As 2021 comes to a close, I wanted to reflect on the technical challenges, solutions, and lessons learned from a year spent building large-scale distributed systems. This year brought massive growth in scale, team size, and system complexity—here’s what I learned.

The Year in Numbers

The systems I worked on in 2021 grew significantly:

  • 100M+ events processed per day (up from 20M)
  • 400K+ monthly active users (up from 150K)
  • 60+ microservices (up from 30)
  • 12 engineers on the platform team (up from 6)
  • 6 geographic regions (up from 3)
  • Latency reduced from 400ms to 50ms (p95)

These numbers represent real technical and organizational challenges.

Technical Lessons

1. Observability is Not Optional

The biggest technical lesson: you cannot operate what you cannot observe.

# What worked: Comprehensive observability from day one
class Service:
    def __init__(self):
        self.metrics = MetricsCollector()
        self.tracer = DistributedTracer()
        self.logger = StructuredLogger()

    def handle_request(self, request):
        # Trace every request
        with self.tracer.start_span('handle_request') as span:
            span.set_attribute('user_id', request.user_id)
            span.set_attribute('request_type', request.type)

            # Log structured data
            self.logger.info('request_received', extra={
                'user_id': request.user_id,
                'endpoint': request.endpoint,
                'correlation_id': request.correlation_id
            })

            # Record metrics
            start = time.time()
            try:
                result = self.process(request)
                self.metrics.counter('requests.success').inc()
                return result
            except Exception as e:
                self.metrics.counter('requests.error',
                                   tags={'error_type': type(e).__name__}).inc()
                self.logger.error('request_failed', exc_info=True)
                raise
            finally:
                duration = time.time() - start
                self.metrics.histogram('request.duration', duration)

Key insight: The cost of adding observability later is 10x higher than building it in from the start.

2. Start with Boring Technology

We made a conscious decision to use proven, “boring” technologies:

  • Kafka for event streaming (not a newer alternative)
  • PostgreSQL for transactional data (not a trendy NoSQL database)
  • Redis for caching (simple, reliable)
  • Kubernetes for orchestration (mature ecosystem)

Why this worked: When operating at scale, you want well-understood failure modes and a large community for support. Exciting new tech is great for side projects, not production systems serving millions of users.

3. Latency Optimization Requires Measurement

Reducing latency from 400ms to 50ms wasn’t one big win—it was dozens of small improvements:

Database query optimization:     180ms → 40ms  (N+1 queries, indexing)
External service calls:          120ms → 60ms  (parallelization)
Caching layer:                   -     → -35ms (Redis caching)
Serialization:                   30ms  → 8ms   (Protobuf)
Connection pooling:              25ms  → ~0ms  (HikariCP)
Code-level optimizations:        20ms  → 10ms  (algorithm improvements)

Key insight: Profile everything. The bottleneck is never where you think it is.

4. Event-Driven Architecture Scales Differently

Synchronous request-response doesn’t scale the same way as event-driven systems:

Synchronous challenges:

  • Cascading failures
  • Tight coupling
  • Hard to scale read vs write
  • Difficult to add new consumers

Event-driven benefits:

  • Natural decoupling
  • Independent scalability
  • Temporal decoupling (services don’t need to be online simultaneously)
  • Easy to add new consumers

But event-driven brings its own challenges:

  • Eventual consistency
  • Complex debugging (events flow through multiple services)
  • Ordering guarantees
  • Exactly-once processing

Key insight: Event-driven architecture is essential at scale, but it’s not a silver bullet. Understand the trade-offs.

5. Multi-Region is Complex

Operating across 6 regions taught us that distribution is hard:

Challenge: Data consistency across regions
Solution: Eventual consistency with conflict resolution

Challenge: Cross-region latency (200ms+)
Solution: Read-local, write-global pattern

Challenge: Regional failures
Solution: Circuit breakers and graceful degradation

Challenge: Data residency regulations
Solution: Region-specific data stores

Key insight: Don’t go multi-region until you must. The complexity cost is high.

Organizational Lessons

1. Conway’s Law is Real

Our architecture reflected our team structure:

Initial team structure (2020):
├── Backend Team (monolith)
├── Frontend Team
└── Data Team

Result: Monolithic backend, cleanly separated frontend/data

New team structure (2021):
├── User Platform Team → User services
├── Activity Platform Team → Activity services
├── Analytics Platform Team → Analytics services
└── Infrastructure Team → Platform tools

Result: Microservices aligned with teams

Key insight: If you want microservices, first create autonomous teams. Architecture follows organization.

