Spot instances for cost savings in Apache Spark - Step-by-Step Execution

Concept Flow - Spot instances for cost savings

Request Spot Instance

↓

Instance Launched if Available

↓

Run Spark Job

↓

Spot Instance May be Interrupted

↓

Job Interrupted

↓

Handle Interruption: Retry or Save State

↓

Cost Savings Achieved

This flow shows how spot instances are requested, run Spark jobs, may get interrupted, and how handling interruptions leads to cost savings.

Execution Sample

Apache Spark

spark.conf.set("spark.dynamicAllocation.enabled", "true")
spark.conf.set("spark.executor.instances", "2")
spark.conf.set("spark.executor.spot", "true")

rdd = spark.sparkContext.parallelize(range(10))
print(rdd.collect())

This code configures Spark to use spot instances and runs a simple job collecting numbers 0 to 9.

Execution Table

Step	Action	Spot Instance Status	Job Status	Output
1	Request spot instances	Requested	Pending	No output yet
2	Spot instances launched	Active	Starting job	No output yet
3	Run Spark job tasks	Active	Running	Partial results processed
4	Spot instance interruption check	Active	Running	Partial results processed
5	Spot instance interrupted	Interrupted	Job failed	Job interrupted error
6	Handle interruption: retry job	Requested	Retrying	No output yet
7	Spot instances relaunched	Active	Running	Partial results processed
8	Job completes successfully	Active	Completed	[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
9	Calculate cost savings	N/A	N/A	Cost reduced by using spot instances

💡 Job completes successfully or is interrupted and retried until completion, achieving cost savings.

Variable Tracker

Variable	Start	After Step 2	After Step 5	After Step 7	Final
spot_instance_status	None	Active	Interrupted	Active	Active
job_status	Pending	Starting job	Job failed	Running	Completed
output	None	None	Job interrupted error	Partial results	[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Key Moments - 3 Insights

Why does the job fail at step 5 even though the spot instance was active before?

At step 5, the spot instance is interrupted by the cloud provider, causing the job to fail because the instance is no longer available to run tasks, as shown in the execution_table row 5.

How does Spark handle the interruption to still complete the job?

Spark retries the job by requesting new spot instances and rerunning tasks, as seen in steps 6 and 7 of the execution_table, allowing the job to eventually complete.

What is the main benefit of using spot instances despite interruptions?

The main benefit is cost savings, because spot instances are cheaper than regular instances, and handling interruptions with retries still results in lower overall cost, as summarized in step 9.

Visual Quiz - 3 Questions

Look at the execution table, what is the job status at step 4?

A Pending

B Running

C Completed

D Job failed

💡 Hint

Refer to execution_table row 4 under 'Job Status' column.

At which step does the spot instance get interrupted?

A Step 7

B Step 3

C Step 5

D Step 9

💡 Hint

Check execution_table row 5 under 'Spot Instance Status'.

If the job never got interrupted, which step would be skipped?

A Step 6 (Handle interruption: retry job)

B Step 2 (Spot instances launched)

C Step 8 (Job completes successfully)

D Step 1 (Request spot instances)

💡 Hint

Look at execution_table rows 5 and 6 to see what happens after interruption.

Concept Snapshot

Spot instances are cheaper cloud servers that can be interrupted.
Spark can run jobs on spot instances to save costs.
Jobs may fail if spot instances are interrupted.
Spark retries jobs automatically to handle interruptions.
This approach reduces cost but requires handling possible job restarts.

Full Transcript

Spot instances are cloud servers offered at lower prices but can be taken away anytime. When running Spark jobs on spot instances, the job starts by requesting these instances. If the instances are available, the job runs. However, spot instances can be interrupted, causing the job to fail. Spark handles this by retrying the job on new spot instances until it completes. This retry mechanism allows cost savings while ensuring job completion. The execution table shows each step from requesting spot instances, running the job, handling interruptions, and finally completing the job with cost savings.

Practice

1.

What is the main benefit of using spot instances for Apache Spark jobs?

easy

A. They provide more memory for Spark executors.

B. They increase the speed of Spark jobs.

C. They guarantee zero interruptions during job execution.

D. They reduce cloud computing costs significantly.

Solution

1. Step 1: Understand spot instances purpose

   Spot instances are cheaper cloud resources that can be interrupted but cost less.

2. Step 2: Identify main benefit for Spark jobs

   Using spot instances lowers the cost of running Spark jobs but may cause interruptions.

