---
id: 09c00f03-7f01-44e5-bba4-a69801f70f3a
---

# Apache Spark Out of Memory Error incident.
---

Apache Spark Out of Memory Error is a type of incident that occurs when the Apache Spark application running on a system is unable to allocate enough memory to perform its tasks. This can happen when the system's memory is insufficient or when there is a memory leak in the application's code. When this incident occurs, the application may crash, become unresponsive, or produce incorrect results. It is important to diagnose and resolve this issue as soon as possible to ensure the smooth functioning of the application.

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

export PATH_TO_SPARK_CONFIGURATION_FILE="PLACEHOLDER"

export PID_OF_JAVA_PROCESS="PLACEHOLDER"

export NEW_MEMORY_LIMIT="PLACEHOLDER"
```

## Debug

### Check available memory
```shell
free -m
```

### Check system logs for any relevant error messages
```shell
dmesg | grep -i error
```

### Check Spark logs for any relevant error messages
```shell
grep -i error ${PATH_TO_SPARK_LOGS}
```

### Check Spark configuration settings
```shell
cat ${PATH_TO_SPARK_CONFIGURATION_FILE}
```

### Check for any running processes that may be consuming memory
```shell
ps aux --sort=-%mem | head
```

### Check for any Java processes that may be consuming memory
```shell
jmap -heap ${PID_OF_JAVA_PROCESS}
```

### Check for any other system or application processes that may be consuming memory
```shell
top
```

## Repair

### Increase the memory capacity of the server in use.
```shell


#!/bin/bash



# Set the new memory limit

NEW_MEMORY_LIMIT=${NEW_MEMORY_LIMIT}



# Edit the configuration file to set the new memory limit

sed -i "s|^-Xmx.*| -Xmx${NEW_MEMORY_LIMIT}m|" /etc/systemd/system.conf



# Restart the systemd daemon to apply the changes

systemctl daemon-reload



# Restart the server to apply the new memory limit

reboot





sudo ./increase_memory.sh


```

Apache Spark Out of Memory Error is a type of incident that occurs when the Apache Spark application running on a system is unable to allocate enough memory to perform its tasks. This can happen when the system's memory is insufficient or when there is a memory leak in the application's code. When this incident occurs, the application may crash, become unresponsive, or produce incorrect results. It is important to diagnose and resolve this issue as soon as possible to ensure the smooth functioning 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 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 PATH_TO_SPARK_LOGS="PLACEHOLDER"

export PATH_TO_SPARK_CONFIGURATION_FILE="PLACEHOLDER"

export PID_OF_JAVA_PROCESS="PLACEHOLDER"

export NEW_MEMORY_LIMIT="PLACEHOLDER"
```


### Check available memory

```shell
free -m
```

### Check system logs for any relevant error messages

```shell
dmesg | grep -i error
```

### Check Spark logs for any relevant error messages

```shell
grep -i error ${PATH_TO_SPARK_LOGS}
```

### Check Spark configuration settings

```shell
cat ${PATH_TO_SPARK_CONFIGURATION_FILE}
```

### Check for any running processes that may be consuming memory

```shell
ps aux --sort=-%mem | head
```

### Check for any Java processes that may be consuming memory

```shell
jmap -heap ${PID_OF_JAVA_PROCESS}
```

### Check for any other system or application processes that may be consuming memory

```shell
top
```


### Increase the memory capacity of the server in use.

```shell


#!/bin/bash



# Set the new memory limit

NEW_MEMORY_LIMIT=${NEW_MEMORY_LIMIT}



# Edit the configuration file to set the new memory limit

sed -i "s|^-Xmx.*| -Xmx${NEW_MEMORY_LIMIT}m|" /etc/systemd/system.conf



# Restart the systemd daemon to apply the changes

systemctl daemon-reload



# Restart the server to apply the new memory limit

reboot





sudo ./increase_memory.sh


```


Apache Spark Out of Memory Error incident.

Overview

Parameters

Debug

Check available memory

Check system logs for any relevant error messages

Check Spark logs for any relevant error messages

Check Spark configuration settings

Check for any running processes that may be consuming memory

Check for any Java processes that may be consuming memory

Check for any other system or application processes that may be consuming memory

Repair

Increase the memory capacity of the server in use.

Learn more

Related Runbooks

Host Out of Memory (OOM) Incident

Tomcat JVM OutOfMemory Incident

Spark tasks failing due to out of memory errors.

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

Support