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Set both behaviors when you create the container, or change them later with docker update. A typical manually managed service might use --restart unless-stopped, a hard memory ceiling of 512m, a lower soft reservation of 384m, and --cpus 1.5—but those numbers are examples, not universal defaults.
Restart policies decide when Docker starts a stopped container. Resource limits decide how much memory and CPU it may consume. The correct values depend on the workload, host capacity, available swap, kernel support and whether another system is responsible for process supervision.
Start with a deliberate policy and measured limits
Docker containers have no resource constraints by default; they can consume as much of a resource as the host kernel scheduler allows. Docker’s documentation warns that host memory exhaustion can cause the kernel to kill processes, including Docker or other important host applications. Set limits only after observing the application under representative load, then test the failure behavior before relying on the configuration in production. See Docker’s resource-constraints documentation.
The following command creates a detached container with both restart and resource settings:
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docker run -d
--name app
--restart unless-stopped
--memory 512m
--memory-reservation 384m
--cpus 1.5
example-image:tagunless-stoppedrestarts the container automatically while preserving an explicit manual stop across Docker daemon restarts.--memory 512mis a hard memory ceiling of 512 MB for this example.--memory-reservation 384mis a soft target used under memory contention; it is not a promise that usage will stay below 384 MB.--cpus 1.5applies a CPU quota of up to one and a half CPUs.
Choose values from observed peak use, headroom for bursts and the capacity that must remain available to the host and other containers. A limit that is too low can make a healthy workload fail; no limit can let one container destabilize the machine.
Choose the restart policy that matches the failure you want to recover from
Docker documents four container restart policies. The default is no, which performs no automatic restart. The policy is selected with --restart on docker run or changed later with docker update. The official descriptions and lifecycle rules are in Start containers automatically.
| Option | What it does | When it fits |
|---|---|---|
--restart no |
No automatic restart; this is Docker’s default. | Use when an operator, a separate supervisor or a higher-level orchestrator should decide what happens after exit. |
--restart on-failure[:N] |
Restarts after a non-zero exit. The optional N caps the number of retries. |
Useful for a batch or service that should be retried only when it reports failure. It does not restart merely because the Docker daemon restarted. |
--restart always |
Restarts the container when it stops, including after a daemon restart. An explicit manual stop suppresses policy-driven restarts until the daemon or container is restarted. | Use when the container should generally be running again after any stop, while understanding the manual-stop exception. |
--restart unless-stopped |
Behaves broadly like always, but an explicit manual stop remains in effect across a Docker daemon restart. |
Often matches a manually managed service whose operator wants a stop to stay stopped until intentionally reversed. |
These are container restart policies, not Swarm service restart configuration. Do not combine Docker’s restart policy with a host-level process manager unless you have designed the interaction; two supervisors can repeatedly start and stop the same workload. Restart policies also are not the same as --live-restore, which addresses daemon upgrades and has a different purpose.
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Successful startup must last at least 10 seconds
Docker does not apply a restart policy immediately to every short-lived launch. A container must run successfully for at least 10 seconds before Docker considers the start successful for restart-policy purposes. This prevents a rapidly failing initialization from being treated as a stable service. If a process exits during that initial period, do not assume the policy will create an unlimited retry loop.
on-failure reacts to exit status, not daemon restarts
on-failure is tied to a non-zero container exit. If the Docker daemon restarts while the container is otherwise stopped or has not failed in that way, this policy alone does not bring it back. Select always or unless-stopped when recovery after daemon restart is part of the requirement.
Manual stops are intentional overrides
Stopping a container manually suppresses policy-driven restarting until the Docker daemon is restarted or the container is manually restarted. With unless-stopped, the stopped state also survives a daemon restart. This difference matters during maintenance: a command such as docker stop app is not equivalent to a transient crash.
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A restart is not a health check
Restart policies respond to container process termination and Docker’s lifecycle events. They do not determine whether an application is serving correct responses. If a process remains running while the application is unhealthy, a restart policy by itself will not detect that condition; use an appropriate health-monitoring design at the platform level rather than treating the policy as an application probe.
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Create a container with restart and resource settings
Build the command from the workload requirements
Place the option values before the image name. The image and its command come last. The example below is intentionally conservative for a small service, not a recommendation for every application:
docker run -d
--name app
--restart unless-stopped
--memory 512m
--memory-reservation 384m
--cpus 1.5
example-image:tagThe hard limit and soft reservation can be used together, but the reservation should be lower than the hard limit. If the application normally needs more than 512 MB, increasing the hard limit—or reducing the application’s working set—is safer than hoping the soft reservation will protect it. If the host cannot spare the requested capacity, adding a limit does not create physical memory or CPU.
