The ELK Stack—Elasticsearch, Logstash, and Kibana—centralizes events from distributed applications so teams can search, visualize, alert on, and investigate problems in one place. The practical workflow is to collect events, parse them into a consistent structure, enrich them with context, store them in Elasticsearch, alert on important conditions, and analyze related activity in Kibana.
That model comes from John Vester’s DZone Refcard “Monitoring and the ELK Stack”. Modern Elastic deployments can use the broader Elastic Stack, where Elastic Agent, APM, OpenTelemetry, ingest pipelines, and Logstash are selected according to the data and operating model.
What the ELK Stack does
ELK refers to three core technologies with different responsibilities:
| Component | Primary role | What it gives an operations team |
|---|---|---|
| Elasticsearch | Scalable, near-real-time search and storage | A central place to index, retain, query, and aggregate events |
| Logstash | Data collection and processing | Inputs, filters, parsing, routing, and transformations for multiple sources |
| Kibana | Visualization and analysis | Search screens, dashboards, charts, and investigation workflows |
A typical deployment also needs a collection method at the edge. The Refcard describes Beats as lightweight shippers for logs, metrics, uptime, network, audit, and Windows data. Elastic’s current guidance says Elastic Agent has replaced Beats for most use cases, while OpenTelemetry, APM, Logstash, and Elasticsearch ingest pipelines remain available for different collection and processing requirements. See Elastic’s current Elastic Stack overview for the supported options.
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How application monitoring flows through ELK
Centralization is useful only when events retain enough structure and context to explain what happened. The Refcard’s six-stage flow provides a practical design checklist.
1. Collect events where they occur
Connect application servers, containers, databases, proxies, operating systems, and cloud services to a collector. Capture events as they are produced rather than relying only on logs copied after an incident. Choose Elastic Agent, an OpenTelemetry collector, APM, Logstash inputs, or another supported method based on the source and telemetry type.
2. Parse messages into fields
Turn raw lines into fields such as timestamp, service, environment, severity, request ID, status code, and exception type. Consistent field names make a query work across services. Preserve the original message when possible so an analyst can verify the parsed result.
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3. Enrich events with context
Add deployment version, host or Kubernetes metadata, region, team ownership, user or transaction identifiers, and trace information where appropriate. Enrichment allows an alert or search to distinguish a production failure from a development event and to connect activity across components.
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Send transformed events to Elasticsearch through the chosen ingestion path. Plan index or data-stream naming, retention, access controls, and capacity around event volume and investigative needs. Storage is not merely an archive: field mappings and timestamps determine whether queries and aggregations are useful.
5. Alert before an incident grows
Define conditions that need attention, such as a rising error rate, repeated authentication failures, or a service that stops reporting. Alerts should include the affected service, time range, severity, and a link or query that lets the responder investigate. An alerting rule is a detection mechanism, not proof that the underlying issue has been fixed.
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6. Analyze the situation in Kibana
Use searches, filters, aggregations, and dashboards to establish when a problem began, which components are affected, and whether a deployment or dependency changed at the same time. Correlating request IDs, host names, timestamps, and service fields is often more valuable than reading one server’s log in isolation.
Current collection choices: ELK versus the broader Elastic Stack
“ELK Stack” is still the clearest name for the Elasticsearch-Logstash-Kibana combination described by the Refcard. In current Elastic terminology, the Elastic Stack is broader:
- Elastic Agent: a unified way to collect logs and metrics and manage integrations; it replaces Beats for most use cases.
- APM: captures application performance details such as requests, responses, database transactions, and errors.
- OpenTelemetry: provides vendor-neutral collection for supported traces, metrics, and logs.
- Logstash: remains useful when you need its broad input, filter, and output processing model.
- Elasticsearch ingest pipelines: perform transformations close to the destination, which can reduce the need for a separate processing tier.
These are alternatives and combinations, not mandatory stages in every installation. A small application may use Elastic Agent and an ingest pipeline; a heterogeneous estate may add Logstash; a tracing-heavy system may add APM or OpenTelemetry.
