Why should you avoid treating DuckDB as a high-concurrency, multi-writer OLTP database?

DuckDB's traditional embedded model is fundamentally single-process: while it supports full ACID transactions and MVCC-based concurrency within that one process, it isn't designed around the pattern a production OLTP system needs, many entirely separate client processes, potentially on different machines, concurrently reading and writing the same database with low-latency, high-throughput small transactions.

Its columnar storage format and vectorized execution engine are also optimized for the opposite access pattern: scanning and aggregating large batches of data efficiently, not for the kind of single-row point lookups and small, frequent writes that dominate a typical OLTP workload, like processing individual e-commerce orders one at a time. Using DuckDB this way would mean fighting against, rather than benefiting from, its core architectural strengths.

This isn't a limitation so much as a reflection of DuckDB's deliberate specialization: a workload with genuinely high write concurrency, low-latency small transactions, and many independent concurrent clients is exactly the profile a system like PostgreSQL, MySQL, or another purpose-built OLTP database handles well, and DuckDB's own documentation and design are explicit that analytical, not transactional-application, workloads are its target. The newer Quack client-server protocol extends DuckDB's reach toward shared, multi-client deployment, but even there, DuckDB's underlying engine remains columnar and analytics-oriented rather than becoming a general-purpose OLTP replacement.