---
id: 47ecb069-8c0c-4eaf-b4cf-bd28daf3bf78
---

# Spark HDFS Connectivity Issue Incident
---

The Spark HDFS Connectivity Issue Incident refers to a situation where there is a problem with the connectivity between Apache Spark and Hadoop Distributed File System (HDFS). This can result in Spark being unable to access data stored in HDFS or facing performance issues due to slow connectivity. This type of incident can be caused by various factors such as network issues, misconfiguration, or software bugs. It can impact the overall functioning of Spark-based applications and require immediate attention to ensure smooth operations.

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

export NAMENODE="PLACEHOLDER"

export PATH_TO_HADOOP_CONFIGURATION_DIRECTORY="PLACEHOLDER"

export PATH_TO_SPARK_CONFIGURATION_DIRECTORY="PLACEHOLDER"

export HDFS_SERVICE_NAME="PLACEHOLDER"

export SPARK_SERVICE_NAME="PLACEHOLDER"
```

## Debug

### Check if Spark is running
```shell
systemctl status spark | grep 'Active: active (running)' || echo 'Spark is not running'
```

### Check if HDFS is running
```shell
systemctl status hdfs | grep 'Active: active (running)' || echo 'HDFS is not running'
```

### Check if Spark can connect to HDFS via Hadoop command line
```shell
spark-submit --class org.apache.spark.examples.SparkPi --master yarn --deploy-mode client --driver-memory 1g --executor-memory 1g --executor-cores 1 --num-executors 1 /path/to/examples.jar yarn-client hdfs://${NAMENODE}:${PORT}/ || echo 'Spark cannot connect to HDFS'
```

### Check if HDFS can be accessed via Hadoop command line
```shell
hdfs dfs -ls hdfs://${NAMENODE}:${PORT}/ || echo 'Cannot access HDFS via Hadoop command line'
```

### Incorrect configuration of Spark or HDFS settings
```shell
bash

#!/bin/bash



# Set the paths to the Spark and Hadoop configuration files

SPARK_CONF_DIR=${PATH_TO_SPARK_CONFIGURATION_DIRECTORY}

HADOOP_CONF_DIR=${PATH_TO_HADOOP_CONFIGURATION_DIRECTORY}



# Check if the configuration files exist

if [ ! -f "${SPARK_CONF_DIR}/spark-defaults.conf" ] || [ ! -f "${HADOOP_CONF_DIR}/core-site.xml" ] || [ ! -f "${HADOOP_CONF_DIR}/hdfs-site.xml" ]; then

  echo "ERROR: Spark or Hadoop configuration files not found"

  exit 1

fi



# Check if the configuration files have the correct settings

if ! grep -q "spark.master" "${SPARK_CONF_DIR}/spark-defaults.conf"; then

  echo "ERROR: spark.master configuration not found in Spark configuration file"

  exit 1

fi



if ! grep -q "fs.defaultFS" "${HADOOP_CONF_DIR}/core-site.xml"; then

  echo "ERROR: fs.defaultFS configuration not found in Hadoop core-site.xml file"

  exit 1

fi



if ! grep -q "dfs.replication" "${HADOOP_CONF_DIR}/hdfs-site.xml"; then

  echo "ERROR: dfs.replication configuration not found in Hadoop hdfs-site.xml file"

  exit 1

fi



# If all checks pass, print a success message

echo "Spark and Hadoop configurations are correct"

exit 0


```

## Repair

### Try restarting the Spark and HDFS services to see if that resolves the connectivity issue.
```shell


#!/bin/bash



# Restart Spark and HDFS services

service ${SPARK_SERVICE_NAME} restart

service ${HDFS_SERVICE_NAME} restart



# Check if services have started successfully

if (( $(ps -ef | grep -v grep | grep ${SPARK_SERVICE_NAME} | wc -l) > 0 )) && (( $(ps -ef | grep -v grep | grep ${HDFS_SERVICE_NAME} | wc -l) > 0 )); then

    echo "Spark and HDFS services restarted successfully."

else

    echo "Failed to restart Spark and HDFS services."

