300 Articles of System Design: The Complete Guide

This is article number 300. What started as a handful of notes on load balancers and caching layers has grown into a comprehensive system design library covering distributed systems, cloud-native architecture, security, data engineering, and everything in between. This capstone organizes the entire collection into categories, traces learning paths through the material, and distills the key lessons from the journey.

The Full Catalog by Category#

Fundamentals#

The building blocks that every system depends on:

• Networking — DNS, CDNs, load balancers, reverse proxies, TCP vs UDP, HTTP/2, HTTP/3, gRPC, WebSockets.
• Storage — Disk I/O, file systems, block vs object storage, RAID, replication.
• Compute — Processes, threads, concurrency models, event loops, coroutines.
• Operating Systems — Linux internals, cgroups, namespaces, syscalls.

Databases and Data Stores#

From single-node Postgres to planet-scale distributed databases:

• Relational — PostgreSQL, MySQL, indexing strategies, query optimization, ACID guarantees, connection pooling.
• NoSQL — Document stores (MongoDB), wide-column (Cassandra, ScyllaDB), key-value (Redis, DynamoDB), graph (Neo4j).
• Search — Elasticsearch, inverted indexes, vector search, hybrid retrieval.
• Time-series — InfluxDB, TimescaleDB, Prometheus TSDB.
• Data modeling — Normalization, denormalization, schema evolution, polyglot persistence.

Caching#

The fastest request is the one you never make:

• Strategies — Cache-aside, read-through, write-through, write-behind, refresh-ahead.
• Layers — Browser cache, CDN, API gateway cache, application cache, database cache.
• Invalidation — TTL, event-driven invalidation, cache stampede prevention.
• Tools — Redis, Memcached, Varnish, CDN edge caching.

Messaging and Event-Driven Architecture#

Decoupling services through asynchronous communication:

• Message brokers — Kafka, RabbitMQ, Pulsar, NATS, Amazon SQS.
• Patterns — Pub/sub, event sourcing, CQRS, saga, outbox, dead letter queues.
• Schema management — Avro, Protobuf, schema registries, event versioning.
• Consumer patterns — Consumer groups, partitioning, idempotency, exactly-once semantics.

Distributed Systems#

The hard problems that emerge at scale:

• Consensus — Raft, Paxos, leader election, split-brain prevention.
• Consistency — CAP theorem, PACELC, linearizability, eventual consistency, CRDTs.
• Coordination — ZooKeeper, etcd, distributed locks, fencing tokens.
• Time — Logical clocks, vector clocks, hybrid clocks, NTP drift.
• Failure modes — Byzantine faults, partial failures, cascading failures, gray failures.

Scalability and Performance#

Making systems handle 10x, 100x, and 1000x growth:

• Horizontal scaling — Stateless services, sharding, consistent hashing, read replicas.
• Rate limiting — Token bucket, sliding window, distributed rate limiting.
• Back pressure — Queue-based load leveling, circuit breakers, bulkheads.
• Performance — Latency budgets, P99 optimization, profiling, benchmarking.

Reliability and Resilience#

Keeping systems running when things go wrong:

• Patterns — Circuit breaker, retry with backoff, timeout budgets, fallbacks, graceful degradation.
• Chaos engineering — Fault injection, game days, Chaos Monkey, Litmus.
• Disaster recovery — RTO/RPO, active-active, active-passive, backup strategies.
• Incident management — On-call, runbooks, postmortems, SLOs/SLIs/SLAs.

Security#

Protecting systems from the inside out:

• Authentication — OAuth 2.0, OIDC, JWT, SAML, passkeys, MFA.
• Authorization — RBAC, ABAC, ReBAC, policy engines (OPA, Cedar).
• Network security — Zero trust, mTLS, VPN vs ZTNA, WAF, DDoS mitigation.
• Application security — OWASP Top 10, input validation, secrets management, supply chain security.
• Cryptography — TLS, encryption at rest, key management, hashing.

Cloud-Native and Infrastructure#

The platform layer beneath your services:

• Containers — Docker, OCI images, multi-stage builds, image scanning.
• Orchestration — Kubernetes, Helm, operators, CRDs, autoscaling.
• Service mesh — Istio, Linkerd, Envoy, mTLS, traffic shaping.
• Serverless — AWS Lambda, edge functions, cold starts, event-driven compute.
• GitOps — Argo CD, Flux, declarative infrastructure, drift detection.
• IaC — Terraform, Pulumi, CloudFormation, Crossplane.

