Spark UI for debugging performance in Apache Spark - Step-by-Step Execution

Concept Flow - Spark UI for debugging performance

Start Spark Job

↓

Spark UI Opens

↓

View Jobs Tab

↓

Select Job to Inspect

↓

Analyze Stages and Tasks

↓

Check Task Duration and Shuffle

↓

Identify Bottlenecks

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Optimize Code or Resources

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Rerun Job and Verify Improvements

The Spark UI shows job progress and details step-by-step to help find slow parts and improve performance.

Execution Sample

Apache Spark

spark.read.csv('data.csv')
  .filter('age > 30')
  .groupBy('country')
  .count()
  .show()

This code reads data, filters rows, groups by country, counts, and shows results.

Execution Table

Step	UI Section	Action	Details	Result
1	Jobs Tab	Open Spark UI and select job	Job 1 running with 3 stages	Job overview displayed
2	Stages Tab	Select Stage 1	Tasks: 100, Duration: 5s	Stage details shown
3	Tasks Tab	View tasks of Stage 1	Some tasks take 10s, others 1s	Uneven task duration noticed
4	SQL Tab	Check query plan	Shuffle read/write high	Shuffle identified as bottleneck
5	Storage Tab	Check cached data	No cached RDDs	Opportunity to cache data
6	Executors Tab	Check executor metrics	One executor overloaded	Resource imbalance found
7	Optimize	Add caching and repartition	Reduce shuffle and balance load	Expected performance gain
8	Rerun Job	Run job again	Stages complete faster	Performance improved

💡 Job completes with improved performance after optimization

Variable Tracker

Variable	Start	After Step 3	After Step 6	Final
Task Duration (s)	N/A	Range 1-10	Range 1-10	Reduced after optimization
Shuffle Size (MB)	High	High	High	Reduced after caching
Executor Load	Balanced	Imbalanced	Imbalanced	Balanced after repartition

Key Moments - 3 Insights

Why do some tasks take much longer than others in the Tasks Tab?

Because of data skew or uneven partitioning, some tasks process more data causing longer duration, as seen in Step 3 of the execution table.

What does high shuffle read/write indicate in the SQL Tab?

High shuffle means a lot of data is moved between nodes, which slows down the job. This is shown in Step 4 where shuffle is identified as a bottleneck.

How does caching data improve performance?

Caching stores data in memory to avoid recomputing or shuffling it repeatedly, reducing task time as suggested in Step 5 and confirmed after rerunning the job in Step 8.

Visual Quiz - 3 Questions

Look at the execution table, at which step do we identify the shuffle as a bottleneck?

A Step 4

B Step 6

C Step 2

D Step 8

💡 Hint

Check the 'SQL Tab' row in the execution table where shuffle read/write is mentioned.

According to the variable tracker, what happens to executor load after optimization?

A It becomes more imbalanced

B It becomes balanced

C It stays the same

D It is not tracked

💡 Hint

Look at the 'Executor Load' row in the variable tracker after the final step.

If caching was not added, which step in the execution table would likely show no change?

A Step 5

B Step 7

C Step 8

D Step 3

💡 Hint

Step 8 shows rerun results; without caching, performance would not improve here.

Concept Snapshot

Spark UI helps debug performance by showing jobs, stages, and tasks.
Check task durations and shuffle data to find slow parts.
Use Storage tab to see cached data.
Executors tab shows resource use.
Optimize by caching and repartitioning.
Rerun and verify improvements.

Full Transcript

The Spark UI is a tool to watch how your Spark job runs. You start your job and open the Spark UI. In the Jobs tab, you see all jobs and their stages. Selecting a job shows stages and tasks. Tasks with long durations may mean data skew or heavy work. The SQL tab shows query plans and shuffle data, which can slow jobs if large. The Storage tab shows cached data; caching can speed up repeated work. The Executors tab shows how resources are used; imbalance can cause slow tasks. By analyzing these, you find bottlenecks. Then you optimize your code or cluster setup, like adding caching or repartitioning data. Finally, rerun the job and check the UI again to see if performance improved.

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