---
id: 956f46e3-7f56-442d-938b-43f0c1281ad9
---

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

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.

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

export INTERVAL="PLACEHOLDER"

export APPLICATION_ID="PLACEHOLDER"

export NEW_MEMORY_FRACTION_VALUE="PLACEHOLDER"

export CONFIGURATION_PARAMETER_TO_UPDATE="PLACEHOLDER"

export PATH_TO_SPARK_CONF="PLACEHOLDER"
```

## Debug

### Check the disk I/O usage
```shell
iostat -x ${INTERVAL} ${COUNT}
```

### Check the network bandwidth usage
```shell
sar -n DEV ${INTERVAL} ${COUNT}
```

### Check the Spark task metrics
```shell
yarn logs -applicationId ${APPLICATION_ID} | grep "TaskMetrics" | grep "ExecutorRunTime"
```

### Check the shuffle size and spill metrics
```shell
yarn logs -applicationId ${APPLICATION_ID} | grep "ShuffleMetrics" | grep "spilled"
```

### Check the system resource usage
```shell
top
```

### Insufficient memory allocated for shuffle operations.
```shell


#!/bin/bash



# set the Spark configuration file path

SPARK_CONF=${PATH_TO_SPARK_CONF}



# get the current value of spark.memory.fraction

MEMORY_FRACTION=$(grep "spark.memory.fraction" $SPARK_CONF | awk '{print $3}')



# calculate the current value of spark.shuffle.memoryFraction

SHUFFLE_MEMORY_FRACTION=$(echo "scale=2; $MEMORY_FRACTION * 0.2" | bc)



# get the current value of spark.shuffle.memoryFraction

CURRENT_SHUFFLE_MEMORY_FRACTION=$(grep "spark.shuffle.memoryFraction" $SPARK_CONF | awk '{print $3}')



# compare the current value of spark.shuffle.memoryFraction with the calculated value

if (( $(echo "$CURRENT_SHUFFLE_MEMORY_FRACTION < $SHUFFLE_MEMORY_FRACTION" | bc -l) )); then

    # if the current value is less than the calculated value, increase the memory fraction for shuffle operations

    sed -i "s/spark.shuffle.memoryFraction=.*/spark.shuffle.memoryFraction=$SHUFFLE_MEMORY_FRACTION/g" $SPARK_CONF

    echo "Increased memory fraction for shuffle operations to $SHUFFLE_MEMORY_FRACTION"

else

    echo "Memory fraction for shuffle operations is already sufficient"

fi


```

## Repair

### Increase the memory allocation for shuffle operations to avoid spills. This can be done by increasing the `spark.shuffle.memoryFraction` or `spark.memory.fraction` configuration parameters.
```shell
bash

#!/bin/bash



# Set the path to the Spark configuration file

SPARK_CONF=${PATH_TO_SPARK_CONF}



# Set the new memory fraction value

NEW_MEM_FRACTION=${NEW_MEMORY_FRACTION_VALUE}



# Set the configuration parameter to be updated

CONF_PARAM=${CONFIGURATION_PARAMETER_TO_UPDATE}



# Update the configuration parameter in the Spark configuration file

sed -i "s/$CONF_PARAM=.*/$CONF_PARAM=$NEW_MEM_FRACTION/g" $SPARK_CONF



# Restart the Spark cluster to apply the configuration changes

sudo systemctl restart spark


```

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.


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 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.

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.

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.


Spark driver failure incident.

```shell
export COUNT="PLACEHOLDER"

export INTERVAL="PLACEHOLDER"

export APPLICATION_ID="PLACEHOLDER"

export NEW_MEMORY_FRACTION_VALUE="PLACEHOLDER"

export CONFIGURATION_PARAMETER_TO_UPDATE="PLACEHOLDER"

export PATH_TO_SPARK_CONF="PLACEHOLDER"
```


### Check the disk I/O usage

```shell
iostat -x ${INTERVAL} ${COUNT}
```

### Check the network bandwidth usage

```shell
sar -n DEV ${INTERVAL} ${COUNT}
```

### Check the Spark task metrics

```shell
yarn logs -applicationId ${APPLICATION_ID} | grep "TaskMetrics" | grep "ExecutorRunTime"
```

### Check the shuffle size and spill metrics

```shell
yarn logs -applicationId ${APPLICATION_ID} | grep "ShuffleMetrics" | grep "spilled"
```

### Check the system resource usage

```shell
top
```

### Insufficient memory allocated for shuffle operations.

