Cluster sizing and auto-scaling in Apache Spark - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is cluster sizing in Apache Spark?

Cluster sizing means choosing the right number and type of machines (nodes) to run your Spark jobs efficiently without wasting resources.

beginner

Why is auto-scaling useful in Spark clusters?

Auto-scaling automatically adds or removes machines based on workload, so your Spark cluster uses just enough resources to handle the job, saving cost and improving performance.

intermediate

What factors affect cluster sizing decisions?

Factors include the size of data, complexity of tasks, memory needs, CPU power, and how fast you want results.

intermediate

How does Spark's dynamic allocation feature help with auto-scaling?

Dynamic allocation lets Spark add or remove executors automatically during a job, adjusting resources to the current workload without manual changes.

beginner

What is a risk of under-sizing a Spark cluster?

Under-sizing can cause slow job execution, failures, or out-of-memory errors because there are not enough resources to handle the workload.

What does cluster sizing primarily determine in Spark?

A The programming language used

B The version of Spark installed

C Number and type of machines to run jobs

D The network speed between nodes

Cluster sizing is about choosing the right number and type of machines to run Spark jobs efficiently.

Which Spark feature helps automatically adjust executors during a job?

A Dynamic allocation

B Static partitioning

C RDD caching

D Broadcast variables

Dynamic allocation lets Spark add or remove executors automatically based on workload.

What is a common benefit of auto-scaling Spark clusters?

A Saving costs by using resources only when needed

B Increasing code complexity

C Reducing data size

D Changing Spark version automatically

Auto-scaling saves costs by adjusting resources to match workload demand.

Which factor is NOT directly related to cluster sizing?

A Data size

B User interface design

C Task complexity

D Memory needs

User interface design does not affect cluster sizing decisions.

What happens if a Spark cluster is under-sized?

A Data size decreases

B Jobs run faster than expected

C More machines are automatically added

D Jobs may run slowly or fail

Under-sizing means not enough resources, causing slow or failed jobs.

Explain how cluster sizing and auto-scaling work together to optimize Spark job performance and cost.

Think about how choosing the right machines and adjusting them automatically helps.

Describe the risks of poor cluster sizing and how dynamic allocation can help mitigate these risks.

Consider what happens when resources are too few and how Spark can fix that during runtime.