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

Concept Flow - Spark UI for debugging performance

Start Spark Job

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Spark UI Opens

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View Jobs Tab

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Select Job to Inspect

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Analyze Stages and Tasks

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Check Task Duration and Shuffle

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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.