Troubleshooting RDS ACK Serverless Capacity Definition Issues

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Introduction

Hey guys! Today, we're diving deep into a tricky issue encountered while trying to define RDS ACK (Amazon Cloud Services Kubernetes) serverless values within a Kubernetes environment. Specifically, we're focusing on the challenge of setting minimum capacity values for Aurora serverless v2 using Resource Graph Definitions (RGD) in Kro. If you've ever wrestled with Kubernetes and cloud services, you know how crucial it is to get these configurations right. So, let's break down the problem, explore the symptoms, and figure out how to tackle it like pros.

Understanding the Core Issue

The heart of the matter lies in the inability to accurately define the minimum capacity for an Aurora serverless v2 database using RDS ACK within a Kubernetes Resource Graph. The main problem arises when trying to set a minimum capacity value other than 0. For instance, attempting to set a minimum capacity of 0.5 results in errors because ACK (AWS Controllers for Kubernetes) expects this value to be an integer, not a floating-point number. This limitation prevents users from configuring their databases to scale from a non-zero minimum capacity, which is essential for workloads that require continuous availability without incurring the cost of a fully provisioned database. Let's be real, nobody wants their database to shut down unexpectedly, right? This issue highlights a critical gap in the integration between Kubernetes resource management and AWS serverless database capabilities, making it essential to find a robust workaround or a permanent fix. Successfully addressing this issue ensures that applications can leverage the scalability and cost-effectiveness of Aurora serverless v2 without the risk of downtime due to scaling from zero capacity. Think of this as the sweet spot between performance and penny-pinching—exactly where we want to be!

Observed and Expected Behaviors

Observed Behavior

Currently, the observed behavior is that the system state remains Inactive. When attempting to apply a configuration with a non-integer minimum capacity value, the synchronization process fails, and the desired state isn't achieved. This means the Aurora serverless v2 database doesn't scale as expected, potentially leading to performance bottlenecks or unexpected shutdowns if the database scales down to zero capacity. Let’s face it, seeing that Inactive state is a techie's nightmare, signaling that something's definitely not playing ball.

Expected Behavior

The expected behavior, on the other hand, is that the system should synchronize successfully and transition to an Active state. The Aurora serverless v2 database should scale within the specified capacity range (minimum and maximum), ensuring optimal performance and cost efficiency. Essentially, we want our database humming along, scaling smoothly without throwing a tantrum. When the system is working as it should, the database automatically adjusts its capacity based on the workload, maintaining performance while minimizing costs. Imagine the peace of mind knowing your database is flexing its muscles as needed, without you having to babysit it!

Reproduction Steps: Diving into the YAML

To reproduce this issue, you'll need to set up a ResourceGraphDefinition (RGD) for an Aurora serverless v2 database in Kubernetes. Here’s a step-by-step breakdown using a sample YAML configuration.

  1. ResourceGraphDefinition (RGD) Setup: Create a ResourceGraphDefinition YAML file, such as aurora-demo.yaml, with the following structure:

    apiVersion: kro.run/v1alpha1
kind: ResourceGraphDefinition
metadata:
  name: aurora-demo
spec:
  schema:
    apiVersion: v1alpha1
    kind: AuroraDemo
    spec:
      name: string | required=true
      namespace: string | required=true
      maxCapacity: integer | default=8 description="Maximum capacity for the serverless database"
      minCapacity: integer | default=0.5 description="Minimum capacity for the serverless database"
      networkType: string | default="DUAL" description="Network type for the database, e.g., 'DUAL' for dual-stack."
      database:
        dbName: string | required=true
        dbInstanceClass: string | default="db.serverless"
        backupRetentionPeriod: integer | default=14 minimum=7 maximum=90 description="Number of days to retain backups"
        engineMode: string | default="provisioned" description="Database engine mode, e.g., 'provisioned' or 'serverless."
        networkType: string | default="DUAL" description="Network type for the database, e.g., 'DUAL' for dual-stack."
        autoMinorVersionUpgrade: boolean | default=true description="Enable automatic minor version upgrades for the database."
  resources:
    - id: dBSubnetGroup
      template:
        apiVersion: rds.services.k8s.aws/v1alpha1
        kind: DBSubnetGroup
        metadata:
          name: ${schema.spec.name}-db-subnet-group
          namespace: ${schema.spec.namespace}
        spec:
          name: ${schema.spec.name}-subnet-group
          description: Subnet group for the database
          subnetIDs:
            - [bring your
            - own list
            - of subnets]

    - id: dBCluster
      template:
        apiVersion: rds.services.k8s.aws/v1alpha1
        kind: DBCluster
        metadata:
          name: ${schema.spec.name}
          namespace: ${schema.spec.namespace}
        spec:
          dbClusterIdentifier: ${schema.spec.name}-cluster
          masterUsername: democase
          masterUserPassword:
            namespace: ${schema.spec.namespace}
            name: ack-rds-creds
            key: password
          engine: "aurora-postgresql"
          engineVersion: "17"
          dbSubnetGroupName: ${schema.spec.name}-db-subnet-group
          vpcSecurityGroupIDs:
            - [bring your own]
          serverlessV2ScalingConfiguration:
            #maxCapacity: ${schema.spec.maxCapacity}
            maxCapacity: 8
            #minCapacity: ${schema.spec.minCapacity}
            minCapacity: 0.5
          autoMinorVersionUpgrade: true
          backupRetentionPeriod: ${schema.spec.database.backupRetentionPeriod}
          engineMode: "provisioned"
          networkType: ${schema.spec.database.networkType}
          storageEncrypted: true