2. Platform Teams Enable Velocity

Creating a dedicated platform team was transformative:

Before platform team:

  • Each team managed their own infrastructure
  • Inconsistent practices
  • Lots of duplicated work
  • Slow onboarding

After platform team:

  • Self-service deployment platform
  • Standardized monitoring/logging
  • Shared best practices
  • New services up in hours, not days

Key insight: Platform engineering is a force multiplier. One platform engineer can enable 10 product engineers.

3. Documentation is a Product

We treated documentation as seriously as code:

# Service Documentation Template

## Overview
- What does this service do?
- Who owns it?
- Key metrics and SLOs

## Architecture
- System diagram
- Dependencies
- Data flow

## Getting Started
- Local development setup
- Running tests
- Deployment process

## Operations
- Monitoring dashboards
- Common alerts and runbooks
- Troubleshooting guide

## API Reference
- Endpoints
- Request/response examples
- Error codes

Key insight: Poor documentation creates a bus factor. Great documentation enables team autonomy.

4. Incident Reviews Build Culture

We made post-incident reviews blameless and focused on learning:

Our template:

  1. Timeline: What happened when?
  2. Root cause: Why did it happen?
  3. Impact: Who was affected and how?
  4. Detection: How did we find out?
  5. Response: What did we do?
  6. Lessons: What did we learn?
  7. Action items: What will we change?

Key insight: How you handle incidents defines your engineering culture. Make them learning opportunities, not blame sessions.

Technical Decisions I’d Change

1. Earlier Investment in Observability

We added distributed tracing 6 months too late. Debugging distributed systems without traces is painful.

Lesson: Build observability from day one.

2. More Aggressive Data Modeling

We kept some data models from our monolith days that didn’t fit microservices well. Refactoring later was expensive.

Lesson: Data modeling is critical in distributed systems. Get it right early.

3. Load Testing Before Scale

We hit scaling issues we could have found earlier with proper load testing.

Lesson: Load test at 10x expected traffic before launching new features.

Looking Forward to 2022

Based on 2021’s lessons, here are my focus areas for 2022:

1. Data Mesh Architecture

Moving beyond centralized data warehouses to domain-oriented data ownership:

Current: All data → Central warehouse → Analytics
Future: Domain services expose data products → Federated queries

2. WebAssembly for Edge Computing

Exploring WASM for running compute at the edge:

  • Lower latency for users
  • Reduced bandwidth costs
  • Better security model

3. eBPF for Deep Observability

eBPF enables observability without application changes:

  • Kernel-level tracing
  • Network analysis
  • Security policies

4. Platform Engineering Maturity

Continuing to build internal developer platforms:

  • Self-service everything
  • Golden paths for common patterns
  • Reduce cognitive load

Key Takeaways for 2021

  1. Observability is foundational: You can’t operate what you can’t see
  2. Boring technology scales: Proven tech > exciting new tech
  3. Measure everything: Latency, errors, capacity, cost
  4. Events > Requests: At scale, event-driven wins
  5. Teams shape architecture: Conway’s Law is real
  6. Platform engineering is leverage: Enable teams to move faster
  7. Documentation is product: Treat it accordingly
  8. Incidents are learning: Blameless post-mortems build culture

Final Thoughts

2021 was a year of tremendous growth and learning. The jump from 30 to 60 microservices, from 150K to 400K users, and from 6 to 12 engineers brought challenges we couldn’t have anticipated.

The most important lesson: technology is the easy part. The hard parts are:

  • Communication across teams
  • Maintaining velocity as systems grow
  • Building reliability while moving fast
  • Creating a culture of ownership

Technical excellence matters, but organizational excellence matters more.

Here’s to 2022 and the next set of challenges.

Resources That Helped

Books:

  • “Designing Data-Intensive Applications” by Martin Kleppmann
  • “Site Reliability Engineering” by Google
  • “Building Microservices” by Sam Newman

Blogs:

  • High Scalability
  • Martin Fowler’s blog
  • Charity Majors on observability

Tools that were game-changers:

  • Jaeger for distributed tracing
  • Prometheus + Grafana for metrics
  • Kafka for event streaming
  • DataDog for integrated observability

Looking forward to sharing more learnings in 2022!