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Centralize Docker logs by sending each container’s stdout and stderr through Docker’s fluentd logging driver to Fluentd, routing events with tags, buffering them, and writing them with the Fluentd Elasticsearch output plugin. Elasticsearch stores the indexed events; Kibana can then search and visualize them.
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The same design works in Docker Compose and Kubernetes, but the deployment boundary changes: Compose usually runs one Fluentd service for the project, while Kubernetes commonly runs Fluentd as a DaemonSet so every node can collect its local container logs.
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The logging path, from container to search result
The pipeline has five distinct stages. Keeping them separate makes failures easier to diagnose:
| Stage | What it does | Configuration that controls it |
|---|---|---|
| Container process | Writes application messages to stdout and stderr. | The application’s logging behavior and message format. |
| Docker logging driver | Hands each container event to Fluentd instead of retaining it only in Docker’s default local log path. | logging.driver: fluentd, fluentd-address, and tag. |
| Fluentd input | Receives events using the Fluent Forward protocol. | A <source> block with @type forward, a bind address, and port 24224. |
| Fluentd routing and buffering | Matches tags, optionally copies an event to more than one destination, and queues data while an output is unavailable. | <match> patterns, @type copy, and each output’s <buffer>. |
| Elasticsearch and Kibana | Elasticsearch indexes the records; Kibana provides an optional interface for searching and dashboards. | The Elasticsearch host, port, index naming options, credentials, TLS settings, and Kibana’s connection settings. |
Docker tags are not decoration. They are routing keys. A tag such as httpd.access can be matched by a specific Fluentd rule, while a broad pattern such as *.** catches all tags that contain one or more segments.
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What you need before configuring the pipeline
- A Docker Engine installation with the Fluentd logging driver available.
- A Fluentd process reachable from the Docker containers on TCP/UDP port 24224, depending on the transport behavior of the driver and deployment.
- The Fluentd Elasticsearch output plugin and a compatible Elasticsearch Ruby client.
- An Elasticsearch node or service reachable from Fluentd on port 9200 unless your deployment uses another port.
- Optional Kibana connected to the same Elasticsearch deployment.
- A plan for authentication, TLS, index retention, persistent storage, and access control before exposing the stack beyond a test environment.
The official Compose example pins Elasticsearch to 8.17.1, the Elasticsearch Ruby gem to 8.19.0, and fluent-plugin-elasticsearch to 5.4.3. Those are example pins, not performance results or universal compatibility requirements. Check the plugin’s compatibility with the Elasticsearch version you actually deploy, and review all pins during upgrades.
| Component | Example pin in the documented Compose sample | How to treat the value |
|---|---|---|
| Elasticsearch image | 8.17.1 | Example deployment pin; validate before production use. |
| Elasticsearch Ruby gem | 8.19.0 | Example client version installed for Fluentd. |
fluent-plugin-elasticsearch |
5.4.3 | Example output-plugin version; verify against the client and server versions. |
Build the pipeline with Docker Compose
The documented Compose shape has four services: an application container, Fluentd, Elasticsearch, and Kibana. The application enables Docker’s Fluentd driver; Fluentd listens on 24224 and writes to Elasticsearch; Kibana is an optional viewer.
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1. Create the project layout
Keep the Fluentd configuration in a file mounted into the Fluentd container. The Docker image’s default configuration path is /fluentd/etc/fluent.conf.
project/
├── compose.yaml
└── fluent.confUse a persistent volume for Elasticsearch data in any environment where logs must survive a container replacement. The exact volume and filesystem arrangement depends on your host and operational policy, so treat storage as a deployment decision rather than an incidental Compose setting.
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2. Configure the application’s Docker logging driver
A representative application service is:
services:
web:
image: httpd
logging:
driver: fluentd
options:
fluentd-address: localhost:24224
tag: httpd.accessThe driver options in this example do three things: select Fluentd, identify the listener address and port, and assign the fixed tag httpd.access. The official example uses localhost:24224; that address is correct only when the Docker logging driver can reach Fluentd through the host or network arrangement being used. If the application and Fluentd are separate Compose containers on a user-defined network, use the Fluentd service’s reachable name and port instead of assuming that localhost means the other container.
You can also use Docker tag templates. For example, templates such as docker.{{.ID}} and docker.{{.Name}} produce tags from container metadata. Choose one convention and make every Fluentd <match> rule follow it.
