System Design
Deep dives into system design concepts, from databases and caching to distributed systems and scalability patterns.
The pulkitxm/systems repository on GitHub has supplementary sketches, runnable implementations, and small code-heavy examples keyed to themes in this series. Pair it with these posts when you want reference code alongside the write-ups.
Databases
Understanding Database Scaling and Sharding Patterns
Master database scaling from vertical to horizontal scaling, read replicas, sharding strategies, and partitioning techniques. Learn when to use synchronous vs asynchronous replication and how to choose the right shard key.
Performance & Caching
Bloom Filters
Learn how Bloom filters work: a probabilistic data structure that tells you with 100% certainty when something doesn't exist. Understand hash functions, bit arrays, false positives, and real-world use cases like recommendation engines and web crawlers.
Data Processing
Introduction to Big Data Tools
Learn why big data processing exists, how distributed computing works, and why tools like Apache Spark handle the heavy lifting so you can focus on business logic.
Distributed Systems
High Availability
Learn how to build resilient systems through data redundancy, automatic failover, and leader election. Understand backup strategies, database replication, disaster recovery patterns, and how leader election enables zero-downtime auto-recovery.
Consistent Hashing
Learn how consistent hashing solves the data ownership problem in distributed systems. Understand hash-based routing, the ring abstraction, and how to scale up and down with minimal data movement.
Infrastructure & Networking
Rate Limiting
Learn how rate limiters protect your systems from being overwhelmed. Understand where to place them, why Redis is the go-to database, five popular algorithms, and how to scale with sharding. Includes the practical insight of building rate limiters as libraries instead of services.