---
id: 02c7010b-81e5-46c9-b274-a25a96c788dd
---

# Spark tasks failing due to out of memory errors.
---

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.

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

export SPARK_APP_DIR="PLACEHOLDER"

export SPARK_APP_LOG_DIR="PLACEHOLDER"

export NEW_MEMORY_ALLOCATION="PLACEHOLDER"

export PATH_TO_SPARK_CONFIGURATION_FILE="PLACEHOLDER"

export MEMORY_SIZE_FOR_THE_SPARK_DRIVER="PLACEHOLDER"

export URL_OF_SPARK_MASTER="PLACEHOLDER"

export MEMORY_SIZE_FOR_EACH_SPARK_EXECUTOR="PLACEHOLDER"
```

## Debug

### Check system memory usage
```shell
free -m
```

### Check if the system is running low on memory
```shell
vmstat -s
```

### Check the amount of available memory
```shell
cat /proc/meminfo
```

### Check the amount of memory used by Spark processes
```shell
ps -e -o pid,user,%mem,command | grep "spark"
```

### Check the logs for out of memory errors
```shell
grep -i "out of memory" /var/log/messages
```

### Check the Spark configuration for memory settings
```shell
cat ${SPARK_HOME}/conf/spark-defaults.conf | grep -E "spark.executor.memory|spark.driver.memory"
```

### Check the Spark application code for memory-intensive operations
```shell
grep -r "cache" ${SPARK_APP_DIR}
```

### Check the Spark application logs for memory-related errors
```shell
grep -i "out of memory" ${SPARK_APP_LOG_DIR}/*
```

### Check the system logs for memory-related errors
```shell
dmesg | grep -i "oom\|out of memory"
```

### Insufficient memory allocation for Spark tasks.
```shell


#!/bin/bash



# Define variables

SPARK_HOME=${SPARK_HOME}

SPARK_MASTER_URL=${URL_OF_SPARK_MASTER}

SPARK_EXECUTOR_MEMORY=${MEMORY_SIZE_FOR_EACH_SPARK_EXECUTOR}

SPARK_DRIVER_MEMORY=${MEMORY_SIZE_FOR_THE_SPARK_DRIVER}



# Check memory allocation configuration

echo "Checking memory allocation configuration..."

if grep -q "spark.executor.memory" $SPARK_HOME/conf/spark-defaults.conf && grep -q "spark.driver.memory" $SPARK_HOME/conf/spark-defaults.conf; then

  echo "Memory allocation configuration is already set."

else

  echo "Memory allocation configuration is not set. Setting configuration..."

  echo "spark.executor.memory $SPARK_EXECUTOR_MEMORY" >> $SPARK_HOME/conf/spark-defaults.conf

  echo "spark.driver.memory $SPARK_DRIVER_MEMORY" >> $SPARK_HOME/conf/spark-defaults.conf

fi



# Check Spark cluster status

echo "Checking Spark cluster status..."

if $SPARK_HOME/bin/spark-submit --master $SPARK_MASTER_URL --class org.apache.spark.examples.SparkPi $SPARK_HOME/examples/jars/spark-examples_2.11-2.4.0.jar 10; then

  echo "Spark cluster is running."

else

  echo "Spark cluster is not running. Please check the Spark cluster status."

fi



# Check Spark task status

echo "Checking Spark task status..."

if $SPARK_HOME/bin/spark-submit --master $SPARK_MASTER_URL --class org.apache.spark.examples.SparkPi --num-executors 1 --executor-memory $SPARK_EXECUTOR_MEMORY --driver-memory $SPARK_DRIVER_MEMORY $SPARK_HOME/examples/jars/spark-examples_2.11-2.4.0.jar 10; then

  echo "Spark task is running with the allocated memory."

else

  echo "Spark task is failing due to insufficient memory allocation. Please increase memory allocation for Spark tasks."

fi


```

## Repair

### Increase the memory allocation for the Spark executor. This can be done by adjusting the `spark.executor.memory` property in the Spark configuration.
```shell


#!/bin/bash



# Set the path to the Spark configuration file

SPARK_CONF=${PATH_TO_SPARK_CONFIGURATION_FILE}



# Set the new memory allocation for the Spark executor

NEW_MEMORY=${NEW_MEMORY_ALLOCATION}



# Replace the old memory allocation with the new memory allocation in the Spark configuration file

sed -i "s/spark.executor.memory=.*/spark.executor.memory=$NEW_MEMORY/" $SPARK_CONF



# Restart the Spark application to apply the new configuration

systemctl restart spark


```

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.


The Host Out of Memory (OOM) Incident occurs when a server or system runs out of memory, causing it to crash or become unresponsive. This can be caused by various factors, such as an unexpected surge in traffic or insufficient resources allocated to the system. Resolving this type of incident requires identifying the root cause of the memory issue and taking appropriate measures such as optimizing system resources or increasing memory capacity.


Host Out of Memory (OOM) Incident

Tomcat JVM OutOfMemory incident occurs when the Java Virtual Machine (JVM) running the Apache Tomcat server runs out of memory, which causes the server to crash or become unresponsive. This type of incident can happen due to a variety of reasons such as incorrect configuration settings, memory leaks, or insufficient memory allocation. When this incident occurs, it can impact the availability and performance of the web application running on the Tomcat server.


Tomcat JVM OutOfMemory Incident

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_HOME="PLACEHOLDER"

export SPARK_APP_DIR="PLACEHOLDER"

export SPARK_APP_LOG_DIR="PLACEHOLDER"

export NEW_MEMORY_ALLOCATION="PLACEHOLDER"

export PATH_TO_SPARK_CONFIGURATION_FILE="PLACEHOLDER"

export MEMORY_SIZE_FOR_THE_SPARK_DRIVER="PLACEHOLDER"

export URL_OF_SPARK_MASTER="PLACEHOLDER"

export MEMORY_SIZE_FOR_EACH_SPARK_EXECUTOR="PLACEHOLDER"
```


