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Computer Science

Distributed Systems

APIs in Distributed Systems

Basic Concepts of Distributed Systems

CAP Theorem

Consensus Protocols Overview

Consistency Models

Data Replication Strategies

Distributed System Patterns

Distributed Hash Tables

Distributed Caching Techniques

Distributed Databases

Distributed Monitoring and Logging

Distributed Transactions

Distributed Messaging Systems

Distributed Computing Models

Distributed File Systems

Eventual Consistency and Trade-offs

Fault Tolerance Techniques

Green Computing in Distributed Systems

Load Balancing Strategies

Leader Election Algorithms

Network Partitioning and Bridging

Paxos Consensus Algorithm

Peer-to-Peer Systems

Notable Distributed Algorithms

Vector Clocks and Their Uses

Raft Consensus Algorithm

Security Considerations in Distributed Systems

Service Discovery in Distributed Systems

Scalability Design Patterns

Synchronization Mechanisms

CAP Theorem

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Availability

Every request receives a (non-error) response – without the guarantee that it contains the most recent write. In distributed systems, availability ensures that the system remains operational even when some nodes fail or become isolated from the network.

Consistency

Every read receives the most recent write or an error. In distributed systems, consistency ensures that all nodes see the same data at the same time. Strong consistency can be challenging to achieve in the presence of partitions.

Partition Tolerance

The system continues to operate despite an arbitrary number of messages being dropped (or delayed) by the network between nodes. In distributed systems, partition tolerance means that the system can sustain any network failure that doesn't result in a total network failure.

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