Stop treating system design like a trivia test. Learn how to think like an architect by mastering load balancing, sharding, and scaling trade-offs.
System design isn't about finding the 'one right answer'—it’s about being able to see the consequences of every choice. It’s about recognizing that simplicity has a cost and scalability has a complexity tax.
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Vertical scaling, or "scaling up," involves adding more power to a single server, such as increasing RAM or CPU cores. While simple to implement because it avoids distributed systems complexity, it is limited by physical hardware ceilings and creates a single point of failure. Horizontal scaling, or "scaling out," involves adding more commodity-grade servers to a fleet. This approach offers nearly unlimited growth and better resilience, but it requires the application to be stateless so that any server can handle any incoming request.
Load balancers use various strategies depending on the application's needs. Round Robin sends requests sequentially down a list of servers, while Least Connections directs traffic to the server currently handling the fewest active tasks. For environments with mixed hardware, Weighted Round Robin can send more traffic to more powerful nodes. Additionally, Layer 4 load balancing routes traffic quickly based on IP addresses and ports, whereas Layer 7 load balancing "peeks" inside the data to route based on specific URLs, headers, or cookies.
The CAP Theorem states that in the event of a network partition, a distributed system can only provide either Consistency or Availability, not both. A "CP" system (Consistent and Partition Tolerant) ensures all nodes see the same data but will stop accepting requests if it cannot guarantee that consistency, prioritizing being "down" over being "wrong." An "AP" system (Available and Partition Tolerant) stays online and accepts requests even if it cannot sync with other nodes, prioritizing uptime while accepting that some users may see "stale" or eventually consistent data.
Idempotency is the property where performing an operation multiple times has the same effect as performing it once. In distributed systems, network calls often time out, leaving the sender unsure if a request succeeded or failed. This necessitates retries. If an operation like a bank transfer is not idempotent, a retry could result in a customer being charged twice. Using "Idempotency Keys" allows a server to recognize a repeated request and return the original success message without executing the underlying logic a second time.
Message queues act as "shock absorbers" by decoupling services through asynchronous communication. Instead of Service A waiting for Service B to respond, it drops a message into a queue and moves on. This prevents a slow or failing downstream service from causing a chain reaction of failures across the entire system. Queues also help manage traffic spikes, such as during a flash sale, by buffering requests so that downstream services can process them at a steady, manageable pace rather than being overwhelmed.
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