Category
scalability
10 articles across 5 sub-topics
Sharding Approaches in SQL and NoSQL: Range, Hash, and Directory-Based Strategies Compared
TLDR: Sharding splits your database across multiple physical nodes so no single machine carries all the data or absorbs all the writes. The strategy you choose — range, hash, consistent hashing, or directory — determines whether range queries stay ch...
Partitioning Approaches in SQL and NoSQL: Horizontal, Vertical, Range, Hash, and List Partitioning
TLDR: Partitioning splits one logical table into smaller physical pieces called partitions. The database planner skips irrelevant partitions entirely — turning a 30-second full-table scan into a 200ms single-partition read. Range partitioning is best...
System Design Sharding Strategy: Choosing Keys, Avoiding Hot Spots, and Resharding Safely
TLDR: Sharding means splitting one logical dataset across multiple physical databases so no single node carries all the data and traffic. The hard part is not adding more nodes. The hard part is choosing a shard key that keeps data balanced and queri...
ID Generation Strategies in System Design: Base62, UUID, Snowflake, and Beyond
TLDR: Short shareable IDs need Base62 (URL shorteners). Database primary keys at scale need time-ordered IDs (Snowflake, UUID v7). Security tokens need random IDs (UUID v4, NanoID). Picking the wrong strategy either causes B-tree fragmentation at 50M...
Consistent Hashing: Scaling Without Chaos
TLDR: Standard hashing (key % N) breaks when $N$ changes — adding or removing a server reshuffles almost all keys. Consistent Hashing maps both servers and keys onto a ring (0–360°). When a server is added, only its immediate neighbors' keys move, mi...
Write-Time vs Read-Time Fan-Out: How Social Feeds Scale
TLDR: Fan-out is the act of distributing one post to many followers' feeds. Write-time fan-out (push) pre-computes feeds at post time — fast reads but catastrophic write amplification for celebrities. Read-time fan-out (pull) computes feeds on demand...
System Design: Caching and Asynchronism
TLDR: Caching stores hot data in fast RAM so you skip slow database round-trips. Asynchronism moves slow tasks (email, video processing) off the critical path via message queues. Together, they turn a blocking, slow system into a responsive, scalable...
Serverless Architecture Pattern: Event-Driven Scale with Operational Guardrails
TLDR: Serverless is strongest for spiky asynchronous workloads when cold-start, observability, and state boundaries are intentionally designed. TLDR: Serverless works best for spiky, event-driven workloads when you design for idempotency, observabili...
Little's Law: The Secret Formula for System Performance
TLDR: Little's Law ($L = \lambda W$) connects three metrics every system designer measures: $L$ = concurrent requests in flight, $\lambda$ = throughput (RPS), $W$ = average response time. If latency spikes, your concurrency requirement explodes with ...