3. Expose Fluentd’s Forward input
Put this source block in fluent.conf:
<source>
@type forward
port 24224
bind 0.0.0.0
</source>@type forward selects Fluentd’s Forward protocol input. Port 24224 must be published or otherwise reachable from the Docker logging driver. Binding to 0.0.0.0 makes the listener accept connections on the container’s available interfaces; restrict network access with Compose networking, host firewalls, or equivalent controls rather than exposing the port indiscriminately.
4. Route events to Elasticsearch and keep a diagnostic copy
The following pattern receives every matching tag, writes an indexed copy to Elasticsearch, and prints a second copy to Fluentd’s stdout:
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<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>host elasticsearchassumes that the Compose service is namedelasticsearchand is discoverable on the Compose network.logstash_format true,logstash_prefix fluentd, andlogstash_dateformat %Y%m%dcreate date-oriented index names with thefluentdprefix.include_tag_key truepreserves the routing tag in the event, andtag_key @log_namechooses the field name used for that value.@type copysends the event to both stores. The stdout store is valuable while diagnosing tags, parsing, and connectivity; remove or reduce it when its volume is no longer useful.- The one-second
flush_intervalis the sample’s setting, not a promise of one-second delivery under load. Tune buffering and flush behavior for your event rate, storage, and recovery requirements.
5. Install and pin the Elasticsearch output dependencies
Fluentd must have the Elasticsearch output plugin and a compatible Elasticsearch Ruby gem installed in the Fluentd image or runtime. The documented sample uses elasticsearch gem 8.19.0 and fluent-plugin-elasticsearch 5.4.3. Install them in the image you deploy, record the versions, and test upgrades together. A Fluentd configuration can parse correctly while the output still fails because a plugin, client, and server disagree about supported APIs.
6. Start services in a safe order
Fluentd should not race an unavailable Elasticsearch service. The Compose example waits for Elasticsearch health before bringing up the dependent logging path. Implement an equivalent readiness check in your Compose workflow, and ensure Elasticsearch has durable storage and enough time to initialize before treating the stack as ready.
The sample disables Elasticsearch security with xpack.security.enabled=false for testing. Do not carry that convenience setting into a production deployment. Production requires authentication, encryption, access controls, and storage and retention settings appropriate to the sensitivity and volume of the logs.
7. Verify each boundary instead of testing only Kibana
- Start Fluentd and confirm that it binds to port 24224 without configuration or plugin errors.
- Start Elasticsearch and verify that its health state reaches the readiness condition used by your deployment.
- Start the application container and generate a known request or startup message.
- Check Fluentd’s stdout copy. If it is empty, investigate the Docker logging driver address, network path, and tag before looking at Elasticsearch.
- Check Elasticsearch indices for the date-oriented
fluentdprefix. If Fluentd receives events but no index appears, inspect the Elasticsearch output plugin log, host, port, credentials, TLS settings, and index permissions. - Use Kibana only after the ingestion path is confirmed; create a data view that matches the generated index names and include the tag field when filtering by source.
Use tags as a routing design, not an afterthought
A tag identifies the event’s source and determines which <match> block handles it. A fixed tag is easy to understand for a single service. Metadata templates are more useful when many containers are created and destroyed, but they require stable matching rules.
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| Tag approach | Example | Strength | Risk |
|---|---|---|---|
| Fixed service tag | httpd.access |
Simple, readable, and easy to route. | Several replicas can become indistinguishable unless the event includes container metadata. |
| Container ID template | docker.{{.ID}} |
Uniquely identifies a container instance. | Rules and searches must handle rapidly changing IDs. |
| Container name template | docker.{{.Name}} |
Readable source identity for operators. | Names can change with redeployments and scaling. |
Keep the number of tag segments and separators consistent. A rule written for httpd.** will not necessarily match a differently shaped tag, and a rule that is too broad can send test, audit, and application records into the same index. Start with a narrow route for important services, then add a deliberate catch-all such as *.** only when you have decided where unmatched events belong.
The Docker logging driver supports fluentd-address and tag. Fluentd v1 also has an optional sub-second precision setting. Older implementations did not support sub-second precision, so confirm the behavior of the Docker and Fluentd versions you run before relying on event timestamps for ordering at sub-second resolution.
