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Plaintext application logs

Ingest and parse plaintext logs, including existing logs, from any programming language or framework without modifying your application or its configuration.

Plaintext logs require some additional setup that structured logs do not require:

To search, filter, and aggregate effectively, you need to parse plaintext logs using an ingest pipeline to extract structured fields. Parsing is based on log format, so you might have to maintain different settings for different applications.
To correlate plaintext logs, you need to inject IDs into log messages and parse them using an ingest pipeline.

To ingest, parse, and correlate plaintext logs:

Ingest plaintext logs with Filebeat or Elastic Agent and parse them before indexing with an ingest pipeline.
Correlate plaintext logs with an APM agent.
View logs in Logs Explorer

Ingest logs

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Send application logs to your project using one of the following shipping tools:

Filebeat: A lightweight data shipper that sends log data to your project.
Elastic Agent: A single agent for logs, metrics, security data, and threat prevention. With Fleet, you can centrally manage Elastic Agent policies and lifecycles directly from your project.

Ingest logs with Filebeatedit

Use Filebeat version 8.11+ for the best experience when ingesting logs with Filebeat.

Follow these steps to ingest application logs with Filebeat.

Step 1: Install Filebeatedit

Install Filebeat on the server you want to monitor by running the commands that align with your system:

Download the Filebeat Windows zip file: https://artifacts.elastic.co/downloads/beats/filebeat/filebeat-8.17.0-windows-x86_64.zip
Extract the contents of the zip file into C:\Program Files.
Rename the filebeat-((version))-windows-x86_64 directory to ((filebeat)).
Open a PowerShell prompt as an Administrator (right-click the PowerShell icon and select Run As Administrator).

From the PowerShell prompt, run the following commands to install Filebeat as a Windows service:

PS > cd 'C:\Program Files{filebeat}'
PS C:\Program Files{filebeat}> .\install-service-filebeat.ps1

If script execution is disabled on your system, you need to set the execution policy for the current session to allow the script to run. For example: PowerShell.exe -ExecutionPolicy UnRestricted -File .\install-service-filebeat.ps1.

Step 2: Connect to your projectedit

Connect to your project using an API key to set up Filebeat. Set the following information in the filebeat.yml file:

output.elasticsearch:
  hosts: ["your-projects-elasticsearch-endpoint"]
  api_key: "id:api_key"

Set the hosts to your project’s Elasticsearch endpoint. Locate your project’s endpoint by clicking the help icon () and selecting Endpoints. Add the Elasticsearch endpoint to your configuration.

From Developer tools, run the following command to create an API key that grants manage permissions for the cluster and the filebeat-* indices using:

POST /_security/api_key
{
  "name": "your_api_key",
  "role_descriptors": {
    "filebeat_writer": {
      "cluster": ["manage"],
      "index": [
        {
          "names": ["filebeat-*"],
          "privileges": ["manage", "create_doc"]
        }
      ]
    }
  }
}

Refer to Grant access using API keys for more information.

Step 3: Configure Filebeatedit

Add the following configuration to the filebeat.yaml file to start collecting log data.

filebeat.inputs:
- type: filestream   
  enabled: true
  paths: /path/to/logs.log

	Reads lines from an active log file.
	Paths that you want Filebeat to crawl and fetch logs from.

You can add additional settings to the filebeat.yml file to meet the needs of your specific set up. For example, the following settings would add a parser to manage messages that span multiple lines and add service fields:

  parsers:
  - multiline:
      type: pattern
      pattern: '^[0-9]{4}-[0-9]{2}-[0-9]{2}'
      negate: true
      match: after
  fields_under_root: true
  fields:
    service.name: your_service_name
    service.environment: your_service_environment
    event.dataset: your_event_dataset

Step 4: Set up and start Filebeatedit

From the Filebeat installation directory, set the index template by running the command that aligns with your system:

from the Filebeat installation directory, start filebeat by running the command that aligns with your system:

Step 5: Parse logs with an ingest pipelineedit

Use an ingest pipeline to parse the contents of your logs into structured, Elastic Common Schema (ECS)-compatible fields.

