Spark UI for debugging performance in Apache Spark - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the Spark UI used for?

The Spark UI is a web interface that helps you see how your Spark jobs run. It shows details about tasks, stages, and executors to help find performance problems.

beginner

What does the 'Stages' tab in Spark UI show?

The 'Stages' tab shows each step of your Spark job. It displays how long each stage took, how many tasks succeeded or failed, and helps find slow parts.

intermediate

How can the 'Executors' tab help debug performance?

The 'Executors' tab shows details about each worker node running tasks. You can see memory use, CPU time, and if any executors are slow or failing.

intermediate

What information does the 'SQL' tab provide in Spark UI?

The 'SQL' tab shows details about SQL queries run by Spark. It helps find slow queries and shows query plans to optimize performance.

intermediate

Why is it important to check 'Task Time' and 'Shuffle Read/Write' in Spark UI?

Task Time shows how long each task takes. Shuffle Read/Write shows data moved between nodes. High shuffle or long tasks can slow jobs, so checking these helps improve speed.

Which Spark UI tab shows the progress of each job step?

A Environment

B Executors

C SQL

D Stages

The 'Stages' tab shows each step or stage of a Spark job and its progress.

What does high shuffle read/write in Spark UI usually indicate?

A Lots of data moving between nodes

B Low memory usage

C Fast task completion

D No data movement

High shuffle read/write means a lot of data is moving between nodes, which can slow down the job.

Where can you find memory and CPU usage of worker nodes in Spark UI?

A SQL tab

B Stages tab

C Executors tab

D Jobs tab

The 'Executors' tab shows memory and CPU usage for each worker node.

What is a common sign of a slow Spark job in the UI?

A No tasks shown

B Long task times and many failed tasks

C All tasks finish instantly

D No shuffle data

Long task times and many failed tasks usually mean the job is slow or has problems.

Which tab helps optimize SQL queries in Spark?

A SQL

B Executors

C Storage

D Environment

The 'SQL' tab shows query plans and performance details to help optimize SQL queries.

Explain how you would use the Spark UI to find why a job is running slowly.

Think about what parts of the UI show task details and resource use.

Describe the role of the Executors tab in debugging Spark performance.

Focus on what information about workers the tab provides.

Practice

1. What is the primary purpose of the Spark UI when running Spark jobs?

easy

A. To monitor and debug the performance of Spark jobs

B. To write Spark code interactively

C. To store Spark job results permanently

D. To schedule Spark jobs on a cluster

Solution

1. Step 1: Understand Spark UI role

   The Spark UI is designed to help users see how their Spark jobs are running and where delays or resource issues occur.

2. Step 2: Differentiate from other tools

   It is not for writing code, storing results, or scheduling jobs but for monitoring and debugging performance.

3. Final Answer:

   To monitor and debug the performance of Spark jobs -> Option A

4. Quick Check:

   Spark UI = Performance monitoring [OK]

Hint: Spark UI shows job speed and resource use [OK]

Common Mistakes:

• Thinking Spark UI is for coding
• Confusing Spark UI with data storage
• Assuming Spark UI schedules jobs

2. Which URL do you typically use to access the Spark UI on the driver node?

easy

A. http://localhost:8080

B. http://localhost:5000

C. http://localhost:4040

D. http://localhost:7077

Solution

1. Step 1: Recall default Spark UI port

   The Spark UI runs on port 4040 by default on the driver node.

2. Step 2: Verify other ports

   Ports 8080, 5000, and 7077 are used for other services, not Spark UI.

3. Final Answer:

   http://localhost:4040 -> Option C

4. Quick Check:

   Default Spark UI port = 4040 [OK]

Hint: Spark UI default port is 4040 [OK]

Common Mistakes:

• Using common web ports like 8080 or 5000
• Confusing Spark master port 7077 with UI port
• Not including the correct port in URL

3. Given the Spark UI's 'Stages' tab shows a stage with a high 'Task Time' and many 'Shuffle Read' bytes, what does this indicate?

medium

A. The stage is CPU-bound with little data movement

B. The stage is slow due to heavy data shuffling between nodes

C. The stage has no tasks running

D. The stage is waiting for user input

Solution

1. Step 1: Interpret 'Task Time' and 'Shuffle Read'

   High 'Task Time' means tasks take long to complete; many 'Shuffle Read' bytes mean lots of data moves between nodes.

2. Step 2: Understand impact on performance

   Heavy shuffling often slows stages because data transfer over network is costly.

3. Final Answer:

   The stage is slow due to heavy data shuffling between nodes -> Option B

4. Quick Check:

   High shuffle + task time = slow stage due to data movement [OK]

Hint: High shuffle read means slow data transfer stage [OK]

Common Mistakes:

• Assuming high task time means CPU only
• Ignoring shuffle data impact
• Thinking no tasks are running

4. You notice in the Spark UI 'Executors' tab that one executor has very high CPU usage but low task completion rate. What is the likely issue?

medium

A. Executor is overloaded or stuck on a task

B. Executor is idle and not processing tasks

C. Executor has finished all tasks successfully

D. Executor is waiting for input data

Solution

1. Step 1: Analyze CPU usage vs task completion

   High CPU usage with low task completion suggests the executor is busy but not finishing tasks, possibly stuck or overloaded.

2. Step 2: Rule out other options

   Idle executors have low CPU; finished executors don't show high CPU; waiting executors usually show low CPU.

3. Final Answer:

   Executor is overloaded or stuck on a task -> Option A

4. Quick Check:

   High CPU + low completion = stuck executor [OK]

Hint: High CPU but few tasks done means stuck executor [OK]

Common Mistakes:

• Confusing idle with high CPU
• Assuming high CPU means finished tasks
• Ignoring stuck task possibility

5. You want to improve a Spark job's performance by reducing shuffle overhead. Which Spark UI tab and metric should you focus on to identify the main shuffle bottleneck?

hard

A. SQL tab; review query text

B. Jobs tab; check job submission time

C. Executors tab; monitor memory usage only

D. Stages tab; look for high Shuffle Read and Shuffle Write bytes

Solution

1. Step 1: Identify shuffle bottleneck metrics

   The 'Stages' tab shows detailed metrics including Shuffle Read and Shuffle Write bytes, which indicate shuffle data volume.

2. Step 2: Understand why other tabs are less relevant

   Jobs tab shows overall job info but not shuffle details; Executors tab shows resource use but not shuffle specifics; SQL tab shows query but not shuffle metrics.

3. Final Answer:

   Stages tab; look for high Shuffle Read and Shuffle Write bytes -> Option D

4. Quick Check:

   Shuffle bottleneck = Stages tab shuffle bytes [OK]

Hint: Check Stages tab for shuffle bytes to find bottlenecks [OK]

Common Mistakes:

• Looking only at job submission time
• Ignoring shuffle metrics in Stages tab
• Focusing on memory without shuffle data