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This guide explains the choices, installation sequence, security decisions, validation checks, and recovery paths for a self-managed Elasticsearch, Kibana, and Logstash deployment.
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What the Elastic Stack contains
The core stack has three services with distinct jobs:
- Elasticsearch stores, indexes, and searches documents. It is the dependency that the other core services use.
- Kibana provides the web interface for searching data, building visualizations, managing data views, and administering the deployment.
- Logstash receives events, optionally transforms them, and sends them to Elasticsearch or another destination.
Elastic’s self-managed installation guidance places additional components after these three. Add Elastic Agent, Beats, APM Server, or Elasticsearch Hadoop only when a specific collection, application-monitoring, or integration requirement justifies them.
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Choose an installation method before installing anything
The right method depends more on the environment you need to operate than on the stack’s features. Docker is the recommended starting point for trying the stack on a personal machine. DEB and RPM packages fit Linux servers where systemd, host-level logging, and distribution filesystem conventions matter. Docker Compose is suited to repeatable deployments that describe several nodes as configuration.
| Installation method | Setup speed | Repeatability | Service-manager integration | Multi-node suitability | Default exposure | Operational profile |
|---|---|---|---|---|---|---|
Docker local trial using start-local |
Strongest for a quick local evaluation | Good when the workflow and image versions are kept together | Container lifecycle rather than native host services | Designed for a local start, not the primary multi-node choice | Elasticsearch and Kibana bind to localhost by default | Low initial burden; protect generated values in .env |
| DEB or RPM packages | Conventional Linux installation | Repeatable when repository or package versions are pinned | Native service management and host filesystem conventions | Works for server deployments when nodes are configured deliberately | Exposure is determined by host and service configuration | Best fit for system-integrated Linux services |
| Docker Compose | More planning than a local single-stack start | Strong: topology and versions are represented as configuration | Container orchestration rather than native systemd units | Designed for deploying multiple nodes at once | Defined by the Compose network and published interfaces | Useful for version-pinned, repeatable environments |
Do not mix methods casually. A package-managed Elasticsearch with containers for the rest can be made to work, but it creates separate lifecycle, networking, and certificate decisions. Unless you have a deliberate reason, keep the core services in one deployment style.
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Plan the release, certificates, and access model
Align every Elastic component to one supported release
Select a supported Elastic release at the time you install. Use that release for Elasticsearch, Kibana, Logstash, and any Beats, Elastic Agent, APM Server, or related components you add. Do not combine arbitrary major or minor versions. Kibana should match Elasticsearch as closely as possible, including the patch version, because compatibility is not a substitute for a deliberately aligned stack.
Pin the chosen image tags or package versions in any repeatable deployment. Record the exact release in your deployment configuration so a rebuild does not silently pull a different version.
Decide where certificates will come from
Certificate planning must happen before production Fleet or Elastic Agent deployment. Choose the trusted CA and certificate names that clients will use, then keep that plan stable. Elastic warns that changing certificates later can require agents to be reinstalled, turning what looks like a routine endpoint change into a fleet-wide recovery task.
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Keep the initial deployment private
The official local-start workflow keeps the stack on localhost by default. That is appropriate for evaluation, not a complete production exposure design. For a server deployment, decide intentionally which interfaces clients need, how cluster communication is protected, where TLS terminates, and which firewall rules are required. Open only the interfaces and ports required by the chosen topology.
Protect generated credentials
The local-start workflow generates a random password for the elastic user and writes local values to a .env file. Treat that password, the file, API keys, and certificate material as secrets. Restrict file access, keep secrets out of source control, and include the environment file in your secret-management and backup policy.
Install Elasticsearch first
Elasticsearch is the foundation of the stack. Kibana connects to it for saved objects and searches, while Logstash sends it indexed events. Installing it first makes the dependency chain explicit and gives you an endpoint and credentials to use in the next phases.
Docker local trial
- Use Elastic’s maintained Docker-based
start-localworkflow for a personal-machine evaluation. - Start the workflow according to its current documentation. It creates the Elasticsearch and Kibana containers, generates the random
elasticpassword, writes local connection values to.env, and keeps access on localhost by default. - Record the generated credentials without placing them in a repository or public shell history.
