Microservices: Custom Filters for Debouncing, Throttling, Rate Limits & Backoff

In a microservices architecture, implementing custom filters for Debouncing, Throttling, Rate Limiting, and Exponential Backoff helps in managing traffic efficiently and improving the overall performance of your services. These filters are usually applied at the API gateway or individual microservice level.

Setting up Custom Filters using Debouncing, Throttling, Rate Limiting, and Exponential Backoff

You can create custom filters in a Spring Boot microservice and configure them using the application.yml file. These filters will be applied globally or to specific endpoints to control traffic and manage retries.

1. Debouncing Filter

Debouncing helps to ensure that multiple quick successive requests are handled with a delay to avoid overwhelming the service. It is typically a client-side technique, but server-side, you can debounce request handling using filters to prevent too frequent handling of the same type of request.

YAML Setup for Debouncing:

This is uncommon in microservices but can be implemented using a filter with a configurable delay between similar request types.

You could implement a custom filter that delays requests and configure it in application.yml.

YAML Configuration:

yaml

debounce:

enabled: true

delay: 500 # Delay in milliseconds before handling the next similar request

Debouncing Filter in Java:

java

@Component

public class DebounceFilter extends OncePerRequestFilter {

@Value("${debounce.delay}")

private long debounceDelay;

private final Map<String, Long> requestTimestamps = new ConcurrentHashMap<>();

@Override

protected void doFilterInternal(HttpServletRequest request, HttpServletResponse response, FilterChain filterChain)

throws ServletException, IOException {

String key = request.getRemoteAddr() + request.getRequestURI();

long currentTime = System.currentTimeMillis();

if (requestTimestamps.containsKey(key)) {

long lastRequestTime = requestTimestamps.get(key);

if (currentTime - lastRequestTime < debounceDelay) {

response.setStatus(HttpStatus.TOO_MANY_REQUESTS.value());

return;

}

requestTimestamps.put(key, currentTime);

filterChain.doFilter(request, response);

}

2. Throttling Filter

Throttling limits the number of requests handled by the server in a fixed time interval.It controls the frequency of request handling, ensuring that the server handles only a certain number of requests in a fixed time window.

YAML Setup for Throttling:

You can configure throttling by setting a fixed request limit in a specific time window.

yaml

throttle:

enabled: true

max-requests: 10 # max number of requests

time-window: 60s # time window (60 seconds)

Throttling Filter in Java:

java

@Component

public class ThrottleFilter extends OncePerRequestFilter {

@Value("${throttle.max-requests}")

private int maxRequests;

@Value("${throttle.time-window}")

private long timeWindow; // in milliseconds

private final Map<String, List<Long>> requestTimestamps = new ConcurrentHashMap<>();

@Override

protected void doFilterInternal(HttpServletRequest request, HttpServletResponse response, FilterChain filterChain)

throws ServletException, IOException {

String clientIP = request.getRemoteAddr();

long currentTime = System.currentTimeMillis();

List<Long> timestamps = requestTimestamps.getOrDefault(clientIP, new ArrayList<>());

timestamps.removeIf(timestamp -> currentTime - timestamp > timeWindow);

if (timestamps.size() >= maxRequests) {

response.setStatus(HttpStatus.TOO_MANY_REQUESTS.value());

return;

}

timestamps.add(currentTime);

requestTimestamps.put(clientIP, timestamps);

filterChain.doFilter(request, response);

}

3. Rate Limiting Filter

Rate Limiting restricts the number of API requests that a user or client can send over a specific time window. It allows you to restrict the number of requests a client can send within a certain period. It’s often used to prevent abuse of the API by malicious users.

