Peak load: 25k reads / 5k writes per second Data volume: 10 TB per year Peak parallelism: 120k concurrent requests Annual user growth: 30% Target p99 latency: <150 ms for reads, <400 ms for writes Target availability: 99.95% Your task is to develop an architectural solution that addresses current issues, ensures consistency and fault tolerance, and implements tracing mechanisms for full lifecycle monitoring of orders. Describe the failure handling mechanism, migration plan from the current architecture, and explain how the proposed solution will improve system reliability and transparency.

To handle peak loads (25k reads and 5k writes per second) and ensure scalability at 120k concurrent requests, a microservices architecture with distributed data storage and asynchronous processing of records is proposed.

Main components of the solution:

• Database: Use a scalable distributed storage (e.g., Cassandra, ScyllaDB, or CockroachDB) supporting horizontal scaling and replication for fault tolerance and consistency.
• Caching: Implement a distributed cache (Redis Cluster or Memcached) to reduce read latency and offload the database.
• Message queues: Use a queue (Kafka, RabbitMQ) for processing records with guaranteed delivery and retries, ensuring asynchronous operation and fault tolerance.
• Consistency mechanism: Use patterns like "Event Sourcing" or "CQRS" to separate read and write operations, optimizing performance and ensuring data consistency.
• Monitoring and tracing: Implement distributed tracing (Jaeger, Zipkin) and metrics (Prometheus + Grafana) for comprehensive monitoring of the order lifecycle, including delays, errors, and throughput.

Failure handling mechanisms:

• Automatic failover to backup database nodes upon failure.
• Retry sending messages to the queue during transient errors.
• Timeouts and circuit breakers to prevent cascading failures.
• Logging and alerts for delay or error thresholds.

Migration plan:

1. Analyze current architecture and identify bottlenecks.
2. Gradually implement caching to reduce database load.
3. Introduce queues for asynchronous record processing.
4. Migrate data to the distributed database with minimal downtime.
5. Integrate tracing and monitoring systems.
6. Conduct load testing and optimize.

Improvements:

• Increased fault tolerance through replication and automatic failover.
• Reduced read latency via caching.
• Guaranteed delivery and processing of records through queues.
• Full transparency of the order lifecycle via tracing.
• Scalability to accommodate growth in users and data volume.

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Example of simplified Go code for writing to a queue with retries:

func writeToQueue(ctx context.Context, msg Message) error {
    const maxRetries = 3
    var err error
    for i := 0; i < maxRetries; i++ {
        err = queue.Publish(ctx, msg)
        if err == nil {
            return nil
        }
        time.Sleep(time.Duration(i) * time.Second) // exponential backoff
    }
    return fmt.Errorf("failed to publish message after retries: %w", err)
}