Sarath Pillai

Scaling Elasticsearch is not an easy task. In this article, we go over different methods to make a High-Availability Logstash Indexing Solution using Qbox Hosted Elasticsearch.

Logstash Indexer is the component that indexes events and sends them to Elasticsearch for faster searches. We will use multiple logstash indexers with the exact same configuration. Having multiple indexers with the same configuration opens up different possibilities to make a highly available logstash solution for your ELK stack. These indexer nodes with identical configuration can easily be created using configuration management tools like Puppet or Chef.

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Although elasticsearch can scale indefinitely, you should store required data only. This will speed up the search operation, as well as response time to retrieve the data, and even reduce resource utilization substantially.

Elasticsearch uses an “Inverted Index” to retrieve data that you are searching for. Although this algorithm is one of the best when it comes to text searching, keeping only the data that you need in the index is the best approach.

In this tutorial, we discuss data retention techniques that you can use in elasticsearch. This will obviously depend on the kind of data and your application, because some might need longer retention policies compared to others.

Imagine an application that deals with finance and money transactions. Such applications will need all of the records forever. But, do these records need to always exist in elasticsearch? Does all of this data need to be quickly searchable?

Logstash provides methods where you can segregate different events, and then store it in standard file storage rather than elasticsearch for long-term storage.

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Filebeat is extremely lightweight compared to its predecessors when it comes to efficiently sending log events. It uses lumberjack protocol, compression, and is easy to configure using a yaml file. It can send events directly to elasticsearch as well as logstash. It keeps track of files and position of its read, so that it can resume where it left of.

The goal of this tutorial is to set up a proper environment to ship Linux system logs to Elasticsearch with Filebeat. It then shows helpful tips to make good use of the environment in Kibana.

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