In another project, I implemented the ELK Stack (Elasticsearch, Logstash, and Kibana) for log aggregation, centralized logging, and real-time monitoring of a high-traffic e-commerce platform. The platform was handling thousands of concurrent users, and real-time visibility into logs and application metrics was essential for operational intelligence and troubleshooting.
Here’s an example of how we used Logstash to process and filter logs before sending them to Elasticsearch:
bash
input {
file {
path => “/var/log/app/*.log”
start_position => “beginning”
}
}
filter {
# Filter log entries for error levels and add tags
if [level] == “ERROR” {
mutate { add_tag => [“error_logs”] }
}
}
output {
elasticsearch {
hosts => [“http://localhost:9200”%5D
index => “app-logs-%{+YYYY.MM.dd}”
}
}
In this case, Logstash reads log files from the /var/log/app/ directory, filters out error logs, and forwards them to Elasticsearch for indexing. We also used Kibana to visualize these logs and set up real-time alerts based on thresholds:
{
“alert”: {
“actions”: [
{
“actionType”: “email”,
“to”: “admin@example.com”,
“subject”: “Error Alert”,
“message”: “Error detected in application logs”
}
]
}
}
This configuration helps us proactively monitor errors and ensure a quick response to system anomalies.
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