Auto-scaling configuration in DynamoDB - Step-by-Step Execution

Concept Flow - Auto-scaling configuration

Monitor Usage Metrics

↓

Compare Usage to Thresholds

↓

Increase

↓

Capacity

↓

Update Table Capacity

↓

Repeat Monitoring

Auto-scaling watches usage and adjusts capacity up or down automatically to keep performance steady.

Execution Sample

DynamoDB

1. Monitor read/write capacity usage
2. If usage > 70%, increase capacity
3. If usage < 30%, decrease capacity
4. Update table settings
5. Repeat monitoring

This process automatically adjusts DynamoDB table capacity based on usage thresholds.

Execution Table

Step	Read Capacity Usage (%)	Write Capacity Usage (%)	Condition	Action	New Read Capacity	New Write Capacity
1	50	40	Usage within thresholds	No change	5	5
2	75	80	Usage > 70%	Increase capacity	7	7
3	65	60	Usage within thresholds	No change	7	7
4	25	20	Usage < 30%	Decrease capacity	5	5
5	55	50	Usage within thresholds	No change	5	5
6	85	90	Usage > 70%	Increase capacity	7	7
7	70	65	Usage within thresholds	No change	7	7
8	28	25	Usage < 30%	Decrease capacity	5	5
9	50	45	Usage within thresholds	No change	5	5
Exit	-	-	Monitoring stops	Manual stop or no changes needed	-	-

💡 Monitoring stops when manually stopped or no capacity changes are needed.

Variable Tracker

Variable	Start	After Step 2	After Step 4	After Step 6	After Step 8	Final
Read Capacity	5	7	5	7	5	5
Write Capacity	5	7	5	7	5	5

Key Moments - 3 Insights

Why does capacity increase when usage is above 70%?

Because high usage means the table needs more capacity to handle requests without slowing down, as shown in execution_table rows 2 and 6.

Why does capacity decrease when usage falls below 30%?

To save costs by reducing unused capacity, as seen in execution_table rows 4 and 8 where capacity is lowered.

Why are there steps where no capacity change happens even if usage is close to thresholds?

Because usage is within acceptable limits (between 30% and 70%), so no adjustment is needed, like in rows 1, 3, 5, 7, and 9.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what action is taken when read capacity usage is 75%?

A No change

B Increase capacity

C Decrease capacity

D Stop monitoring

💡 Hint

Check the 'Action' column at step 2 in the execution_table.

At which step does the capacity decrease due to low usage?

A Step 6

B Step 3

C Step 4

D Step 7

💡 Hint

Look for 'Usage < 30%' condition and 'Decrease capacity' action in the execution_table.

If the read capacity usage stays at 50% continuously, what would the capacity changes be?

A No capacity change

B Capacity increases

C Capacity decreases

D Capacity fluctuates randomly

💡 Hint

Refer to execution_table rows where usage is around 50% and see the action taken.

Concept Snapshot

Auto-scaling in DynamoDB:
- Monitors read/write usage continuously
- Increases capacity if usage > 70%
- Decreases capacity if usage < 30%
- Keeps capacity stable otherwise
- Helps balance performance and cost automatically

Full Transcript

Auto-scaling configuration in DynamoDB works by monitoring the read and write capacity usage of a table. When usage goes above 70%, the system increases capacity to handle more requests smoothly. When usage falls below 30%, it decreases capacity to save costs. If usage stays between 30% and 70%, no changes are made. This cycle repeats continuously to keep the table performing well without wasting resources. The execution table shows step-by-step how capacity changes based on usage percentages, and the variable tracker shows how capacity values update over time.

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