Log management and troubleshooting in Hadoop - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to display the last 10 lines of the Hadoop log file.

Hadoop

tail -n [1] /var/log/hadoop/hadoop.log

Drag options to blanks, or click blank then click option'

A5

B10

C20

D50

Attempts:

3 left

💡 Hint

Common Mistakes

Using too few lines like 5 might miss important info.

Using too many lines like 50 can be overwhelming.

The tail -n 10 command shows the last 10 lines of the log file, which is useful for quick troubleshooting.

2fill in blank

medium

Complete the command to search for error messages in the Hadoop log file.

Hadoop

grep '[1]' /var/log/hadoop/hadoop.log

Drag options to blanks, or click blank then click option'

Awarning

Bdebug

Cinfo

Derror

Attempts:

3 left

💡 Hint

Common Mistakes

Searching for info or debug shows too much detail.

Using warning might miss actual errors.

The keyword error helps find error messages in the log for troubleshooting.

3fill in blank

hard

Fix the error in the command to continuously monitor the Hadoop log file.

Hadoop

tail [1] /var/log/hadoop/hadoop.log

Drag options to blanks, or click blank then click option'

A-c

B-n

C-f

D-r

Attempts:

3 left

💡 Hint

Common Mistakes

Using -n only shows a fixed number of lines and exits.

Using -r reverses the output order, not follow.

The -f option makes tail follow the file, showing new lines as they are added.

4fill in blank

hard

Fill both blanks to create a dictionary of log levels and their counts from a log file.

Hadoop

import collections
log_counts = [1](line.split()[2] for line in open('/var/log/hadoop/hadoop.log'))
print(log_counts.[2]())

Drag options to blanks, or click blank then click option'

Acollections.Counter

Bmost_common

Citems

Ddefaultdict

Attempts:

3 left

💡 Hint

Common Mistakes

Using defaultdict alone does not count automatically.

Using items() returns all items but not sorted by frequency.

collections.Counter counts occurrences of log levels. most_common() shows counts sorted by frequency.

5fill in blank

hard

Fill all three blanks to filter and print unique IP addresses from Hadoop access logs.

Hadoop

with open('/var/log/hadoop/access.log') as f:
    ips = set()
    for line in f:
        if '[1]' in line:
            ip = line.split()[[2]]
            ips.[3](ip)
print(ips)

Drag options to blanks, or click blank then click option'

AGET

B0

Cadd

DPOST

Attempts:

3 left

💡 Hint

Common Mistakes

Using POST instead of GET filters different requests.

Using append instead of add on a set causes errors.

This code filters lines with GET requests, extracts the IP address at index 0, and adds it to a set to keep unique IPs.

Practice

1. What is the main purpose of Hadoop logs in managing a cluster?

easy

A. To speed up data processing

B. To show what happens inside Hadoop and help find problems

C. To store user data permanently

D. To backup Hadoop configuration files

Solution

1. Step 1: Understand the role of logs

   Logs record events and actions inside Hadoop components.

2. Step 2: Identify the purpose of logs in troubleshooting

   Logs help find and fix problems by showing errors and system behavior.

3. Final Answer:

   To show what happens inside Hadoop and help find problems -> Option B

4. Quick Check:

   Logs = Problem detection [OK]

Hint: Logs help spot problems by showing system events [OK]

Common Mistakes:

• Confusing logs with data storage
• Thinking logs speed up processing
• Assuming logs backup configs

2. Which command is used to view logs of a YARN application in Hadoop?

easy

A. yarn logs -applicationId <app_id>

B. hdfs dfs -ls

C. hadoop fs -put

D. yarn application -status

Solution

1. Step 1: Identify commands related to logs

   Only yarn logs fetches logs for YARN apps.

2. Step 2: Match command with correct syntax

   yarn logs -applicationId <app_id> shows logs for the given app.

3. Final Answer:

   yarn logs -applicationId <app_id> -> Option A

4. Quick Check:

   YARN logs command = yarn logs -applicationId [OK]

Hint: Use 'yarn logs -applicationId' to get app logs fast [OK]

Common Mistakes:

• Using 'hdfs dfs -ls' which lists files, not logs
• Confusing 'hadoop fs -put' with log commands
• Using 'yarn application -status' which shows status, not logs

3. Given the command hdfs dfs -cat /logs/app1.log | grep ERROR, what is the expected output?

medium

A. A list of files in /logs directory

B. All lines from app1.log

C. Only lines without the word 'ERROR'

D. Only lines containing the word 'ERROR' from app1.log

Solution

1. Step 1: Understand the command parts

   hdfs dfs -cat /logs/app1.log outputs the file content; grep ERROR filters lines with 'ERROR'.

2. Step 2: Combine command effects

   The pipe sends file content to grep, which shows only lines containing 'ERROR'.

3. Final Answer:

   Only lines containing the word 'ERROR' from app1.log -> Option D

4. Quick Check:

   grep ERROR filters lines with ERROR [OK]

Hint: Pipe file content to grep ERROR to find error lines [OK]

Common Mistakes:

• Thinking grep removes ERROR lines
• Expecting full file output
• Confusing grep with listing files

4. You run yarn logs -applicationId app_12345 but get an error saying 'Application not found'. What is the likely cause?

medium

A. The logs directory is empty

B. The command syntax is wrong

C. The application ID is incorrect or does not exist

D. YARN is not installed on the cluster

Solution

1. Step 1: Analyze the error message

   'Application not found' means the given app ID is not recognized by YARN.

2. Step 2: Check command correctness

   The command syntax is correct; error is about app ID validity.

3. Final Answer:

   The application ID is incorrect or does not exist -> Option C

4. Quick Check:

   Invalid app ID = Application not found error [OK]

Hint: Check app ID spelling if 'Application not found' error appears [OK]

Common Mistakes:

• Assuming command syntax is wrong
• Thinking YARN is missing
• Believing logs directory emptiness causes this error

5. You want to quickly find all error messages in Hadoop logs stored in HDFS at /var/log/hadoop. Which command sequence will help you achieve this efficiently?

hard

A. hdfs dfs -cat /var/log/hadoop/* | grep ERROR

B. yarn logs -applicationId * | grep ERROR

C. hdfs dfs -ls /var/log/hadoop | grep ERROR

D. hadoop fs -copyToLocal /var/log/hadoop /tmp && grep ERROR /tmp/*

Solution

1. Step 1: Identify command to read all logs in HDFS directory

   hdfs dfs -cat /var/log/hadoop/* outputs all files in that directory.

2. Step 2: Filter output for error lines

   Piping to grep ERROR shows only lines with 'ERROR'.

3. Final Answer:

   hdfs dfs -cat /var/log/hadoop/* | grep ERROR -> Option A

4. Quick Check:

   Cat all logs + grep ERROR = quick error search [OK]

Hint: Cat all logs then grep ERROR to find errors fast [OK]

Common Mistakes:

• Using yarn logs without specifying app ID
• Using ls which lists files, not content
• Copying logs locally is slower and unnecessary