Log Aggregation — Centralizing Logs with Aggregation Systems
DodaTech
Updated 2026-06-28
1 min read
In this tutorial, you'll learn about Log Aggregation. We cover key concepts, practical examples, and best practices to help you understand and apply this topic effectively.
Log aggregation centralizes logs from multiple services into a searchable, queryable platform for debugging and analysis.
# Filebeat configuration for log shipping
filebeat.inputs:
- type: container
paths:
- '/var/lib/docker/containers/*/*.log'
json.keys_under_root: true
json.add_error_key: true
processors:
- add_docker_metadata:
host: "unix:///var/run/docker.sock"
- decode_json_fields:
fields: ['message']
target: ''
overwrite_keys: true
output.elasticsearch:
hosts: ['https://elastic:9200']
index: "logs-scanapp-%{+yyyy.MM.dd}"
ssl.verification_mode: none
# Logstash pipeline
input {
beats { port => 5044 }
}
filter {
if [service] == "scan-api" {
ruby {
code => 'event.set("[@metadata][target_index]", "scan-api-logs")'
}
}
}
output {
elasticsearch {
hosts => ["https://elastic:9200"]
index => "%{[@metadata][target_index]}-%{+YYYY.MM.dd}"
}
}
Log aggregation provides a single pane of glass for debugging across distributed microservice architectures.
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Structured JSON Logging — Implementing Structured Logging in Backend Apps
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Correlation IDs — Tracing Requests Across Microservices with Correlation IDs
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