I'm preparing for
Topic
distributed systems
60 shorts across 1 course, in learning order
🎛️ Narrow downsubject · level · topic2▾
Results · 23 for “Basic server architecture”
← Front page✨ Smart search: matched by meaning, not just words
⚡ One HLD concept. 60 seconds. Interview ready
Understanding gRPC Architecture: Behind the Call
gRPC allows efficient communication between services using a structured request-response model.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Vertical and Horizontal Scaling in System Design
Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.
⚡ One HLD concept. 60 seconds. Interview ready
Elasticsearch Architecture: How Distributed Search Works
Elasticsearch uses distributed architecture to search data quickly across multiple servers.
⚡ One HLD concept. 60 seconds. Interview ready
Consistent Hashing: Key to Distributed Systems Scalability
Consistent hashing helps distribute data across servers with minimal movement.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Network Partitions in Distributed Systems
Network partitions occur when servers are operational but can't communicate, affecting system performance.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding RPC: Remote Procedure Calls in Distributed Systems
RPC allows one service to call a function in another service over the network.
⚡ One HLD concept. 60 seconds. Interview ready
HLD: Understanding Server Failure Detection and Timeouts
Timeouts in distributed systems indicate suspicion, not confirmed failure.
⚡ One HLD concept. 60 seconds. Interview ready
When to Use Microservices in Software Design
Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.
⚡ One HLD concept. 60 seconds. Interview ready
Log Replication and Majority Commit in Distributed Systems
Log replication ensures data consistency by requiring majority acknowledgment before committing changes.
⚡ 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
ZooKeeper Architecture and ZAB Explained
ZooKeeper coordinates distributed systems using an ensemble of servers and the ZAB protocol.
⚡ One HLD concept. 60 seconds. Interview ready
Graceful Degradation in System Design
Graceful degradation allows apps to function partially during service failures.
⚡ One HLD concept. 60 seconds. Interview ready
Failover and Split-Brain in High-Level Design
Failover ensures systems remain operational by managing leader changes during failures.
⚡ 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
Understanding Consensus in Distributed Systems
Consensus ensures that distributed systems can agree on decisions even during failures.
⚡ One HLD concept. 60 seconds. Interview ready
Leader Election in Distributed Systems Explained
Leader election helps distributed systems select one coordinator from many nodes.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Elasticsearch Node Roles for Scaling Clusters
Elasticsearch has different node roles that help manage data and scale clusters effectively.
⚡ One HLD concept. 60 seconds. Interview ready
HLD: Fail-Stop vs Fail-Recover Explained
Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.
⚡ One HLD concept. 60 seconds. Interview ready
Leader Election Algorithms: Bully vs Raft vs ZooKeeper
Bully, Raft, and ZooKeeper are distinct algorithms for leader election in distributed systems.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Distributed Clocks in Systems Design
In distributed systems, timestamps don't always reflect the true order of events.
⚡ One HLD concept. 60 seconds. Interview ready
Understanding Apache Kafka for Scalable Event Streaming
Apache Kafka is a system for managing large volumes of events efficiently.
⚡ 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
HLD: Crash Failures vs Network Failures in Distributed Systems
Crash failures stop a service, while network failures disrupt communication.
🧠Related by meaning
Not tagged “distributed systems”, but closely connected
