The Tool Desk
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Centralize Docker logs by sending each container’s stdout and stderr through Docker’s Fluentd logging driver to a Fluentd forward input, routing events by tag, buffering them, and writing them with Fluentd’s Elasticsearch output plugin. Elasticsearch stores the indexed records; Kibana can then search and visualize them. This pattern works in Docker Compose and, with a node-level DaemonSet, in Kubernetes.
The pipeline and why each component exists
The complete path is:
- A container writes application messages to stdout or stderr.
- The Docker Fluentd logging driver forwards those messages to Fluentd over port 24224.
- Fluentd accepts the events with its
forwardinput, attaches or preserves tags, and applies matching rules. - Fluentd buffers events and sends them through the Elasticsearch output plugin.
- Elasticsearch indexes the records. Kibana is optional and provides search, dashboards, and visual exploration.
The driver is the handoff from Docker to Fluentd; it is not an Elasticsearch client. Fluentd needs both an input that understands the driver’s protocol and an output plugin that understands Elasticsearch. Tags are routing keys: a tag such as httpd.access lets one <match> rule select web logs while another rule could send database or security events elsewhere.
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What you need before configuring it
- A Docker Engine that supports the Fluentd logging driver and permission to configure logging options.
- A Fluentd image or installation with the
forwardinput andfluent-plugin-elasticsearchinstalled. - An Elasticsearch node or cluster reachable from Fluentd on its HTTP port, normally 9200.
- Optional Kibana access if people need a browser interface rather than direct Elasticsearch queries.
- A plan for credentials, TLS, index retention, disk capacity, and recovery before exposing the service beyond a test machine.
The official Compose example uses Elasticsearch image 8.17.1, Elasticsearch Ruby gem 8.19.0, and fluent-plugin-elasticsearch 5.4.3. These are example pins, not a compatibility guarantee or a performance benchmark. Check the plugin’s supported Elasticsearch client versions and the versions you intend to deploy together; do not copy the pins blindly into a production upgrade.
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A working Docker Compose implementation
A minimal stack has four services: an application, Fluentd, Elasticsearch, and Kibana. The application selects the Fluentd driver, Fluentd publishes port 24224, Elasticsearch provides storage, and Kibana is the optional interface.
1. Create the Compose file
services:
web:
image: httpd
logging:
driver: fluentd
options:
fluentd-address: localhost:24224
tag: httpd.access
fluentd:
image: fluentd-custom
ports:
- "24224:24224"
- "24224:24224/udp"
volumes:
- ./fluent.conf:/fluentd/etc/fluent.conf:ro
depends_on:
elasticsearch:
condition: service_healthy
elasticsearch:
image: docker.elastic.co/elasticsearch/elasticsearch:8.17.1
environment:
- discovery.type=single-node
- xpack.security.enabled=false
healthcheck:
test: ["CMD-SHELL", "curl -fsS http://localhost:9200/_cluster/health || exit 1"]
interval: 10s
timeout: 5s
retries: 20
kibana:
image: docker.elastic.co/kibana/kibana:8.17.1
depends_on:
elasticsearch:
condition: service_healthyThe application’s fluentd-address must resolve to the listener from the Docker daemon’s point of view. The value localhost:24224 is the value used by the official example; in other deployment layouts you may need a host address, a published port, or a service name. Confirm the address for your Docker Engine and network rather than assuming that “localhost” means the Fluentd service from every container.
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2. Build Fluentd with the Elasticsearch output plugin
FROM fluent/fluentd:v1.16-debian
USER root
RUN gem install elasticsearch -v 8.19.0 &&
gem install fluent-plugin-elasticsearch -v 5.4.3
USER fluentUse a Fluentd base image and plugin versions that are supported together. The versions above reproduce the documented example’s pins; they are not a recommendation to remain on those versions. Rebuild and test the image whenever you change Elasticsearch, the Ruby client, or the output plugin.
