Catalog
concept#Architecture#Reliability#Platform

Distributed Systems

An architectural paradigm where multiple independent computers coordinate to appear as a single coherent system to users.

Distributed systems are collections of independent computers that appear to users as a single coherent system.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Message brokers (e.g. Kafka)Configuration and service discovery systems (e.g. etcd, Consul)Service mesh and sidecar architectures

Principles & goals

Partitioning for scalability and fault isolationExplicit decisions on consistency, availability and latencyObservability and automated failure detection
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Data inconsistencies due to network partitions
  • Hidden performance bottlenecks and thundering herd
  • Lack of resilience measures can cause cascading failures
  • Explicit and documented consistency requirements
  • Careful partitioning by domain and data access patterns
  • Automated monitoring and regular resilience tests

I/O & resources

  • Architectural requirements and SLAs
  • Network and infrastructure overview
  • Data access and consistency requirements
  • Design decisions on partitioning and replication
  • Operationalized deployment and observability pipelines
  • SLA-compliant operational guidelines

Description

Distributed systems are collections of independent computers that appear to users as a single coherent system. They enable scalability, fault tolerance and geographic distribution but introduce concurrency, consistency and coordination challenges. Design requires trade-offs among performance, availability and complexity across networked nodes.

✔

  • Scalability through horizontal expansion
  • Increased fault tolerance and resilience
  • Geographic proximity to users reduces latency

✖

  • Complexity in design, testing and operations
  • Challenges achieving strong consistency across partitions
  • Increased need for observability and debugging tools

  • Average response time

    Average duration for requests across distributed components.

  • Error rate

    Proportion of failed requests or operations.

  • Replication lag

    Time difference between primary and replicated state.

Global key-value database

A distributed database uses replication and sharding to achieve global availability.

Service mesh in a microservices architecture

A service mesh manages communication, security and observability between distributed services.

Distributed stream processing with exactly-once semantics

Stream processors and cooperative consumers ensure consistent processing under partitions.

1

Analyze requirements and choose consistency models

2

Partition the system into components and responsibility boundaries

3

Implement replication, sharding and failover strategies

4

Introduce observability and chaos testing

⚠️ Technical debt & bottlenecks

  • Ad-hoc replication logic without documentation
  • Monolithic database singleton as a bottleneck
  • Incomplete test coverage for partition scenarios
Network latencyCoordination overheadState management
  • Attempting to enforce strong consistency without coordination
  • Scaling by indiscriminately replicating all data
  • Ignoring network partition tests in QA
  • Underestimating operationalization costs
  • Neglecting observability before production
  • Missing rollback strategies for schema or process changes
Understanding of distributed algorithms (consensus, replication)Network and performance engineeringObservability, monitoring and debugging distributed systems
Availability and fault toleranceScalability and elasticityConsistency requirements and latency goals
  • • Limited bandwidth and variable latencies
  • • Regulatory requirements for data locality
  • • Heterogeneous infrastructure and operations teams