Perfect choice for big datacenters
Use Cases and Deployment Scope
Logstash is a part of the ELK stack in the private Telcocloud datacenter I used in Nokia. The Telco datacenter infrastructure has a lot of logs to be analyzed, and Logstash acts as a log shipper on the Opensatck-based infrastructure. It helped collect logs from various sources and then processed them in the required format to be displayed on Kibana for analysis & Grafana for visulisation for graphs
Pros
- Supports unstructured log data into searchable fields
- Wide integration with almost any data source and backend
- Powerful searchable fields, including unstructured log data
- Supports various formats like JSON, CSV, XML, key-value, etc
Cons
- It is heavy i.e., intensive as of now. Need to reduce overhead to save CPU/RAM consumption
- Need to be more Kubernetes-friendly. Should support auto-scaling and K8s observability
- Initial configuration is still complex. A seamless config procedure is still required
Return on Investment
- It is very difficult to give any figures on ROI, as it depends on many factors, and in a Telcocloud environment, it is much complex to find out; however, I would give some points below on ROI
- ROI based on flexibility is very high, as it reduces the time to find RCA
- ROI based on integration is very high because it supports multi-vendor environments, avoiding vendor lock-in & works across multi-cloud setups
- ROI on resource consumption is less because Logstash in 2-3 times more resource-intensive as compared to its lightweight alternatives resulting in latency
Usability
Alternatives Considered
Enterprise Fluentd, fluentbit and Apache Kafka