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Elasticsearch will occasionally be unavailable during startup, maintenance, network disruption, or resource pressure. Fluentd’s output buffer is the boundary that can absorb a temporary interruption, but a one-second flush interval alone does not define how much data can be retained or for how long.
- Choose a buffer type and storage location that match your loss tolerance. Memory-only buffering can disappear when Fluentd restarts; durable buffering requires suitable disk capacity and permissions.
- Set limits and retry behavior deliberately. An unbounded queue can fill the host, while an undersized queue can discard events during a short outage.
- Monitor buffer queue length, retry counts, output errors, Fluentd restarts, and Elasticsearch rejection responses.
- Keep the stdout diagnostic store during initial rollout, then remove it or route it to a controlled destination so troubleshooting output does not become an accidental second log stream.
- Use readiness checks for startup, but remember that readiness does not protect against a later Elasticsearch outage. Buffering and alerting handle that operational period.
There is no published implementation-specific throughput or latency figure for this configuration. The documented version numbers are configuration pins only; benchmark your own message sizes, event rates, index mappings, and storage before setting capacity targets.
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Security and data protection for a real deployment
The test-oriented Compose sample turns Elasticsearch security off. A production pipeline should instead define who may send logs, who may read them, and how traffic is protected in transit.
- Enable Elasticsearch authentication and assign the Fluentd output identity only the index and template permissions it needs.
- Use TLS for Fluentd-to-Elasticsearch traffic and validate certificates. Do not disable certificate verification merely to make a connection succeed.
- Protect the Fluentd Forward listener with network policy or firewall rules so arbitrary hosts cannot inject records.
- Keep credentials out of the Compose file when your deployment platform provides secrets; rotate them without rebuilding every application image.
- Classify logs before indexing. Request headers, account identifiers, tokens, and user-generated text can contain sensitive data that should be filtered or redacted at the application or Fluentd layer.
- Give Elasticsearch persistent storage, snapshots, and a retention policy. Centralization is not a backup strategy unless you operate the storage and recovery process accordingly.
Kubernetes: collect logs with a Fluentd DaemonSet
In Kubernetes, the common Fluentd pattern is a node-level DaemonSet. A DaemonSet schedules one Fluentd pod on each node, allowing it to collect the container logs local to that node and forward them to Elasticsearch.
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The documented Kubernetes image is quay.io/fluent/fluentd-kubernetes-daemonset. Its configuration uses environment variables for the Elasticsearch destination:
| Environment variable | Purpose | Documented default or behavior |
|---|---|---|
FLUENT_ELASTICSEARCH_HOST |
Hostname of the Elasticsearch service. | Defaults assume a service named elasticsearch-logging; change it to your service name. |
FLUENT_ELASTICSEARCH_PORT |
Elasticsearch service port. | Default is 9200. |
FLUENT_ELASTICSEARCH_SSL_VERIFY |
Whether the client verifies the Elasticsearch TLS certificate. | Default is true. |
FLUENT_ELASTICSEARCH_SSL_VERSION |
TLS protocol version used for the connection. | Default is TLSv1_2. |
Change the host when your cluster uses another Service name, namespace, or external endpoint. Keep certificate verification enabled unless you have a documented, temporary test reason to do otherwise. Kubernetes service discovery, Secret objects, and rolling DaemonSet updates replace the corresponding Compose concerns, but the data path remains the same: container output, collection agent, tag and route, Elasticsearch output, and optional Kibana exploration.
Compose and Kubernetes: choose by operating boundary
| Decision axis | Docker Compose | Kubernetes |
|---|---|---|
| Deployment scope | One project or host-level stack with an application, Fluentd, Elasticsearch, and optional Kibana services. | Cluster-wide collection, normally one Fluentd pod per node. |
| Collection locality | The Fluentd service must be reachable from the containers whose logs it receives. | The DaemonSet runs on each node to collect local container logs. |
| Service discovery | Compose service names and published ports, subject to the network topology. | Kubernetes Services, namespaces, and cluster DNS. |
| Secrets | Use the secret mechanism available to your Compose environment. | Kubernetes Secrets and workload identity patterns. |
| Upgrades | Coordinate image, plugin, and configuration changes for the stack. | Roll the DaemonSet and backend changes while monitoring node coverage and buffer health. |
| Best fit | Development, a small controlled deployment, or a single-host logging domain. | Multiple nodes where node-level collection and cluster scheduling are already standard. |
Self-hosted Elasticsearch versus a managed backend
Elasticsearch remains the documented storage target in either model. The meaningful comparison is operational ownership, not a different Fluentd architecture.
