Category
These database design guides cover how data is stored, copied and split as a system grows. Each post weighs the costs, not just the gains.
First, database indexing explains why reads get fast. Then sharding vs replication helps you pick a scaling path. Also, SQL vs NoSQL covers the model choice itself.
10 articles
Elasticsearch architecture from the inside: the inverted index and its term dictionary, segments and the near-real-time refresh, shards and replicas as first-class partitioning and replication, and…
Data partitioning explained: the umbrella concept under sharding, horizontal vs vertical partitioning, the partitioning vs sharding vocabulary sorted,…
Read/write separation: routing reads to replicas with the staleness audit, replica routing mechanics, replication lag as the governing…
Search engine system design: search vs database queries, when to add elasticsearch, the indexing pipeline that keeps the…
Object storage explained: the flat key-space and HTTP model, blob vs object naming, object vs block vs file…
SQL vs NoSQL decided on real constraints: what relational databases guarantee, the types of nosql databases, when to…
How do database indexes work: the b tree index, clustered vs non clustered indexes, composite index column order,…
Sharding vs replication compared: what each mechanism scales, when replication is enough, when to shard, and why production…
Database sharding explained: range, hash, directory, and geographic strategies, choosing a shard key, hot shards, cross-shard queries, and…
Database replication explained: leader-follower, multi-leader, and leaderless topologies, synchronous vs asynchronous trade-offs, replication lag, and the failover failure…
