Technology Stack

Rationale

Each technology choice was made based on production requirements and industry best practices.

Go (Core Services)

Why Go

High performance: Compiled, fast execution
Concurrency: Goroutines for high throughput
gRPC native: First-class protobuf support
Industry adoption: Used by Google, Uber, Dropbox
Binary distribution: Single executable, easy deployment

Services built in Go

Product Service
Order Service
User Service
Payment Service
Delivery Service
API Gateway

Rust (Inventory Service)

Why Rust

Memory safety: No null pointer dereferences
Zero-cost abstractions: Performance comparable to C++
Concurrency: Fearless concurrency with ownership
Growing ecosystem: tokio async runtime

Use case

High-frequency inventory updates
Optimistic locking for concurrent stock modifications
Performance-critical operations

Python (Analytics Worker)

Why Python

Rich ML/Data ecosystem: pandas, numpy, scikit-learn
Rapid prototyping: Quick iteration on analytics
Batch processing: Ideal for scheduled jobs
Libraries: Data visualization, reporting

Use case

Sales reports generation
Customer analytics
Inventory forecasting
Data aggregation

Protocol Buffers

Why Protobufs

Language-agnostic: Generate code for Go, Rust, Python, JavaScript
Backward compatible: Field numbers ensure evolution safety
Efficient: Binary serialization, smaller than JSON
Strong typing: Schema-enforced contracts

Benefits

Single source of truth
Auto-generate clients
Versioned APIs
Documentation as code

PostgreSQL

Why PostgreSQL

ACID compliance: Reliable transactions
Advanced features: JSONB, triggers, row-level security
Extensibility: PostGIS for geospatial data
Replication: High availability support
Mature tooling: pgAdmin, psql, migration tools

Schema organization

catalog - Products, categories, variants
orders - Orders, order items
users - Users, addresses
payments - Payments, transactions
inventory - Stock items, movements, reservations
delivery - Delivery zones, slots, shipments

Redis

Why Redis

In-memory: Fast reads/writes (microsecond latency)
Data structures: Strings, hashes, lists, sets, sorted sets
Persistence: AOF and RDB snapshots
Clustering: Horizontal scaling
Pub/Sub: Message passing

Use cases

Product listing cache (5 min TTL)
User sessions (24 hr TTL)
Rate limiting counters
Distributed locks

Kafka

Why Kafka

High throughput: Millions of messages per second
Durability: Replicated log storage
Consumer groups: Parallel processing
Schema registry: Avro integration (optional)

Use cases

Order events (created, confirmed, shipped)
Payment events (completed, failed, refunded)
Inventory events (reserved, released)
User events (registered, updated)

Docker & Kubernetes

Why Docker

Consistency: Same environment everywhere
Microservices: Each service in own container
Resource isolation: CPU, memory limits
Easy testing: Reproducible builds

Why Kubernetes

Orchestration: Automated deployment, scaling
Self-healing: Restart failed containers
Service discovery: Internal DNS
Load balancing: Distribute traffic
Rolling updates: Zero-downtime deployments

Technology Comparison

Category	Technology Chosen	Alternatives Considered
Language	Go, Rust, Python	Java, Node.js, C++
Database	PostgreSQL	MySQL, MongoDB, CockroachDB
Cache	Redis	Memcached, etcd
Message Queue	Kafka	RabbitMQ, AWS SQS, NATS
API Protocol	gRPC	REST, GraphQL, Thrift
Container	Docker	Podman, LXC
Orchestration	Kubernetes	Docker Swarm, Nomad

Industry Examples

Go

Google: Source language for Go itself
Uber: Microservices infrastructure
Dropbox: Storage systems
Twitch: Real-time messaging

Rust

Microsoft: Windows components
AWS: Lambda runtime
Cloudflare: Workers
Mozilla: Firefox components

gRPC

Google: Protocol Buffers origin
Netflix: API communication
Square: Microservices
Cisco: Network automation

Kubernetes

Google: GKE origin
Red Hat: OpenShift
Amazon: EKS
Microsoft: AKS

Future Considerations

Potential Upgrades

PostgreSQL 16: Latest stable version
Redis 8: New data structures, improved performance
Kafka 4: Enhanced features, better scalability
Go 1.23: Performance improvements, new stdlib

Emerging Technologies

Service Mesh: Istio for traffic management
Observability: OpenTelemetry for tracing
Serverless: AWS Lambda for sporadic workloads
Edge Computing: Cloudflare Workers for global distribution

Decision Process

Technology selection followed these principles:

Production maturity: Must be battle-tested
Community support: Active community, documentation
Long-term viability: Backed by major organizations
Developer experience: Good tooling, ease of use
Performance: Meets scalability requirements
Cost efficiency: Open-source preferred

Next Steps