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3. Add Fluentd’s configuration
<source>
@type forward
port 24224
bind 0.0.0.0
</source>
<match *.**>
@type copy
<store>
@type elasticsearch
host elasticsearch
port 9200
logstash_format true
logstash_prefix fluentd
logstash_dateformat %Y%m%d
include_tag_key true
type_name access_log
tag_key @log_name
<buffer>
flush_interval 1s
</buffer>
</store>
<store>
@type stdout
</store>
</match>By default, Fluentd reads its configuration from /fluentd/etc/fluent.conf in the documented container layout. The forward source listens on all interfaces so the Docker driver can reach it. The wildcard match catches tags with at least one dot; if your tags do not contain dots, change the pattern to match your naming scheme.
The copy output sends every event to Elasticsearch and to stdout. The stdout store is deliberately useful during setup: it proves that Fluentd received and routed a record even when Elasticsearch indexing is failing. Remove or restrict that store after troubleshooting if duplicate console output is undesirable.
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4. Start and inspect the stack
- Build the Fluentd image:
docker compose build fluentd. - Start all services:
docker compose up -d. - Check service state:
docker compose ps. - Read Fluentd’s startup and routing output:
docker compose logs -f fluentd. - Read Elasticsearch health from inside its container:
docker compose exec elasticsearch curl -fsS http://localhost:9200/_cluster/health. - Generate a request against the web service, then watch for the record in Fluentd stdout and in the Elasticsearch index.
Do not treat a running container as proof that logs are indexed. Confirm all three transitions: the Docker driver reaches port 24224, Fluentd emits the event, and Elasticsearch accepts an indexed document.
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Tags and routing rules
A tag should identify the source and remain predictable. The fixed httpd.access value is easy to match and search. Docker also supports templates such as docker.{{.ID}} and docker.{{.Name}}. Template variables must be written exactly as Docker expects; in documentation and shell files, keep them inside a quoted or code-formatted value so another templating system does not expand them first.
Design a tag taxonomy before adding match rules. For example, use a stable first component for the workload and a second component for the stream or severity. Then route specific streams before a catch-all rule:
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<match api.error>
@type elasticsearch
# error-specific index and retention settings
</match>
<match api.**>
@type elasticsearch
# general API logs
</match>
<match **>
@type stdout
</match>Put narrow rules before broad rules. A wildcard that catches everything can hide a typo in a more specific rule, while a final stdout or secondary destination makes unmatched events visible. Keep the same naming convention across Compose projects, hosts, and Kubernetes namespaces so dashboards and retention policies do not depend on deployment location.
The Fluentd logging driver supports fluentd-address, tag, and optional sub-second precision in Fluentd v1. Older implementations did not support sub-second precision, so verify the behavior of the Docker Engine and Fluentd versions you actually run before relying on ordering at sub-second resolution.
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Buffering, readiness, and delivery behavior
Elasticsearch can be temporarily unavailable during startup, rolling maintenance, network failure, or disk pressure. Fluentd’s buffer is the component that absorbs a short outage instead of making every application request depend on an immediately healthy indexer. The sample uses a one-second flush interval, but a production buffer also needs explicit limits and a failure policy.
- Choose memory or file-backed buffering according to loss tolerance. File-backed buffers survive a Fluentd process restart more reliably, provided the mount is durable and has enough capacity.
- Set chunk, queue, and retry limits so a prolonged Elasticsearch outage cannot consume the host’s entire disk or memory.
- Monitor retry counts, dropped chunks, buffer utilization, and output latency. An apparently healthy Fluentd process can still be accumulating undelivered data.
- Use an Elasticsearch health check and a startup dependency, but remember that readiness at startup does not protect against later outages.
- Decide whether a full buffer should block new writes, drop the oldest data, or route failures elsewhere. The correct choice depends on whether completeness or application availability is more important.
Docker’s logging driver is on the path before Fluentd. Test what happens when Fluentd is stopped, when port 24224 is unreachable, and when Elasticsearch rejects writes. Record the observed behavior for your Docker version and document the recovery procedure for operators.
