Alexey Vidanov for AWS Community Builders

Posted on Oct 11 • Edited on Oct 14 • Originally published at tecracer.com

API Gateway and Lambda Throttling with Terraform. Part 1

#aws #lambda #apigateway #serverless

In today's cloud-native world, effectively managing API and serverless function performance is crucial for building reliable and cost-effective applications. This guide explores advanced throttling techniques for AWS API Gateway and Lambda using Terraform, incorporating best practices from the AWS Well-Architected Framework and real-world implementation patterns.

Why Throttling Matters

When building cloud-native applications, it's easy to focus on the business logic and forget key infrastructure components such as throttling and usage limits. However, this oversight can lead to unpredictable performance, increased costs, and even service outages. Setting the right throttling limits ensures that your applications:

Adapt to varying load patterns and sudden traffic spikes
Protect backend services from overload and cascading failures
Optimize costs across different environments while maintaining service quality
Provide meaningful monitoring and alerting for proactive management
Support different user tiers and business requirements effectively
Enable graceful degradation during high-load scenarios

In real-world projects, teams often realize the importance of throttling limits only after encountering performance or cost issues. It can be challenging to set proper limits without historical data, but this guide provides you with the framework to get those metrics in place, simplifying budgeting and operational planning. Furthermore, proper throttling configuration serves as a critical defense mechanism against denial-of-service attacks, whether intentional or accidental.

Implementation Overview

Let's dive into a comprehensive throttling implementation that addresses these needs using Terraform. We'll build a solution that's both flexible and production-ready.

1. Dynamic Configuration Management

One of the key challenges in multi-environment setups is managing environment-specific configurations. In the example below, we set up environment-specific throttling limits for API Gateway:

variable "environment" {
  type        = string
  description = "Environment name (e.g., dev, staging, prod)"
}

variable "api_throttling_configs" {
  type = map(object({
    burst_limit = number
    rate_limit  = number
  }))
  default = {
    dev = {
      burst_limit = 1000
      rate_limit  = 500
    }
    prod = {
      burst_limit = 5000
      rate_limit  = 1000
    }
  }
}

This setup allows you to manage different limits for development and production environments. For example, the dev environment has lower limits, ensuring that your resources are not overwhelmed during testing, while prod has higher limits to handle real-world traffic.

2. Lambda Configuration

AWS Lambda has a default maximum concurrency limit of 1,000 concurrent executions per account per region. You can control concurrency at the function level to avoid unplanned spikes and protect backend services. Here's how to set concurrency limits for Lambda:

resource "aws_lambda_function" "example" {
  function_name = "my_lambda_function_${var.environment}"
  ... // other lambda function configurations
  reserved_concurrent_executions = lookup(var.lambda_concurrency_limits, var.environment, 100)
}

By setting reserved_concurrent_executions, we control how many instances of the Lambda function can run simultaneously, protecting backend services from excessive traffic. The default value in this example is 100 concurrent executions, but this can be adjusted depending on the environment.

Real-World Observation

A common oversight in development is forgetting to configure these limits early on. Teams often realize the importance of limiting Lambda concurrency when they see unexpected bills or performance degradation. This configuration helps prevent such scenarios by establishing clear boundaries from the start.

3. Comprehensive Monitoring

Monitoring is critical for ensuring that throttling is functioning as expected. Use CloudWatch alarms to proactively track throttling metrics:

resource "aws_cloudwatch_metric_alarm" "lambda_throttles" {
  alarm_name          = "lambda-throttles-${aws_lambda_function.example.function_name}"
  comparison_operator = "GreaterThanThreshold"
  evaluation_periods  = 2
  metric_name         = "Throttles"
  namespace           = "AWS/Lambda"
  period              = 300
  statistic           = "Sum"
  threshold           = 5
  alarm_description   = "Lambda function throttling detected"
  alarm_actions       = [aws_sns_topic.alerts.arn]

  dimensions = {
    FunctionName = aws_lambda_function.example.function_name
  }
}

4. API Gateway Configuration

API Gateway can experience heavy loads during peak traffic times. To prevent backend services from being overwhelmed, we use throttling limits:

resource "aws_api_gateway_method_settings" "example" {
  rest_api_id = aws_api_gateway_rest_api.example.id
  stage_name  = aws_api_gateway_stage.example.stage_name
  method_path = "*/*"

  settings {
    metrics_enabled         = true
    logging_level          = "INFO"
    data_trace_enabled     = true
    throttling_burst_limit = lookup(var.api_throttling_configs[var.environment], "burst_limit", 1000)
    throttling_rate_limit  = lookup(var.api_throttling_configs[var.environment], "rate_limit", 500)
  }
}