Quick Recap
| Concern | Self-hosted Elasticsearch | Managed Elasticsearch |
|---|---|---|
| Operations | Your team provisions nodes, upgrades versions, handles failures, and manages capacity. | The provider operates much of the platform layer, subject to its service limits and procedures. |
| Security and TLS | You configure certificates, authentication, roles, network boundaries, and key rotation. | The service supplies controls, but you still configure identities, certificates, roles, and network access. |
| Persistence and retention | You choose disks, snapshots, replication, and deletion schedules. | Storage tiers and retention features are provider-specific; verify recovery and export behavior. |
| Scaling | You add capacity and rebalance indices. | Scaling may be simpler, but limits and cost increase with ingestion and retention. |
| Compatibility | You control Elasticsearch, plugin, and Ruby client versions together. | You must match Fluentd’s plugin and client behavior to the service’s supported API and TLS requirements. |
| Total cost | Infrastructure, operations time, storage, upgrades, and incident response. | Service charges plus ingestion, storage, network, and retention costs. |
Troubleshoot by locating the first broken boundary
| Symptom | Likely boundary | Checks |
|---|---|---|
| No events in Fluentd stdout | Docker driver to Fluentd. | Confirm the driver is fluentd, verify fluentd-address, check port 24224 reachability, and inspect the tag. |
| Fluentd receives events but no Elasticsearch index appears | Fluentd output to Elasticsearch. | Check the Elasticsearch hostname, port 9200, plugin installation, client compatibility, credentials, TLS verification, and index permissions. |
| Only some services are indexed | Tag matching. | Compare actual tags with the <match> patterns; check segment count, punctuation, and whether a catch-all rule exists. |
| Events arrive late during an outage | Buffer and retry behavior. | Inspect buffer queue depth, retry errors, disk capacity, flush settings, and whether the buffer survives a Fluentd restart. |
| Fluentd starts before Elasticsearch is usable | Service readiness. | Use an Elasticsearch health check or equivalent readiness gate and confirm that Fluentd retries while the backend initializes. |
| TLS connection fails | Certificate or protocol settings. | Verify the CA, hostname, certificate validity, FLUENT_ELASTICSEARCH_SSL_VERIFY, and TLS version. Do not solve a certificate problem by permanently disabling verification. |
| Duplicate or unexpectedly large ingestion | Fan-out or repeated collection. | Check the @type copy stores, ensure only one collector handles each node’s files, and confirm that diagnostic stdout is not being re-ingested. |
Maintenance checklist
- Record the Docker Engine, Fluentd, Elasticsearch, Ruby client, and output-plugin versions.
- Test a plugin and Elasticsearch upgrade in a staging stack before changing the production pipeline.
- Review index naming, mappings, retention, and access permissions as log fields evolve.
- Alert on Fluentd process restarts, output errors, buffer growth, Elasticsearch health, disk watermarks, and rejected writes.
- Exercise recovery: stop Elasticsearch briefly, confirm buffering, restore the backend, and verify that queued events drain without exhausting disk.
- For Kubernetes, verify that the DaemonSet has one healthy collector on every intended node after scheduling or upgrade changes.
- Remove test-only security settings and temporary diagnostic outputs before declaring the deployment production-ready.
End-to-end implementation checklist
- Choose Compose or Kubernetes based on whether collection is host-scoped or cluster-wide.
- Deploy Elasticsearch with persistent storage and a documented retention policy.
- Install a Fluentd image containing a compatible Elasticsearch Ruby gem and
fluent-plugin-elasticsearch. - Expose Fluentd’s
forwardinput on port 24224 and restrict who can reach it. - Set the Docker logging driver and a consistent tag scheme.
- Write a Fluentd
<match>rule that routes those tags to Elasticsearch and, during rollout, to stdout for diagnosis. - Add buffering, retry monitoring, and an Elasticsearch readiness check.
- Enable authentication, TLS, certificate verification, least-privilege access, and secret management.
- Generate a known test event and verify it at the Docker, Fluentd, Elasticsearch, and Kibana layers in that order.
- Document versions and rehearse outage and upgrade procedures.
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