Auto-scaling configuration in DynamoDB - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is auto-scaling in DynamoDB?

Auto-scaling in DynamoDB automatically adjusts the read and write capacity of your tables based on traffic patterns to maintain performance and control costs.

beginner

Which two capacity types can DynamoDB auto-scale?

DynamoDB auto-scales both Read Capacity Units (RCUs) and Write Capacity Units (WCUs) to match the workload demand.

intermediate

What is the role of a target utilization percentage in DynamoDB auto-scaling?

The target utilization percentage defines the desired average usage of provisioned capacity. Auto-scaling adjusts capacity to keep usage near this target.

beginner

Name one benefit of enabling auto-scaling for DynamoDB tables.

Auto-scaling helps avoid throttling by adjusting capacity automatically and reduces manual effort to manage capacity, saving costs and improving reliability.

intermediate

What happens if the traffic exceeds the maximum capacity set in DynamoDB auto-scaling?

If traffic exceeds the maximum capacity, requests may be throttled, causing delays or errors until capacity is increased or traffic decreases.

What does DynamoDB auto-scaling adjust automatically?

A Number of indexes

B Table size in GB

C Read and write capacity units

D Backup frequency

DynamoDB auto-scaling adjusts the provisioned read and write capacity units to match workload demand.

Which metric does DynamoDB auto-scaling use to decide when to scale?

A CPU usage

B Target utilization percentage

C Disk space used

D Number of items in the table

Auto-scaling uses the target utilization percentage to maintain capacity near the desired usage level.

What happens if you set a very low maximum capacity in auto-scaling?

A Capacity will never scale above that max, risking throttling

B Auto-scaling will ignore the max capacity

C Capacity will scale infinitely

D The table will delete automatically

The maximum capacity limits scaling; if traffic exceeds it, throttling can occur.

Which AWS service is used to configure DynamoDB auto-scaling?

A AWS Application Auto Scaling

B Amazon CloudWatch

C AWS Lambda

D Amazon S3

AWS Application Auto Scaling manages auto-scaling for DynamoDB tables.

Why is auto-scaling important for DynamoDB tables?

A To automatically backup data

B To create indexes automatically

C To encrypt data at rest

D To adjust capacity to workload and avoid manual changes

Auto-scaling adjusts capacity automatically to match workload, reducing manual effort and preventing throttling.

Explain how DynamoDB auto-scaling works and why it is useful.

Think about how capacity changes with traffic.

Describe the risks of setting incorrect minimum or maximum capacity limits in DynamoDB auto-scaling.

Consider what happens if limits are too strict or too loose.

Practice

1. What is the main purpose of auto-scaling in DynamoDB?

easy

A. To encrypt data stored in the table

B. To manually set fixed capacity limits

C. To backup data at regular intervals

D. To automatically adjust read and write capacity based on traffic

Solution

1. Step 1: Understand auto-scaling function

   Auto-scaling automatically changes capacity to match traffic demands.

2. Step 2: Compare options

   Only To automatically adjust read and write capacity based on traffic describes automatic adjustment of capacity based on traffic.

3. Final Answer:

   To automatically adjust read and write capacity based on traffic -> Option D

4. Quick Check:

   Auto-scaling adjusts capacity automatically = A [OK]

Hint: Auto-scaling means automatic capacity adjustment [OK]

Common Mistakes:

• Confusing auto-scaling with manual capacity setting
• Thinking auto-scaling handles backups
• Mixing encryption with scaling

2. Which of the following is the correct way to specify the minimum capacity units in a DynamoDB auto-scaling policy JSON snippet?

easy

A. "MinCapacity": 5

B. "MinimumCapacityUnits": 5

C. "min_capacity": 5

D. "Min_Capacity": 5

Solution

1. Step 1: Recall DynamoDB auto-scaling JSON keys

   The correct key for minimum capacity is "MinCapacity" with camel case and no underscores.

2. Step 2: Check options

   Only "MinCapacity": 5 matches the exact key syntax "MinCapacity".