5. Monitoring with CloudWatch Dashboards

Once the throttling is configured, it's important to visualize the data to make informed decisions. CloudWatch dashboards offer a great way to monitor both API Gateway and Lambda throttling:

resource "aws_cloudwatch_dashboard" "throttling_monitoring" {
  dashboard_name = "throttling-monitoring-${var.environment}"

  dashboard_body = jsonencode({
    widgets = [
      {
        type   = "metric"
        width  = 12
        height = 6
        properties = {
          metrics = [
            ["AWS/ApiGateway", "ThrottleCount", "ApiName", aws_api_gateway_rest_api.example.name],
            ["AWS/Lambda", "Throttles", "FunctionName", aws_lambda_function.example.function_name]
          ]
          period = 300
          stat   = "Sum"
          region = var.aws_region
          title  = "Throttling Overview"
        }
      }
    ]
  })
}

6. Cost Management with Usage Plans

API Gateway usage plans are a practical way to control costs and ensure that API consumers do not abuse the service:

resource "aws_api_gateway_usage_plan" "tiered" {
  name = "tiered-usage-plan-${var.environment}"

  api_stages {
    api_id = aws_api_gateway_rest_api.example.id
    stage  = aws_api_gateway_stage.example.stage_name
  }

  quota_settings {
    limit  = 10000
    period = "MONTH"
  }

  throttle_settings {
    burst_limit = lookup(var.api_throttling_configs[var.environment], "burst_limit", 1000)
    rate_limit  = lookup(var.api_throttling_configs[var.environment], "rate_limit", 500)
  }

  tags = {
    Environment = var.environment
    CostCenter  = "API-Gateway"
  }
}

Without proper throttling in place, API Gateway costs can spiral out of control. Budgeting becomes much easier when you set usage plans early and monitor actual consumption via dashboards.

7. Budget and Billing Alerts

An important aspect of managing API Gateway and Lambda throttling is cost control. By setting up budget and billing alerts, you can monitor and track usage costs to avoid unexpected charges. Here’s how you can approach it:

AWS Budgets: Set a monthly budget for API Gateway and Lambda usage, and configure notifications to alert you when costs exceed a certain threshold. This allows proactive management of expenses and ensures that your application remains cost-efficient.
Cost Anomaly Detection: Enable AWS Cost Anomaly Detection to spot unusual usage patterns that may indicate misconfigurations or unexpected traffic spikes, helping you address cost-related issues promptly.

These measures, combined with your throttling configurations, provide a robust approach to managing both application performance and cost efficiency.

Testing and Validation

Testing your throttling configurations ensures reliability in production:

Load Testing: Simulate high traffic to verify the throttling limits are being respected, including edge cases and boundary conditions.
Scenario Testing: Test burst traffic and sustained load to validate both limits and system resilience, particularly focusing on recovery patterns.
Monitoring Validation: Ensure your CloudWatch alarms are firing during test scenarios and verify the accuracy of metrics collection.

Best Practices for Production

1. Regular Review

Continuously monitor usage trends and adjust throttling settings as your traffic patterns evolve
Periodically review cost implications of your throttling configurations
Analyze throttling patterns to identify potential optimization opportunities
Consider seasonal variations in traffic when setting limits

2. Documentation

Maintain detailed runbooks for handling throttling-related incidents
Document any configuration changes, including justifications, in a version-controlled manner
Keep a historical record of throttling adjustments and their impacts

3. Compliance

Perform regular audits of your throttling configurations to ensure they meet compliance and security standards
Document throttling decisions as part of your compliance framework
Ensure throttling mechanisms align with SLA commitments

Further Considerations and Possible Ways to Go

As you refine your throttling strategy, here are some additional techniques you can consider:

Budget and Billing Alerts: Set up budget limits and enable cost anomaly detection to avoid unexpected charges.
Time-Based Throttling Adjustments: Use AWS EventBridge to adjust throttling limits during peak hours vs. off-hours to optimize resource allocation.
WAF Integration: Add an extra layer of security by integrating AWS WAF for IP-based throttling, blocking suspicious IP addresses.
Request Validation: Ensure that API requests conform to expected formats to reduce the chances of invalid requests causing backend overload.
Dead Letter Queues (DLQs): Ensure that throttled requests are not lost by sending them to DLQs for later reprocessing.

See the follow up API Gateway and Lambda Throttling with Terraform. Part 2

Conclusion

Advanced throttling is a critical aspect of modern cloud applications. By implementing these patterns with Terraform, you can create a robust, scalable, and maintainable throttling solution that protects your applications while optimizing costs. The key is to approach throttling as a dynamic system that requires ongoing attention and refinement, rather than a set-and-forget configuration.

Key Reminders:

Adapt and Review: Continuously evaluate and adjust throttling configurations based on real-world usage patterns
Monitor and Alert: Track throttling metrics for actionable insights and maintain comprehensive dashboards
Security and Compliance: Maintain rigorous security checks and documentation while ensuring throttling aligns with business requirements
Performance Balance: Strike the right balance between protection and performance to avoid over-throttling

With the configurations and best practices outlined in this guide, you can ensure your applications are prepared to handle varying traffic loads while maintaining predictable performance and cost efficiency. Remember that throttling is not just about limiting requests – it's about creating a resilient system that can gracefully handle any load condition while protecting your infrastructure and budget.

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