fi


```

The Spark HDFS Connectivity Issue Incident refers to a situation where there is a problem with the connectivity between Apache Spark and Hadoop Distributed File System (HDFS). This can result in Spark being unable to access data stored in HDFS or facing performance issues due to slow connectivity. This type of incident can be caused by various factors such as network issues, misconfiguration, or software bugs. It can impact the overall functioning of Spark-based applications and require immediate attention to ensure smooth operations.


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 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.

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 PORT="PLACEHOLDER"

export NAMENODE="PLACEHOLDER"

export PATH_TO_HADOOP_CONFIGURATION_DIRECTORY="PLACEHOLDER"

export PATH_TO_SPARK_CONFIGURATION_DIRECTORY="PLACEHOLDER"

export HDFS_SERVICE_NAME="PLACEHOLDER"

export SPARK_SERVICE_NAME="PLACEHOLDER"
```


### Check if Spark is running

```shell
systemctl status spark | grep 'Active: active (running)' || echo 'Spark is not running'
```

### Check if HDFS is running

```shell
systemctl status hdfs | grep 'Active: active (running)' || echo 'HDFS is not running'
```

### Check if Spark can connect to HDFS via Hadoop command line

```shell
spark-submit --class org.apache.spark.examples.SparkPi --master yarn --deploy-mode client --driver-memory 1g --executor-memory 1g --executor-cores 1 --num-executors 1 /path/to/examples.jar yarn-client hdfs://${NAMENODE}:${PORT}/ || echo 'Spark cannot connect to HDFS'
```

### Check if HDFS can be accessed via Hadoop command line

```shell
hdfs dfs -ls hdfs://${NAMENODE}:${PORT}/ || echo 'Cannot access HDFS via Hadoop command line'
```

### Incorrect configuration of Spark or HDFS settings

```shell
bash

#!/bin/bash



# Set the paths to the Spark and Hadoop configuration files

SPARK_CONF_DIR=${PATH_TO_SPARK_CONFIGURATION_DIRECTORY}

HADOOP_CONF_DIR=${PATH_TO_HADOOP_CONFIGURATION_DIRECTORY}



# Check if the configuration files exist

if [ ! -f "${SPARK_CONF_DIR}/spark-defaults.conf" ] || [ ! -f "${HADOOP_CONF_DIR}/core-site.xml" ] || [ ! -f "${HADOOP_CONF_DIR}/hdfs-site.xml" ]; then

  echo "ERROR: Spark or Hadoop configuration files not found"

  exit 1

fi



# Check if the configuration files have the correct settings

if ! grep -q "spark.master" "${SPARK_CONF_DIR}/spark-defaults.conf"; then

  echo "ERROR: spark.master configuration not found in Spark configuration file"

  exit 1

fi



if ! grep -q "fs.defaultFS" "${HADOOP_CONF_DIR}/core-site.xml"; then

  echo "ERROR: fs.defaultFS configuration not found in Hadoop core-site.xml file"

  exit 1

fi



if ! grep -q "dfs.replication" "${HADOOP_CONF_DIR}/hdfs-site.xml"; then

  echo "ERROR: dfs.replication configuration not found in Hadoop hdfs-site.xml file"

  exit 1

fi



# If all checks pass, print a success message

echo "Spark and Hadoop configurations are correct"

exit 0


```


### Try restarting the Spark and HDFS services to see if that resolves the connectivity issue.

```shell


#!/bin/bash



# Restart Spark and HDFS services

service ${SPARK_SERVICE_NAME} restart

service ${HDFS_SERVICE_NAME} restart



# Check if services have started successfully

if (( $(ps -ef | grep -v grep | grep ${SPARK_SERVICE_NAME} | wc -l) > 0 )) && (( $(ps -ef | grep -v grep | grep ${HDFS_SERVICE_NAME} | wc -l) > 0 )); then

    echo "Spark and HDFS services restarted successfully."

else

    echo "Failed to restart Spark and HDFS services."

fi


```


Spark HDFS Connectivity Issue Incident

Overview

Parameters

Debug

Check if Spark is running

Check if HDFS is running

Check if Spark can connect to HDFS via Hadoop command line

Check if HDFS can be accessed via Hadoop command line

Incorrect configuration of Spark or HDFS settings

Repair

Try restarting the Spark and HDFS services to see if that resolves the connectivity issue.

Learn more

Related Runbooks

YARN ResourceManager Failure Impacting Spark Jobs.

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

Spark executor failure during job execution.

Spark driver program crash during job runtime.

Support