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What you can monitor and investigate
Development troubleshooting
Search aggregated events to follow an exception across services, compare a failing request with successful requests, and identify malformed input or a dependency timeout.
Production support
Dashboards can show error trends, traffic, latency-related fields, and service health indicators. Support staff can move from a visual spike to the underlying events without logging into every host.
Application performance
APM complements ordinary logs by recording transaction and dependency detail. Combining traces or APM data with application and infrastructure events helps connect a slow request to a database call, downstream service, or deployment.
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Compliance and security analysis
Centralized audit, authentication, network, and endpoint events can support threat investigation, anti-DDoS analysis, and SIEM-related workflows. The stack itself does not guarantee compliance or prevent attacks; those outcomes also depend on policies, controls, retention, access management, and response procedures.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Monitoring the Elastic Stack itself
Elastic Stack Monitoring collects logs and metrics from Elasticsearch, Logstash, Kibana, APM Server, and Beats. Monitoring data is stored in Elasticsearch and viewed in Kibana; Elastic Agent or Metricbeat can collect it. This creates a useful distinction between application observability and platform observability: the same platform can display application failures and reveal that the ingestion or search layer is under pressure.
For a separate monitoring cluster, Elastic advises that the monitoring deployment should generally use the same stack version as the monitored cluster and cannot monitor a newer version. Check the current Stack Monitoring documentation before selecting versions or changing the topology.
Choosing a deployment model
| Decision | Self-managed | Hosted or managed |
|---|---|---|
| Operations | Your team runs upgrades, capacity, backups, security, and incident response for the stack. | A provider operates much of the platform; confirm exactly which responsibilities remain yours. |
| Architecture control | Maximum control over networking, plugins, data placement, and retention design. | Faster standardization, with provider-specific limits and supported configurations. |
| Collection and processing | You design collectors, pipelines, mappings, and integrations. | Review supported data sources, agents, parsing, enrichment, and export options before committing. |
| Cost planning | Budget infrastructure, engineering time, storage, and retention. | Evaluate ingest, storage, query, retention, support, and egress charges; no current provider price comparison is established here. |
| Version compatibility | You control upgrade sequencing, including any monitoring cluster. | Verify the provider’s supported versions and upgrade policy. |
The Refcard lists Docker, Docker Compose, Kubernetes, and managed services as possible starting routes and names Logz.io, Logit.io, and Coralogix as examples of providers discussed at the time. Those mentions do not establish their current features, prices, quality, or availability.
Starting safely with an implementation plan
- Define the questions first: decide which incidents you need to detect and which fields would answer the investigation questions.
- Inventory sources: list services, hosts, containers, platforms, and security systems, noting formats, volumes, timestamps, and ownership.
- Select collectors: choose Elastic Agent, OpenTelemetry, APM, Logstash, or a combination based on each source and telemetry type.
- Standardize fields: establish timestamp, service, environment, severity, host, request or trace ID, and error fields before building dashboards.
- Design retention and access: set retention by operational and regulatory need, restrict sensitive fields, and separate production access from administration.
- Build detection and investigation views: create a small set of actionable alerts and dashboards, then test that each alert leads to searchable evidence.
- Monitor the monitoring path: enable Stack Monitoring so dropped events, unhealthy nodes, or overloaded ingestion do not remain invisible.
- Test failure and recovery: verify behavior when a collector disconnects, a timestamp is malformed, Elasticsearch is unavailable, or event volume spikes.
Important caveat about the Refcard’s sample setup
The Refcard includes a worked installation using the deviantony/docker-elk repository, example ports and credentials, and index-pattern steps. Treat those as historical teaching material, not current production instructions. Repository defaults, passwords, ports, security settings, and Kibana labels may have changed. Use current Elastic documentation, secure credentials, and version-matched configuration for an actual deployment.
What success looks like
John Vester describes the goal as giving teams the ability to identify issues or unexpected behavior “within minutes, if not seconds.” That is a stated objective, not a measured guarantee. In practice, the result depends on complete collection, accurate parsing, useful enrichment, sensible retention, actionable rules, access controls, and a team that knows how to investigate the resulting data.
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