3. Final Answer:

   They reduce cloud computing costs significantly. -> Option D

4. Quick Check:

   Spot instances = cost savings [OK]

Hint: Spot instances save money but may interrupt jobs [OK]

Common Mistakes:

• Thinking spot instances speed up jobs
• Assuming spot instances never interrupt
• Believing spot instances add more memory

2.

Which of the following is the correct way to specify spot instances in a Spark cluster configuration on a cloud platform?

spark-submit --conf spark.executor.instances=5 --conf spark.executor.spot=true

easy

A. You must use --spot-instance flag in spark-submit.

B. This is correct syntax to request spot instances.

C. Spot instances are configured outside Spark code, not with spark-submit options.

D. Spot instances require setting spark.executor.memory.spot=true.

Solution

1. Step 1: Understand where spot instance setup happens

   Spot instance configuration is done in the cloud platform, not inside Spark submit commands.

2. Step 2: Analyze the given spark-submit command

   The command tries to set spot instances via Spark configs, which is incorrect.

3. Final Answer:

   Spot instances are configured outside Spark code, not with spark-submit options. -> Option C

4. Quick Check:

   Spot config = cloud platform setup [OK]

Hint: Spot instance setup is outside Spark code [OK]

Common Mistakes:

• Trying to set spot instances via spark-submit options
• Using incorrect flags like --spot-instance
• Confusing memory settings with spot instance flags

3.

Consider this Spark job running on a cluster with spot instances enabled. The job is flexible and can handle interruptions. What is the expected effect on the job's cost and runtime?

Cost = ?; Runtime = ?

medium

A. Cost decreases; runtime may increase due to interruptions.

B. Cost increases; runtime decreases due to faster nodes.

C. Cost and runtime both decrease.

D. Cost stays the same; runtime increases.

Solution

1. Step 1: Understand spot instance behavior

   Spot instances are cheaper but can be interrupted, causing retries or delays.

2. Step 2: Predict cost and runtime impact

   Cost goes down because spot instances are cheaper; runtime may increase due to interruptions and restarts.

3. Final Answer:

   Cost decreases; runtime may increase due to interruptions. -> Option A

4. Quick Check:

   Spot = cheaper but possibly slower [OK]

Hint: Spot instances cut cost but may slow jobs [OK]

Common Mistakes:

• Assuming spot instances speed up jobs
• Thinking cost increases with spot instances
• Ignoring possible interruptions

4.

You configured your Spark cluster to use spot instances, but your job keeps failing unexpectedly. Which of the following is the most likely cause?

medium

A. You did not allocate enough memory in Spark configs.

B. Spot instances were interrupted by the cloud provider.

C. Spark code has syntax errors.

D. The Spark version is incompatible with spot instances.

Solution

1. Step 1: Identify common failure reasons with spot instances

   Spot instances can be interrupted anytime by the cloud provider, causing job failures.

2. Step 2: Evaluate other options

   Syntax errors or memory issues cause different errors; incompatibility is rare and unrelated to spot instances.

3. Final Answer:

   Spot instances were interrupted by the cloud provider. -> Option B

4. Quick Check:

   Spot interruptions cause failures [OK]

Hint: Spot interruptions cause unexpected failures [OK]

Common Mistakes:

• Blaming Spark code syntax for spot instance failures
• Ignoring cloud provider interruptions
• Assuming memory or version issues cause spot failures

5.

You want to run a Spark job that processes large data but can tolerate interruptions. You have a limited budget. How should you configure your cluster to save costs using spot instances?

Choose the best approach.

hard

A. Use spot instances for all executors and implement checkpointing to recover from interruptions.

B. Use only on-demand instances to avoid interruptions and save costs.

C. Mix spot and on-demand instances without any fault tolerance in your job.

D. Use spot instances but disable retries to save time.

Solution

1. Step 1: Understand workload tolerance and cost constraints

   The job can handle interruptions and budget is limited, so cost saving is priority.

2. Step 2: Choose cluster setup for cost and fault tolerance

   Using spot instances saves money; checkpointing helps recover from interruptions safely.

3. Step 3: Evaluate other options

   Only on-demand is costly; mixing without fault tolerance risks failures; disabling retries risks data loss.

4. Final Answer:

   Use spot instances for all executors and implement checkpointing to recover from interruptions. -> Option A

5. Quick Check:

   Spot + checkpointing = cost saving + reliability [OK]

Hint: Combine spot instances with checkpointing for savings and safety [OK]

Common Mistakes:

• Avoiding spot instances despite budget limits
• Not implementing fault tolerance with spot instances
• Disabling retries risking job failure