17 posts · page 1 of 1
Understand what makes distributed systems hard. Learn consensus algorithms (Paxos, Raft), leader election, distributed locking, vector clocks, gossip protocols, and how CockroachDB achieves distributed SQL.
An honest look at microservices vs monoliths. Learn service communication, API gateways, database-per-service, distributed tracing, and how Amazon's migration shaped the industry.
Design a distributed unique ID generator — compare UUIDs, Snowflake IDs, database tickets, and ULID for generating globally unique, sortable identifiers at scale.
Design a web crawler like Googlebot — URL frontier, politeness, deduplication, distributed architecture, and strategies for crawling billions of pages.
Compare RabbitMQ, Apache Kafka, and Amazon SQS with working code examples, architecture diagrams, and decision criteria for choosing the right message queue.
Implement distributed locks with Redis. Covers single-instance locks, the Redlock algorithm, fencing tokens, lock renewal, and common pitfalls to avoid.
Learn how backpressure prevents overload in distributed systems. Covers load shedding, rate limiting, buffering, and flow control patterns with examples.
Go beyond basic consistent hashing. Learn how virtual nodes solve imbalance, how rebalancing works during scale events, and real-world usage in Cassandra and DynamoDB.
Understand event sourcing fundamentals, how it differs from CRUD, when to use it, and the real-world trade-offs you need to consider before adopting it.
Learn how to design idempotent APIs and achieve exactly-once semantics using idempotency keys, deduplication stores, and transactional outbox patterns.
How the saga pattern coordinates long running business transactions across services using local commits and compensating actions instead of distributed two phase commit.
What ACID and BASE actually mean, where each shines, and how to reason about consistency, availability, and durability when you design data systems.
A pragmatic look at the CAP theorem: what consistency and availability mean for real workloads, and how PACELC describes the trade-offs better.
How consistent hashing actually works in production: virtual nodes, rebalancing, hot keys, and why naive modulo hashing fails at scale.
A senior-engineer guide to designing rate limiters: algorithms, distributed coordination, trade-offs, and production patterns that actually scale.
A practical look at distributed locking with Redis: SET NX EX, Redlock, fencing tokens, and the failure modes that cause data corruption.
Design a distributed cache using consistent hashing, replication, and eviction policies to speed up reads at internet scale.