Security and production hardening
The Compose sample disables Elasticsearch security with xpack.security.enabled=false. That setting is explicitly a testing convenience, not a production baseline. A real deployment needs authentication, authorization, encrypted transport where traffic crosses a trust boundary, protected secrets, and durable storage.
- Enable Elasticsearch security and create a least-privilege identity that can write only the intended indices and templates.
- Use TLS for Fluentd-to-Elasticsearch traffic when the network is not fully trusted. Configure certificate verification deliberately; do not turn verification off merely to clear a connection error.
- Keep Elasticsearch and Fluentd management ports off public interfaces. Restrict port 24224 to the Docker hosts or Kubernetes nodes that must send logs.
- Store passwords, API keys, and certificates as secrets rather than embedding them in a Compose file committed to source control.
- Give Elasticsearch persistent volumes and monitor free disk space. Log indexing stops being reliable when the cluster reaches disk watermarks.
- Define index lifecycle and retention rules. Date-based names make it possible to delete or archive old indices without rewriting active ones.
- Scrub credentials, session tokens, and personal data before indexing when application logs may contain them.
Security and retention are operational requirements rather than optional add-ons: centralized logs often contain the credentials and personal information attackers seek, and their value depends on remaining available during an incident.
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Kubernetes: run Fluentd on every node
In Kubernetes, the common pattern is a Fluentd DaemonSet. A DaemonSet places one collector on each node, allowing it to read that node’s local container log files and forward them to Elasticsearch. This differs from Compose, where one Fluentd service can receive logs from the application services in the project.
The official Kubernetes pattern uses the quay.io/fluent/fluentd-kubernetes-daemonset image. Configure its Elasticsearch connection with environment variables:
| Variable | Meaning and documented default |
|---|---|
FLUENT_ELASTICSEARCH_HOST |
Hostname or service address of the Elasticsearch cluster. Set it to the service used by your cluster. |
FLUENT_ELASTICSEARCH_PORT |
Elasticsearch port; the documented default is 9200. |
FLUENT_ELASTICSEARCH_SSL_VERIFY |
Certificate verification; the documented default is true. |
FLUENT_ELASTICSEARCH_SSL_VERSION |
TLS protocol version; the documented default is TLSv1_2. |
Those defaults assume a service named elasticsearch-logging. Change the host to match your service discovery name, namespace, and security configuration. A DaemonSet also needs the host paths and permissions required to read container logs, resource requests and limits, and a strategy for tolerations if control-plane nodes should be collected. Keep credentials in Kubernetes Secrets and grant the collector only the permissions it needs.
Use labels and namespaces in your tag or record fields so the same Elasticsearch cluster can distinguish workloads, environments, and nodes. Validate one node first, then roll the DaemonSet out while watching buffer utilization and Elasticsearch indexing rates.
Compose versus Kubernetes
| Decision area | Docker Compose | Kubernetes |
|---|---|---|
| Deployment scope | Project or host-oriented stack with an application, Fluentd, Elasticsearch, and optional Kibana. | Cluster-oriented collection, normally one Fluentd pod per node. |
| Collection path | Docker logging driver sends events to a reachable Fluentd listener on port 24224. | Node collectors commonly read local container logs and forward them to Elasticsearch. |
| Service discovery | Compose service names such as elasticsearch work on the project network; host-published addresses may be needed for the logging driver. |
Use Kubernetes Service DNS and configure FLUENT_ELASTICSEARCH_HOST accordingly. |
| Secrets | Use an external secret mechanism or protected environment files; do not commit credentials. | Use Kubernetes Secrets, RBAC, and mounted certificates. |
| Upgrade workflow | Rebuild images and recreate services, checking plugin and client compatibility. | Roll the DaemonSet and Elasticsearch changes in controlled stages with resource and buffer monitoring. |
| Best fit | Small environments, development, and a bounded set of Docker hosts. | Multi-node clusters where every node needs local log collection and workload metadata. |
Self-hosted Elasticsearch or a managed backend
The Fluentd pipeline can target self-managed Elasticsearch or a managed Elasticsearch service. The Fluentd configuration shape remains similar, but the operational responsibilities differ.
