Difference Between Using Filebeat and Logstash to Push Log File to Elasticsearch

Both Filebeat and Logstash are popular tools in the Elastic Stack used for shipping logs to Elasticsearch, but they have different strengths and use cases. Here's a comparison to help you decide which tool might be more appropriate for your needs:

Filebeat

1. Purpose:
   • Filebeat is a lightweight shipper designed specifically for forwarding and centralizing log data. It is optimized for log data collection and forwarding with minimal overhead.
2. Deployment:
   • Filebeat is typically installed on the servers where logs are generated. It reads log files, parses log lines, and ships them to a central system, such as Logstash or directly to Elasticsearch.

3. Configuration:

   • Configuration is relatively simple. Filebeat configurations are focused on specifying which files to read, where to ship the logs, and basic processing rules.

   • Example configuration:

      

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     filebeat.inputs:
       - type: log
         paths:
           - /var/log/myapp/*.log
     
     output.elasticsearch:
       hosts: ["localhost:9200"]
     

4. Processing Capabilities:

   • Filebeat includes basic processing capabilities such as multiline log handling, filtering, and simple transformations. It is generally used for lightweight, straightforward data forwarding.

5. Resource Usage:

   • Filebeat is designed to be lightweight and efficient, making it suitable for resource-constrained environments. It has minimal overhead on the source machine.

6. Use Case:

   • Best suited for environments where the primary need is to collect and forward logs with minimal processing. Ideal for scenarios where you want to quickly and efficiently forward logs to Elasticsearch or Logstash.

Logstash

1. Purpose:
   • Logstash is a powerful data processing pipeline that can collect, transform, and ship data. It provides more advanced processing and transformation capabilities compared to Filebeat.
2. Deployment:
   • Logstash is typically deployed on a separate server or cluster to handle data processing before sending it to Elasticsearch or other outputs.

3. Configuration:

   • Logstash configurations are more complex and flexible. They include inputs, filters, and outputs, allowing extensive data transformation and enrichment.

   • Example configuration:

      

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     input {
       file {
         path => "/var/log/myapp/*.log"
         start_position => "beginning"
       }
     }
     
     filter {
       grok {
         match => { "message" => "%{COMBINEDAPACHELOG}" }
       }
       date {
         match => [ "timestamp", "dd/MMM/yyyy:HH:mm:ss Z" ]
       }
     }
     
     output {
       elasticsearch {
         hosts => ["localhost:9200"]
         index => "myapp-logs"
       }
     }
     
     

4. Processing Capabilities:

   • Logstash offers extensive data processing features, including complex filtering, enrichment, and transformation capabilities using plugins. This makes it suitable for more complex log processing tasks.

5. Resource Usage:

   • Logstash is generally more resource-intensive compared to Filebeat due to its advanced processing features. It requires more CPU and memory, which is something to consider for deployment.

6. Use Case:

   • Best suited for scenarios where you need to perform complex data transformations, enrichments, or aggregations before sending logs to Elasticsearch. Ideal for situations where logs need significant processing or integration with other data sources.

Summary

• Filebeat: Lightweight, efficient log shipper ideal for forwarding logs with minimal processing. Best for straightforward log collection and forwarding.
• Logstash: Powerful, flexible data processing tool suitable for complex log transformations and integrations. Best for scenarios requiring advanced log processing and enrichment.

Depending on your needs, you might even use both in tandem: Filebeat for lightweight log forwarding and Logstash for more complex processing tasks.

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