Distributed Systems
Explain Like I'm 5...
Imagine you and your friends are working on a BIG group project - building a huge LEGO castle! But instead of everyone sitting at the same table, each friend works at their own house on different parts:
The Group Project:
- • Sarah builds the castle towers at her house
- • Mike makes the walls at his house
- • Emma creates the gate at her house
- • They all share their progress using video calls!
- • Even if one friend gets sick, others can keep working!
Why Is This Useful?
- • Too big for one person - need many computers working together!
- • If one computer breaks, others keep the system running
- • Faster! Multiple computers do work at the same time
- • Users around the world get fast responses from nearby computers
What Are Distributed Systems?
A distributed system is a collection of independent computers that appear to users as a single coherent system. These computers communicate and coordinate their actions by passing messages to one another.
- ✓Multiple computers working together
- ✓Connected through a network
- ✓Coordinate by message passing
- ✓Appear as a unified system to users
Distributed System Architecture
Load Balancer
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+---------------+---------------+
| | |
[Server 1] [Server 2] [Server 3]
| | |
+----+----+ +----+----+ +----+----+
| | | | | |
[Cache 1] [DB Replica] [Cache 2] [DB Replica] [Cache 3] [DB Primary]
| | | | | |
+---------+-----+---------+-----+---------+
| |
[Message Queue] [Service Discovery]
| |
+----+----+ [ZooKeeper]
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[Worker 1] [Worker 2]
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[Analytics] [Logging]CAP Theorem
The CAP theorem states that a distributed system can only guarantee TWO of these three properties at the same time:
Consistency
All nodes see the same data at the same time. Every read receives the most recent write.
Availability
Every request receives a response (success or failure). The system is always operational.
Partition Tolerance
The system continues to operate despite network failures that partition the system into isolated groups.
Consistency (C)
/\
/ \
/ \
/ CA \
/ (Not \
/ Realistic)\
/_____________\
/ | \
/ CP | AP \
/ Systems| Systems \
/ (e.g. | (e.g. \
/ MongoDB) |Cassandra)\
/__________________________\
Availability (A) Partition Tolerance (P)
You can only pick TWO!The Trade-off:
- •CP Systems (Consistency + Partition Tolerance): May become unavailable during network issues. Examples: MongoDB, HBase, Redis
- •AP Systems (Availability + Partition Tolerance): May return stale data during network issues. Examples: Cassandra, CouchDB, DynamoDB
- •CA Systems (Consistency + Availability): Only work in single-location systems, not truly distributed. Examples: Traditional RDBMS
Consistency Models
Strong Consistency
After an update, all subsequent reads will return the updated value. Highest consistency but may sacrifice availability and performance.
Eventual Consistency
If no new updates are made, eventually all reads will return the last updated value. Better availability and performance, but temporary inconsistencies possible.
Causal Consistency
Operations that are causally related are seen by all nodes in the same order. Provides a middle ground between strong and eventual consistency.
Consensus Algorithms
Algorithms that help distributed systems agree on a single value or state, even when some nodes fail:
Paxos
Classic consensus algorithm that's proven correct but complex to implement. Uses proposers, acceptors, and learners to reach agreement.
Raft
Easier-to-understand alternative to Paxos. Uses leader election and log replication. Popular in modern systems like etcd and Consul.
Raft Consensus - Leader Election
Normal Operation:
[Leader] --heartbeat--> [Follower]
| |
[Follower] <--ack----- [Follower]
Leader Failure:
[FAILED] X [Follower] --request vote--> [Follower]
| |
[Follower] <--grant vote---- [Follower]
|
[NEW LEADER] --heartbeat--> [Follower]
Log Replication:
Client --> [Leader] --append entries--> [Follower]
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[Follower] <--ack------------- [Follower]
|
[Commit] --success--> ClientService Discovery & Coordination
Apache ZooKeeper
Centralized service for maintaining configuration, naming, synchronization, and group services. Provides distributed coordination primitives.
HashiCorp Consul
Service mesh solution providing service discovery, health checking, and key-value store. Built on Raft consensus.
