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distributed systems
60 shorts across 1 course, in learning order
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Results · 7 for “API Rate Limiting”
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⚡ One HLD concept. 60 seconds. Interview ready
Token Bucket Rate Limiting Explained for Interviews
The Token Bucket algorithm helps manage how many requests a server can handle.
⚡ One HLD concept. 60 seconds. Interview ready
Kafka Backpressure Strategies: Handling Traffic Spikes
Backpressure helps manage message flow in Kafka during traffic spikes.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Idempotency in System Design
Idempotency ensures that repeated operations in APIs do not cause duplicate effects.
⚡ One HLD concept. 60 seconds. Interview ready
Distributed Backpressure: Managing Service Overload
Distributed backpressure helps manage the flow of work between services to prevent overload.
⚡ One HLD concept. 60 seconds. Interview ready
HLD Timeout Pattern: Understanding Timeouts in Distributed Systems
A timeout indicates a deadline was exceeded, not necessarily a failure.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding CAP Theorem: Trade-offs in Distributed Systems
The CAP Theorem explains the trade-offs in distributed systems during network failures.
⚡ One HLD concept. 60 seconds. Interview ready
Load Shedding in Distributed Systems: Protecting Capacity
Load shedding helps systems reject excess requests to maintain performance during high demand.
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Not tagged “distributed systems”, but closely connected
