Guides on SQL, NoSQL, database optimization, migrations, and indexing for developers.
Cassandra is built for write-heavy workloads at massive scale — but only if you model data for your queries, not your intuition. This post explains the model and the write path.
Redis is not just a cache. Its five core data structures solve problems that would require complex code otherwise. This post covers all of them plus persistence and Pub/Sub.
MongoDB is easy to start and hard to optimize. This post covers document schema design, the full aggregation pipeline, index types, and the patterns that separate fast from slow.
NoSQL is not better than SQL — it is different. This post maps the four NoSQL families to their use cases, explains their consistency models, and helps you choose deliberately.
Distributed transactions are hard. 2PC gives safety at the cost of availability. Saga gives availability at the cost of complexity. This post explains both and when each is right.
CAP theorem is misunderstood constantly. PACELC is more useful but almost unknown. This post explains both with concrete database examples and the decisions they actually inform.
Sharding splits data across multiple nodes and creates a new class of problems. This post covers range, hash, and consistent hashing with trade-offs that matter in production.
Automatic failover sounds simple — consensus makes it hard. This post explains leader election, Raft vs Paxos trade-offs, and what high availability means in distributed databases.
Replication lag creates bugs that only appear under load. Read-your-own-writes, monotonic reads, and consistent prefix reads are the guarantees that prevent them.
Replication keeps databases available when servers fail. Single-leader, multi-leader, and leaderless each trade consistency differently — this post explains all three clearly.
How does a database survive a server crash without losing data? The Write-Ahead Log is the answer. This post explains WAL, ARIES recovery, and what durability actually guarantees.
InnoDB uses B-Tree. RocksDB uses LSM-Tree. The choice determines read speed, write speed, and space amplification. Understanding both unlocks every storage engine decision.
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