- Confirm that the Elasticsearch container is running and that the local endpoint responds before moving to Kibana verification.
This workflow is intentionally optimized for trying the stack, not for exposing a production cluster to a wider network. Replace its local-only assumptions with an explicit network, certificate, backup, and access design before treating it as a server deployment.
DEB or RPM Linux installation
Use the DEB package on Debian- and Ubuntu-family systems. Use RPM packages on Red Hat, CentOS, SLES, OpenSuSE, and other RPM-based systems. Install from the appropriate Elastic repository or package file for the release you selected, then start Elasticsearch through the operating system’s service manager.
- Keep the package source and version consistent across every node.
- Use the service manager to check that Elasticsearch starts cleanly after installation and after a reboot.
- Capture the HTTP endpoint, generated or configured credentials, and certificate locations before installing clients.
- Apply the host firewall and network policy before making the endpoint reachable beyond the administration network.
Docker Compose deployment
Use Compose when you need several nodes or want the topology represented as version-controlled configuration. Pin every Elastic image to the same supported release, define persistent storage, and make the network and published interfaces intentional. Bring up Elasticsearch and verify the cluster before adding Kibana or ingestion services.
Install and connect Kibana second
Kibana belongs after Elasticsearch because it must authenticate to and communicate with the Elasticsearch instance or cluster. Installing it before Elasticsearch gives you a web service with no usable backend and makes diagnosis harder.
Configure the connection
- Install the Kibana package or image using the exact Elasticsearch release you selected.
- Set Kibana’s Elasticsearch connection to the endpoint created in the first phase.
- Provide the credentials and certificate trust required by that Elasticsearch deployment.
- Start Kibana through the package service manager or container deployment method.
- Open Kibana through the intentionally configured local or server interface and verify that it authenticates successfully.
Do not add Logstash until Kibana can reach Elasticsearch. A successful Kibana login proves that the core endpoint, credentials, and basic trust relationship are coherent; it does not yet prove that ingestion is working.
Local versus production access
With the local-start workflow, Kibana remains reachable through localhost by default. That protects an evaluation machine from accidental exposure. A production deployment requires a deliberate reverse-proxy or direct-access design, trusted certificates, authentication, firewall rules, and a decision about which users and networks may reach the interface.
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Logstash is the ingestion and transformation stage. Install it after Elasticsearch and Kibana so its output target, credentials, and certificate trust can be tested against an already functioning core stack.
Package-based Logstash
Install Logstash from the matching Elastic repository or DEB/RPM package. Package installations run Logstash as a service. Keep its configuration in the host’s managed configuration location and use the operating system’s service manager for startup, restart, and status checks.
Containerized Logstash
For Docker deployments, use the official Logstash image at the same release as Elasticsearch and Kibana. Mount the pipeline configuration and any required certificate or secret material through the container deployment rather than baking secrets into an image.
Build one small pipeline first
A useful first pipeline has all three required stages:
- Input: accept a deliberately small test event through the input type appropriate to your environment.
- Filter: parse or normalize only what you need to prove the pipeline works. Keep the first filter simple so failures are easy to isolate.
- Elasticsearch output: send the event to the intended Elasticsearch endpoint using the configured credentials and TLS trust.
Once this path succeeds, add additional inputs, filters, routing rules, and outputs one change at a time. A large first configuration makes it difficult to tell whether a failure is caused by the input, transformation, authentication, or destination.
Validate the complete data path
Validation should prove more than “the processes are running.” Follow the event from ingestion to search:
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- Confirm Elasticsearch is running and reachable from the service that will write to it.
- Confirm Kibana authenticates to the same Elasticsearch deployment.
- Send one small test event through the Logstash input.
- Check Logstash’s service or container logs for input, filter, authentication, and output errors.
- Check the target index in Elasticsearch and confirm that the event was indexed.
- In Kibana, create or select the corresponding data view and search for the test event.
