Express courier. Direct.
Also overnight.
x25pronto Switzerland takes goods direct to the destination. Small loads go by car. Larger loads go by van or Van XL. Direct, express and overnight trips run Monday to Sunday when a slot is free. Free times are shown in the booking calendar. This keeps the plan clear.
Practical patterns for asynchronous order processing
Actionable patterns for robust asynchronous order processing. Focus on queues, idempotent workers, visibility timeouts and dead‑letter queues.
Quick answer
Asynchronous order processing improves scalability and reliability. Use queues to decouple systems, implement idempotent workers, manage retries with visibility timeouts, and route failed messages to a dead‑letter queue. These patterns lower downtime and simplify troubleshooting in production.
Why choose asynchronous processing?
Synchronous calls tie up resources and increase latency. Asynchronous processing separates acceptance from work. That helps with traffic spikes and long tasks like generating reports or calling external APIs. For Swiss SMEs this lowers operational risk and improves responsiveness.
Queues as a decoupling layer
A queue buffers incoming orders. Managed options include AWS SQS or Google Pub/Sub; self‑hosted options include RabbitMQ or Kafka. Store minimal payloads or pointers to full records (e.g., DB IDs). This keeps the sender fast and lets workers process later.
Idempotent workers: accept retries
Workers must handle repeated deliveries safely. Use deduplication IDs and database safeguards (UPSERT, unique constraints). Keep processing state (pending, processing, done) and design steps so repeating them does not corrupt the result.
Visibility timeouts control reprocessing
When a worker reads a message, many queues make it invisible for a time. Set visibility longer than normal processing. If a worker crashes, the message becomes visible again and another worker can pick it up. Adjust timeouts dynamically for variable tasks.
Dead‑letter queues for problematic orders
After repeated failures, move messages to a dead‑letter queue (DLQ). That separates unrecoverable items from retriable ones. Inspect DLQ messages manually or with debugging jobs. Store metadata like error codes, attempt count and original payload.
Retry strategies and backoff
Use exponential backoff with jitter to avoid thundering herds. Different errors need different handling: transient network errors retry; validation errors go to DLQ. Cap retries and document rules so operators can respond predictably.
Frequently asked questions
How do I test idempotence effectively?
Send duplicate messages with the same ID and check for duplicates in storage or incorrect state transitions. Combine unit tests with end‑to‑end tests against a test queue.
When should I use a DLQ?
Use a DLQ after several automated retry attempts fail and no automatic fix is possible. DLQs keep the primary queue flowing and allow manual examination.
Which tools suit Swiss companies?
Managed cloud queues (AWS SQS, Google Pub/Sub) are common. For on‑premises, RabbitMQ or Kafka work well. Choose based on compliance, latency and operations budget.
What should I monitor?
Track queue length, throughput, error rate, DLQ size and average processing time. Alert on sudden growth in queue depth or rising error counts.