### Check system memory usage

```shell
free -m
```

### Check if the system is running low on memory

```shell
vmstat -s
```

### Check the amount of available memory

```shell
cat /proc/meminfo
```

### Check the amount of memory used by Spark processes

```shell
ps -e -o pid,user,%mem,command | grep "spark"
```

### Check the logs for out of memory errors

```shell
grep -i "out of memory" /var/log/messages
```

### Check the Spark configuration for memory settings

```shell
cat ${SPARK_HOME}/conf/spark-defaults.conf | grep -E "spark.executor.memory|spark.driver.memory"
```

### Check the Spark application code for memory-intensive operations

```shell
grep -r "cache" ${SPARK_APP_DIR}
```

### Check the Spark application logs for memory-related errors

```shell
grep -i "out of memory" ${SPARK_APP_LOG_DIR}/*
```

### Check the system logs for memory-related errors

```shell
dmesg | grep -i "oom\|out of memory"
```

### Insufficient memory allocation for Spark tasks.

```shell


#!/bin/bash



# Define variables

SPARK_HOME=${SPARK_HOME}

SPARK_MASTER_URL=${URL_OF_SPARK_MASTER}

SPARK_EXECUTOR_MEMORY=${MEMORY_SIZE_FOR_EACH_SPARK_EXECUTOR}

SPARK_DRIVER_MEMORY=${MEMORY_SIZE_FOR_THE_SPARK_DRIVER}



# Check memory allocation configuration

echo "Checking memory allocation configuration..."

if grep -q "spark.executor.memory" $SPARK_HOME/conf/spark-defaults.conf && grep -q "spark.driver.memory" $SPARK_HOME/conf/spark-defaults.conf; then

  echo "Memory allocation configuration is already set."

else

  echo "Memory allocation configuration is not set. Setting configuration..."

  echo "spark.executor.memory $SPARK_EXECUTOR_MEMORY" >> $SPARK_HOME/conf/spark-defaults.conf

  echo "spark.driver.memory $SPARK_DRIVER_MEMORY" >> $SPARK_HOME/conf/spark-defaults.conf

fi



# Check Spark cluster status

echo "Checking Spark cluster status..."

if $SPARK_HOME/bin/spark-submit --master $SPARK_MASTER_URL --class org.apache.spark.examples.SparkPi $SPARK_HOME/examples/jars/spark-examples_2.11-2.4.0.jar 10; then

  echo "Spark cluster is running."

else

  echo "Spark cluster is not running. Please check the Spark cluster status."

fi



# Check Spark task status

echo "Checking Spark task status..."

if $SPARK_HOME/bin/spark-submit --master $SPARK_MASTER_URL --class org.apache.spark.examples.SparkPi --num-executors 1 --executor-memory $SPARK_EXECUTOR_MEMORY --driver-memory $SPARK_DRIVER_MEMORY $SPARK_HOME/examples/jars/spark-examples_2.11-2.4.0.jar 10; then

  echo "Spark task is running with the allocated memory."

else

  echo "Spark task is failing due to insufficient memory allocation. Please increase memory allocation for Spark tasks."

fi


```


### Increase the memory allocation for the Spark executor. This can be done by adjusting the `spark.executor.memory` property in the Spark configuration.

```shell


#!/bin/bash



# Set the path to the Spark configuration file

SPARK_CONF=${PATH_TO_SPARK_CONFIGURATION_FILE}



# Set the new memory allocation for the Spark executor

NEW_MEMORY=${NEW_MEMORY_ALLOCATION}



# Replace the old memory allocation with the new memory allocation in the Spark configuration file

sed -i "s/spark.executor.memory=.*/spark.executor.memory=$NEW_MEMORY/" $SPARK_CONF



# Restart the Spark application to apply the new configuration

systemctl restart spark


```


Spark tasks failing due to out of memory errors.

Overview

Parameters

Debug

Check system memory usage

Check if the system is running low on memory

Check the amount of available memory

Check the amount of memory used by Spark processes

Check the logs for out of memory errors

Check the Spark configuration for memory settings

Check the Spark application code for memory-intensive operations

Insufficient memory allocation for Spark tasks.

Repair

Increase the memory allocation for the Spark executor. This can be done by adjusting the `spark.executor.memory` property in the Spark configuration.

Learn more

Related Runbooks

Host Out of Memory (OOM) Incident

Tomcat JVM OutOfMemory Incident

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

Spark job failures due to cluster resource contentions.

Support

Spark tasks failing due to out of memory errors.

Overview

Parameters

Debug

Check system memory usage

Check if the system is running low on memory

Check the amount of available memory

Check the amount of memory used by Spark processes

Check the logs for out of memory errors

Check the Spark configuration for memory settings

Check the Spark application code for memory-intensive operations

Check the Spark application logs for memory-related errors

Check the system logs for memory-related errors

Insufficient memory allocation for Spark tasks.

Repair

Increase the memory allocation for the Spark executor. This can be done by adjusting the spark.executor.memory property in the Spark configuration.

Learn more

Related Runbooks

Host Out of Memory (OOM) Incident

Tomcat JVM OutOfMemory Incident

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

Spark job failures due to cluster resource contentions.

Increase the memory allocation for the Spark executor. This can be done by adjusting the `spark.executor.memory` property in the Spark configuration.