For Spring Boot microservices, use Elasticsearch to store and search telemetry, Kibana to explore and visualize it, and a collection path suited to the data: Elastic Agent for straightforward log forwarding, Logstash when you need parsing or enrichment, and Actuator-based integration for application metrics and HTTP traces. Give every service stable identity and consistent time and correlation fields; secure Actuator endpoints and Elastic credentials before the system is reachable beyond a local development network.
What the ELK stack does in a Spring Boot environment
“ELK” is shorthand for Elasticsearch, Logstash, and Kibana. Elastic’s broader Elastic Stack also includes Elastic Agent and other products. Elasticsearch stores and searches ingested data; Kibana is where you explore it, build visualizations, and manage the deployment. Logstash and Elastic Agent are collection and forwarding options, not substitutes for Elasticsearch or Kibana.
A useful design separates two paths that may complement each other:
- Logs: The application writes structured events; Elastic Agent or Logstash collects and forwards them to Elasticsearch.
- Actuator observability data: An Elastic integration can query Spring Boot Actuator web endpoints and ingest supported application data into Elasticsearch.
Spring Boot describes observability as the ability to observe a running system’s internal state from the outside. Its three pillars are logs, metrics, and traces. Spring uses Micrometer Observation for metrics and traces and provides basic OpenTelemetry support. Decide which signals you need before configuring collection: shipping logs alone will not automatically give you metrics or traces.
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Choose a deployment and collection path
| Choice | Use it when | Trade-off |
|---|---|---|
| Elastic Cloud | You want a hosted Elastic Stack and less infrastructure operation. | It reduces operational work around upgrades, certificates, scaling, and backups, but assess data residency, retention, integration limits, and incident-response responsibilities for your environment. |
| Self-managed Elastic Stack | You need infrastructure, compliance, or network control and have a team to operate the stack. | You control deployment and infrastructure, and must manage version alignment, certificates, capacity, backups, and upgrades. |
| Elastic Agent | You need a relatively straightforward way to collect and forward application logs. | Use a different or additional processing path if you need complex transformations, enrichment, or routing. |
| Logstash | You need parsing, enrichment, routing, or more complex ETL before data is indexed. | It adds a processing component to configure and operate. |
| Elastic Spring Boot integration | You want supported Actuator-derived application observability data in addition to, or instead of, separately collected logs. | Check its stated requirements and compatibility against your actual Spring Boot and Elastic versions before adopting it. |
Elastic’s Spring Boot integration documentation recommends Elastic Cloud, but the choice is not universal. Compare operating effort and control needs for your deployment rather than treating either hosted or self-managed as the default for every team.
Set up the stack in dependency order
- Choose hosted or self-managed Elasticsearch. If self-managing, install Elasticsearch first, then Kibana, followed by the collection components you need—Logstash, Elastic Agent or Beats—and APM if your design requires it. Elastic’s installation example advises using the same version across stack products; keep versions aligned rather than upgrading components independently without checking compatibility.
- Add Actuator to each Spring Boot service that needs it. Include the Actuator starter in the service build:
<dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-actuator</artifactId> </dependency> - Expose only the operational endpoints you need. Spring Boot’s usual web endpoint pattern is
/actuator/{id}; for example, the standard health endpoint is/actuator/health. Do not expose the full endpoint set by default. Apply authentication, network restrictions, and least privilege, especially before making the application reachable outside a local development network. - Choose how to collect each signal. Configure Elastic Agent for simpler log forwarding, or Logstash if you need richer parsing and processing. If using Elastic’s Spring Boot integration, verify the integration’s Actuator and Jolokia requirements and make the relevant endpoints reachable to the integration.
- Standardize destination and retention. Use consistent index or data-stream naming, and define lifecycle and retention policies appropriate to the data. Keep service logs and metrics discoverable through the patterns your Kibana users will select.
- Validate the end-to-end path. Confirm a known application event arrives in Kibana Discover under the intended
logs-*ormetrics-*pattern, then check timestamps, parsed fields, and dashboard filters. Exercise alert rules with a controlled failure before relying on them.
Use Actuator for application-level observability
Elastic’s Spring Boot integration is designed to fetch observability data from Spring Boot Actuator web endpoints and ingest it into Elasticsearch. Its documentation says it collects audit-event and HTTP-trace data, along with garbage-collection, memory, and threading metrics, and includes Kibana dashboards. These capabilities are distinct from simply shipping application log files.
