Provisioned Mode and Auto Scaling are the two primary capacity modes offered by AWS DynamoDB for growing database workloads. Each mode is appropriate for a variety of applications due to its unique use cases, advantages, and disadvantages. To assist you in choosing the appropriate mode for your workload, we'll go over the greatest use cases for each mode in this post, as well as a thorough analysis of its benefits and drawbacks.

1. Provisioned Mode: Overview and Use Cases

Based on your anticipated workload, you manually configure the read and write capacity units (RCUs and WCUs) in Provisioned Mode. If you have consistent or predictable traffic growth, this approach is reliable and economical.

Use Cases

👉🏻 Consistent, Predictable Workloads: Suitable for applications with stable or predictable traffic, such as backend services that operate on fixed schedules.

👉🏻 Cost-Conscious Applications: Provisioned mode is more cost-effective when you know exactly how much capacity your application needs, allowing you to avoid over-provisioning costs.

Advantages of Provisioned Mode

👉🏻 Cost Predictability: You only pay for the provisioned capacity, giving you consistent billing.

👉🏻 Fine Control Over Capacity: You can manually adjust RCUs and WCUs to optimize your cost-performance balance.

Disadvantages of Provisioned Mode

👉🏻 Manual Scaling: If traffic surges unexpectedly, you may run into throttling issues until you manually increase capacity.

👉🏻 Less Flexibility: Provisioned mode isn’t ideal for workloads with unpredictable traffic spikes, as unexpected load can lead to throttling and service degradation.

Example in AWS CDK (TypeScript)

import * as dynamodb from 'aws-cdk-lib/aws-dynamodb';

const table = new dynamodb.Table(this, 'MyTable', {
  partitionKey: { name: 'id', type: dynamodb.AttributeType.STRING },
  billingMode: dynamodb.BillingMode.PROVISIONED,
  readCapacity: 5,
  writeCapacity: 5,
});

2. Auto Scaling Mode: Overview and Use Cases

Applications with fluctuating or unexpected traffic can benefit from Auto Scaling Mode, which automatically modifies RCUs and WCUs in response to demand. By ramping up during periods of high traffic and scaling down during off-peak hours, auto scaling can assist in maintaining a balance between cost and performance.

Use Cases

👉🏻 Unpredictable Traffic Patterns: Best for applications where traffic fluctuates, such as seasonal applications or APIs experiencing frequent traffic spikes.

👉🏻 Demand-Based Scaling: Ideal for applications with peak traffic during certain hours or events, like e-commerce sites during sales.

Advantages of Auto Scaling Mode

👉🏻 Automatic Scaling: Auto Scaling adjusts capacity based on load, reducing the risk of throttling.

👉🏻 Cost Efficiency for Variable Workloads: Scales down during low traffic periods, reducing costs compared to over-provisioning for peak capacity.

Disadvantages of Auto Scaling Mode

👉🏻 Slight Lag in Scaling: Auto Scaling doesn’t happen instantaneously, so sudden spikes may still lead to short periods of throttling.

👉🏻 Potentially Higher Costs During High Demand: If traffic is consistently high, costs may exceed those of a fixed provisioned capacity due to frequent scaling adjustments.

Example in AWS CDK (TypeScript)

const table = new dynamodb.Table(this, 'MyTable', {
  partitionKey: { name: 'id', type: dynamodb.AttributeType.STRING },
  billingMode: dynamodb.BillingMode.PROVISIONED,
  readCapacity: 5,
  writeCapacity: 5,
});

table.autoScaleReadCapacity({
  minCapacity: 5,
  maxCapacity: 50,
}).scaleOnUtilization({ targetUtilizationPercent: 70 });

table.autoScaleWriteCapacity({
  minCapacity: 5,
  maxCapacity: 50,
}).scaleOnUtilization({ targetUtilizationPercent: 70 });

3. Comparison: Choosing Between Provisioned Mode and Auto Scaling

Conclusion

Choosing the right capacity mode in DynamoDB depends largely on your application’s traffic pattern and cost sensitivity. Provisioned Mode offers predictable costs for consistent workloads, while Auto Scaling provides flexibility for fluctuating traffic, ensuring optimal performance. By understanding the strengths and weaknesses of each, you can align DynamoDB’s capabilities with your workload’s needs, optimizing both cost and performance.