Observability#

You cannot fix what you cannot see:

• Pillars — Metrics, logs, traces (and the emerging fourth pillar: profiles).
• Tools — Prometheus, Grafana, OpenTelemetry, Jaeger, Loki, Datadog.
• Practices — Structured logging, distributed tracing, SLO-based alerting, dashboards that tell a story.

API Design#

The contract between your services and the world:

• Styles — REST, GraphQL, gRPC, AsyncAPI, WebSockets.
• Versioning — URL path, header, content negotiation.
• Gateway — API gateway patterns, rate limiting, authentication at the edge.
• Documentation — OpenAPI, schema-first design, contract testing.

Data Engineering#

Moving and transforming data at scale:

• Pipelines — Batch (Spark, dbt), streaming (Flink, Kafka Streams), ELT vs ETL.
• Warehousing — Snowflake, BigQuery, Redshift, lakehouse architecture.
• Orchestration — Airflow, Dagster, Prefect.
• Quality — Data contracts, schema validation, great expectations.

Learning Paths#

Not everyone needs to read all 300 articles. Here are curated paths based on your goal.

Path 1 — System Design Interview Prep#

Focus on breadth. Cover fundamentals, then practice end-to-end designs:

1. Start with networking, storage, and compute fundamentals.
2. Study databases: relational vs NoSQL, indexing, replication, sharding.
3. Learn caching strategies and CDN architecture.
4. Understand messaging: Kafka, pub/sub, event-driven patterns.
5. Practice distributed systems concepts: CAP, consensus, consistent hashing.
6. Study scalability: horizontal scaling, rate limiting, back pressure.
7. Cover reliability: circuit breakers, retries, disaster recovery.
8. Design 10-15 real systems end-to-end (URL shortener through social media feed).

Path 2 — Backend Engineer Going Senior#

Focus on depth in areas that distinguish senior engineers:

1. Master database internals: query planning, lock contention, connection pooling.
2. Deep-dive into distributed systems: consensus protocols, consistency models.
3. Study observability: OpenTelemetry, SLO-based alerting, distributed tracing.
4. Learn reliability engineering: chaos engineering, incident management, SLOs.
5. Understand security: zero trust, OAuth 2.0 flows, secrets management.

Path 3 — Platform / Infrastructure Engineer#

Focus on the systems beneath the application:

1. Containers and Kubernetes from first principles.
2. Service mesh: when and why, Istio vs Linkerd.
3. GitOps and infrastructure as code.
4. Cloud-native maturity model and CNCF landscape.
5. Observability stack: Prometheus, Grafana, OTel Collector.
6. Security: network policies, pod security, supply chain signing.

Path 4 — Data-Intensive Applications#

Focus on data movement, storage, and processing:

1. Database internals and data modeling.
2. Messaging and event-driven architecture.
3. Stream processing: Kafka Streams, Flink, windowing, watermarks.
4. Data warehousing and lakehouse architecture.
5. Data quality, contracts, and governance.

Key Takeaways from 300 Articles#

After writing 300 articles on system design, these lessons keep recurring:

1. Simplicity wins. The best architecture is the simplest one that meets your requirements. Premature complexity is the number one cause of system failure.

2. Understand the trade-offs. Every design decision is a trade-off. CAP theorem is just the beginning — latency vs consistency, cost vs availability, flexibility vs performance. Name the trade-off explicitly before choosing.

3. Design for failure. Distributed systems will fail. The question is not "if" but "when" and "how gracefully." Circuit breakers, retries, timeouts, and fallbacks are not optional.

4. Observability is not a luxury. You cannot debug a distributed system with print statements. Invest in structured logging, distributed tracing, and meaningful metrics from day one.

5. Data outlives code. Frameworks change, languages evolve, but your data model persists. Invest disproportionately in getting the data model right.

6. Incremental adoption beats big bang. Whether it is microservices, Kubernetes, or event-driven architecture — migrate incrementally. Run the old and new systems in parallel and cut over gradually.

7. Security is a feature, not a phase. Bolt-on security fails. Threat model early, encrypt by default, enforce least privilege, and treat every network as hostile.

8. Measure before you optimize. Latency budgets, load tests, profiling, and benchmarks should drive optimization decisions — not intuition.

What Comes Next#

Three hundred articles is a milestone, not a finish line. The field continues to evolve: AI-native architectures, edge computing, WebAssembly runtimes, and new consensus protocols are reshaping how we build systems. The fundamentals — networking, storage, concurrency, trade-off analysis — remain constant. Master those, and every new technology becomes a variation on a theme you already understand.

Thank you for reading, sharing, and building alongside us. Here is to the next 300.

300 articles on system design at codelit.io/blog.