    - id: dBInstance
      template:
        apiVersion: rds.services.k8s.aws/v1alpha1
        kind: DBInstance
        metadata:
          name: ${schema.spec.name}
          namespace: ${schema.spec.namespace}
        spec:
          dbInstanceClass: db.serverless
          dbInstanceIdentifier: ${schema.spec.name}-instance
          dbClusterIdentifier: ${schema.spec.name}-cluster
          dbSubnetGroupName: ${schema.spec.name}-db-subnet-group
          engine: "aurora-postgresql"
          engineVersion: "17"
          publiclyAccessible: false

  2. Apply the RGD: Use kubectl to apply the ResourceGraphDefinition:

    kubectl apply -f aurora-demo.yaml

  3. Observe the Error: You’ll likely encounter an error similar to the following:

    failed to patch CRD: CustomResourceDefinition.apiextensions.k8s.io "aurorademoes.kro.run" is invalid: spec.validation.openAPIV3Schema.properties[spec].properties[minCapacity].default: Invalid value: "number":  in body must be of type integer: "number"

    Or this one:

    failed to build resourcegraphdefinition 'aurora-demo': failed to build OpenAPI schema for instance: failed to build OpenAPI schema for instance: unknown type: number

    These errors indicate that the minCapacity field, which expects an integer, is receiving a floating-point number (e.g., 0.5).

Root Cause Analysis: Peeling Back the Layers

The root cause of this issue stems from a type mismatch in the schema validation within ACK and Kro. ACK expects the minCapacity parameter to be an integer, whereas the desired configuration often requires a floating-point number to represent fractions of Aurora Capacity Units (ACUs). This discrepancy leads to validation errors and prevents the successful deployment of the resource graph. Think of it as trying to fit a square peg in a round hole—the system just can't process the conflicting data types. This technical hiccup underscores the importance of precise data type definitions in infrastructure-as-code configurations.

Workarounds and Solutions: Navigating the Maze

Current Workaround

The current workaround involves setting the minCapacity value to 0. While this allows the resource graph to deploy successfully, it’s not ideal. Setting the minimum capacity to zero means the database can shut down completely during periods of inactivity, potentially leading to cold starts and increased latency when the database needs to serve requests again. It’s like putting your car in park on a steep hill—technically, it works, but you might roll backward a bit! This workaround is a temporary fix, but it doesn’t address the core need for a non-zero minimum capacity.

Potential Solutions and Long-Term Fixes

  1. Schema Modification: The most straightforward solution is to modify the schema definition to allow floating-point numbers for the minCapacity field. This would involve updating the CustomResourceDefinition (CRD) for AuroraDemo in Kro to correctly interpret and validate the minCapacity value. This is the equivalent of widening the round hole to fit our peg—a direct and effective solution.

  2. ACK Update: Another approach is to request an update to ACK itself to support floating-point numbers for Aurora serverless v2 scaling configurations. This would align ACK with the actual capabilities of Aurora serverless v2 and provide a more seamless integration experience. This is more like getting a new peg that fits the existing hole perfectly—a clean and elegant fix.

  3. Custom Validation Logic: Implement custom validation logic within Kro to handle the minCapacity field. This could involve creating a custom validator that checks the value and ensures it’s within the acceptable range for Aurora serverless v2. This is akin to building a custom adapter to bridge the gap—a flexible but potentially more complex solution.

Version Information: Know Your Tools

It's crucial to be aware of the versions of the tools you're using when troubleshooting issues. Here’s the version information for the environment where this issue was observed:

  • kro version: helm.sh/chart=kro-0.3.0
  • Kubernetes Version: Server Version: v1.32.5-eks-5d4a308
  • RDS-ACK: helm.sh/chart=rds-chart-1.4.22

Knowing these versions helps in pinpointing compatibility issues and identifying potential updates or patches that might address the problem. Think of it as checking the manufacturing dates on your tools—you want to make sure they're up to the job!

Involved Controllers: The Orchestrators

Understanding which controllers are involved can provide additional context for troubleshooting. In this case, the primary controllers involved are those responsible for managing ResourceGraphDefinitions in Kro and the RDS controllers in ACK. Identifying the specific controller versions and URLs can help in diagnosing issues related to controller behavior or misconfigurations. It's like knowing who's conducting the orchestra—you need to know the conductor to understand the music.

Error Logs: Deciphering the Clues

Error logs are your best friends when debugging. They provide valuable clues about what went wrong and where. The error logs observed in this case include messages indicating a type mismatch for the minCapacity field:Let’s see the Error logs again.

failed to patch CRD: CustomResourceDefinition.apiextensions.k8s.io "aurorademoes.kro.run" is invalid: spec.validation.openAPIV3Schema.properties[spec].properties[minCapacity].default: Invalid value: "number":  in body must be of type integer: "number"

Or

failed to build resourcegraphdefinition 'aurora-demo': failed to build OpenAPI schema for instance: failed to build OpenAPI schema for instance: unknown type: number

These logs clearly point to the issue of the minCapacity field expecting an integer but receiving a floating-point number. It’s like reading the detective's notes—each log entry brings you closer to solving the mystery.

Conclusion: Charting a Path Forward

In summary, the challenge of defining RDS ACK serverless values, particularly the minimum capacity, highlights a critical area for improvement in the integration between Kubernetes and AWS services. While the current workaround of setting minCapacity to 0 exists, it’s not a long-term solution. The ideal fix involves either modifying the schema in Kro to support floating-point numbers or updating ACK to align with Aurora serverless v2 capabilities. By addressing this issue, we can unlock the full potential of serverless databases in Kubernetes environments, ensuring efficient scaling and cost management.

Keep an eye out for updates and new approaches to tackling this problem. Until then, happy coding, and may your deployments always be smooth sailing!Remember, if this helped you, don’t forget to share and give a thumbs up! Let’s keep the conversation rolling and make cloud deployments a breeze for everyone.