If an FFmpeg YouTube stream stops on a Linux server, first establish whether the kernel or a service/container memory limit killed FFmpeg. A stream can also end because of an FFmpeg error, exhausted input, a network or RTMP failure, or an ended or invalid YouTube broadcast. Restarting FFmpeg may hide the symptom without fixing its cause.
How to check if Linux OOM killer killed FFmpeg
Preserve logs before restarting
Record the failure time and retain FFmpeg stderr, service logs, and kernel logs. On a systemd host, inspect the service and kernel journals around that time, replacing the example service name and time window with your own:
journalctl -u YOUR_SERVICE --since "2026-10-03 12:00:00"
journalctl -k --since "2026-10-03 12:00:00"Look for kernel OOM-killer messages naming ffmpeg or a related process, the service result and exit status, and any container or cgroup OOM indicators. The exact log details and commands depend on the host setup.
If the kernel identifies an OOM victim, continue with memory accounting. If not, check FFmpeg’s final error and exit status, whether its input was exhausted or became unavailable, the YouTube stream key and broadcast state, and network or RTMP errors. Do not assume a stopped YouTube stream proves that the kernel killed FFmpeg.
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Check both the host and FFmpeg’s memory limit
Find the cgroup that contains FFmpeg
A systemd service or container can have a memory limit of its own. That limit can be reached while the host still has memory available, so check system-wide pressure and the process’s actual service or container boundary.
On a cgroup v2 system, identify the cgroup containing FFmpeg and inspect these files where available:
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memory.current: current memory usage.memory.peak: recorded peak usage.memory.highandmemory.max: the configured pressure threshold and hard limit.memory.eventsand, if present,memory.events.local: event counters, includinghigh,max,oom, andoom_kill.
Compare counter values before and after a failure if possible. The kernel documents that memory.events counters can be hierarchical; use memory.events.local where available when you need to determine whether that specific cgroup recorded an event. File availability and the cgroup path depend on the deployed kernel and system layout. See the Linux kernel cgroup v2 documentation.
Understand the difference between memory.high and memory.max
| Setting | What it indicates | What to look for |
|---|---|---|
memory.high |
A threshold above which processes are throttled and put under reclaim pressure. Crossing it does not itself invoke the OOM killer. | Rising high events and memory pressure may explain slowing or degraded work, but do not alone establish an OOM kill. |
memory.max |
The hard memory limit. If usage reaches it and cannot be reduced, the cgroup OOM killer is invoked. | Inspect the limit and relevant max, oom, and oom_kill counters alongside logs. |
On cgroup v1, the hierarchy and controls differ. Do not copy cgroup v2 filenames or commands into a v1 deployment; consult the kernel’s cgroup v1 memory-controller documentation, which marks its OOM-control interface deprecated and points to v2 controls for some corresponding functions.
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Measure usage while the stream runs
Observe FFmpeg’s resident memory and cgroup usage long enough to catch both gradual growth and short peaks, including the period around the failure. Record the number of concurrent FFmpeg jobs, input resolution and frame rate, filters, encoder, and other services sharing the same memory boundary. A single reading after a restart may miss the peak that caused the failure.
FFmpeg documents -benchmark and -benchmark_all for performance and resource reporting, but maximum-memory reporting is unsupported on some systems and may display zero. Treat host and cgroup measurements as the primary evidence, not a zero FFmpeg statistic. Check the documentation for the version matching your installed binary: FFmpeg command-line documentation.
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Do not switch encoders, lower resolution, or remove filters by guesswork. FFmpeg supports varied inputs, filters, transcodes, and outputs; the memory demand depends on the actual pipeline. Hardware acceleration is capability-dependent, and some paths copy frames from GPU memory to system memory, so acceleration does not automatically mean lower system RAM use. See FFmpeg’s advanced video options.
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- A service or container limit is the boundary: If the measured peak shows that
memory.maxis too low, consider revising the allocation in that service or container’s configuration. Check host capacity and competing services before raising it; the host must be able to support the added demand. - The host is under broad pressure: Reduce concurrent jobs or other memory consumers, or provision capacity based on measured demand. There is no universal RAM requirement for an FFmpeg YouTube stream.
- Usage grows over time: Investigate the FFmpeg command, wrapper, input and filter path, supervision, and installed version using logs and a minimal reproducible workload. The available sources do not establish a universal FFmpeg memory leak or YouTube-streaming defect.
- A pipeline component drives the measured peak: Test a targeted simplification, such as reducing resolution, removing an unnecessary filter, avoiding needless transcoding, or lowering concurrency. These are experiments, not guaranteed fixes.
Do not routinely disable OOM killing. It does not remove a hard shortage; address the measured usage, memory boundary, or available host capacity instead.
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Keep FFmpeg manageable as a background process
FFmpeg normally checks console input. As the FFmpeg FAQ explains, a background job can be suspended by terminal input behavior. Add -nostdin to the invocation or redirect standard input from /dev/null:
ffmpeg -nostdin [your existing input, processing, and output options]Use the options and input/output arguments appropriate to your existing command; the example is not a complete streaming command. -nostdin prevents interactive-input checks from causing a background-job suspension. It does not fix OOM, network, input, or YouTube broadcast failures.
Run a long-lived process under a service manager so logs, boot behavior, and restart limits are explicit. Set bounded restart behavior and alert on repeated exits. A supervisor can restart FFmpeg after a failure, but repeated OOM kills will continue until the underlying pressure or limit is addressed. Check the YouTube broadcast and input independently: a running local process does not by itself prove the remote stream is healthy.
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