```shell


#!/bin/bash



# set the Spark configuration file path

SPARK_CONF=${PATH_TO_SPARK_CONF}



# get the current value of spark.memory.fraction

MEMORY_FRACTION=$(grep "spark.memory.fraction" $SPARK_CONF | awk '{print $3}')



# calculate the current value of spark.shuffle.memoryFraction

SHUFFLE_MEMORY_FRACTION=$(echo "scale=2; $MEMORY_FRACTION * 0.2" | bc)



# get the current value of spark.shuffle.memoryFraction

CURRENT_SHUFFLE_MEMORY_FRACTION=$(grep "spark.shuffle.memoryFraction" $SPARK_CONF | awk '{print $3}')



# compare the current value of spark.shuffle.memoryFraction with the calculated value

if (( $(echo "$CURRENT_SHUFFLE_MEMORY_FRACTION < $SHUFFLE_MEMORY_FRACTION" | bc -l) )); then

    # if the current value is less than the calculated value, increase the memory fraction for shuffle operations

    sed -i "s/spark.shuffle.memoryFraction=.*/spark.shuffle.memoryFraction=$SHUFFLE_MEMORY_FRACTION/g" $SPARK_CONF

    echo "Increased memory fraction for shuffle operations to $SHUFFLE_MEMORY_FRACTION"

else

    echo "Memory fraction for shuffle operations is already sufficient"

fi


```


### Increase the memory allocation for shuffle operations to avoid spills. This can be done by increasing the `spark.shuffle.memoryFraction` or `spark.memory.fraction` configuration parameters.

```shell
bash

#!/bin/bash



# Set the path to the Spark configuration file

SPARK_CONF=${PATH_TO_SPARK_CONF}



# Set the new memory fraction value

NEW_MEM_FRACTION=${NEW_MEMORY_FRACTION_VALUE}



# Set the configuration parameter to be updated

CONF_PARAM=${CONFIGURATION_PARAMETER_TO_UPDATE}



# Update the configuration parameter in the Spark configuration file

sed -i "s/$CONF_PARAM=.*/$CONF_PARAM=$NEW_MEM_FRACTION/g" $SPARK_CONF



# Restart the Spark cluster to apply the configuration changes

sudo systemctl restart spark


```


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

Overview

Parameters

Debug

Check the disk I/O usage

Check the network bandwidth usage

Check the Spark task metrics

Check the shuffle size and spill metrics

Check the system resource usage

Insufficient memory allocated for shuffle operations.

Repair

Increase the memory allocation for shuffle operations to avoid spills. This can be done by increasing the `spark.shuffle.memoryFraction` or `spark.memory.fraction` configuration parameters.

Learn more

Related Runbooks

Spark tasks failing due to out of memory errors.

Spark job failures due to cluster resource contentions.

Spark executor failure during job execution.

Spark driver program crash during job runtime.

Support

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

Overview

Parameters

Debug

Check the disk I/O usage

Check the network bandwidth usage

Check the Spark task metrics

Check the shuffle size and spill metrics

Check the system resource usage

Insufficient memory allocated for shuffle operations.

Repair

Increase the memory allocation for shuffle operations to avoid spills. This can be done by increasing the spark.shuffle.memoryFraction or spark.memory.fraction configuration parameters.

Learn more

Related Runbooks

Spark tasks failing due to out of memory errors.

Spark job failures due to cluster resource contentions.

Spark executor failure during job execution.

Spark driver program crash during job runtime.

Increase the memory allocation for shuffle operations to avoid spills. This can be done by increasing the `spark.shuffle.memoryFraction` or `spark.memory.fraction` configuration parameters.