The thundering herd problem is a critical challenge in distributed systems that can bring even robust architectures to their knees. This article explores the nature of this issue, its recurring variant, and how jitter serves as a crucial defense mechanism. We'll also examine practical solutions for Java developers, including standard libraries and customization options for REST-based clients. Understanding the Thundering Herd The thundering herd problem occurs when a large number of processes or clients simultaneously attempt to access a shared resource, overwhelming the system. This can happen in various scenarios: After a service outage, when all clients try to reconnect at once When a popular cache item expires, causing multiple requests to hit the backend During scheduled events or cron jobs that trigger at the same time across many servers The impact can be severe, leading to: Increased latency Service unavailability Cascading failures across dependent systems Recurring Thundering Herd: A Persistent Threat While a single thundering herd event can be disruptive, recurring instances pose an even greater danger. This phenomenon happens when: Clients use fixed retry intervals, causing repeated traffic spikes Periodic tasks across multiple servers align over time IoT devices or smart home appliances check for updates on a fixed schedule Jitter: The Unsung Hero of Distributed Systems Jitter introduces controlled randomness into timing mechanisms, effectively dispersing potential traffic spikes. Here's why it's crucial: Prevents synchronization: By adding small random delays, jitter keeps processes from aligning their actions. Smooths traffic: Instead of sharp spikes, jitter creates a more even distribution of requests over time. Improves resilience: Systems with jitter can better handle load variations and recover from failures. Implementing Jitter in Java Java developers have several options for implementing jitter: Standard Libraries java.util.concurrent.ThreadLocalRandom: javalong jitter = ThreadLocalRandom.current().nextLong(0, maxJitterMs); java.util.Random: javaRandom random = new Random(); long jitter = random.nextLong(maxJitterMs); Third-Party Libraries Guava's ExponentialBackOff: javaExponentialBackOff backoff = ExponentialBackOff.builder() .setInitialIntervalMillis(500) .setMaxIntervalMillis(1000 * 60 * 5) .setMultiplier(1.5) .setRandomizationFactor(0.5) .build(); Resilience4j's Retry: javaRetryConfig config = RetryConfig.custom() .waitDuration(Duration.ofMillis(1000)) .maxAttempts(3) .build(); Retry retry = Retry.of("myRetry", config); Customizing REST Clients with Jitter When working with REST clients, you can incorporate jitter in several ways: Custom Interceptors: Implement an interceptor that adds a random delay before each request. Retry Policies: Use libraries like OkHttp or Apache HttpClient that allow custom retry policies with jitter. Circuit Breakers: Implement circuit breakers with jittered retry mechanisms using libraries like Hystrix or Resilience4j. IoT and Smart Home Devices: A Special Case The thundering herd problem is particularly relevant for IoT and smart home devices. These devices often use a common pattern of periodically checking for updates or sending data to a central server. To mitigate potential issues: Implement device-side jitter for update checks and data transmissions. Use push notifications instead of frequent polling when possible. Stagger initial boot times and update schedules across device fleets. Conclusion The thundering herd problem remains a significant challenge in distributed systems, but with proper understanding and implementation of jitter, developers can create more resilient and scalable applications. By leveraging Java's built-in libraries and third-party solutions, along with custom REST client configurations, you can effectively tame the herd and ensure your systems remain stable under heavy load. Remember, in the world of distributed systems, a little randomness goes a long way in maintaining order and preventing chaos. References: [1] Distributed Systems Horror Stories: The Thundering Herd Problem https://encore.dev/blog/thundering-herd-problem [2] Retry policy to avoid Thundering Herd Problem - Temporal Community https://community.temporal.io/t/retry-policy-to-avoid-thundering-herd-problem/790 [3] This is known generally as the "Thundering Herd" problem https://news.ycombinator.com/item?id=1722213 [4] Using the REST Client - Quarkus https://quarkus.io/guides/rest-client [5] Thundering Herd Problem and How not to do API retries - YouTube https://www.youtube.com/watch?v=8sTuCPh3s0s [6] YouTube Strategy: Adding Jitter isn't a Bug - High Scalability - https://highscalability.com/youtube-strategy-adding-jitter-isnt-a-bug/ [7] Timeouts, retries and