}
```

### Check the system logs for any relevant error messages

```shell
journalctl -u spark.service | grep ERROR
```

### Insufficient resources allocated to the Spark executor leading to failure during job execution.

```shell


#!/bin/bash



# Define parameters

SPARK_EXECUTOR_MEMORY=${EXECUTOR_MEMORY}

SPARK_EXECUTOR_CORES=${EXECUTOR_CORES}



# Check available system memory

SYS_MEM=$(free -m | awk 'NR==2{print $2}')

if (( $SYS_MEM < $SPARK_EXECUTOR_MEMORY )); then

  echo "Insufficient system memory available for Spark executor"

fi



# Check available CPU cores

CORES=$(nproc)

if (( $CORES < $SPARK_EXECUTOR_CORES )); then

  echo "Insufficient CPU cores available for Spark executor"

fi


```


### Check if the executor has sufficient resources such as memory, CPU cores, and disk space. Increase the resources if necessary.

```shell


#!/bin/bash



# Set the resource limits

MEMORY=${EXECUTOR_MEMORY}

CPU=${EXECUTOR_CORES}

DISK=${DISK_LIMIT}



# Get the current executor resources

CURRENT_MEMORY=$(grep -oP 'spark\.executor\.memory\s*\K\S+' $SPARK_HOME/conf/spark-defaults.conf)

CURRENT_CPU=$(grep -oP 'spark\.executor\.cores\s*\K\S+' $SPARK_HOME/conf/spark-defaults.conf)

CURRENT_DISK=$(grep -oP 'spark\.executor\.dir\s*\K\S+' $SPARK_HOME/conf/spark-defaults.conf)



# Check if the current resources are less than the desired limits

if [ $CURRENT_MEMORY -lt $MEMORY ] || [ $CURRENT_CPU -lt $CPU ] || [ $CURRENT_DISK -lt $DISK ]; then

  # Increase the resources

  sed -i "s/spark\.executor\.memory.*/spark\.executor\.memory $MEMORY/" $SPARK_HOME/conf/spark-defaults.conf

  sed -i "s/spark\.executor\.cores.*/spark\.executor\.cores $CPU/" $SPARK_HOME/conf/spark-defaults.conf

  sed -i "s/spark\.executor\.dir.*/spark\.executor\.dir $DISK/" $SPARK_HOME/conf/spark-defaults.conf

  echo "Executor resources updated"

else

  echo "Executor resources are sufficient"

fi


```


Spark executor failure during job execution.

Overview

Parameters

Debug

Check the status of the Spark application

View the logs for the failed executor

Check the resource usage of the executor

Check the system logs for any relevant error messages

Insufficient resources allocated to the Spark executor leading to failure during job execution.

Repair

Check if the executor has sufficient resources such as memory, CPU cores, and disk space. Increase the resources if necessary.

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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