DDIA-style deep dives and summaries of real engineering decisions from Netflix, Uber, Discord, and others. Every post connects back to a concept or system you can explore interactively.
Token buckets, sliding windows, Redis counters, and the distributed rate limiting problem nobody talks about in interviews.
Microservices give you independent deployability and fault isolation. They also give you distributed transactions, network latency, and a debugging problem that gets exponentially harder as you add services.
Idempotency is not a theoretical property. It's the difference between a payment that charges once and one that charges three times when the client retries. Here's how to build it correctly.
Adding one server to a cache cluster shouldn't invalidate 90% of your cache. Consistent hashing is why it doesn't, and the math is simpler than you think.
Every modern database handles concurrent reads and writes without locking readers out. The mechanism is Multi-Version Concurrency Control, and it's one of the most elegant ideas in database engineering.
CAP gets cited in every system design interview and misunderstood in most. Here's what it actually says, what it doesn't say, and what it means for your design decisions.
A distributed lock seems simple: one process holds it, others wait. In practice, clocks drift, processes pause, and networks lie, which makes every simple lock scheme subtly broken.
Most engineers know indexes make queries faster. Few know why, or when they make things slower. Here's what's happening inside the storage engine.
2PC is the textbook solution for distributed transactions. It's also why most distributed systems avoid distributed transactions entirely.
Every database makes a fundamental choice between write-optimized and read-optimized storage. Here's what that means for your workload.
Every replicated database has replication lag. Most engineers don't fully understand what happens when reads hit a stale replica, until production teaches them.
Discord migrated from MongoDB to Cassandra to ScyllaDB as their message store grew from millions to trillions. Here's what they learned.
Uber's dispatch and pricing systems need sub-second latency while reading live supply/demand across millions of driver and rider events per minute.
A deep look at Netflix's Active-Active multi-region architecture and what it actually took to get there.