---
id: 33315d35-d99e-4702-9c1c-962386a48dc4
---

# Spark job failures due to cluster resource contentions.
---

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.

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

export NUM_NODES="PLACEHOLDER"

export LOG_FILE="PLACEHOLDER"

export NEW_CPU_VALUE="PLACEHOLDER"

export CLUSTER_PROCESS_NAME="PLACEHOLDER"

export NEW_MEMORY_VALUE="PLACEHOLDER"
```

## Debug

### Get the status of the Spark cluster
```shell
spark-status
```

### Check the resource usage of the Spark jobs
```shell
yarn application -status ${JOB_ID}
```

### Check if any nodes in the cluster are overloaded
```shell
top -d 1 -b | grep -E '(Cpu|Memory)' | head -n ${NUM_NODES}
```

### Analyze the Spark logs for any errors or exceptions
```shell
grep -i -e 'error' -e 'exception' ${LOG_FILE}
```

### Check the network usage of the cluster nodes
```shell
netstat -s | grep -E 'segments retransmitted' | head -n ${NUM_NODES}
```

### Not enough resources allocated to the Spark job causing it to compete with other jobs running on the cluster.
```shell
bash

#!/bin/bash



# Set the parameters

SPARK_JOB=${JOB_ID}

CLUSTER=${CLUSTER_PROCESS_NAME}



# Check the resource allocation for the Spark job

allocated_resources=$(grep $SPARK_JOB /var/log/spark-resource-manager.log | grep "Allocated resources")

if [ -z "$allocated_resources" ]; then

  echo "No allocation found for the Spark job $SPARK_JOB"

  exit 1

fi



# Check the total available resources in the cluster

total_resources=$(grep $CLUSTER /var/log/spark-resource-manager.log | grep "Total resources")

if [ -z "$total_resources" ]; then

  echo "No resource information found for the cluster $CLUSTER"

  exit 1

fi



# Parse the allocated and total resources

allocated_cpu=$(echo $allocated_resources | awk '{print $5}')

allocated_memory=$(echo $allocated_resources | awk '{print $7}')

total_cpu=$(echo $total_resources | awk '{print $5}')

total_memory=$(echo $total_resources | awk '{print $7}')



# Check if the allocated resources are less than the total resources

if [ $allocated_cpu -lt $total_cpu ] && [ $allocated_memory -lt $total_memory ]; then

  echo "The Spark job $SPARK_JOB is not allocated enough resources"

else

  echo "The Spark job $SPARK_JOB has enough resources"

fi


```

## Repair

### Increasing the resources allocated to the cluster, like memory and CPU, to avoid contention.
```shell
bash

#!/bin/bash



# Set the new values for memory and CPU

NEW_MEMORY=${NEW_MEMORY_VALUE}

NEW_CPU=${NEW_CPU_VALUE}



# Find the PID of the cluster process

PID=$(ps aux | grep ${CLUSTER_PROCESS_NAME} | grep -v grep | awk '{print $2}')



# Increase the memory and CPU limits for the cluster process

sudo renice -n -10 $PID

sudo cpulimit -p $PID -l $NEW_CPU &

sudo cgroups -g memory:cluster_group_name/ -m $NEW_MEMORY"m" $PID


```

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.


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 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.

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 JOB_ID="PLACEHOLDER"

export NUM_NODES="PLACEHOLDER"

export LOG_FILE="PLACEHOLDER"

export NEW_CPU_VALUE="PLACEHOLDER"

export CLUSTER_PROCESS_NAME="PLACEHOLDER"

export NEW_MEMORY_VALUE="PLACEHOLDER"
```


### Get the status of the Spark cluster

```shell
spark-status
```

### Check the resource usage of the Spark jobs

```shell
yarn application -status ${JOB_ID}
```

### Check if any nodes in the cluster are overloaded

```shell
top -d 1 -b | grep -E '(Cpu|Memory)' | head -n ${NUM_NODES}
```

### Analyze the Spark logs for any errors or exceptions

```shell
grep -i -e 'error' -e 'exception' ${LOG_FILE}
```

### Check the network usage of the cluster nodes

```shell
netstat -s | grep -E 'segments retransmitted' | head -n ${NUM_NODES}
```

### Not enough resources allocated to the Spark job causing it to compete with other jobs running on the cluster.

```shell
bash

#!/bin/bash



# Set the parameters

SPARK_JOB=${JOB_ID}

CLUSTER=${CLUSTER_PROCESS_NAME}



# Check the resource allocation for the Spark job

allocated_resources=$(grep $SPARK_JOB /var/log/spark-resource-manager.log | grep "Allocated resources")

if [ -z "$allocated_resources" ]; then

  echo "No allocation found for the Spark job $SPARK_JOB"

  exit 1

fi



# Check the total available resources in the cluster

total_resources=$(grep $CLUSTER /var/log/spark-resource-manager.log | grep "Total resources")

if [ -z "$total_resources" ]; then

  echo "No resource information found for the cluster $CLUSTER"

  exit 1

fi



# Parse the allocated and total resources

allocated_cpu=$(echo $allocated_resources | awk '{print $5}')

allocated_memory=$(echo $allocated_resources | awk '{print $7}')

total_cpu=$(echo $total_resources | awk '{print $5}')

total_memory=$(echo $total_resources | awk '{print $7}')



# Check if the allocated resources are less than the total resources

if [ $allocated_cpu -lt $total_cpu ] && [ $allocated_memory -lt $total_memory ]; then

  echo "The Spark job $SPARK_JOB is not allocated enough resources"

else

  echo "The Spark job $SPARK_JOB has enough resources"

fi


```


### Increasing the resources allocated to the cluster, like memory and CPU, to avoid contention.

```shell
bash

#!/bin/bash



# Set the new values for memory and CPU

NEW_MEMORY=${NEW_MEMORY_VALUE}

NEW_CPU=${NEW_CPU_VALUE}



# Find the PID of the cluster process

PID=$(ps aux | grep ${CLUSTER_PROCESS_NAME} | grep -v grep | awk '{print $2}')



# Increase the memory and CPU limits for the cluster process

sudo renice -n -10 $PID

sudo cpulimit -p $PID -l $NEW_CPU &

sudo cgroups -g memory:cluster_group_name/ -m $NEW_MEMORY"m" $PID


```


Spark job failures due to cluster resource contentions.

Overview

Parameters

Debug

Get the status of the Spark cluster

Check the resource usage of the Spark jobs

Check if any nodes in the cluster are overloaded

Analyze the Spark logs for any errors or exceptions

Check the network usage of the cluster nodes

Not enough resources allocated to the Spark job causing it to compete with other jobs running on the cluster.

Repair

Increasing the resources allocated to the cluster, like memory and CPU, to avoid contention.

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 executor failure during job execution.

Support