Throttle requests

Throttling requests is an essential practice for maintaining stability in distributed systems during high demand periods. It protects resources from becoming overwhelmed, ensuring that the system can remain operational and responsive even when faced with unexpected spikes in traffic.

Best Practices

Implement Request Throttling

• Define throttling limits: Establish a baseline for the maximum number of requests your system can handle per unit of time to prevent resource exhaustion.
• Use automated throttling mechanisms: Implement services such as AWS API Gateway, AWS Lambda, or AWS Application Load Balancer that support built-in throttling capabilities.
• Provide clear feedback: When a request is throttled, ensure the system responds with a specific error message indicating the request has been rejected due to throttling limits. This helps clients understand the status.
• Monitor and adjust throttling parameters: Continuously monitor request patterns and performance metrics to adjust throttling settings as needed to adapt to changing demand and maintain reliability.
• Implement exponential backoff: Encourage clients to reduce the rate of their requests after receiving a throttled response, gradually increasing their attempts to re-establish a connection without overwhelming the system.
• Test your throttling strategy: Regularly conduct load testing to evaluate the effectiveness of your throttling implementation and identify potential weaknesses in the system.

Questions to ask your team

• What mechanisms are in place to monitor request rates and identify potential throttling conditions?
• How do you define and manage your throttling limits for different components of the system?
• What strategies do you use to inform users or calling services when their requests have been throttled?
• How do you handle retries for requests that are throttled, and what backoff strategies do you implement?
• Are there any specific performance metrics you track to evaluate the effectiveness of your throttling implementation?
• How do you ensure that throttling does not lead to cascading failures across services in the distributed system?
• What tools or frameworks are you using to implement and manage request throttling in your architecture?

Who should be doing this?

Cloud Architect

• Design and implement a throttling strategy for system requests to ensure reliability under varying loads.
• Define thresholds for request limits based on system capacity and performance metrics.
• Monitor and adjust throttling parameters in response to usage patterns and system performance.

DevOps Engineer

• Implement the throttling mechanisms in the application code and infrastructure.
• Automate the deployment of throttling configurations using CI/CD pipelines.
• Continuously monitor system metrics to identify trends and adjust throttling limits accordingly.

Quality Assurance Engineer

• Test the throttling mechanisms to ensure that they correctly reject excess requests and provide appropriate feedback to users.
• Perform stress testing to validate system behavior under high load scenarios.
• Document and report on the effectiveness of throttling in maintaining application reliability.

Product Owner

• Define user requirements and acceptance criteria for request throttling features.
• Collaborate with stakeholders to understand the impact of request throttling on user experience.
• Ensure alignment between business goals and technical implementations of throttling.

What evidence shows this is happening in your organization?

• Throttle Request Policy Template: A policy template outlining the protocols for throttling requests in distributed systems. This document includes thresholds for request limits, procedures for monitoring demand, and guidelines for communicating with clients when requests are throttled.
• Throttling Strategy Guide: A comprehensive guide that explains the strategies for effectively implementing request throttling in distributed applications. It includes best practices, common use cases, and examples of configuration settings for various cloud services.
• Throttling Dashboard: An interactive dashboard that visualizes system performance metrics related to request throttling. It displays real-time data on incoming requests, throttled requests, and system resource utilization, enabling teams to monitor the impact of throttling in real-time.
• Incident Response Playbook for Throttled Requests: A playbook that provides step-by-step instructions for responding to incidents caused by throttled requests. It outlines the roles and responsibilities of team members, escalation procedures, and remedial actions to ensure system reliability.
• Request Throttling Checklist: A checklist that helps teams verify that all aspects of request throttling have been considered. It includes items such as identifying thresholds, configuring alerts, and ensuring proper logging and documentation are in place.

Cloud Services

AWS

• Amazon API Gateway: Enables you to create, publish, maintain, monitor, and secure APIs at any scale. It can throttle requests to protect your backend services from being overwhelmed.
• AWS Lambda: Allows you to run code in response to events and automatically manages the compute resources, helping to handle varying loads effectively.
• Amazon CloudWatch: Provides monitoring and observability of your AWS cloud resources and applications, allowing you to set up alarms and triggers to throttle or manage requests.

Azure

• Azure API Management: Helps to create and manage APIs securely, including rate limiting and throttling capabilities to protect backend resources from overload.
• Azure Functions: Serverless compute service that enables you to run event-driven code without provisioning infrastructure, scaling based on demand.
• Azure Monitor: Provides full-stack monitoring for applications and services, enabling you to analyze requests and set up alerts based on usage patterns.

Google Cloud Platform

• Google Cloud Endpoints: Monitors and manages APIs, allowing for request throttling and ensuring that backend services are not overwhelmed by excessive traffic.
• Cloud Functions: Enables you to run code in response to events, automatically scaling based on the incoming request load.
• Google Cloud Monitoring: Provides insights into the performance of your applications and services, allowing for proactive management of traffic and resource usage.

Question: How do you design interactions in a distributed system to mitigate or withstand failures?
Pillar: Reliability (Code: REL)