backoff with jitter - AWS https://aws.amazon.com/builders-library/timeouts-retries-and-backoff-with-jitter/ [8] Connect to a REST API - Jitterbit Documentation https://success.jitterbit.com/design-studio/design-studio-reference/sources-and-targets/http/rest-api-tutorial/connect-to-a-rest-api/ [9] Figure 1: Figure 1: The thundering herd problem : Image generated using DALL-E 3 from the prompt "The Thundering Herd Problem: Taming the Stampede in Distributed Systems" (OpenAI, 2023) The thundering herd problem is a critical challenge in distributed systems that can bring even robust architectures to their knees. This article explores the nature of this issue, its recurring variant, and how jitter serves as a crucial defense mechanism. We'll also examine practical solutions for Java developers, including standard libraries and customization options for REST-based clients. Understanding the Thundering Herd The thundering herd problem occurs when a large number of processes or clients simultaneously attempt to access a shared resource, overwhelming the system. This can happen in various scenarios: After a service outage, when all clients try to reconnect at once When a popular cache item expires, causing multiple requests to hit the backend During scheduled events or cron jobs that trigger at the same time across many servers After a service outage, when all clients try to reconnect at once When a popular cache item expires, causing multiple requests to hit the backend During scheduled events or cron jobs that trigger at the same time across many servers The impact can be severe, leading to: Increased latency Service unavailability Cascading failures across dependent systems Increased latency Service unavailability Cascading failures across dependent systems Recurring Thundering Herd: A Persistent Threat While a single thundering herd event can be disruptive, recurring instances pose an even greater danger. This phenomenon happens when: Clients use fixed retry intervals, causing repeated traffic spikes Periodic tasks across multiple servers align over time IoT devices or smart home appliances check for updates on a fixed schedule Clients use fixed retry intervals, causing repeated traffic spikes Periodic tasks across multiple servers align over time IoT devices or smart home appliances check for updates on a fixed schedule Jitter: The Unsung Hero of Distributed Systems Jitter introduces controlled randomness into timing mechanisms, effectively dispersing potential traffic spikes. Here's why it's crucial: Prevents synchronization: By adding small random delays, jitter keeps processes from aligning their actions. Smooths traffic: Instead of sharp spikes, jitter creates a more even distribution of requests over time. Improves resilience: Systems with jitter can better handle load variations and recover from failures. Prevents synchronization: By adding small random delays, jitter keeps processes from aligning their actions. Prevents synchronization: Smooths traffic: Instead of sharp spikes, jitter creates a more even distribution of requests over time. Smooths traffic: Improves resilience: Systems with jitter can better handle load variations and recover from failures. Improves resilience: Implementing Jitter in Java Java developers have several options for implementing jitter: Standard Libraries java.util.concurrent.ThreadLocalRandom: javalong jitter = ThreadLocalRandom.current().nextLong(0, maxJitterMs); java.util.Random: javaRandom random = new Random(); long jitter = random.nextLong(maxJitterMs); java.util.concurrent.ThreadLocalRandom: javalong jitter = ThreadLocalRandom.current().nextLong(0, maxJitterMs); java.util.concurrent.ThreadLocalRandom: java.util.concurrent.ThreadLocalRandom: javalong jitter = ThreadLocalRandom.current().nextLong(0, maxJitterMs); javalong jitter = ThreadLocalRandom.current().nextLong(0, maxJitterMs); java.util.Random: javaRandom random = new Random(); long jitter = random.nextLong(maxJitterMs); java.util.Random: java.util.Random: javaRandom random = new Random(); long jitter = random.nextLong(maxJitterMs); javaRandom random = new Random(); long jitter = random.nextLong(maxJitterMs); Third-Party Libraries Guava's ExponentialBackOff: javaExponentialBackOff backoff = ExponentialBackOff.builder() .setInitialIntervalMillis(500) .setMaxIntervalMillis(1000 * 60 * 5) .setMultiplier(1.5) .setRandomizationFactor(0.5) .build(); Resilience4j's Retry: javaRetryConfig config = RetryConfig.custom() .waitDuration(Duration.ofMillis(1000)) .maxAttempts(3) .build(); Retry retry = Retry.of("myRetry", config); Guava's ExponentialBackOff: javaExponentialBackOff backoff = ExponentialBackOff.builder() .setInitialIntervalMillis(500) .setMaxIntervalMillis(1000 * 60 * 5) .setMultiplier(1.5) .setRandomizationFactor(0.5) .build(); Guava's