Catalog
concept#Architecture#Platform#Observability#Reliability

Distributed Computing

A concept for distributing computation across multiple networked nodes to achieve scalability, fault tolerance and performance. Includes coordination, consistency models and communication protocols.

Distributed computing denotes architectures where computational tasks are spread across multiple networked nodes.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Advanced

Technical context

Kubernetes for orchestrationApache Kafka as distributed messaging layeretcd or Consul for service discovery and configuration

Principles & goals

Make explicit failure and network assumptionsUnderstand trade-offs between consistency, partition tolerance, and latencyDesign for observability and automated recovery
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Inconsistent data due to incorrect replication strategy
  • Network partitions causing unexpected behavior
  • Operational overhead and troubleshooting can consume resources
  • Small, independent services with clear interfaces
  • Idempotent operations and explicit retries
  • Automated tests for network failures and partitions

I/O & resources

  • Network topology and latency profiles
  • Consistency and availability requirements
  • Monitoring and observability tooling
  • Architecture design with distributed components
  • Operational metrics, SLAs and recovery plans
  • Implemented replication and consistency mechanisms

Description

Distributed computing denotes architectures where computational tasks are spread across multiple networked nodes. It covers consistency, fault tolerance, coordination mechanisms, and communication protocols. The goal is scalable, resilient, and efficient processing of distributed applications. Typical domains include distributed databases, microservices, edge computing, and large-scale data platforms.

✔

  • Scalability via horizontal distribution of load
  • Increased fault tolerance through redundancy
  • Proximity to data sources reduces latency (edge options)

✖

  • More complex failure cases and coordination required
  • Stricter requirements for observability and testing
  • Consistency models can increase developer complexity

  • Request success rate

    Ratio of successful to failed requests over time; indicates stability and error frequency.

  • P95/P99 latency

    Percentile-based latency measurements, important for service-level requirements.

  • Replication lag

    Delay between primary update and visibility on replicas.

Distributed key-value stores (etcd)

etcd provides distributed configuration and service discovery with strong consistency via Raft.

MapReduce clusters for batch processing

Batch processing of distributed data sets across a cluster with coordinated tasks.

Globally distributed databases

Databases that provide geographically distributed replication and specialized consistency models.

1

Requirements analysis: define consistency, latency, throughput

2

Build and test prototypes for critical paths

3

Introduce observability (metrics, tracing, logging)

4

Perform incremental rollouts and chaos testing

⚠️ Technical debt & bottlenecks

  • Ad-hoc replication without clear consistency guarantees
  • Lack of observability standards causes later remediation work
  • Tight coupling between services hinders later scaling
Coordination/leader electionNetwork bandwidthConflict resolution in replication
  • Incorrect replication strategy leads to data loss
  • Optimizing solely for throughput without latency considerations breaks SLAs
  • Deploying without chaos tests causes undetected weaknesses
  • Overestimating deterministic network conditions
  • Neglecting data locality requirements
  • Insufficient fallback strategies for consistency conflicts
Understanding of distributed algorithms (e.g., Raft, Paxos)Network knowledge and fault-tolerance designExperience with observability and debugging tools
Scalability for growing user baseFault tolerance and availabilityNetwork latency and data locality
  • • Limited network capacity and variable latency
  • • Regulatory requirements for data locality
  • • Budget for redundant infrastructure and observability