---
id: c29d5853-c4d0-400b-933e-a9c1c65a00bc
---

# High Latency Incident for Spark Job Execution.
---

This incident type indicates that there is a high latency issue in the execution of a Spark job. Spark is a distributed computing framework that is used for processing large datasets. High latency in this context means that the time taken to execute the Spark job is significantly longer than expected or normal. This can result in delays in processing data and can impact the performance of the application or system that is utilizing Spark.

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

export PATH_TO_SPARK_LOGS="PLACEHOLDER"

export PATH_TO_SPARK_CONF="PLACEHOLDER"

export JOB_FILE="PLACEHOLDER"

export PATH_TO_LOG_FILE="PLACEHOLDER"

export LATENCY_THRESHOLD="PLACEHOLDER"
```

## Debug

### Check system resource utilization
```shell
top
```

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

### Check network latency
```shell
traceroute ${HOSTNAME}
```

### Check Spark logs for errors
```shell
tail -f ${PATH_TO_SPARK_LOGS}
```

### Check network connectivity
```shell
ping ${HOSTNAME}
```

### Check disk usage
```shell
df -h
```

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

### Check CPU usage
```shell
mpstat
```

### Inefficient Code: Inefficient code can cause high latency during Spark job execution. This can happen when a developer writes code that doesn't optimize the use of Spark resources. For example, if a developer writes code that doesn't take advantage of Spark's in-memory processing capabilities, it can cause high latency during Spark job execution.
```shell


#!/bin/bash



# Set the path to the Spark application log file

LOG_FILE=${PATH_TO_LOG_FILE}



# Set the threshold for high latency

LATENCY_THRESHOLD=${LATENCY_THRESHOLD}



# Find the lines in the log file that indicate slow tasks

slow_tasks=$(grep -E "^(.*INFO.*TaskSchedulerImpl.*:.*Task.*failed.*to.*launch.*.*ms.*)(.*)$" $LOG_FILE)



# If there are no slow tasks, exit with success status

if [ -z "$slow_tasks" ]; then

    echo "No slow tasks found"

    exit 0

fi



# Loop through the slow tasks and check their latency

while read -r line; do

    # Extract the latency from the log line

    latency=$(echo $line | grep -oE "([0-9]+)ms" | tr -d 'ms')



    # If the latency is above the threshold, print a warning

    if [ "$latency" -ge "$LATENCY_THRESHOLD" ]; then

        echo "Warning: Slow task detected with latency of $latency ms"

    fi

done <<< "$slow_tasks"


```

## Repair

### Optimize the Spark job code and ensure that it is running efficiently without any unnecessary operations that could slow down the execution.
```shell


#!/bin/bash



# Replace ${JOB_FILE} with the path to the Spark job code file

job_file=${JOB_FILE}



# Create a backup of the original job file

cp $job_file $job_file.bak



# Optimize the job code by removing any unnecessary operations

# This example removes all lines that contain the word "slow"

sed '/slow/d' $job_file > $job_file.temp

mv $job_file.temp $job_file



# Print a message indicating that the job code has been optimized

echo "Spark job code has been optimized."


```

This incident type indicates that there is a high latency issue in the execution of a Spark job. Spark is a distributed computing framework that is used for processing large datasets. High latency in this context means that the time taken to execute the Spark job is significantly longer than expected or normal. This can result in delays in processing data and can impact the performance of the application or system that is utilizing Spark.


This incident type refers to a situation where the Tomcat server's JDBC connection pool usage surpasses its configured limits. This can lead to a shortage of available connections for incoming requests, resulting in slow response times or even server crashes. It is important to monitor and manage the JDBC connection pool usage to prevent such incidents from occurring.


Tomcat High JDBC Connection Pool Usage Incident.

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 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 HOSTNAME="PLACEHOLDER"

export PATH_TO_SPARK_LOGS="PLACEHOLDER"

export PATH_TO_SPARK_CONF="PLACEHOLDER"

export JOB_FILE="PLACEHOLDER"

export PATH_TO_LOG_FILE="PLACEHOLDER"

export LATENCY_THRESHOLD="PLACEHOLDER"
```


### Check system resource utilization

```shell
top
```

### Check memory usage

```shell
free -m
```

### Check network latency

```shell
traceroute ${HOSTNAME}
```

### Check Spark logs for errors

```shell
tail -f ${PATH_TO_SPARK_LOGS}
```

### Check network connectivity

```shell
ping ${HOSTNAME}
```

### Check disk usage

```shell
df -h
```

### Check Spark configuration settings

```shell
cat ${PATH_TO_SPARK_CONF}
```

### Check CPU usage

```shell
mpstat
```

### Inefficient Code: Inefficient code can cause high latency during Spark job execution. This can happen when a developer writes code that doesn't optimize the use of Spark resources. For example, if a developer writes code that doesn't take advantage of Spark's in-memory processing capabilities, it can cause high latency during Spark job execution.

```shell


#!/bin/bash



# Set the path to the Spark application log file

LOG_FILE=${PATH_TO_LOG_FILE}



# Set the threshold for high latency

LATENCY_THRESHOLD=${LATENCY_THRESHOLD}



# Find the lines in the log file that indicate slow tasks

slow_tasks=$(grep -E "^(.*INFO.*TaskSchedulerImpl.*:.*Task.*failed.*to.*launch.*.*ms.*)(.*)$" $LOG_FILE)



# If there are no slow tasks, exit with success status

if [ -z "$slow_tasks" ]; then

    echo "No slow tasks found"

    exit 0

fi



# Loop through the slow tasks and check their latency

while read -r line; do

    # Extract the latency from the log line

    latency=$(echo $line | grep -oE "([0-9]+)ms" | tr -d 'ms')



    # If the latency is above the threshold, print a warning

    if [ "$latency" -ge "$LATENCY_THRESHOLD" ]; then

        echo "Warning: Slow task detected with latency of $latency ms"

    fi

done <<< "$slow_tasks"


```


### Optimize the Spark job code and ensure that it is running efficiently without any unnecessary operations that could slow down the execution.

```shell


#!/bin/bash



# Replace ${JOB_FILE} with the path to the Spark job code file

job_file=${JOB_FILE}



# Create a backup of the original job file

cp $job_file $job_file.bak



# Optimize the job code by removing any unnecessary operations

# This example removes all lines that contain the word "slow"

sed '/slow/d' $job_file > $job_file.temp

mv $job_file.temp $job_file



# Print a message indicating that the job code has been optimized

echo "Spark job code has been optimized."


```


High Latency Incident for Spark Job Execution.

Overview

Parameters

Debug

Check system resource utilization

Check memory usage

Check network latency

Check Spark logs for errors

Check network connectivity

Check disk usage

Check Spark configuration settings

Check CPU usage

Repair

Optimize the Spark job code and ensure that it is running efficiently without any unnecessary operations that could slow down the execution.

Learn more

Related Runbooks

Tomcat High JDBC Connection Pool Usage Incident.

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.

Support