Filebeat vs Logstash: Which is Better for Shipping Spring Boot Logs?

Shipping logs efficiently from Spring Boot microservices to a centralized logging system is essential for observability and troubleshooting. But when it comes to choosing between Filebeat and Logstash, developers often face a dilemma. Both are widely used tools in the ELK stack ecosystem, but they serve different purposes and come with unique strengths.

This blog compares Filebeat and Logstash to help you decide the better fit for shipping Spring Boot logs. We’ll look at their ideal use cases, pros and cons in various environments, sample configurations, and performance benchmarks.

Table of Contents

1. When to Use Filebeat or Logstash
2. Pros and Cons for Different Environments
3. Sample Configurations for Filebeat and Logstash
4. Performance Benchmarks
5. Summary

When to Use Filebeat or Logstash

Filebeat and Logstash have overlapping capabilities, but their roles differ significantly:

What is Filebeat?

Filebeat is a lightweight log shipper that reads log files and forwards the data to a destination like Elasticsearch or Logstash. It’s designed for simplicity and efficiency, making it perfect for ingesting plain text logs.

Ideal Use Cases for Filebeat:

• Lightweight Applications: Perfect for Spring Boot services with minimal data transformation needs.
• Log Aggregation: Collect logs from multiple sources (e.g., logs stored in files like /var/log).
• Low Overhead: When system resource usage needs to be minimal, Filebeat is optimal.

What is Logstash?

Logstash is a powerful data processing pipeline tool that ingests, transforms, and sends logs to Elasticsearch or other storage systems. Unlike Filebeat, Logstash supports complex data enrichment and filtering.

Ideal Use Cases for Logstash:

• Complex Data Transformation: For applications where raw logs need to be enriched with metadata, parsed, or filtered.
• Diverse Inputs: Logstash supports a range of input types, such as Kafka, databases, and TCP sockets.
• Custom Pipelines: Complex pipelines that modify logs for analytics benefit from Logstash’s flexibility.

Choosing Between Filebeat and Logstash:

Use Filebeat when you want a lightweight log shipper with minimal configuration. Choose Logstash when your application requires complex filtering, parsing, or enrichment.

Pros and Cons for Different Environments

The decision between Filebeat and Logstash often depends on resource availability, log complexity, and deployment context. Below is a breakdown of their strengths and weaknesses:

Filebeat

Pros:

• Lightweight Design: Minimal CPU and memory usage, making it ideal for resource-constrained environments.
• Easy to Configure: Simple YAML configuration files allow quick setup.
• Direct Integration: Push logs directly to Elasticsearch or Logstash without intermediaries.
• Fast Performance: High throughput with low latency for streaming plain text logs.

Cons:

• Limited Processing Capabilities: No advanced data transformation or enrichment features.
• Dependency on Logstash/Elasticsearch: Raw logs may require further processing in Elasticsearch or Logstash.

Best Fit:

• Autoscaling cloud-native Spring Boot applications where logs are stored locally.
• Scenarios where the focus is speed and simplicity rather than heavy data enrichment.

Logstash

Pros:

• Powerful Data Processing: Transform, filter, and enrich data with custom logic.
• Wide Input Protocol Support: Fetch data from Kafka, JDBC, and more.
• Data Flexibility: Modify, aggregate, and clean logs before passing them to Elasticsearch.

Cons:

• Heavier Resource Usage: Higher memory and CPU consumption compared to Filebeat.
• Complex Configuration: Requires more effort to write and maintain pipeline configurations.
• Latency Overhead: Additional processing adds slight delays.

Best Fit:

• Distributed systems requiring advanced enrichment for traces and metrics.
• Complex enterprise setups where logs need data manipulation before indexing.

Sample Configurations for Filebeat and Logstash

Filebeat Configuration

Here’s a basic configuration to ship Spring Boot application logs to Elasticsearch using Filebeat:

Install Filebeat

1. Download and install Filebeat on your application server.
2. Enable the system module using: filebeat modules enable system

filebeat.yml Configuration:

filebeat.inputs:
  - type: log
    enabled: true
    paths:
      - /var/log/spring-boot/*.log
    fields:
      app_name: my-spring-boot-app
      environment: production

output.elasticsearch:
  hosts: ["http://localhost:9200"]

• Inputs: Defines the log file source.
• Fields: Adds custom metadata like app_name and environment.
• Output: Sends logs directly to Elasticsearch.

Start Filebeat with:

filebeat -e -c filebeat.yml

Logstash Configuration

Here’s a Logstash configuration example for consuming logs from Kafka and enriching them before storing them in Elasticsearch.

logstash.conf Configuration:

input {
  kafka {
    bootstrap_servers => "localhost:9092"
    topics => ["spring-logs"]
  }
}

filter {
  json {
    source => "message"
  }
  mutate {
    add_field => {
      "processed_by" => "logstash"
      "environment" => "production"
    }
  }
}

output {
  elasticsearch {
    hosts => ["http://localhost:9200"]
    index => "spring-logs-%{+yyyy.MM.dd}"
  }
}

• Input Section: Reads logs from Kafka.
• Filter Section: Parses JSON logs and adds custom fields.
• Output Section: Sends logs to Elasticsearch.

Run Logstash with:

logstash -f logstash.conf

Performance Benchmarks

The choice between Filebeat and Logstash can impact the performance of your log pipeline. Here’s a comparison based on common metrics like throughput, latency, and resource usage:

• Throughput: Filebeat outperforms Logstash in cases where logs don’t require transformation.
• Resource Efficiency: Filebeat’s lightweight nature makes it more suitable for edge devices and small-scale applications.
• Flexibility: Logstash wins with its ability to process and enrich log data, albeit at the cost of higher resource consumption.

Summary

Choosing between Filebeat and Logstash boils down to your application’s needs:

• Use Filebeat for lightweight log shipping where simplicity and performance are key.
• Opt for Logstash if you need advanced log transformation and flexibility.

Key Takeaways:

1. Setup Simplicity: Filebeat is the go-to choice for fast and easy log streaming.
2. Data Transformation: Logstash provides the muscle for enriching, filtering, and shaping log data.
3. Performance Matters: Filebeat is lightweight and fast, while Logstash is resource-intensive but powerful.
4. Use Cases: Filebeat shines in Spring Boot microservices with raw log transport, whereas Logstash fits complex enterprise scenarios.

For most Spring Boot applications with straightforward logging needs, Filebeat’s simplicity and speed are hard to beat. However, if your pipeline demands data enrichment, Logstash remains a pivotal choice.

Implement either tool today to enhance your centralized logging strategy, and consider pairing both Filebeat and Logstash for a hybrid approach in sophisticated environments!