Auto Scaling For DynamoDB: Tables And GSIs Enhancement

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Auto Scaling for DynamoDB: Tables and GSIs Enhancement

Hey guys! Today, we're diving deep into an exciting enhancement for DynamoDB: Auto Scaling support for provisioned tables and Global Secondary Indexes (GSIs). This is a game-changer for anyone looking to optimize performance and costs while ensuring their DynamoDB setup can handle traffic spikes without breaking a sweat. Let's get started!

Current Behavior: The Need for Auto Scaling

Currently, provisioned DynamoDB tables and GSIs are configured with fixed capacity. This means you set a specific readCapacity and writeCapacity, and that's what you get. For example, a table might be set with readCapacity: 1 and writeCapacity: 1, while a GSI could have readCapacityUnits: 2 and writeCapacityUnits: 2. The problem? No Auto Scaling.

This fixed capacity approach can lead to issues. During peak traffic, you might hit the dreaded ProvisionedThroughputExceededException. Not cool, right? On the flip side, during low traffic periods, you're paying for capacity you're not even using. It's like driving a massive truck to the grocery store – overkill!

Auto Scaling is crucial because it allows your DynamoDB tables and GSIs to automatically adjust their capacity based on demand. This ensures you have enough resources to handle traffic spikes while scaling down during quiet times to save costs. It's all about being efficient and smart with your resources.

Proposed Enhancement: Auto Scaling to the Rescue

So, how do we fix this? We're proposing to add Auto Scaling configuration for both table-level and GSI-level capacity. This means DynamoDB can automatically increase or decrease read and write capacities based on actual usage. Think of it as cruise control for your database – smooth sailing ahead!

1. Table-Level Auto Scaling

First up, let's tackle table-level Auto Scaling. This involves configuring the provisioned table in our lib/cdk-ddb-multi-gsi-stack.ts file. Here’s a snippet of what the code might look like:

const provisionedDdb = new dynamodb.Table(this, "ProvisionedTable", {
  // ... existing config ...
});

// Read Auto Scaling
const readScaling = provisionedDdb.autoScaleReadCapacity({
  minCapacity: 1,
  maxCapacity: 10,
});
readScaling.scaleOnUtilization({
  targetUtilizationPercent: 70,
});

// Write Auto Scaling
const writeScaling = provisionedDdb.autoScaleWriteCapacity({
  minCapacity: 1,
  maxCapacity: 10,
});
writeScaling.scaleOnUtilization({
  targetUtilizationPercent: 70,
});

In this example, we're setting up Auto Scaling for both read and write capacities. We define a minimum capacity (minCapacity) and a maximum capacity (maxCapacity). The scaleOnUtilization method is where the magic happens. We set a targetUtilizationPercent of 70%, meaning DynamoDB will try to keep the utilization around this level by scaling up or down as needed. This ensures optimal performance without over-provisioning.

2. GSI-Level Auto Scaling

Now, let's talk about GSI-level Auto Scaling. This is a bit more complex, and we've identified two potential implementation options:

Option A: Use CDK Native GSI Creation with Auto Scaling

The first option is to replace our custom resource with CDK's built-in addGlobalSecondaryIndex() method. CDK (Cloud Development Kit) is a powerful tool that allows you to define your cloud infrastructure as code. By using CDK's native method, we can leverage its Auto Scaling capabilities for GSIs directly. This approach simplifies the process and aligns with best practices for infrastructure as code.

Option B: Extend GsiManager to Support Auto Scaling

Alternatively, we can extend our existing GsiManager to include Auto Scaling settings. This involves several steps:

  • Extend GSIConfiguration Interface: We'll need to update the GSIConfiguration interface to include settings for Auto Scaling. This might involve adding properties like minReadCapacity, maxReadCapacity, minWriteCapacity, and maxWriteCapacity.
  • Manage Application Auto Scaling Policies in Custom Resource Lambda: Our custom resource Lambda function will need to manage Application Auto Scaling policies. This means creating, updating, and deleting policies as GSIs are created, modified, or removed.
  • Use PutScalingPolicy API: We'll use the PutScalingPolicy API to create scaling policies. This API allows us to define the scaling behavior, such as the minimum and maximum capacity, and the target utilization.
  • Delete Policies When GSIs Are Removed: When a GSI is removed, we need to make sure to delete the associated scaling policies to avoid orphaned resources.

Benefits: Why Auto Scaling Matters

So, why go through all this effort? The benefits of adding Auto Scaling are significant:

  1. Improved Availability: Auto Scaling prevents those pesky ProvisionedThroughputExceededException errors during traffic spikes. Your application stays responsive, and your users stay happy. It's like having a safety net for your database.
  2. Cost Optimization: By scaling down during low traffic periods, you're not paying for idle capacity. This can lead to significant cost savings, especially for applications with variable traffic patterns. Think of it as only paying for the electricity you use.
  3. Reduced Operational Overhead: No more manual capacity adjustments! Auto Scaling takes care of this for you, freeing up your time to focus on other important tasks. It's like having an automated database administrator.
  4. Best Practice Compliance: Auto Scaling aligns with AWS's recommended approach for provisioned mode DynamoDB tables and GSIs. You're following the best practices in the industry.

Related Code: Where the Magic Happens

If you're curious about where this all comes together in the code, here are some key areas:

  • lib/cdk-ddb-multi-gsi-stack.ts (lines 87-112): This is where the provisioned table definition lives.
  • lib/types/gsi-types.ts: Here, you'll find the GSIConfiguration type definition.
  • lambda/gsi-manager/src/dynamodb-gsi-service.ts: This is the GSI management service.
  • lib/gsi-manager-construct.ts: You'll find the GsiManager construct here.

Implementation Approach: Let's Get It Done

We're planning a phased approach to implement this enhancement:

Phase 1: Table-Level Auto Scaling (Can Be Implemented Immediately)

  1. Add Auto Scaling configuration to cdk-ddb-multi-gsi-stack.ts (as shown in the code snippet above).
  2. Deploy the changes and verify the behavior. Make sure the table scales up and down as expected.

Phase 2: GSI Auto Scaling Design Review

  1. Compare Option A (CDK native GSI creation) vs. Option B (extend GsiManager).
  2. Extend type definitions to include Auto Scaling settings.
  3. Decide on the best implementation approach based on our needs and constraints.

Phase 3: GSI Auto Scaling Implementation

  1. Extend the custom resource or migrate to the CDK native approach based on our decision in Phase 2.
  2. Add thorough test cases to ensure Auto Scaling works correctly for GSIs.
  3. Update the documentation to reflect the changes.

Priority: Why This Matters Now

We've assigned this a medium priority. It's important because it improves availability and cost efficiency, but there are workarounds, such as manual scaling or using on-demand mode. However, Auto Scaling is the way to go for a robust and efficient DynamoDB setup.

Environment: Our Toolkit

For those curious about our environment, here are the key details:

  • AWS CDK version: 2.152.0
  • Node.js version: 18.18.2
  • Runtime: NodeJS_20_X
  • AWS region: ap-northeast-1

Additional Context: The Big Picture

This enhancement addresses a limitation in our current custom resource implementation. While it manages GSI CRUD operations, it doesn't handle Auto Scaling policies for provisioned capacity mode. By adding Auto Scaling, we're making our DynamoDB setup more robust, efficient, and aligned with AWS best practices.

So, there you have it! Auto Scaling for DynamoDB tables and GSIs is a significant step forward in optimizing our database performance and costs. Stay tuned for updates as we move through the implementation phases. Let's make our DynamoDB setup scale like a pro! 🚀