How to develop an architecture for processing about a hundred million messages daily, including data updates and interaction with a third-party system?

To process hundreds of millions of messages per day with data updates and interaction with an external system, the architecture must be scalable, fault-tolerant, and provide high performance.

Main components and approaches:

1. Message Queue — use distributed message brokers (e.g., Kafka, RabbitMQ) for receiving and buffering incoming messages. This will smooth out load peaks and ensure reliable delivery.

2. Scalable processing (Consumer Pool) — multiple message consumers that process data in parallel. In Java, you can use a thread pool or reactive frameworks (e.g., Reactor, Akka).

3. Idempotency and data updating — when updating data, it is important to ensure idempotency of operations to avoid inconsistency during reprocessing of messages.

4. Caching and database — use databases optimized for high load (NoSQL or distributed SQL), as well as caching (Redis, Memcached) to speed up access to frequently used data.

5. Interaction with external system — implement asynchronous calls with retries and timeouts to avoid blocking the main processing. You can use the Circuit Breaker pattern for resilience.

6. Monitoring and logging — implement monitoring systems (Prometheus, Grafana) and centralized logging to track status and quickly respond to failures.

An example of a simplified Java scheme using Kafka and a thread pool:

KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Collections.singletonList("topic"));
ExecutorService executor = Executors.newFixedThreadPool(10);

while (true) {
    ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
    for (ConsumerRecord<String, String> record : records) {
        executor.submit(() -> processMessage(record.value()));
    }
}

void processMessage(String message) {
    // Parsing, data updating, calling external system with retry
}

This approach will allow efficient and reliable processing of a large message stream.