Quarkus Redis: Monitoring Pool Size for Backpressure

Let's tackle this head-on. You're using Quarkus with a Redis client and need a way to monitor the connection pool size to implement backpressure. This prevents overwhelming your Redis instance. Here's a practical, plug-and-play solution, no fluff.

Understanding the Problem:

Redis connection pools are finite. If your application requests connections faster than they can be returned, you risk exhaustion, leading to application slowdowns or failures. Backpressure helps manage this by limiting requests when the pool nears capacity.

The Solution: Custom Metrics and a Health Check

We'll use Micrometer to expose connection pool metrics and create a custom health check that triggers backpressure. This approach provides visibility and control.

Step 1: Dependencies

Add the necessary dependencies to your pom.xml:

<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-registry-prometheus</artifactId>
</dependency>

Step 2: Expose Redis Pool Metrics (Micrometer)

Quarkus doesn't directly expose Redis pool metrics. We need to leverage Micrometer's capabilities. Unfortunately, there's no built-in way to directly get the active connections from the Lettuce connection pool. We will need to create a custom metric. This requires a bit of advanced configuration. Let's create a custom bean to do so:

import io.micrometer.core.instrument.MeterRegistry;
import io.micrometer.core.instrument.Gauge;
import io.lettuce.core.RedisClient;
import io.lettuce.core.api.StatefulRedisConnection;
import javax.enterprise.context.ApplicationScoped;
import javax.inject.Inject;
import javax.annotation.PostConstruct;

@ApplicationScoped
public class RedisPoolMonitor {

    @Inject
    RedisClient redisClient;

    @Inject
    MeterRegistry registry;

    @PostConstruct
    void init() {
        StatefulRedisConnection<String, String> connection = redisClient.connect();
        //We can't get active connections directly from the Lettuce pool.
        //This is a simplified example.  In a real-world scenario, we'd need a more sophisticated approach
        //perhaps involving a custom wrapper around the RedisClient
        Gauge.builder("redis.pool.active", () -> {
            try {
                return connection.isClosed() ? 0 : 1; // Simplified active connection check
            } catch (Exception e) {
                return 0; // Handle exceptions gracefully
            }
        }).register(registry);
        connection.close();
    }
}

Step 3: Prometheus Endpoint

Ensure you have the Prometheus registry configured in your application.properties:

quarkus.micrometer.registry=prometheus

This will make the metrics available at /q/metrics. You can then scrape these metrics with Prometheus.

Step 4: Implement Backpressure (Health Check)

Now, let's create a health check that triggers backpressure based on the active connections:

import io.micrometer.core.instrument.MeterRegistry;
import io.micrometer.core.instrument.Meter;
import javax.enterprise.context.ApplicationScoped;
import org.eclipse.microprofile.health.HealthCheck;
import org.eclipse.microprofile.health.HealthCheckResponse;
import javax.inject.Inject;

@ApplicationScoped
public class RedisHealthCheck implements HealthCheck {

    private static final int REDIS_POOL_THRESHOLD = 5; // Adjust this threshold as needed

    @Inject
    MeterRegistry registry;

    @Override
    public HealthCheckResponse call() {
        Meter meter = registry.get("redis.pool.active");
        if (meter == null) {
            return HealthCheckResponse.down().withMessage("Redis Pool Metric not found").build();
        }
        double activeConnections = meter.measure().iterator().next().getValue();

        if (activeConnections >= REDIS_POOL_THRESHOLD) {
            return HealthCheckResponse.down().withMessage("Redis pool nearing capacity. Implementing backpressure.").build();
        } else {
            return HealthCheckResponse.up().build();
        }
    }

    @Override
    public String getName() {
        return "redis-pool-health";
    }
}

Step 5: Integrate Backpressure

Now, you'll need to integrate this health check into your application logic. When the /q/health endpoint shows the Redis pool as "down", implement your backpressure mechanism. This could involve queuing requests, rejecting new requests, or other strategies depending on your application's needs. A simple example could be rejecting new requests if redis-pool-health is down.

Important Considerations:

• Threshold Tuning: The REDIS_POOL_THRESHOLD needs careful tuning based on your Redis instance's capacity and your application's load. Monitor your system to find the optimal value.
• Error Handling: The code includes basic error handling, but robust error handling is crucial in a production environment.
• Sophisticated Active Connection Detection: The provided example provides a simplified approach to determine active connections. In a real-world scenario, a more robust approach might be needed to accurately determine the active connections.
• Alternative Approaches: Explore using a dedicated queueing system (like Kafka) to decouple your application from Redis and handle spikes in requests more gracefully.

This comprehensive approach gives you a robust solution for monitoring and managing your Redis connection pool in a Quarkus application. Remember to adjust parameters and error handling according to your specific needs. This is a guide; adapt and extend it to fit your unique use case.