Elasticsearch Speeds Up Log Analysis in Logstash
Elasticsearch Inc. has announced the latest version of Logstash, a log data storage tool. Logstash is used to store data of various types in a consolidated archive, and version 4.1 promises to streamline the startup process with several new features. Logstash is one of three components of the Elasticsearch ELK stack (ELK stands for Elasticsearch, Logstash, Kibana).
Logstash is an open source tool that formats any data received into the JSON format for readability. Log data is any data from a time-based transaction or event; this kind of data can be useful in determining which parts of your business' services are being used frequently and successfully.
Version 4.1 of Elasticsearch's storage tool includes several improvements:
- A more streamlined installation process, as well as a simplified Getting Started guide.
- A load time that is up to three times faster than the previous version.
- A more intuitive plugin management system.
- Automatic configuration of Puppet modules on virtual machines and servers.
"Logstash can get data from unknown places and from any source and will clean it up so you don't have to worry about the exact log types or reconciling different data formats," says Jordan Sissel, software engineer and creator of Logstash. "We handle it all and let you slice and dice that data with Elasticsearch."
Logstash represents only one part of the Elasticsearch ELK stack. Used together, the components provide a toolset for the storage, visualization and analysis of log data. Elasticsearch represents the analysis tool and provides powerful search functionality, and Kibana is the visualization tool, allowing for interaction with your data via customized dashboards.
"Prior to using the ELK stack, some of our departments had no visibility into our operational data. Now, we can provide all of our teams with the insights they require to improve our business offerings," says Don Son, Director of Product Management at The Control Group.
For more information on Logstash and the Elasticsearch ELK stack visit elasticsearch.org.