One of our client’s PostgreSQL 16.8 production databases started logging what looked like a memory error: ERROR: invalid memory alloc request size The error immediately pointed toward two likely suspects: - Memory exhaustion - Memory corruption As it turned out, neither was the culprit. Instead, it had encountered a known PostgreSQL bug that trapped the checkpointer in an infinite retry loop. The only way to recover was a forced restart, followed by an extended period of WAL replay during crash recovery. This article explains what happened, why manual checkpoints couldn't fix it, and how a PostgreSQL minor version upgrade permanently resolved the issue. Understanding the purpose of a checkpoint When a transaction modifies data, PostgreSQL does not immediately write the changed page to disk. Instead, it follows a two-step process: Write the change to the Write-Ahead Log (WAL) - a sequential, append-only record of every modification. Keep the modified page in shared memory as a dirty buffer until it is written later. This design is intentional. WAL writes are sequential and therefore inexpensive, whereas writing data pages directly to their final location requires random disk I/O, which is much more costly. Decoupling these two operations is a fundamental part of PostgreSQL's I/O architecture. Eventually, however, the dirty buffers in memory must be synchronized with the actual data files on disk. That is the job of a checkpoint. During a checkpoint, the checkpointer: Flushes every dirty buffer from shared memory to its corresponding data file. Calls fsync() on those files to ensure the data has reached durable storage rather than remaining in the operating system's cache. Records the checkpoint location in the WAL once all writes have been safely persisted. This checkpoint record is critical for crash recovery. If PostgreSQL crashes, recovery only needs to replay WAL generated after the most recent completed checkpoint, because everything before that point has already been written safely to disk. Without checkpoints, PostgreSQL would have to replay the entire WAL history from the beginning, making recovery increasingly slow as WAL accumulates. To keep track of which files still require an fsync() before a checkpoint can finish, the checkpointer maintains an internal structure called the fsync request queue. Every data file modified during checkpoint processing is added to this queue. As each file is successfully fsynced, its entry is removed. Under normal conditions, the queue drains steadily until the checkpoint completes. The problem begins when it doesn't.

AI agents are becoming increasingly capable at operational tasks: summarizing logs, analyzing query plans, identifying anomalies, and assisting with incident response. For databases in particular, this creates an obvious opportunity. Much of day-to-day troubleshooting follows repeatable workflows that lend themselves well to automation. As someone who spends most of my time working with PostgreSQL, I find the interesting question isn't whether an LLM can help analyze a slow database. It can. The harder question is how to do that safely. Production databases sit behind layers of controls, processes, and accountability. Access is granted carefully because mistakes are expensive. When an engineer investigates an incident, that trust comes from experience and clearly defined responsibilities. Extending those capabilities to an AI agent raises a different challenge: how do you give it enough access to be useful without giving it enough access to be dangerous? That problem is exactly what Model Context Protocol (MCP) attempts to address. Rather than exposing a database directly to an LLM, MCP introduces a layer of controlled capabilities. Instead of unrestricted access, the model receives a set of predefined tools with well-defined boundaries.