ELK Stack is the top open-source IT log management solution for businesses seeking the benefits of centralized logging without the high cost of enterprise software. When Elasticsearch, Logstash, and Kibana are combined, they form an end-to-end stack (ELK Stack) and real-time data analytics platform that can give actionable insights from practically any structured or unstructured data source.
What is ELK Stack?
ELK Stack is designed to manage massive volumes of data efficiently because of its distributed architecture. Scalability requires the correct configuration of Elasticsearch nodes, as well as the use of features such as sharding and indexing. To avoid performance bottlenecks, best practices for scaling include monitoring cluster health, managing storage, and assuring query efficiency.
To utilize ELK to monitor the performance of your platform, a few tools and integrations are necessary. Probes must be running on each host to collect various system performance data. The data must then be delivered to Logstash, saved and aggregated in Elasticsearch, and finally transformed into Kibana graphs.
Usage of ELK Stack
Applications with complex search requirements: Any application with complicated search needs can greatly benefit from employing the Elastic Stack as the underlying engine for advanced searches.
Big data: Companies that handle huge amounts of unstructured, semi structured, and structured data can use the Elastic Stack to run their data operations. Netflix, Facebook, and LinkedIn are examples of successful organizations that have implemented the stack.
Other significant usage cases: The Elastic Stack is used for infrastructure metrics and container monitoring, logging and log analytics, application performance monitoring, geospatial data analysis and visualization, security and business analytics, and scraping and aggregating publicly available data.
ELK Stack Application for Monitoring and Log Analysis
Alert: Detects events before they progress to a greater intensity.
Enrich: Adds the ability to define log events further.
Parse: It converts source log messages into a uniform format.
Collect: Connects to a source system and ingests logs as they are created.
Store: Saves the gathered, parsed, and enriched logs.
Analyze: This allows you to search, filter, and review all occurrences connected to a specific circumstance.
How to Monitor Using the ELK Stack?
Step 1: Docker Installtion
Make sure Docker is installed and running. You can modify the docker-compose.yml or Logstash configuration files, but the default settings should work for initial testing.
$ docker-compose.yml
Output:
Docker Installation
Step 2: Execute compose up
Within the docker-elk folder, perform the following command in a terminal session:
$ docker-compose up
Output:
Output
Step 3: Open Kibana
After the ELK Stack has ingested some data, open Kibana with the URL localhost:5601 to access the dashboard.
Kibana
Step 4: Configure settings
Configure the settings, pick the @timestamp time filter, and then single-click the Create index pattern button to save the new index pattern.
Configure settings
Step 5: Collecting and Shipping
We used Collect, a tool for collecting and shipping data to Logstash. This excellent open-source project includes a plethora of choices that enable operations to measure numerous indicators from many IT systems and save the data for subsequent examination.
$ collectl -sjmf -oT
Output:
Output
Step 6: Monitor the ELK Stack
If you have a fast ELK stack, you will receive the data almost instantaneously. This relies on the performance of your ELK, but you may expect results in half a minute or less, providing you with a very current stream of information.
Monitor ELK Stack
Conclusion
In this article, we have learned about monitoring using the ELK Stack. The ELK stack has evolved significantly since its introduction. Initially focused on log management, it has evolved into a comprehensive application for managing a variety of analytics activities.