Choose a Docker logging driver with a disk and outage plan

For Docker logging driver, the safest approach is a bounded operational change, not a command pasted without context. This runbook starts with effective state, shows the smallest candidate action, and finishes by repeating the real user or system path.

TL;DR: The logging driver receives container stdout and stderr; delivery mode, rotation, metadata, and remote availability determine whether logs block, disappear, or fill disk. Capture a baseline with docker info --format '{{.LoggingDriver}}', make the reviewed change only when the evidence matches, then verify with docker inspect example-app-1 --format '{{json .HostConfig.LogConfig}}' and keep the rollback ready.

Audience: developers and operators familiar with Dockerfiles, images, containers, and Compose projects. This guide assumes familiarity with Docker operations and a change window appropriate to the system.

The direct answer

Logs remain queryable across a bounded window, disk growth is capped, and remote failure behavior is documented. That is the success condition for Docker logging driver; command completion by itself is not enough.

The important boundary is build context, immutable image, runtime configuration, namespace isolation, and persistent state. The logging driver receives container stdout and stderr; delivery mode, rotation, metadata, and remote availability determine whether logs block, disappear, or fill disk. If an observation does not identify which side of that boundary failed, collect a narrower observation before changing state.

How the mechanism works

For Docker logging driver, use this mental model: Docker turns a build graph into content-addressed image layers, then combines an image with runtime namespaces, mounts, networks, limits, and a process. The model prevents a common mistake—treating configuration text, control-plane acceptance, process state, and end-user behavior as the same proof.

Follow four stages:

1. Observe: identify the exact host, object, version, owner, and active configuration.
2. Interpret: write the expected result, the abnormal result, and what would remain inconclusive.
3. Change: apply one reviewed action at the narrowest layer that contradicts the baseline.
4. Verify: repeat the original path and compare the same evidence, including adjacent safety controls.

Preflight and safety boundary

Record the current image digest, Compose rendering, mounts, and container state before rebuilding or recreating anything that serves production traffic.

Before Docker logging driver, record UTC time, the current version or digest, the exact target, recent changes, and who owns the workload. The rollback for this runbook is: recreate the service with the previous logging configuration and preserve old log files until the new path is proven.

Do not continue if the target identity is ambiguous, the current state cannot be saved, the only recovery session would be at risk, or the proposed command affects more objects than the brief names.

Capture the read-only baseline

Run these commands one at a time. Replace example names and addresses deliberately; do not paste production secrets into a transcript.

docker info --format '{{.LoggingDriver}}'
docker inspect example-app-1 --format '{{.HostConfig.LogConfig.Type}} {{json .HostConfig.LogConfig.Config}}'
du -sh /var/lib/docker/containers/* 2>/dev/null

Interpret the baseline before moving on:

• Expected: logs remain queryable across a bounded window, disk growth is capped, and remote failure behavior is documented.
• Abnormal: the application bypasses stdout, the driver blocks on a collector, or rotation removes the only incident evidence.
• Inconclusive: missing output can also mean the wrong context, permissions, namespace, log window, binary, or target. Prove those assumptions before treating absence as health.

Save the decisive output, exit status, and timestamp. Redact credentials, customer data, private topology, tokens, and complete environment dumps.

Apply the smallest candidate change

The following is state-changing example syntax, not an instruction to run it unchanged:

docker update --log-driver local example-app-1

For Docker logging driver, the proposed change is acceptable only when the read-only baseline predicts its effect and the rollback is available. The key risk is: changing drivers often requires container recreation and can create an observability gap during the rollout.

Prefer an immutable artifact, validated configuration, dry-run, transaction, candidate object, or staged target when the tool supports one. Record the exact command and UTC time so later telemetry can be correlated to the change.

Verify the result from the outside in

docker inspect example-app-1 --format '{{json .HostConfig.LogConfig}}'
docker logs --since 5m example-app-1
df -h /var/lib/docker

Verification for Docker logging driver has three layers:

1. The control plane or command reports the intended effective state.
2. The process, resource, or data path reflects that state without a new pressure signal.
3. The original user-visible or dependent-system path succeeds from an independent vantage point.

If docker inspect example-app-1 --format '{{json .HostConfig.LogConfig}}' succeeds but the original path still fails, stop. The change may have repaired a local symptom while DNS, policy, routing, caching, dependency, or client state remains broken.

Failure branches

The baseline does not match this runbook

When the application bypasses stdout, the driver blocks on a collector, or rotation removes the only incident evidence, do not force the candidate command. Return to identity and scope, compare a healthy peer only through effective settings, and name a new falsifiable mechanism.

The change succeeds but behavior does not

A successful docker update --log-driver local example-app-1 proves that one interface accepted a request. It does not prove convergence, readiness, data compatibility, external routing, or client recovery. Re-run the same evidence at each downstream boundary.

The change makes the system worse

Execute the written rollback: recreate the service with the previous logging configuration and preserve old log files until the new path is proven. Preserve the failed candidate and relevant logs long enough to explain the outcome; do not destroy the evidence with broad cleanup or repeated restarts.

Operator checklist

• Confirm the exact target, context, identity, version, and active owner.
• Capture the read-only baseline and one disconfirming observation.
• Label docker update --log-driver local example-app-1 as state-changing during review.
• Keep recovery access and rollback independent of the path being edited.
• Change one layer, record UTC time, and wait for its real convergence boundary.
• Verify the original path, adjacent controls, resource pressure, and persistence.
• Update the runbook when observed behavior differs from the source-reviewed model.

Investigate it in Tryssh

$ docker inspect example-app-1 --format '{{json .HostConfig.LogConfig}}'
Expected: logs remain queryable across a bounded window, disk growth is capped, and remote failure behavior is documented.

Tryssh can preserve this evidence loop and show a state-changing command for human approval. It does not make the operator's identity, recovery access, rollback, or platform authority decisions.

Evidence and review status

This Docker logging driver runbook was source-reviewed on 2026-07-29 against current first-party documentation. The commands are illustrative and use example targets. The page does not claim that the change was reproduced across every distribution, managed service, version, network, or workload.

Limitations and trade-offs

Changing drivers often requires container recreation and can create an observability gap during the rollout. Managed platforms may generate configuration, restrict privileges, replace local state, or expose a different control plane than the upstream project. Confirm the installed version and provider contract before applying a repair.

Search visibility is not proof of operational correctness. Treat this page as a decision aid, preserve independent recovery, and stop when the evidence contradicts its assumptions.

Related operator runbooks

Continue with Docker image digest pinning, docker network inspect, the Docker operations foundation guide, and the SSH hardening checklist.

Sources and further reading

• Docker logging driver first-party reference
• Docker Engine documentation
• Docker Build documentation
• Docker Compose documentation