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The integration page currently lists version 1.9.1 and a minimum Kibana version of 9.0.0. Elastic reports compatibility testing with Spring Boot 2.7.17 and LTS JDKs 8, 11, 17, and 21. Those are the stated tested combinations, not proof that every later Spring Boot or JDK version is supported. Confirm the current integration requirements for your deployed versions. The listed requirements include Elasticsearch, Kibana, a reachable Spring Boot host, the Actuator dependency, and Jolokia for endpoint access.
Spring Boot’s web starter brings in the logging starter transitively, and Logback is the first-choice logging system when it is present. Configure logback-spring.xml or another supported logging configuration to emit structured, parseable events. For metrics and traces, favor bounded, low-cardinality dimensions. Spring’s guidance is to keep high-cardinality attributes on traces rather than turning them into metric dimensions.
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Give every service a consistent event identity
Telemetry is useful across a fleet only when events can be attributed to the right service and time, and related across a request. Standardize a common field set across services rather than relying on each team’s logger defaults.
- Service and deployment:
service.name,service.version, environment, and instance identifier. - Time and severity: UTC
@timestamp, log level, and logger name. - Request context: HTTP method, route template, status, duration, request or correlation ID, and trace/span IDs when available.
- Failure details: exception type and stack trace, with secrets and personal data removed.
- Infrastructure context when useful: host, container, pod, region, and instance identifiers.
Do not use unbounded values such as user IDs, request bodies, or arbitrary labels as metric dimensions. They can create high cardinality and make metrics less reliable to query and operate. Put suitable high-cardinality context on traces or carefully filtered log fields instead. Keep log, metric, and trace naming and correlation conventions consistent so that a service, environment, and request can be followed across the system.
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Build Kibana views around operational questions
Start in Kibana Discover to verify records and field names before building dashboards. Then organize views around the questions on-call engineers need to answer, using the integration’s included dashboards where they fit or building views from your own data:
- Are requests succeeding, and how are request rate, error rate, and latency changing?
- Are JVM memory, garbage collection, or thread behavior changing alongside application errors?
- Which audit events or HTTP traces are relevant to an incident?
- Can an operator move from a service-level symptom to the associated request, trace, and exception?
Use stable service and environment fields as filters. Check the selected data view or pattern, time range, time zone, and dashboard filters when results appear empty or inconsistent. Create alerts against signals with a clear operational meaning, and validate each one with a known, controlled condition.
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Secure endpoints and control diagnostic changes
Actuator endpoints can expose operational details; some can also change application behavior. Restrict access with authentication, network controls, and least privilege. Do not treat an endpoint as safe merely because its path is difficult to guess. Apply equivalent care to Elastic credentials and access to the stack itself.
The Actuator /actuator/loggers endpoint can view and change logger levels at runtime. Supported levels include TRACE, DEBUG, INFO, WARN, ERROR, FATAL, and OFF. Restrict this endpoint to trusted operators: increasing verbosity can sharply increase log volume and may reveal diagnostic information that should not be broadly available. Return a changed logger to its normal level after controlled troubleshooting.
Troubleshoot missing or unusable telemetry
Follow the data path from the application toward Kibana; this helps distinguish an application problem from collection, parsing, indexing, or visualization issues.
Quick Recap
- Check the application output. Confirm that the service emits valid structured events and includes the expected service, environment, timestamp, and request context.
- Check collection. Confirm the Elastic Agent or Logstash input receives the events. For Actuator integration, confirm the configured host and required endpoints are reachable.
- Inspect processing. Look for parsing or enrichment failures before indexing; malformed fields can prevent useful filtering even when events arrive.
- Check Elasticsearch acceptance and mappings. Review index or data-stream mappings and rejected documents for field conflicts or ingestion failures.
- Check Kibana selection. Open Discover with the correct
logs-*ormetrics-*pattern, a suitable time range, and the expected filters. - Check clocks and time handling. Verify UTC timestamps, clock synchronization, time zones, and dashboard filters when records seem absent or out of order.
- Test alerts deliberately. Trigger a controlled error, verify the expected alert behavior, and restore normal logger levels afterward.
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