With the release of Postgres 18 Beta 1 this week, a significant architectural shift in how the database handles Input/Output (I/O) operations is underway. The introduction of Asynchronous I/O (AIO) represents a fundamental change that promises substantial performance gains, particularly in cloud environments where network-attached storage often introduces latency. Why Asynchronous I/O Matters Postgres has traditionally relied on a synchronous I/O model, where each read request involves a blocking system call. The database pauses to wait for the operating system to fetch the data, which can introduce significant delays, especially in cloud environments like Amazon Elastic Block Store (EBS) where storage is network-attached and I/O latencies can exceed 1 millisecond. In contrast, asynchronous I/O allows the database to issue multiple read requests concurrently, without waiting for each prior request to complete. This can significantly reduce I/O wait times, leading to faster query execution and better overall system performance. Imagine a librarian who can retrieve several books simultaneously rather than fetching one at a time, thereby reducing the total retrieval time. How Postgres 17 Prepared the Ground The groundwork for AIO in Postgres was laid in version 17 with the introduction of read stream APIs and the use of posix_fadvise() to request data prefetching from the operating system. However, this approach had limitations. Despite the kernel's efforts to preload data into the OS page cache, Postgres still had to issue individual syscalls for each read, which could introduce inconsistencies and inefficiencies. New io_method Setting in Postgres 18 Postgres 18 introduces a new configuration parameter, io_method, which determines how read operations are managed. This setting can be configured in the postgresql.conf file and requires a restart to apply changes. Here are the three options: io_method = sync: This maintains the synchronous behavior of Postgres 17, with reads still blocking the main process. io_method = worker: Utilizes dedicated I/O workers that run in the background to fetch data independently. The main backend process enqueues read requests, and these workers handle them, delivering data to shared buffers without blocking the main process. The default number of workers is 3, but this can be configured via the io_workers setting. io_method = io_uring: This Linux-specific method uses io_uring, a high-performance I/O interface introduced in kernel version 5.1. It minimizes syscall overhead by establishing a shared ring buffer between Postgres and the kernel, making it the most efficient option. However, it is only compatible with newer Linux kernels and specific file systems. Performance Impact in Cloud Environments The benefits of AIO are most pronounced in cloud environments, where individual disk reads can take multiple milliseconds, leading to substantial idle CPU time and degraded throughput. To quantify these gains, a benchmark was conducted on an AWS c7i.8xlarge instance (32 vCPUs, 64 GB RAM) with a 100GB io2 EBS volume provisioned for 20,000 IOPS. The test involved a 3.5GB table, and the OS page cache was cleared before each test run to simulate cold cache conditions. Synchronous I/O (Postgres 17 and 18): Performance remained similar, confirming that the synchronous behavior was unchanged. Worker Method (Postgres 18): Provided a consistent 2-3x improvement in read performance, especially with the default setting of 3 I/O workers. io_uring Method (Postgres 18): Delivered the best results, showing even greater improvements in cold cache scenarios. The lower syscall overhead and reduced process coordination make it the preferred method for maximizing I/O performance. Tuning Parameters for AIO In Postgres 18, the effective_io_concurrency setting becomes more critical when using asynchronous io_methods. This parameter directly controls the number of asynchronous read-ahead requests Postgres issues, affecting performance in high-latency environments. The default value has been increased from 1 in Postgres 17 to 16 in Postgres 18, reflecting the new capabilities. Monitoring I/O in Postgres 18 The introduction of asynchronous I/O changes how I/O activity is monitored. With the worker method, the backend process appears idle during read operations, and the new IO / AioIoCompletion wait event must be considered. When using io_uring, the backend does not block on traditional I/O syscalls, making it challenging to identify active I/O operations using standard monitoring tools. The new pg_aios view helps by providing visibility into in-flight I/O requests, crucial for debugging and performance tuning. Asynchronous I/O and Query Performance Asynchronous I/O can alter the interpretation of query performance metrics. For instance, using EXPLAIN ANALYZE with the worker method might show a shorter runtime because it reflects the wait time for I/O completion, not the actual I/O effort. This can make it appear as though queries are more efficient than they actually are, necessitating careful analysis. Conclusion The arrival of Postgres 18 marks a significant step forward in I/O management, with asynchronous reads offering notable performance boosts in high-latency cloud environments. However, the transition to AIO comes with challenges, such as the need to adjust observability practices and understand new timing metrics. Engineers and database administrators will need to adapt their strategies to fully leverage these enhancements. Future versions of Postgres may further expand AIO capabilities, potentially including support for asynchronous writes and direct I/O, to address even more performance bottlenecks in modern workloads. Industry Evaluation and Company Profiles Industry insiders are enthusiastic about the potential of Postgres 18's AIO features, particularly for cloud-native applications. Companies like Amazon, which offer scalable cloud infrastructure, stand to benefit significantly from reduced I/O latency and improved database efficiency. The Postgres community has been actively involved in testing and benchmarking these features, ensuring they are both robust and performant. The integration of io_uring is particularly noteworthy, as it aligns with the latest advancements in kernel technology, making Postgres 18 a cutting-edge solution for database management in cloud environments.