Some Distributed Systems Exploration

🌍 Distributed Systems Exploration #

Distributed systems are the backbone of scalable and fault-tolerant applications. In this phase, we explore 6+ distributed technologies, understanding how they work, what problems they solve, and where they fit in microservices.

🔍 Exploration Methodology #

For this phase, students will work in collaborative teams to deeply investigate distributed systems technologies. Each team will:

  1. Research & Analyze: Thoroughly explore an assigned distributed system technology
  2. Document Findings: Create comprehensive technical documentation
  3. Present Solutions: Deliver an engaging presentation to peers and faculty

Your exploration should focus on these key aspects:

Focus Area Key Questions to Address
🏗️ Architecture How is the system structured? What are its core components?
⚙️ Working Principles What algorithms and techniques make it function? How does data flow?
🛡️ High Availability How does it handle failures? What redundancy mechanisms exist?
💼 Use Cases Where is it most effectively applied? What problems does it solve best?
🔄 Integration How does it fit within a microservices ecosystem?

Teams will be evaluated on technical depth, presentation quality, and practical insights provided.

📦 1. Apache Cassandra – Decentralized NoSQL DB #

Architecture Highlights:

  • Peer-to-peer ring-based system using consistent hashing
  • Eventual consistency with tunable quorum reads/writes
  • Replication across data centers
  • Uses SSTables and Memtables for fast write-heavy workloads

Use Case: High-volume write system for analytics or logs.

⚡ 2. Apache Kafka – Distributed Event Streaming Platform #

Architecture Highlights:

  • Distributed commit log with partitioned topics
  • Producer → Kafka Broker → Consumer
  • Message durability via segment files on disk
  • High-throughput stream processing with horizontal scaling

Use Case: Decoupled microservice communication, event sourcing, stream analytics.

🧠 3. Redis Cluster – In-Memory Key-Value Store (Distributed Mode) #

Architecture Highlights:

  • Hash-slot-based sharding (16,384 slots)
  • Master-replica architecture with automatic failover (via Sentinel or Cluster)
  • High-speed data access with optional persistence

Use Case: Caching for microservices, pub/sub for chat or real-time updates.

🧮 4. Etcd – Distributed Key-Value Store for Configuration & Coordination #

Architecture Highlights:

  • Strongly consistent store using Raft consensus
  • Frequently used in Kubernetes for state/config storage
  • Designed for leader election, distributed locking, and configuration management

Use Case: Service registry, feature flag storage, cluster coordination.

🔎 5. Consul – Service Discovery and Key/Value Configuration #

Architecture Highlights:

  • Gossip-based peer discovery
  • Offers DNS + HTTP APIs for service registration and health checking
  • KV store with ACLs for configuration sharing
  • Integrates with Envoy for service mesh features

Use Case: Discover microservices dynamically, share config globally across services.

🌐 6. CockroachDB – Distributed SQL Database #

Architecture Highlights:

  • Follows Google Spanner-inspired architecture
  • Distributed ACID transactions via Raft
  • Multi-region, horizontally scalable SQL
  • Strong consistency with PostgreSQL compatibility

Use Case: Global backend for services needing relational queries and strong consistency.

📘 7. MinIO – Distributed Object Storage (S3-Compatible) #

Architecture Highlights:

  • Distributed erasure-coded storage
  • Horizontal scaling with node auto-discovery
  • API-compatible with AWS S3
  • High-performance object store for cloud-native workloads

Use Case: Store file uploads, logs, media in a microservices app without relying on AWS.

🔄 Summary Table #

System Type Architecture Keywords Ideal Use Case
Cassandra NoSQL DB Peer-to-peer, AP, Sharding High-speed writes, analytics
Kafka Event Streaming Broker, Partitioning, Log Messaging, decoupling, events
Redis Cluster In-Memory KV Store Sharding, Replication Caching, real-time data
Etcd Config Store Raft, Leader Election Cluster state/config (e.g., Kubernetes)
Consul Service Discovery/KV Gossip, DNS-based discovery Microservices service registry
CockroachDB Distributed SQL DB Raft, ACID, SQL + Scale Transactional workloads, global apps
MinIO Object Storage Erasure coding, S3 API Distributed storage for files/media

🧠 Concepts Reinforced: #

  • Consistency models (Strong, Eventual, Tunable)
  • Consensus protocols (Raft, Gossip)
  • Data partitioning and replication
  • Service coordination and discovery
  • State management in stateless systems