ExponentialBackOff: Guava's ExponentialBackOff: javaExponentialBackOff backoff = ExponentialBackOff.builder() .setInitialIntervalMillis(500) .setMaxIntervalMillis(1000 * 60 * 5) .setMultiplier(1.5) .setRandomizationFactor(0.5) .build(); javaExponentialBackOff backoff = ExponentialBackOff.builder() .setInitialIntervalMillis(500) .setMaxIntervalMillis(1000 * 60 * 5) .setMultiplier(1.5) .setRandomizationFactor(0.5) .build(); Resilience4j's Retry: javaRetryConfig config = RetryConfig.custom() .waitDuration(Duration.ofMillis(1000)) .maxAttempts(3) .build(); Retry retry = Retry.of("myRetry", config); Resilience4j's Retry: Resilience4j's Retry: javaRetryConfig config = RetryConfig.custom() .waitDuration(Duration.ofMillis(1000)) .maxAttempts(3) .build(); Retry retry = Retry.of("myRetry", config); javaRetryConfig config = RetryConfig.custom() .waitDuration(Duration.ofMillis(1000)) .maxAttempts(3) .build(); Retry retry = Retry.of("myRetry", config); Customizing REST Clients with Jitter When working with REST clients, you can incorporate jitter in several ways: Custom Interceptors: Implement an interceptor that adds a random delay before each request. Retry Policies: Use libraries like OkHttp or Apache HttpClient that allow custom retry policies with jitter. Circuit Breakers: Implement circuit breakers with jittered retry mechanisms using libraries like Hystrix or Resilience4j. Custom Interceptors: Implement an interceptor that adds a random delay before each request. Custom Interceptors: Retry Policies: Use libraries like OkHttp or Apache HttpClient that allow custom retry policies with jitter. Retry Policies: Circuit Breakers: Implement circuit breakers with jittered retry mechanisms using libraries like Hystrix or Resilience4j. Circuit Breakers: IoT and Smart Home Devices: A Special Case The thundering herd problem is particularly relevant for IoT and smart home devices. These devices often use a common pattern of periodically checking for updates or sending data to a central server. To mitigate potential issues: Implement device-side jitter for update checks and data transmissions. Use push notifications instead of frequent polling when possible. Stagger initial boot times and update schedules across device fleets. Implement device-side jitter for update checks and data transmissions. Use push notifications instead of frequent polling when possible. Stagger initial boot times and update schedules across device fleets. Conclusion The thundering herd problem remains a significant challenge in distributed systems, but with proper understanding and implementation of jitter, developers can create more resilient and scalable applications. By leveraging Java's built-in libraries and third-party solutions, along with custom REST client configurations, you can effectively tame the herd and ensure your systems remain stable under heavy load. Remember, in the world of distributed systems, a little randomness goes a long way in maintaining order and preventing chaos. References: [1] Distributed Systems Horror Stories: The Thundering Herd Problem https://encore.dev/blog/thundering-herd-problem [2] Retry policy to avoid Thundering Herd Problem - Temporal Community https://community.temporal.io/t/retry-policy-to-avoid-thundering-herd-problem/790 [3] This is known generally as the "Thundering Herd" problem https://news.ycombinator.com/item?id=1722213 [4] Using the REST Client - Quarkus https://quarkus.io/guides/rest-client [5] Thundering Herd Problem and How not to do API retries - YouTube https://www.youtube.com/watch?v=8sTuCPh3s0s [6] YouTube Strategy: Adding Jitter isn't a Bug - High Scalability - https://highscalability.com/youtube-strategy-adding-jitter-isnt-a-bug/ [7] Timeouts, retries and backoff with jitter - AWS https://aws.amazon.com/builders-library/timeouts-retries-and-backoff-with-jitter/ [8] Connect to a REST API - Jitterbit Documentation https://success.jitterbit.com/design-studio/design-studio-reference/sources-and-targets/http/rest-api-tutorial/connect-to-a-rest-api/ https://encore.dev/blog/thundering-herd-problem https://community.temporal.io/t/retry-policy-to-avoid-thundering-herd-problem/790 https://news.ycombinator.com/item?id=1722213 https://quarkus.io/guides/rest-client https://www.youtube.com/watch?v=8sTuCPh3s0s https://highscalability.com/youtube-strategy-adding-jitter-isnt-a-bug/ https://aws.amazon.com/builders-library/timeouts-retries-and-backoff-with-jitter/ https://success.jitterbit.com/design-studio/design-studio-reference/sources-and-targets/http/rest-api-tutorial/connect-to-a-rest-api/ [9] Figure 1: Figure 1: The thundering herd problem : Image generated using DALL-E 3 from the prompt "The Thundering Herd Problem: Taming the Stampede in Distributed Systems" (OpenAI, 2023)
