When the core log buffer becomes full, the logging subsystem can be configured to
CONFIG_LOG_MODE_OVERFLOW=y (default)CONFIG_LOG_MODE_OVERFLOW=n, orCONFIG_LOG_BLOCK_IN_THREAD=y, CONFIG_LOG_BLOCK_IN_THREAD_TIMEOUT_MS=-1.In the last configuration, the log processing thread will block until space becomes available again in the core log buffer.
Warning ⚠️: Blocking the log processing thread is generally not recommended and should only be attempted in advanced use cases.
There are roughly 4 scenarios we care about testing with CONFIG_LOG_BLOCK_IN_THREAD, and they can all be characterized by comparing log message flow rates. Typically, one would describe log message flow rates with units such as [msg/s]. However, in the table below, we are mainly concerned with the ratio of the Output Rate to the Input Rate, and in that case, the units themselves cancel-out. In the table we assume there exists an N such that N > 1.
| Name | Input Rate | Output Rate | Rate |
|---|---|---|---|
| Input-Limited | 1 | N | 1 |
| Matched | 1 | 1 | 1 |
| Output-Limited | 1 | 1/N | 1/N |
| Stalled | 0 | 0 | 0 |
The resultant Rate is always Rate = MIN(Input Rate, Output Rate).
Rate-limiting of any kind can be described approximately as back pressure. Back pressure is fine in short bursts but it can cause delays in application and driver code if the pressure is not relieved promptly.
Many log backends, such as UARTs, have a built-in hardware FIFO that inherently provides back-pressure; output log processing is rate-limited based on the baud rate of the UART. Other backends, such as UDP sockets or DMA, can provide significantly higher throughput but are still inherently rate-limited by the physical layer over which they operate, be it Gigabit Ethernet or PCI express.
Even a trivial message source or message sink is still rate-limited by memory or the CPU. From that perspective, we can infer that there is a finite limit in the log processing rate for practical systems. That may be comforting to know, even if it is something astronomical like 1G [msg/s].
The ideal scenario is when the output “bandwidth” exceeds the input rate. If so configured, we minimize the liklihood that the log processing thread will stall. We can also be sure that the output will be able to relieve backpressure (i.e. the core log buffer usage will tend to zero over time).
When the input rate and output rates are equal, one might think this is the ideal scenario. In reality, it is not. The rates could be matched, but a sustained increase (or several small increases) in the input log rate, could cause the core log buffer to approach 100% capacity. Since the output log rate is still only matched with the input log rate, the core log buffer capacity would not decrease from 100%, and it would remain saturated.
Logging has a tendency to be bursty, so it is definitely preferable to operate in the Input-limited Log Rate regime.
If the rate of output processing is less than the rate of input processing, the core log buffer will approach 100% capacity and, eventually, stall the log processing thread.
When any log backend is unable to process logs for whatever reason, the output rate approaches 0 [msg/s]. If application or driver code continue to submit logs, the core log buffer approaches 100% capacity. Once the core log buffer is full, the log processing thread is unable to allocate new log messages and it will be stalled.
Stalling a real-time application produces unexpected behaviour, so it is advised to avoid this for any non-negligible amount of time.
It is absolutely critical that the log backend is capable of operating correctly even when the log processing thread is blocking in order to automatically recover from a stall.
On a live system, it may be necessary to manually perform remediation of log backends that are unable to recover from stalling the log processing thread. Remediation could involve disabling the log backend and freeing any in-use buffers.