3. Final Answer:

   "MinCapacity": 5 -> Option A

4. Quick Check:

   Correct JSON key for minimum capacity = B [OK]

Hint: Use exact key names with correct casing in JSON [OK]

Common Mistakes:

• Using underscores instead of camel case
• Adding extra words like 'Units'
• Incorrect capitalization

3. Given this auto-scaling target tracking policy snippet:

{
  "TargetValue": 70.0,
  "ScaleInCooldown": 60,
  "ScaleOutCooldown": 60
}

What does the TargetValue represent?

medium

A. The maximum capacity units allowed

B. The cooldown time in seconds

C. The desired utilization percentage of capacity

D. The minimum capacity units allowed

Solution

1. Step 1: Understand TargetValue meaning

   TargetValue sets the target utilization percentage for capacity usage.

2. Step 2: Analyze options

   The desired utilization percentage of capacity correctly describes TargetValue as desired utilization percent (70%).

3. Final Answer:

   The desired utilization percentage of capacity -> Option C

4. Quick Check:

   TargetValue = utilization percent = C [OK]

Hint: TargetValue sets target usage percent, not capacity units [OK]

Common Mistakes:

• Confusing TargetValue with capacity limits
• Mixing cooldown times with TargetValue
• Thinking TargetValue is a fixed capacity number

4. You have this auto-scaling policy JSON snippet:

{
  "MinCapacity": 5,
  "MaxCapacity": 100,
  "TargetValue": 80.0,
  "ScaleInCooldown": 30,
  "ScaleOutCooldown": 30
}

But the policy is not working as expected. What is the likely error?

medium

A. MinCapacity is greater than MaxCapacity

B. Missing required field for resource ID

C. TargetValue should be between 0 and 1, not 80.0

D. Cooldown times are too short and cause rapid scaling

Solution

1. Step 1: Check capacity and cooldown values

   MinCapacity (5) is less than MaxCapacity (100), cooldowns are valid numbers.

2. Step 2: Identify missing required fields

   Auto-scaling policies require resource identifiers (like ResourceId) to apply the policy.

3. Step 3: Confirm TargetValue range

   TargetValue is a percentage and 80.0 is valid.

4. Final Answer:

   Missing required field for resource ID -> Option B

5. Quick Check:

   Missing resource ID causes policy failure = A [OK]

Hint: Check for all required fields, not just values [OK]

Common Mistakes:

• Assuming cooldown times cause failure
• Misreading TargetValue scale
• Ignoring missing resource identifiers

5. You want to configure auto-scaling for a DynamoDB table with unpredictable traffic spikes. Which combination of settings will best ensure smooth scaling without throttling or overspending?

{
  "MinCapacity": 10,
  "MaxCapacity": 200,
  "TargetValue": 70.0,
  "ScaleInCooldown": 120,
  "ScaleOutCooldown": 30
}

hard

A. Set a low MinCapacity, high MaxCapacity, fast ScaleOutCooldown, slow ScaleInCooldown

B. Set MinCapacity equal to MaxCapacity for fixed capacity

C. Set very fast ScaleInCooldown and ScaleOutCooldown both to 10 seconds

D. Set TargetValue to 100 to maximize usage

Solution

1. Step 1: Understand traffic spikes impact

   Unpredictable spikes need quick scaling out to avoid throttling and slower scaling in to avoid rapid capacity drops.

2. Step 2: Analyze given settings

   Low MinCapacity saves cost, high MaxCapacity allows growth, fast ScaleOutCooldown (30s) reacts quickly, slow ScaleInCooldown (120s) prevents rapid scale down.

3. Step 3: Evaluate other options

   Fixed capacity ignores spikes, very fast cooldowns cause thrashing, setting TargetValue to 100 risks overprovisioning.

4. Final Answer:

   Set a low MinCapacity, high MaxCapacity, fast ScaleOutCooldown, slow ScaleInCooldown -> Option A

5. Quick Check:

   Fast scale out + slow scale in = smooth scaling = D [OK]

Hint: Fast scale out, slow scale in for spike handling [OK]

Common Mistakes:

• Setting fixed capacity ignoring spikes
• Using equal cooldowns causing rapid scaling
• Setting TargetValue too high causing overspending