4 Challenges of Distributed Systems - And Possible Solutions

Things to keep in mind...

Feb 18, 2025

Distributed systems are at the heart of modern technology, powering everything from internet services like Google to online banking platforms and multiplayer gaming networks.

They enable applications to scale, improve fault tolerance, and enhance system performance by leveraging multiple computers (nodes) working together.

However, designing and maintaining distributed systems comes with significant challenges. Let’s look at the major challenges and their possible solutions.

1 - Communication Challenges

Communication between nodes in a distributed system is inherently unreliable due to network failures, latency issues, and security vulnerabilities. When a system relies on multiple servers to work together, ensuring consistent, secure, and reliable communication is crucial.

Key Issues:

Packet Loss: Messages may be dropped due to network failures.
Out-of-Order Delivery: Messages may arrive at different times or out of sequence.
Security Risks: Data transmitted between nodes can be intercepted if not secured.

Techniques to Handle Communication Challenges:

a) Using TCP (Transmission Control Protocol)

Unlike UDP, TCP ensures message reliability by handling lost packets, retransmissions, and ordering.
TCP guarantees that messages arrive in sequence and without loss.

You can play around with the diagram on Eraser.io

b) Securing Communication with TLS (Transport Layer Security)

Encrypting messages using TLS ensures that communication between nodes is secure and prevents data interception.
TLS-based encryption is used in HTTPS, banking transactions, and secure APIs.

c) Service Discovery with DNS

In dynamic environments (e.g., cloud-based applications, microservices), service discovery helps locate nodes without hardcoding IP addresses.
DNS-based service discovery (e.g., AWS Route 53, Kubernetes Service Discovery) ensures dynamic routing of requests to available nodes.

Example Use Case:

A microservices-based e-commerce platform needs to ensure that communication between the Order Service, Payment Service, and Inventory Service is secure, reliable, and properly sequenced. Using TCP for reliability, TLS for encryption, and DNS for service discovery ensures smooth communication.

2 - Coordination Challenges

Coordination among nodes is challenging due to:

Network Failures: Some nodes may go offline unpredictably.
Lack of a Global Clock: There’s no universal time across all servers.
Race Conditions: Multiple nodes modifying shared resources can lead to inconsistent states.

Techniques to Handle Coordination Challenges:

a) Failure Detection

Detecting node failures is critical for maintaining system reliability.
Heartbeat Mechanisms (nodes send periodic "I’m alive" signals).
Leader Election Algorithms (e.g., Raft, Paxos) select a leader to manage coordination.

b) Logical and Vector Clocks

Since nodes don’t share a global clock, they use logical timestamps (e.g., Lamport Timestamps) to track event order.
Vector Clocks help determine causal relationships between events.

c) Consensus Algorithms

When nodes must agree on a decision (e.g., database writes, leader election), consensus algorithms help.
Paxos and Raft are used to ensure distributed consensus.

d) Data Replication

Primary-Replica Model: One primary node accepts writes, and multiple replicas serve reads.
Multi-Leader Replication: Used in high-availability scenarios where multiple leaders accept writes.

Example Use Case:

A distributed database needs to ensure that transactions are synchronized across multiple data centers. Using Raft for consensus, vector clocks for event ordering, and failure detection to handle server crashes ensures consistent coordination.

3 - Scalability Challenges

Scalability is a major advantage of distributed systems, allowing them to handle increasing workloads by adding more nodes. However, choosing the right scalability pattern is essential for performance and efficiency.

Key Scalability Patterns:

a) Microservices Behind a Gateway

Breaking a monolithic system into microservices allows independent scaling.
An API Gateway routes requests to the correct microservice.

b) Load Balancers

Distributes requests across multiple servers, preventing overload.
Types:
- Round Robin: Assigns requests sequentially.
- Least Connections: Routes traffic to the least busy server.
- Geographical Load Balancing: Routes users to the nearest data center.

c) Functional Decomposition with CQRS

CQRS (Command Query Responsibility Segregation) separates read and write operations into different services.
Read-heavy workloads use denormalized views, and write-heavy workloads optimize for consistency.

Example Use Case:

A social media platform with millions of users must scale user profile reads differently from post creation. CQRS ensures that reads scale independently using a read-optimized database, while writes go to a separate system.

4 - Resiliency Challenges

Resiliency refers to the system's ability to recover from failures and continue functioning. Failures in distributed systems are inevitable, so designing for graceful degradation is critical.

Resiliency Techniques:

Timeouts: Prevents waiting indefinitely for slow responses.
Retries: Retries failed requests with exponential backoff to avoid overwhelming services.
Circuit Breakers: Stops sending requests to a failing service until it recovers.
Load Shedding: Drops low-priority requests when overloaded.
Rate Limiting: Restricts API calls per second to prevent abuse.
Bulkheads: Isolate failure-prone components so they don’t crash the whole system.
Health Checks: Automatically detects and removes failing nodes.

Example Use Case:

A video streaming service must handle sudden traffic spikes during a major event. Using rate limiting to manage incoming requests, CDNs to distribute content, and health checks to remove failing servers ensures the system remains available.

👉 So - how do you handle challenges with distributed systems?