"PostgreSQL 18 Async I/O: What io_method Means for Apps"
Postgres io_uring vs worker: PostgreSQL 18's async I/O explained — io_method settings, pg_aios monitoring, io_workers tuning for read-heavy workloads.
The headline PostgreSQL 18 performance feature is invisible: the database can now issue disk reads asynchronously instead of stalling on every one. The practical question for app developers is smaller and sharper — postgres io_uring vs worker : which io_method should your server use, how do you know it's working, and when does it actually matter? This guide answers all three. Async I/O is GA in PostgreSQL 18 (released 2025-09-25) — no beta caveat here — and for read-heavy workloads it delivers the biggest single-version read speedup in years. Last updated: October 4, 2026 What you'll learn Why Postgres 18 async io changes the read path (and what it doesn't change) The three io_method values: worker, io_uring, sync postgres io_uring vs worker: which one to use and why the default is worker Monitoring in-flight I/O with the pg_aios view io_workers tuning and the effective_io_concurrency 1→16 change The Docker/seccomp failure mode and the "could not setup io_uring queue" error Postgres 18 Async IO: Why Reads Were the Bottleneck Before PG18, a Postgres backend reading from disk worked like this: issue a read, block, wait, get the data, issue the next read. For sequential scans, bitmap heap scans, and any query touching more data than fits in shared buffers, the CPU sat idle while the storage subsystem — which could have been fetching the next pages in parallel — did nothing. Asynchronous I/O changes the shape of that loop: the backend submits a batch of read requests and keeps working while they complete. Reported results put cold-cache sequential scans at roughly 2–3x faster, with bitmap heap scans and VACUUM also significantly improved. The key phrase is *cold cache* — async I/O helps when you're actually waiting on the disk. A workload that lives…