YAML Setup for Rate Limiting:

You can configure rate limiting in application.yml by defining the maximum number of requests a user can send in a time frame.

yaml

rate-limiting:

enabled: true

max-requests: 100 # Number of requests allowed

time-window: 60s # Time period (e.g., 60 seconds)

Rate Limiting Filter in Java:

java

@Component

public class RateLimitingFilter extends OncePerRequestFilter {

@Value("${rate-limiting.max-requests}")

private int maxRequests;

@Value("${rate-limiting.time-window}")

private long timeWindow; // in milliseconds

private final Map<String, List<Long>> clientRequests = new ConcurrentHashMap<>();

@Override

protected void doFilterInternal(HttpServletRequest request, HttpServletResponse response, FilterChain filterChain)

throws ServletException, IOException {

String clientKey = request.getRemoteAddr();

long currentTime = System.currentTimeMillis();

List<Long> timestamps = clientRequests.getOrDefault(clientKey, new ArrayList<>());

timestamps.removeIf(timestamp -> currentTime - timestamp > timeWindow);

if (timestamps.size() >= maxRequests) {

response.setStatus(HttpStatus.TOO_MANY_REQUESTS.value());

return;

}

timestamps.add(currentTime);

clientRequests.put(clientKey, timestamps);

filterChain.doFilter(request, response);

}

4. Exponential Backoff Filter

Exponential Backoff allows for handling retries by increasing the delay between retry attempts exponentially. This technique is often used when a service temporarily fails or is overloaded.

It is a technique used to retry failed requests, but the retry intervals increase exponentially. This is often used in microservices to avoid overwhelming the service or server when failures occur.

YAML Setup for Exponential Backoff:

You can configure the initial delay, maximum delay, and the backoff multiplier in application.yml

yaml

exponential-backoff:

enabled: true

initial-delay: 1s # Start with 1 second delay

max-delay: 64s # Maximum retry delay

backoff-multiplier: 2 # Double the delay after each failure

Exponential Backoff Filter in Java:

java

public class ExponentialBackoffRetryHandler {

private final long initialDelay;

private final long maxDelay;

private final int backoffMultiplier;

public ExponentialBackoffRetryHandler(long initialDelay, long maxDelay, int backoffMultiplier) {

this.initialDelay = initialDelay;

this.maxDelay = maxDelay;

this.backoffMultiplier = backoffMultiplier;

}

public void executeWithBackoff(Runnable task) throws InterruptedException {

long delay = initialDelay;

while (true) {

try {

task.run();

return;

} catch (Exception e) {

if (delay > maxDelay) {

throw new RuntimeException("Exceeded max retry attempts");

}

System.out.println("Retrying in " + delay + "ms...");

Thread.sleep(delay);

delay *= backoffMultiplier;

}

Usage Example:

java

public class BackoffRetryExample {

public static void main(String[] args) throws InterruptedException {

ExponentialBackoffRetryHandler backoff = new ExponentialBackoffRetryHandler(1000, 64000, 2);

backoff.executeWithBackoff(() -> {

// Your task to retry

if (Math.random() > 0.3) {

throw new RuntimeException("Simulated failure");

}

System.out.println("Task succeeded");

});

}

Setting Up Filters in Spring Boot with YAML Configuration

Define filters as beans or components in your Spring Boot application.
Use application.yml to configure the filters dynamically.
Activate or deactivate filters based on the configuration settings in application.yml.

Example YAML Configuration:

yaml

filters:

debounce:

enabled: true

delay: 500

throttle:

enabled: true

max-requests: 10

time-window: 60s

rate-limiting:

enabled: true

max-requests: 100

time-window: 60s

exponential-backoff:

enabled: true

initial-delay: 1s

max-delay: 64s

backoff-multiplier: 2

Conclusion

By implementing custom filters for Debouncing, Throttling, Rate Limiting, and Exponential Backoff in a Spring Boot microservices application, you can manage incoming traffic effectively and prevent your services from being overwhelmed. Using YAML configurations allows you to dynamically adjust the filter settings without modifying the source code. This is particularly useful in production environments where flexibility is essential for maintaining service stability and performance.

For more information, visit Mastering Debouncing, Throttling, Rate Limiting, and Exponential Backoff.