Use a restart count when failures should eventually stop
To limit retries after non-zero exits, append a count, for example:
docker run -d --name worker --restart on-failure:5 example-image:tagThis asks Docker to retry up to five times after failure. It does not turn the container into a daemon-restart recovery mechanism, and it does not replace diagnosing the exit cause.
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Change an existing container without recreating it
Use docker update to change restart and resource configuration on an existing container. To change only the restart policy:
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docker update --restart unless-stopped appDocker also documents docker update for resource settings. For example, where the installed Engine version accepts these options and the host can support them:
docker update
--memory 768m
--memory-reservation 512m
--cpus 2
appConsult the Docker container update CLI reference for the exact syntax, option constraints and behavior supported by your Engine release. A change to a limit is not evidence that the new value is safe; observe the workload again after changing it.
Set memory limits without confusing hard and soft controls
--memory: a hard maximum
--memory sets the container’s hard memory maximum. Docker documents a minimum value of 6 MB. A hard limit protects the host from an unbounded container, but it can also trigger out-of-memory handling when the application needs more than the configured ceiling. Pick a value that covers normal use and expected bursts while leaving capacity for the operating system, Docker and other workloads.
docker run -d --name api --memory 1g example-image:tagThe suffix expresses the amount, such as m for megabytes or g for gigabytes. Treat the limit as an enforcement boundary, not as a target the process should reach.
--memory-reservation: a soft limit under contention
--memory-reservation is activated when the host is under memory pressure. It gives Docker a lower target for reclaim and scheduling decisions, but it does not guarantee that the container will remain below that amount. A process can exceed the reservation when capacity is available. When both options are set, keep the reservation below the hard --memory ceiling:
docker run -d
--name api
--memory 1g
--memory-reservation 768m
example-image:tag--memory-swap: combined RAM and swap allowance
--memory-swap controls the combined memory-plus-swap allowance and only has meaning when --memory is also set. The result depends on whether the host has swap enabled and on kernel support. A configured number is therefore not automatically available swap capacity. Check the host’s swap configuration and the documentation for the Docker Engine and platform you operate before relying on this setting. The option relationships and examples are described in Resource constraints and Running containers.
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docker run -d
--name api
--memory 1g
--memory-swap 2g
example-image:tagIn this example, the combined allowance is 2 GB, of which up to 1 GB is the memory limit. It does not promise that 1 GB of swap is physically present or that swapping will have acceptable performance.
Be cautious with out-of-memory behavior
When a container or the host runs out of memory, the kernel can kill processes. Docker advises against casually disabling this protection with --oom-kill-disable; if you use that option, Docker’s guidance says a memory limit must also be set. A disabled kill does not create memory and can leave the host exposed to a broader outage. Prefer sizing, load testing and application-level memory controls first.
Control CPU with a ceiling or a relative priority
--cpus: the simple CPU quota
--cpus expresses a CPU-time ceiling as a count of CPUs. For example, --cpus="1.5" allows up to one and a half CPUs worth of scheduled time when the workload can use it:
docker run -d --name batch --cpus="1.5" example-image:tagThis is a cap, not a reservation. A container may use less when idle, and it can be throttled at the ceiling even if the application would benefit from more CPU.
--cpu-shares: relative weight during contention
CPU shares provide relative weighting when containers compete for CPU. They do not reserve a fixed fraction, guarantee a minimum CPU allocation or cap the container by themselves. A container with a higher share receives more of the CPU available during contention, but if no other workload is competing, it may still use the available CPU subject to other limits.
docker run -d --name reporting --cpu-shares 512 example-image:tagUse shares when the requirement is “prefer this workload over that one under contention.” Use --cpus when the requirement is “never let this workload exceed this CPU amount.” They can be combined when both a ceiling and a relative priority are needed.
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Advanced quota flags and kernel support
Docker’s CPU controls are implemented through kernel scheduling and cgroup features. The documented default CFS period is 100,000 microseconds; lower-level period and quota flags exist for cases that need finer control, but their behavior depends on platform and kernel support. Prefer --cpus unless you have a reason to manage those lower-level values and have verified them against your installed Engine version.
