Advanced Priority Queue Patterns & Production Tuning

Once you have mastered the basics of multi-criteria scoring and age-based escalation, you can implement more sophisticated, production-grade patterns.

Pattern 1: Dedicated Infrastructure for Tiers

For true performance isolation, enterprise or premium customers should be routed to entirely separate infrastructure (e.g., a different Kafka cluster). This prevents "noisy neighbor" problems, where a massive influx of low-priority data could still impact higher-tier customers at the network level.

Dedicated Enterprise Infrastructure

output:
  switch:
    cases:
      # Enterprise customers go to a completely separate Kafka cluster
      - check: this.customer_tier == "enterprise"
        output:
          kafka:
            addresses: [ ${ENTERPRISE_KAFKA_BROKERS} ]
            topic: enterprise-events
            # ... include strict reliability and security settings

      # All other customers go to the standard, shared Kafka cluster
      - output:
          kafka:
            addresses: [ ${KAFKA_BROKERS} ]
            topic: standard-events

Pattern 2: Dynamic Scoring Based on System Load

Your scoring can be made adaptive. If the system is under heavy load, the criteria for achieving "critical" or "high" priority can be made stricter to shed load.

Load-Aware Scoring

- mapping: |
    root = this

    # Fetch current system load from a monitoring endpoint (conceptual)
    let system_load_percent = http_client(
      "http://monitoring.internal/api/load",
      {"timeout": "50ms"}
    ).cpu.or(50)

    # If load is high, apply a penalty to all scores
    let load_penalty = match {
      system_load_percent > 90 => 20, # Heavy penalty
      system_load_percent > 75 => 10, # Moderate penalty
      _ => 0
    }

    # Calculate score as before...
    let base_score = ...

    # Apply the penalty
    root.priority_score = base_score - load_penalty

Pattern 3: SLA Tracking and Violation Alerting

For paying customers, you can explicitly track and alert on SLA (Service Level Agreement) violations.

SLA Violation Tracking

- mapping: |
    root = this

    # Define SLA targets in milliseconds
    let sla_target_ms = match this.customer_tier {
      "enterprise" => 100,
      "premium" => 500,
      _ => 5000
    }

    let processing_time_ms = (now().unix_nano() - this.timestamp.parse_timestamp().unix_nano()) / 1000000

    if processing_time_ms > sla_target_ms {
      root.sla_violation = {
        "tier": this.customer_tier,
        "target_ms": sla_target_ms,
        "actual_ms": processing_time_ms
      }
    }

# Downstream, a 'switch' can route SLA violations to an alerting system.

Production Kafka Tuning

The minimal Kafka configurations in the steps are for learning. A production setup would have much more detailed tuning for each priority queue.

Production-Tuned Critical Queue

- check: this.priority == "critical"
  output:
    kafka:
      addresses: ["${KAFKA_BROKERS}"]
      topic: logs-critical
      batching:
        count: 1 # No batching for speed
        period: 0s
      # Reliability settings
      max_in_flight: 1
      ack_replicas: true
      idempotent_write: true
      max_retries: 10
      backoff:
        initial_interval: 100ms
        max_interval: 2s
      compression: none # No compression for speed

Production-Tuned Bulk Queue

- check: this.priority == "bulk"
  output:
    kafka:
      addresses: ["${KAFKA_BROKERS}"]
      topic: logs-bulk
      batching:
        count: 5000 # Massive batches for throughput
        period: 5m
        byte_size: 10MB
      # Performance settings
      max_in_flight: 20
      compression: gzip # High compression to save network/disk
      # Reliability is less important
      ack_replicas: false
      max_retries: 1

These advanced patterns allow you to build a highly sophisticated, business-aware, and resilient routing system that can be tuned to meet specific performance, cost, and compliance goals.