Distributed-Systems

In static infrastructure, services live at known addresses. Database at 10.0.1.5, cache at 10.0.1.6. Simple, predictable, fragile. In dynamic infrastructure — containers, auto-scaling, cloud — services appear and disappear constantly. IP addresses change. Instances multiply and vanish. Hardcoded addresses become a liability. Service discovery solves this: how do services find each other when everything is moving? The Problem 1 2 3 4 5 6 7 # Hardcoded - works until it doesn't DATABASE_URL = "postgres://10.0.1.5:5432/mydb" # What happens when: # - Database moves to a new server? # - You add read replicas? # - The IP changes after maintenance? DNS-Based Discovery The simplest approach: use DNS names instead of IPs. ...

Synchronous request-response is simple: client asks, server answers, everyone waits. But some operations don’t fit that model. Sending emails, processing images, generating reports — these take time, and your users shouldn’t wait. Message queues decouple the “request” from the “work,” letting you respond immediately while processing happens in the background. The Basic Pattern 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 # API endpoint - returns immediately @app.post("/orders") def create_order(order_data): order = db.create_order(order_data) # Queue async work instead of doing it now queue.enqueue('process_order', order.id) return {"order_id": order.id, "status": "processing"} # Worker - processes queue in background def process_order(order_id): order = db.get_order(order_id) charge_payment(order) send_confirmation_email(order) notify_warehouse(order) update_inventory(order) The user gets a response in milliseconds. The actual work happens whenever the worker gets to it. ...

Networks fail. Databases hiccup. External APIs return 503. In distributed systems, transient failures are not exceptional — they’re expected. The question isn’t whether to retry, but how. Bad retry logic turns a brief outage into a cascading failure. Good retry logic absorbs transient issues invisibly. The Naive Retry (Don’t Do This) 1 2 3 4 5 6 7 # Immediate retry loop - a recipe for disaster def call_api(): while True: try: return requests.get(API_URL) except RequestException: continue # Hammer the failing service This creates a thundering herd. If the service is struggling, every client immediately retrying makes it worse. You’re not recovering from failure — you’re accelerating toward total collapse. ...

Your payment service is down. Every request to it times out after 30 seconds. You have 100 requests per second hitting that endpoint. Do the math: within a minute, you’ve got 6,000 threads waiting on a dead service, and your entire application is choking. This is where circuit breakers earn their keep. The Problem: Cascading Failures In distributed systems, a single failing dependency can take down everything. Without protection, your system will: ...

Learn how circuit breakers prevent cascade failures in distributed systems by detecting failures early and failing fast instead of waiting for timeouts.

Learn how to design idempotent APIs that handle duplicate requests safely, making your systems more resilient to network failures and client retries.

When your application spans multiple services, containers, and regions, print("something went wrong") doesn’t cut it anymore. Structured logging transforms your logs from walls of text into queryable data. Why Structured Logging? Traditional logs are strings meant for humans: [ 2 0 2 6 - 0 2 - 1 3 1 4 : 0 0 : 0 0 ] E R R O R : F a i l e d t o p r o c e s s o r d e r 1 2 3 4 5 f o r u s e r j o h n @ e x a m p l e . c o m Structured logs are data meant for machines (and humans): ...