Measure actual use before tightening limits
Docker provides docker stats for live CPU usage, memory usage and the configured memory limit. Start with the container name:
docker stats appLeave the command running while the application handles representative traffic, scheduled jobs, startup, cache warm-up and its largest expected request. A single snapshot cannot establish a safe production limit. Record the highest sustained and burst memory use, CPU demand and restart behavior, then repeat the test after applying limits. The runtime metrics format is documented at Docker runtime metrics.
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- Measure unconstrained behavior in a controlled environment. Observe normal and peak workload periods before choosing a ceiling.
- Reserve host capacity. Subtract the memory and CPU needed by the operating system, Docker and other services; do not allocate every host resource to containers.
- Set a hard memory ceiling above measured peaks. Include application bursts, runtime overhead and a margin, but keep the value below what the host can safely provide.
- Set a lower reservation only when it represents a useful pressure target. Remember that it is soft and can be exceeded.
- Choose a CPU cap from latency and throughput requirements. A cap that is numerically convenient may still cause throttling during bursts.
- Apply the restart policy that matches the failure contract. Decide whether non-zero exits, daemon restarts, manual stops or retry limits should trigger recovery.
- Load-test the constrained container. Confirm that the application fails in a controlled, observable way when it reaches a limit.
- Recheck after upgrades. Engine versions, kernels, Docker Desktop environments and workload versions can change resource behavior.
Diagnose common surprises
The container does not restart after a daemon reboot
- If the policy is
on-failure, that is expected: the policy responds to non-zero exits, not to a daemon restart by itself. - If the container was manually stopped, Docker suppresses policy-driven restart until the daemon or container is restarted.
- If you need a manual stop to persist across daemon restarts, use
unless-stopped. - Confirm that the policy was actually applied, and check the update command against your Engine version if it was changed after creation.
The container restarts repeatedly
- Inspect the application’s exit condition rather than treating restarts as a fix.
- Check whether the process exits before the documented 10-second successful-start threshold.
- Use
on-failure:Nwhen a bounded retry count is appropriate. - Check whether the memory ceiling is too low and causing out-of-memory termination.
Memory usage exceeds the reservation
That is not necessarily a configuration failure. A reservation is soft and is enforced as a pressure target, not as a hard maximum. The hard boundary is --memory. If usage reaches that ceiling, increase the limit only after verifying host capacity and application needs.
CPU usage is lower than expected
- A
--cpusvalue is a ceiling, so the container cannot exceed it even during a burst. - CPU shares affect distribution only when there is contention; they do not guarantee a fixed percentage.
- Host-level limits, kernel support and other containers can also affect scheduling.
Swap settings do not behave as expected
--memory-swap is a combined memory-plus-swap allowance, not a standalone swap guarantee. Verify that the host has usable swap and that the platform supports the requested cgroup controls. Behavior may differ across Linux Engine installations, Docker Desktop environments and other operating systems.
Platform and operations boundaries
The resource documentation focuses on Linux kernel and cgroup behavior. Some resource features require kernel support, and this guidance does not establish identical behavior across every Docker Engine release, Docker Desktop configuration or operating system. Check the documentation for the exact deployment target and installed version before depending on a specific flag.
Keep responsibility for restarts in one layer. Docker restart policies apply to containers; Swarm services and Kubernetes workloads have their own controllers and configuration models. Likewise, a host process manager may conflict with Docker’s policy if both attempt to supervise the same process. Choose one authority and document what a manual stop means for operators.
Quick Recap
Configuration checklist
- Have you selected
no,on-failure[:N],alwaysorunless-stoppedbased on an explicit failure scenario? - Do operators understand the 10-second successful-start condition and the effect of a manual stop?
- Is
--memoryhigh enough for measured peaks while protecting host capacity? - If you set
--memory-reservation, is it below the hard ceiling and treated as a soft target? - Does the host have the swap and kernel support required by your
--memory-swapplan? - Are you using
--cpusfor a true cap and--cpu-sharesonly for relative priority? - Have you watched
docker statsthrough representative load rather than relying on one snapshot? - Have you tested out-of-memory, CPU throttling, application crashes and daemon restarts before production?
- Is Docker the only restart authority for this container?
Official references
- Docker Docs: Start containers automatically — restart policies, startup conditions and manual-stop behavior.
- Docker Docs: Resource constraints — memory, swap, CPU controls, host risks and kernel requirements.
- Docker Docs: Running containers — run-time resource option reference and examples.
- Docker Docs: docker container update — changing settings on an existing container.
- Docker Docs: Runtime metrics — metrics exposed by
docker stats.
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