Create an ingest pipeline with a dissect processor to extract structured ECS fields from your log messages. In your project, go to Developer Tools and use a command similar to the following example:

PUT _ingest/pipeline/filebeat*  
{
  "description": "Extracts the timestamp log level and host ip",
  "processors": [
    {
      "dissect": {  
        "field": "message",  
        "pattern": "%{@timestamp} %{log.level} %{host.ip} %{message}"  
      }
    }
  ]
}

	`_ingest/pipeline/filebeat*`: The name of the pipeline. Update the pipeline name to match the name of your data stream. For more information, refer to Data stream naming scheme.
	`processors.dissect`: Adds a dissect processor to extract structured fields from your log message.
	`field`: The field you’re extracting data from, `message` in this case.
	`pattern`: The pattern of the elements in your log data. The pattern varies depending on your log format. `%{@timestamp}`, `%{log.level}`, `%{host.ip}`, and `%{message}` are common ECS fields. This pattern would match a log file in this format: `2023-11-07T09:39:01.012Z ERROR 192.168.1.110 Server hardware failure detected.`

Refer to Extract structured fields for more on using ingest pipelines to parse your log data.

After creating your pipeline, specify the pipeline for filebeat in the filebeat.yml file:

output.elasticsearch:
  hosts: ["your-projects-elasticsearch-endpoint"]
  api_key: "id:api_key"
  pipeline: "your-pipeline"

Add the pipeline output and the name of your pipeline to the output.

Ingest logs with Elastic Agentedit

Follow these steps to ingest and centrally manage your logs using Elastic Agent and Fleet.

Step 1: Add the custom logs integration to your projectedit

To add the custom logs integration to your project:

In your Elastic Observability Serverless project, go to Project Settings → Integrations.
Type custom in the search bar and select Custom Logs.
Click Add Custom Logs.
Click Install Elastic Agent at the bottom of the page, and follow the instructions for your system to install the Elastic Agent.
After installing the Elastic Agent, configure the integration from the Add Custom Logs integration page.
Give your integration a meaningful name and description.
Add the Log file path. For example, /var/log/your-logs.log.
An agent policy is created that defines the data your Elastic Agent collects. If you’ve previously installed an Elastic Agent on the host you’re collecting logs from, you can select the Existing hosts tab and use an existing agent policy.
Click Save and continue.

You can add additional settings to the integration under Custom log file by clicking Advanced options and adding YAML configurations to the Custom configurations. For example, the following settings would add a parser to manage messages that span multiple lines and add service fields. Service fields are used for Log correlation.

  parsers:
  - multiline:
      type: pattern
      pattern: '^[0-9]{4}-[0-9]{2}-[0-9]{2}'
      negate: true
      match: after
  fields_under_root: true
  fields:
    service.name: your_service_name  
    service.version: your_service_version  
    service.environment: your_service_environment

for Log correlation, add the service.name (required), service.version (optional), and service.environment (optional) of the service you’re collecting logs from.

Step 2: Add an ingest pipeline to your integrationedit

To aggregate or search for information in plaintext logs, use an ingest pipeline with your integration to parse the contents of your logs into structured, Elastic Common Schema (ECS)-compatible fields.

From the custom logs integration, select Integration policies tab.
Select the integration policy you created in the previous section.
Click Change defaults → Advanced options.
Under Ingest pipelines, click Add custom pipeline.

Create an ingest pipeline with a dissect processor to extract structured fields from your log messages.

Click Import processors and add a similar JSON to the following example:

{
  "description": "Extracts the timestamp log level and host ip",
  "processors": [
    {
      "dissect": {  
        "field": "message",  
        "pattern": "%{@timestamp} %{log.level} %{host.ip} %{message}"  
      }
    }
  ]
}

	`processors.dissect`: Adds a dissect processor to extract structured fields from your log message.
	`field`: The field you’re extracting data from, `message` in this case.
	`pattern`: The pattern of the elements in your log data. The pattern varies depending on your log format. `%{@timestamp}`, `%{log.level}`, `%{host.ip}`, and `%{message}` are common ECS fields. This pattern would match a log file in this format: `2023-11-07T09:39:01.012Z ERROR 192.168.1.110 Server hardware failure detected.`

Click Create pipeline.
Save and deploy your integration.

Correlate logs

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Correlate your application logs with trace events to:

view the context of a log and the parameters provided by a user
view all logs belonging to a particular trace
easily move between logs and traces when debugging application issues

Log correlation works on two levels:

at service level: annotation with service.name, service.version, and service.environment allow you to link logs with APM services
at trace level: annotation with trace.id and transaction.id allow you to link logs with traces

Learn about correlating plaintext logs in the agent-specific ingestion guides:

View logs

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To view logs ingested by Filebeat, go to Discover. Create a data view based on the filebeat-* index pattern. Refer to Create a data view for more information.

To view logs ingested by Elastic Agent, go to Discover and select the Logs Explorer tab. Refer to the Filter and aggregate logs documentation for more on viewing and filtering your log data.

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