| Concern | Self-hosted Elasticsearch | Managed Elasticsearch |
|---|---|---|
| Operations | Your team owns nodes, upgrades, snapshots, capacity, and incident response. | The provider operates the service, while you configure access, data policy, and integration. |
| Security and TLS | You configure identities, certificates, network controls, and audit settings. | The service supplies provider-specific controls; Fluentd still needs correct credentials and certificate verification. |
| Persistence and retention | You choose volumes, replication, snapshots, and deletion schedules. | Plans and service settings determine durability, snapshots, and retention options. |
| Scaling | Add and rebalance nodes, then monitor shard and disk behavior. | Scale through the provider’s controls, subject to its limits and pricing. |
| Compatibility | You control Elasticsearch, Ruby client, and plugin versions together. | Provider versions and endpoints constrain which plugin and TLS settings are valid. |
| Total cost | Infrastructure, storage, bandwidth, engineering time, and on-call work. | Usage, storage, transfer, and service charges, with less infrastructure maintenance. |
Choose based on operational ownership and compliance requirements, not only the initial server cost. Whichever backend you use, test index creation, authentication failures, certificate errors, and recovery from an unavailable node before routing all production logs.
Verification and troubleshooting
No records appear in Fluentd stdout
- Confirm the application actually writes to stdout or stderr rather than only to an in-container file.
- Inspect the application’s effective logging driver with
docker inspectand verify the tag and address. - Check that Fluentd publishes TCP port 24224 and, if your driver uses it, UDP 24224.
- Review Fluentd startup logs for a configuration parse error or a failed bind.
- Check the address semantics of
localhost; the Docker daemon may not see the same network namespace as a Compose container.
Fluentd receives records but Elasticsearch stays empty
- Use the stdout copy to distinguish routing from output failure.
- Resolve the Elasticsearch hostname from the Fluentd container and test port 9200.
- Check that the Elasticsearch output plugin is installed in the image actually running, not only in the Dockerfile you edited.
- Inspect Fluentd retry and buffer messages for authentication, TLS, index, or mapping errors.
- Query Elasticsearch for the expected date-based index after allowing for the configured flush interval.
Events are delayed or disappear during an outage
- Inspect buffer queue length, chunk limits, and the filesystem mount used for file buffering.
- Confirm that the host has free disk space and that the Fluentd process can write to the buffer directory.
- Test the configured retry and overflow policy deliberately; do not infer it from a normal run.
- After recovery, verify that queued chunks drain and that Elasticsearch is not rejecting them because of disk watermarks or authentication changes.
Only some tags are indexed
- Compare the actual tag with the pattern in each
<match>rule. - Remember that
*.**requires a dotted tag; use**or a more suitable pattern for single-component tags. - Place specific rules before catch-all rules and retain a diagnostic output for unmatched events.
Operational checklist
- Pin and test compatible Elasticsearch, Ruby client, Fluentd, and output-plugin versions.
- Give every source a documented tag and keep tag patterns consistent.
- Confirm Docker-to-Fluentd connectivity before debugging Elasticsearch.
- Use readiness checks plus bounded, durable buffering.
- Enable authentication, TLS, access control, and persistent storage outside a test environment.
- Monitor Fluentd retries, buffer usage, dropped events, Elasticsearch health, disk watermarks, and index growth.
- Set retention and deletion policies before indexes fill the cluster.
- Run failure drills for Fluentd downtime, Elasticsearch downtime, expired certificates, full disks, and malformed records.
- Keep Kibana optional: indexing and retention must remain correct even when the user interface is unavailable.
What this setup can and cannot guarantee
This architecture centralizes Docker stdout and stderr, gives Fluentd a routing layer, and provides searchable Elasticsearch indexes. It does not automatically make application logs structured, guarantee delivery through every failure mode, or select secure production defaults. Delivery semantics depend on the Docker Engine, Fluentd buffer configuration, network, output plugin, and Elasticsearch response. Treat the documented version numbers as configuration examples and validate the complete chain under the failure conditions your service must survive.
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