This content originally appeared on HackerNoon and was authored by hackernoon
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)
This content originally appeared on HackerNoon and was authored by hackernoon
hackernoon | Sciencx (2024-10-08T09:46:45+00:00) The Thundering Herd Problem: Taming the Stampede in Distributed Systems. Retrieved from https://www.scien.cx/2024/10/08/the-thundering-herd-problem-taming-the-stampede-in-distributed-systems/
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