Bold question: Postgres in 2025 isn’t just about flashy features—it's about real workloads. Is it read-heavy or write-heavy, and can SQL stay central as AI-driven stacks rise? [1]
Read vs Write: The workload reality From the OLTP angle, Postgres is designed for writes, but many apps are read-heavy in practice. Audits and trailing logs mean writes stay heavy, while caching makes reads cheap. Read replicas and caching help scale the read side. That mix forces tuning choices that lean toward balancing act. [1]
MVCC/WAL and tuning implications MVCC and transaction logging (WAL) add cost to writes through locking and data validation. Those costs shape tuning: pick indexes, craft queries, and decide when to route reads through caches or replicas. In practice, you might tune for read efficiency with caches on the hot data paths. [1]
SQL's continuing centrality in 2025 On the broader data stage, SQL remains the lingua franca. Postgres is booming, and ecosystems around it are thriving as AI-driven infrastructures take shape. Self-service data platforms still lean on SQL, ensuring the old model stays powerful. [2]
Bottom line: SQL isn’t going anywhere, even as workloads tilt and architectures evolve.
References
Is Postgres read heavy or write heavy?
Discusses whether Postgres workloads are read-heavy or write-heavy; compares OLTP/OLAP, MVCC, WAL, and other databases in tuning tradeoffs today overall.
View sourceWhy SQL Still Rules the Data World in 2025
SQL remains essential; Postgres thriving; AI shifts may alter abstractions, but SQL persists across future data stacks and tools everywhere.
View source