Microservices Architecture
Architectural style where applications are composed of small, independent services that communicate over a network:
- •Each service runs in its own process
- •Services communicate via lightweight protocols (HTTP/REST, gRPC, message queues)
- •Services can be deployed independently
- •Each service can use different technology stacks
Benefits:
- ✓Independent scaling of services
- ✓Easier to understand and modify
- ✓Fault isolation - one service failure doesn't crash entire system
- ✓Technology diversity - use best tool for each job
Microservices Architecture
API Gateway
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+-----------------+------------------+
| | |
[User Service] [Product Service] [Order Service]
| | |
[User DB] [Product DB] [Order DB]
| | |
+--------[Message Queue]-------------+
|
[Email Service]
|
[SMTP Server]Fault Tolerance & Redundancy
Techniques to keep systems running despite component failures:
Replication
Keep multiple copies of data across different nodes. If one fails, others continue serving requests.
Health Checks & Heartbeats
Regular signals from nodes to confirm they're alive. Missing heartbeats trigger failover procedures.
Circuit Breaker
Stop calling a failing service to prevent cascading failures. Like a circuit breaker in your house protecting from electrical overload.
Bulkhead Pattern
Isolate resources for different parts of the system. Failure in one area doesn't drain resources from others.
Network Partitions
When network failures split the system into isolated groups that can't communicate:
Split-Brain Problem
Multiple partitions think they're the leader, making conflicting decisions. Solved using quorum-based approaches.
Quorum
Require majority agreement before making decisions. Prevents split-brain by ensuring only one partition has majority.
Real-World Examples
Google Search - Global Distribution
Google operates massive distributed systems across data centers worldwide:
- •Search queries routed to nearest data center for low latency
- •Index replicated across thousands of servers
- •MapReduce processes petabytes of data in parallel
- •Bigtable provides distributed storage with eventual consistency
- •Spanner offers global strong consistency using atomic clocks
WhatsApp - Message Delivery
WhatsApp handles billions of messages across distributed servers:
- •Messages routed through multiple servers for reliability
- •Eventual consistency - messages sync across devices over time
- •Queue-based architecture handles message delivery
- •End-to-end encryption maintained across distributed infrastructure
- •Presence information (online/offline) uses gossip protocols
Dropbox - File Synchronization
Dropbox synchronizes files across multiple devices and cloud storage:
- •Files chunked and stored across distributed storage nodes
- •Metadata service tracks file versions and locations
- •Conflict resolution when same file edited on different devices
- •Delta sync - only changed parts transmitted
- •Eventual consistency model with conflict notification
Netflix - Global Streaming
Netflix serves content to millions from distributed infrastructure:
- •Content replicated to regional CDN servers worldwide
- •Microservices architecture with hundreds of services
- •Chaos engineering (Chaos Monkey) tests fault tolerance
- •Dynamic failover between AWS regions
- •Eventual consistency for user preferences and viewing history
Key Challenges
- ⚠️Network Latency - Communication between nodes takes time
- ⚠️Partial Failures - Some components fail while others work
- ⚠️Concurrency - Multiple nodes accessing same data simultaneously
- ⚠️Clock Synchronization - Hard to agree on time across nodes
- ⚠️Data Consistency - Keeping replicas synchronized
- ⚠️Debugging - Difficult to trace issues across distributed components
Design Principles
- ✓Design for Failure - Assume components will fail and plan for it
- ✓Embrace Asynchrony - Use message queues and async communication
- ✓Eventual Consistency - Accept temporary inconsistencies for availability
- ✓Idempotency - Operations safe to retry without side effects
- ✓Loose Coupling - Services independent and communicate via well-defined interfaces
- ✓Monitoring & Observability - Track system health and behavior
Common Patterns
- →Leader Election - Select one node to coordinate actions
- →Sharding - Partition data across nodes for scalability
- →Read Replicas - Scale reads by distributing across copies
- →Write-Ahead Logging - Ensure durability before acknowledging writes
- →Gossip Protocol - Spread information through peer-to-peer communication
- →Saga Pattern - Manage distributed transactions through compensating actions
Trade-offs
- ⚖️Consistency vs Availability - Can't maximize both during partitions
- ⚖️Latency vs Consistency - Stronger consistency requires more coordination
- ⚖️Complexity vs Performance - Optimizations add operational complexity
- ⚖️Cost vs Redundancy - More replicas cost more but improve reliability
- ⚖️Flexibility vs Standardization - Microservices freedom vs consistency
Best Practices
- 1.Use proven consensus algorithms (Raft, Paxos) for critical coordination
- 2.Implement comprehensive monitoring and distributed tracing
- 3.Design APIs to be idempotent for safe retries
- 4.Use circuit breakers to prevent cascading failures
- 5.Implement graceful degradation when dependencies fail
- 6.Test partition scenarios and failure modes regularly
- 7.Use immutable data structures to avoid coordination
- 8.Implement proper backpressure and rate limiting