If the event appears in Elasticsearch but not in Kibana, inspect the data view pattern, time-field selection, and time range. If it never reaches Elasticsearch, troubleshoot Logstash’s input and output path before changing Kibana.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Troubleshoot by failure stage
| Symptom | Most likely stage | Action |
|---|---|---|
| Elasticsearch does not start | Package, container, storage, or initial configuration | Read the Elasticsearch service or container log, verify the selected release and configuration, and correct the host or volume problem before proceeding. |
| Kibana starts but cannot connect | Endpoint, credentials, certificate trust, or version alignment | Verify the Elasticsearch endpoint, authentication values, certificate chain, and that Kibana matches Elasticsearch’s supported release. |
| Kibana loads but shows no data | Ingestion or data-view configuration | Send a single test event, inspect the Logstash output, confirm the target index, then check Kibana’s data view and time range. |
| Logstash receives no events | Input listener, client routing, or container network | Check that the configured input is reachable from the sender and inspect Logstash logs for binding, parsing, or permission errors. |
| Logstash receives events but indexing fails | Output authentication, TLS, endpoint, or index permissions | Verify the destination endpoint, credentials, certificate trust, and the output configuration. Keep the test pipeline minimal while correcting it. |
| Agents fail after a certificate change | Client trust material | Revisit the production certificate plan. Elastic notes that certificate changes can require agents to be reinstalled. |
| A deployment works once but cannot be reproduced | Unpinned images, packages, or secrets | Pin the release, record configuration as code, and manage generated credentials and certificates outside the image or repository. |
When to add Elastic Agent, Beats, APM, or Hadoop
Do not install every Elastic component by default. Add them only after the core three are healthy:
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- Elastic Agent or Beats: use when you need host, application, or service telemetry collection.
- APM Server: use when application performance traces and transactions are part of the monitoring requirement.
- Elasticsearch Hadoop: use when an existing Hadoop workflow specifically needs Elasticsearch integration.
For production Fleet or Elastic Agent rollouts, complete certificate planning before enrollment. Replacing certificates after agents are distributed can require reinstalling those agents.
Operate and upgrade the stack safely
Keep versions and configuration together
Record the Elasticsearch, Kibana, Logstash, and optional-component versions in one deployment record. In Compose, pin image tags. With packages, pin or otherwise control repository and package versions. Upgrade the stack as a coordinated unit according to Elastic’s compatibility guidance rather than upgrading one core service arbitrarily.
Separate secrets from configuration
Keep passwords, API keys, private keys, and generated .env values in protected secret storage. Configuration should reference those values without exposing them in source control, container images, screenshots, or support bundles.
Control network exposure
Restrict Elasticsearch and Kibana while the installation is being verified. Expose only the interfaces required by clients and cluster communication. The exact firewall and port requirements depend on whether you have one node, several nodes, package services, or containers, so apply the requirements for your topology rather than copying a rule set from a different deployment.
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Observe every layer
Use service or container logs for Elasticsearch, Kibana, and Logstash, then correlate them with the test event’s progress. Host-integrated package installations place these logs within the operating system’s service conventions; container deployments require you to inspect the container log streams and mounted configuration.
A practical installation checklist
- Choose Docker local trial, DEB/RPM, or Compose based on the operating environment.
- Select one supported Elastic release and align every component to it.
- Plan trusted certificates before production Fleet or Elastic Agent enrollment.
- Install and start Elasticsearch.
- Record the endpoint, credentials, and certificate details.
- Install Kibana and verify its authenticated connection to Elasticsearch.
- Install Logstash and create one minimal input-filter-output pipeline.
- Send a small test event and confirm it in the target index.
- Open the matching data view in Kibana and locate the event.
- Protect generated passwords, API keys, certificates, and local
.envfiles. - Pin versions and document the deployment before adding optional agents or integrations.
Which approach should you use?
Use the Docker local-start workflow when your goal is to learn the stack or evaluate it on one machine with localhost-only access. Use DEB or RPM packages when Elasticsearch, Kibana, and Logstash need to behave like native Linux services with host-level logging and service-manager control. Use Docker Compose when you need a repeatable, version-pinned representation of multiple nodes. Whichever method you choose, the reliable sequence remains Elasticsearch, Kibana, then Logstash, followed by only the optional components your use case requires.
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