As the data in the application grows, it is essential that the log analytics system should scale well with the system. Also, there are times when your systems are under a heavy load, and you need your log analytics systems to analyze what is going on with the application. ELK Stack provides that capability where you can easily scale each component as per your needs. You can always add more Elasticsearch nodes (master nodes and data nodes) in the cluster. It is recommended that you have three master nodes (one primary and two backup) for large clusters. Also, load balancing or routing nodes can be added for high volume searches and indexing requirements. You can also get more Logstash and Redis instances, and add more than one Kibana instance too. A typical scaled architecture may look like this:
Learning ELK Stack
By :
Learning ELK Stack
By:
Overview of this book
Table of Contents (17 chapters)
Learning ELK Stack
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Introduction to ELK Stack
Building Your First Data Pipeline with ELK
Collect, Parse and Transform Data with Logstash
Creating Custom Logstash Plugins
Why Do We Need Elasticsearch in ELK?
Finding Insights with Kibana
Kibana – Visualization and Dashboard
Putting It All Together
ELK Stack in Production
Expanding Horizons with ELK
Index
Customer Reviews