Do constant work

In a distributed system, ensuring that components function reliably under varying loads is critical. By designing systems that consistently perform a stable amount of work—regardless of the load—applications can minimize issues arising from peaks in demand, thereby preventing failures and extending their operational uptime.

Best Practices

Implement Constant Workload Patterns

• Design health checks and monitoring systems to send uniform payloads for status updates to avoid sudden spikes in data transfer.
• Use a steady state approach for load balancers to maintain maximum request handling without creating resource contention.
• Perform load testing with predictable patterns to replicate constant workloads in production and identify potential failure points.
• Utilize smoothing algorithms to distribute workloads evenly over time, rather than in bursts, thereby minimizing system shocks.
• Establish a consistent data retention policy for logs and metrics to manage storage without causing sudden resource strain.

Monitor and Adjust Cadence of Health Checks

• Set regular intervals for health checks to avoid overwhelming systems with rapid requests, which can lead to high latency.
• Incorporate exponential backoff strategies for retrying failed health checks to reduce impact on systems during large outages.
• Adapt health check frequencies based on system load levels, increasing intervals during peak loads to ensure ongoing performance.
• Analyze performance metrics after changes in monitoring cadence to assess the impact and adjust based on findings.

Leverage Caching Mechanisms

• Utilize caching for frequently accessed data to reduce network load and decrease response times during health checks.
• Implement tiered caching strategies to provide different cache levels based on data criticality and access frequency.
• Ensure that cache invalidation policies are in place to maintain up-to-date information while keeping interactions predictable.
• Evaluate caching solutions that suit your workloads, such as in-memory caches, to provide low-latency access to data.

Questions to ask your team

• Is your system designed to handle consistent workloads without abrupt changes in resource demand?
• Have you implemented techniques to ensure that health checks and monitoring processes maintain constant flow and size of data?
• Do you have mechanisms in place to control load variations during peak times?
• Are your systems tested under simulated steady-state conditions to observe their performance and responsiveness?
• How do you ensure that dependencies between components can handle stable and predictable loads?

Who should be doing this?

System Architect

• Design the system architecture to ensure stability under varying loads.
• Implement monitoring solutions to track component health and performance.
• Define protocols for consistent payload sizes during health checks.

DevOps Engineer

• Automate deployment processes to maintain consistency in application behavior.
• Monitor network performance and latency to mitigate potential issues.
• Ensure continuous integration and delivery pipelines are resilient to changes in load.

Site Reliability Engineer (SRE)

• Analyze and respond to incidents that disrupt system reliability.
• Develop strategies for load balancing to handle varying traffic gracefully.
• Conduct regular stress testing and failure simulations to improve MTBF.

QA Engineer

• Create test cases that simulate varying load conditions.
• Validate that health check systems and other components behave consistently.
• Report on system performance to help identify potential failure points.

Product Owner

• Collaborate with stakeholders to define reliability requirements.
• Prioritize features and improvements that enhance system reliability.
• Ensure that user needs are considered in the design of reliability features.

What evidence shows this is happening in your organization?

• Distributed System Load Management Plan: A detailed plan outlining strategies for managing load within distributed systems, including guidelines on maintaining constant work during health checks and other operations to prevent sudden load spikes.
• Health Check Payload Consistency Checklist: A checklist that ensures all health check systems are configured to send consistent payload sizes, detailing how to monitor and adjust health checks to avoid large, rapid changes in load.
• Reliability Best Practices Report: A comprehensive report summarizing best practices for designing interactions in distributed systems, emphasizing the importance of constant work and providing case studies and examples of successful implementations.
• Distributed Systems Monitoring Dashboard: An interactive dashboard that visualizes the health and performance of distributed systems, highlighting the state of components and ensuring constant monitoring without causing load fluctuations.
• Incident Response Playbook: A playbook that outlines response strategies for failures in distributed systems, focusing on load management approaches and maintaining consistent operational tasks to mitigate impacts on service reliability.

Cloud Services

AWS

• Amazon CloudWatch: Monitors AWS cloud resources and applications in real-time, providing insights to help ensure the reliability of distributed systems by tracking performance and resource utilization.
• AWS Lambda: Allows running code in response to events without provisioning or managing servers, enabling consistent workloads with predictable performance during variable load scenarios.
• Amazon SQS: A fully managed message queuing service, helping to decouple and scale microservices, distributed systems, and serverless applications, supporting constant work during load spikes.

Azure

• Azure Monitor: Collects, analyzes, and acts on telemetry data from Azure and on-premises environments, providing insights to ensure the reliability and performance of applications.
• Azure Functions: Enables serverless computing that automatically scales with demand, helping to maintain constant workloads without failure during varying loads.
• Azure Queue Storage: Provides a way to store and manage messages in a queue, facilitating communication between distributed components and enabling consistent workloads.

Google Cloud Platform

• Google Cloud Monitoring: Provides visibility into the performance, uptime, and overall health of applications and services, helping maintain reliability in distributed systems.
• Cloud Functions: A serverless execution environment for building and connecting cloud services, allowing for flexible scaling that ensures reliability under varying loads.
• Cloud Pub/Sub: A messaging service for building event-driven systems and real-time analytics, helping to maintain consistent workflows across distributed components.

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