System Design Interview Cheat Sheet
A new cheatsheet for senior/staff engineering interviews compiles 35 real system design questions. The list covers essentials like designing distributed queues, rate limiters, and real-time data pipelines, plus fintech-specific problems like building an ETA prediction service. It's a practical resource for engineers looking to bridge the gap from coding to architectural decision-making.
Modern mortgage lending platforms are increasingly abandoning monolithic architectures in favor of event-driven, microservices-based systems. This shift addresses the data silos and batch processing limitations of legacy Loan Origination Systems (LOS), which can cause significant delays and rework. An event-driven approach provides real-time visibility into the loan pipeline, a stark contrast to the overnight batch updates of traditional systems. At the core of these modern systems are high-throughput distributed message queues, designed for scalability and fault tolerance. To handle massive volumes, these queues are often partitioned across multiple machines, a technique known as sharding. This horizontal scaling allows the system to process millions of messages per second by adding more brokers and reassigning partitions to balance the load. For financial data, "exactly-once" delivery semantics are crucial to prevent duplicate or lost messages, which can be achieved through idempotent producers and atomic transactions. This is a step above the default "at-least-once" guarantee, which protects against loss but not duplication. The entire distributed system relies on a consensus service like ZooKeeper to manage cluster metadata and leader election for partitions. Real-time data pipelines are essential for immediate fraud detection and risk management in fintech. By leveraging stream processing engines like Apache Flink or Spark Streaming, companies can analyze transaction patterns and score for risk in milliseconds. This capability can reportedly cut fraud losses by as much as 60%. API rate limiters are a critical component for protecting these systems from overuse, whether malicious or inadvertent. Common implementation algorithms include the Token Bucket, which handles bursts of traffic well, and the Sliding Window, which offers a balance of accuracy and efficiency. These are typically implemented on the server-side or at the API gateway level to ensure centralized control. In the context of fintech, ETA prediction services are being used to provide customers with precise information on the arrival time of funds. For instant payments, this can be 10 seconds or less, while traditional payments may range from 24 to 72 hours. These systems often use machine learning models that analyze historical data alongside real-time inputs like network traffic and weather to continuously refine predictions. Staff-level system design interviews assess a candidate's ability to identify the true crux of a problem and ruthlessly simplify the architecture. Interviewers expect senior candidates to drive the conversation, define constraints, and discuss trade-offs, moving beyond basic component descriptions. The focus is often on ultra-low-latency and high-reliability systems capable of processing millions of updates without data loss.
