Thoughts, ideas, and things I find interesting.
How multi-leader replication enables local writes, why conflicts happen, and how to design safe resolution rules
How leader-follower replication handles writes, failures, replica lag, and read consistency in production
How databases, APIs, and message brokers keep data compatible through real-world deployments
How data formats and schemas let old and new services safely evolve together
How column stores handle writes, and when materialized views and OLAP cubes make repeated analytics much faster
My practical notes on column storage, bitmap indexes, Cassandra, and vectorized processing
Why column stores make analytical queries faster through selective reads, compression, bitmap indexes, and CPU-friendly scans
Why OLTP and OLAP need different database designs, and how ETL, warehouses, and star schemas support analytics
How secondary, spatial, full-text, and in-memory indexes make real database queries fast, and what each trade-off costs
How Cassandra uses partitioning, replication, compaction, and separate analytics pipelines to make LSM-trees practical at banking scale
A practical comparison of B-trees and LSM-trees: writes, compaction, latency, and transaction trade-offs
A practical mental model for database B-trees: pages, heap rows, MVCC, WAL, page splits, and how they compare with LSM-trees